Syntax and models for urban accessibility: the case of “Porta Nuova” in Turin by Matteo Mandrile

POLYTECHNIC OF TURIN Department of Architecture and Design Master of Science in Architecture and Sustainable Design Syntax and models for urban accessibility: the case of â&#x20AC;&#x153;Porta Nuovaâ&#x20AC;? in Turin

Matteo Mandrile

POLITECNICO DI TORINO Dipartimento di Architettura e Design Corso di Laurea Magistrale

in Architettura per il Progetto Sostenibile

Tesi di Laurea Magistrale

Sintassi e modelli per l’accessibilità urbana: il caso “Porta Nuova” di Torino

Relatore prof. Roberto Pagani Candidato Matteo Mandrile

Febbraio 2015 II

III

TABLE

CONTENTS

1 SOMMARIO ................................................................................ VI 2 INTRODUCTION .......................................................................... 22 2.1

Smart cities .................................................................................................... 22

2.2

A different way to look at the city ....................................................................... 24

2.3

The needs and challenges of analytical methods .................................................. 27

4 MODELS AND ANALYSIS ............................................................... 36 4.1

Notes on flows and networks ............................................................................. 36

4.1.1

Flows analysis ................................................................................................................ 36

4.1.2

Networks analysis .......................................................................................................... 53

4.2

Space Syntax ................................................................................................... 68

4.2.1

4.2.1.1

Relations, configurations, and space ...................................................................... 75

4.2.1.2

Configurational analysis and universal distances ................................................... 77

4.2.1.3

Natural movement, from Hillier et al. (1993)............................................................ 81

4.2.3

Space Syntax analysis .................................................................................................... 86

4.2.3.1

Axial Analysis ......................................................................................................... 86

4.2.3.2

Angular Segment Analysis...................................................................................... 98

4.2.5 4.3

Space Syntax theories .................................................................................................... 75

Space syntax application to regenerate urban areas ................................................... 102

From Space Syntax to Visibility Graph Analysis ................................................. 140

4.3.1

Isovists and isovist fields .............................................................................................. 143

4.3.2

From Isovists to Visibility Graphs ................................................................................ 149

4.3.3

Analyzing the Visibility Graph ..................................................................................... 152

4.3.5

VGA application to regenerate urban area .................................................................. 157

4.4

From Visibility Graph Analysis to Agent Based Models ....................................... 176

4.4.1

Pedestrian modeling.................................................................................................... 178

4.4.2

EVA: Exosomatic Visual Architecture .......................................................................... 181

4.4.4

ABM application to regenerate urban area .................................................................. 189

4.5

Mobility in the city of the future: Smart Intersection Management at MIT Senseable

City Lab .............................................................................................................. 201 4.5.1

Autonomous vehicles ................................................................................................... 201

4.5.2

DriveWave: Smart Intersection Management............................................................... 205

4.5.3

DriveWave: the installation .......................................................................................... 215

6 CONCLUSIONS ........................................................................... 219 7 BIBLIOGRAPHY .......................................................................... 223 9 RINGRAZIAMENTI ...................................................................... 230

1 SOMMARIO Il presente lavoro si propone di presentare delle metodologie descrittive e analitiche che hanno come oggetto l’ambiente urbano, valutato in termini di accessibilità intesa come possibilità di movimento. È indagato in maniera specifica l’effetto della configurazione spaziale nella definizione di centralità urbane emergenti, ovvero l’influenza dell’ambiente costruito nella formazione di potenziali di accessibilità e permeabilità. Sono condotte diverse analisi a scala urbana, comprendendo il territorio all’interno dei confini comunali di Torino, e successivamente è posta enfasi sull’area della stazione ferroviaria Porta Nuova. La suddetta area è stata scelta come zona d’indagine locale per una possibile ricucitura del tessuto urbano, e il lavoro effettuato si configura come analisi preliminare del tessuto esistente, che ha portato alla determinazione di linee guida per un

eventuale

intervento.

Particolare

attenzione

posta

nell’illustrazione, applicazione, e interpretazione di tali analisi dal punto di vista del professionista che opera in ambito architettonico e urbanistico: è mostrato, infatti, come un approccio analitico al progetto possa essere fonte d’ispirazione, permettendo di prendere decisioni informate e diminuendo il rischio di fallimento nel raggiungimento degli obiettivi preposti. Le analisi presentate fanno parte di un’area di studi chiamata sin-

tassi spaziale, che partendo dagli studi di Bill Hillier e Julienne Hanson (1984) – assieme ad altri professori e ricercatori – indaga il sistema di relazioni tra gli spazi che costituiscono l’elemento architettonico e l’ambiente urbano, e le relative implicazioni sociali a livello percettivo e di utilizzo. Tali analisi sono chiamate analisi

assiale ed analisi a segmenti. In seguito, sempre con riferimento alle teorie della sintassi spaziale, è introdotta un’altra tipologia di inVI

dagine che valuta la percezione visiva dell’ambiente costruito, chiamata analisi del grafo di visibilità (VGA). Basandosi su quest’ultima, è poi illustrato un modello ad agenti, che sfrutta i dati sulla visibilità per l’esplorazione di una data configurazione spaziale da parte di agenti virtuali, in grado di “navigare” l’ambiente a loro circostante in maniera autonoma. La presentazione di ogni di analisi è preceduta da una breve introduzione storica e da un sunto delle teorie fondamentali che ne giustificano l’applicazione, ed è seguita da un esempio applicativo commentato. Tuttavia, per una corretta applicazione e valutazione di queste analisi, è stato ritenuto necessario l’inserimento di note su flussi e reti, poiché sono entrambi concetti propedeutici alla sintassi spaziale. Infine, il capitolo conclusivo illustra uno studio sull’accessibilità veicolare in ambiente urbano, condotto presso il Massachusetts Institute of Technology, che valuta le potenzialità di un sistema di attraversamento automatizzato degli incroci, prendendo in considerazione veicoli autoguidati. Questo sommario riassume nelle seguenti pagine il percorso affrontato, permettendo di avere un quadro generale del lavoro svolto e dei risultati che ne sono derivati, mentre si rimanda ai relativi capitoli per descrizioni dettagliate degli argomenti trattati. Nell’introduzione è illustrato come il dibattito odierno riguardo alla comprensione di forma e funzione in ambito urbanistico abbia dato vita al termine Smart City. Le varie definizioni del termine, pur soffrendo differenze di carattere linguistico, hanno in comune una combinazione di azioni coordinate, che mirano a rendere la città più sostenibile attraverso l’uso di tecnologie dell’informazione e della comunicazione (ICT). In termini urbanistici, la sostenibilità è valutata da differenti punti di vista, che includono efficienza energetica, adattabilità funzionale, qualità della vita, impatto ambientale e flessibilità gestionale. Oltre a tali caratteristiche, l’approccio delVII

la smart city pone particolare attenzione al bilanciamento tra capitale

fisico,

sociale

intellettuale,

mirando

aumentare

l’accessibilità all’informazione e all’istruzione nei confronti dei cittadini. Secondo tale approccio, una città può essere definita "smart" se investimenti in capitale sociale e umano, uniti ad investimenti in infrastrutture di comunicazione tradizionali (trasporti) e moderne (ICT) incentivano uno sviluppo economico sostenibile e un’elevata qualità della vita, assieme ad una gestione intelligente delle risorse naturali, attraverso iniziative di inclusione e partecipazione (Caragliu, Nijkamp e Del Bo 2009). Parte

delle

teorie

alla

base

questo

approccio

alla

città,

interptretano l’ambiente urbano come un sistema di reti, relazioni ed interazioni, sia sociali, sia di elementi costitutenti il tessuto urbano. Ciò non vuol dire perdere di vista l’importanza del luogo, ma anzi renderlo parte attiva e determinante nella definizione delle qualità percettive, e dunque dell’utilizzo, da parte dell’essere umano (Batty 2013). Considerando la città come un sistema complesso, formato da molti componenti interagenti a diversi livelli, la peculiarità di questo pensiero risiede nella capacità di analizzare le relazioni – e le interazioni – non lineari tra singole entità, studiandone la formazione di pattern di similarità con l’obiettivo di spiegare l’emergenza di aspetti caratteristici dell’ambiente urbano, tramite un’approccio bottom-up (dal basso). Dal punto di vista del professionista nell’ambito dell’architettura e dell’urbanistica,

co-occorrenza

sempre

più

alti

standard

qualitativi - causata in parte dai profili di competitività urbana – unita alla crescente credibilità delle precedenti teorie, delinea una necessità di analizzare in maniera attenta sia lo “stato di fatto”, per

prendere

decisioni

informate

riguardo

possibili

soluzioni

progettuali, sia l’effetto di tali scelte nel raggiungimento degli VIII

obiettivi di progetto. Si argomenta dunque che un’approccio al progetto di tipo analitico non debba essere visto come un limite posto

all’intuizione

alla

creatività

del

professionista,

piuttosto come uno strumento in grado di facilitare la formulazione di soluzioni progettuali basate su un processo di dipo deduttivo. Citando Karimi (2012) sono dunque elencate quattro caratteristiche che sembrano essere rilevanti per caratterizzare metodologia e strumenti analitici impiegati in fase di progettazione. In primo luogo, l'approccio analitico da utilizzare dev’essere di carattere spaziale e dunque trattare direttamente con questo elemento del progetto. In secondo luogo tale approccio dev’essere in grado di collegare esplicitamente lo spazio con le persone. In terzo luogo, è necessario che sia consistente a diverse scale, ed infine deve poter esaminare un progetto nella sua interezza e nelle sue parti costituenti. È possibile pensare che la sintassi spaziale, formata da un corpus teorico atto a collegare lo spazio ad aspetti sociali e un insieme di tecniche per analizzare una determinata configurazione, sia uno strumento adatto per rispondere a tali esigenze. Prima di entrare nel merito della sitassi spaziale, vengono fatte due note riguardo l’analisi di flussi e l’analisi di reti. Le analisi di flussi e di reti a scala urbana permettono di analizzare le dinamiche dell’ambiente costruito da due punti di vista complementari: mentre i flussi trattano di processi a diverse scale spaziali e temporali, le reti possono essere pensate come i contenitori la cui capacità vincola i flussi di materiali, persone, o informazioni. Considerando i flussi di persone e la rete di strade, si potrebbe sostenere che siano in grado di influenzare in maniera sostanziale parte del funzionamento di una città. Inoltre, entrambi gli argomenti sembrano essere sullo sfondo della sintassi spaziale, poiché teorie quali il movimento naturale (Hillier, Penn, et al. 1993) collegano il flusso IX

di persone con la topologia della rete stradale, e gran parte delle misure della sintassi spaziale derivano dall’analisi delle reti. Dal punto di vista dell’urbanista, analizzare i dati riguardanti la mobilità cittadina fornisce informazioni preziose per la definizione di linee guida generali a supporto della progettazione. Non si vuole, in questo lavoro, fornire una visione dettagliata delle teorie e delle analisi della mobilità urbana. Piuttosto, ci si vuole focalizzare su metodi di visualizzazione e interpretazione dei dati riguardanti la mobilità. I dati IMQ 20081 sono visualizzati attraverso software GIS, limitandosi all’area interna ai confini comunali, per analizzare la mobilità tra i quartieri di Torino. Una prima visualizzazione, mostrata in Figure 4.2 è relativa ai flussi aggregati in entrata ed in uscita – con mezzi pubblici e privati – tra i rispettivi quartieri, in cui il volume degli spostamenti è proporzionale allo spessore ed al colore delle linee che collegano i centroidi delle rispettive zone. Tale rappresentazione ha permesso di rilevare le coppie di quartieri con interazione maggiore in termini di spostamenti. In seguito analizzando i dati riguardanti gli spostamenti senza ritorni, sono evidenziati i quartieri che agiscono principalmente come attrattori, avendo i maggiori flussi in entrata, e quelli che invece presentano i maggiori spostamenti in uscita (Figure 4.3). Due successive visualizzazioni (Figure 4.4 e Figure 4.5) mostrano le medie direzionali degli spostamenti in uscita e in entrata, evidenziando le asimmetrie degli spostamenti cittadini focalizzati rispettivamente a sud-ovest del centro cittadino e a ovest dello stesso. Infine è mostrato un modello d’interazione spaziale senza vincoli (Figure 4.6) basato sulla legge di gravitazione universale di Newton, che valuta l’interazione tra

Indagine sulla Mobilità delle persone e sulla Qualità dei trasporti nell’area me-

tropolitana e nella provincia di Torino

due quartieri basandosi sulla popolazione abitante, con effetto deterrente della distanza (Wilson 1970). Sebbene tale modello non sia applicabile per una simulazione realistica d’interazione tra la popolazione delle aree considerate, è proposto come modello interessante

per

progettista,

poiché

restituisce

una

potenzialità

d’interazione che non è catturata da una visualizzazione degli spostamenti reali, come ad esempio l’alto potenziale d’interazione tra

Crocetta e San Salvario. Di conseguenza, soprattutto per i progetti riguardanti la ricucitura del tessuto urbano, come può essere quello riguardante l’area della stazione ferroviaria di Porta Nuova, questo modello potrebbe essere usato come base per eventuali proposte progettuali che mirano ad aumentare le connessioni tra i due quartieri, basandosi su questa potenzialità di interazione “latente”. Per quel che concerne le analisi di reti, è posta attenzione sulla struttura di connessioni tra i vari quartieri, piuttosto che sul volume di spostamenti in entrata ed in uscita da questi. Viene dunque illustrata la definizione di grafo, composta di archi e nodi, seguita dalla definizione di grafo spaziale, che associa gli elementi costituenti il grafo analizzato con entità esistenti nello spazio euclideo. Prendendo in considerazione il grafo stradale della città di Torino, si mostra come tale modello di infrastruttura stradale possa essere definito una “rete spaziale quasi planare”, intendendo con ciò la possibilità di rappresentarla attraverso il disegno di un grafo spaziale – in cui i nodi rappresentano le intersezioni e gli archi rappresentano le strade – con una percentuale molto bassa di strade che si intersecano senza incroci (ponti o sottopassi). In seguito sono introdotte misure per definire il grado di connettività del sistema, e misure della centralità dei relativi nodi, quali closeness centrali-

ty, ovvero la distanza media in termini topologici di un nodo da tutti gli altri nodi nel sistema, e betweenness centrality, ovvero la XI

frequenza con cui ogni singolo nodo si trova sul percorso più breve che collega ogni altra coppia di nodi. Attraverso l’applicazione di queste due misure sul grafo stradale di Torino (Figure 4.10 e Figure 4.11),

mostrato

come

siano

poco

utilizzo

all’architetto

all’urbanista se applicate ai nodi di un grafo planare ma, come dimostrato in seguito, se applicate agli archi – ovvero alle strade – rivelano importanti informazioni utili al professionista, quali il grado di accessibilità e di permeabilità di una determinata strada o di una determinata area. Queste note sulle analisi di flussi e di reti mostrano che se prese singolarmente, tali analisi possono servire in maniera limitata per un’indagine preliminare. Le analisi sui flussi – senza considerare modelli particolarmente sofisticati come i LUTM2 – non prendendo in considerazione gli effetti del tessuto urbano sugli spostamenti, sono di poco aiuto al professionista che si occupa di architettura o di urbanistica, poiché non sono in grado di valutare gli effetti che modifiche dell’ambiente costruito possono avere sugli spostamenti. L’analisi delle reti definisce misure interessati di centralità, ma la loro applicazione sui nodi del grafo (ossia gli incroci delle strade) non restituisce risultati particolarmente utili al progettista. Tuttavia, se combinate, queste due analisi possono costituire un modello particolarmente efficace: ovvero un’analisi che prenda in considerazione elementi spaziali costituenti il tessuto urbano – come le strade – e che sia in grado di stimarne l’accessibilità e la permeabilità,

Land-Use and Transportation Models, ovvero modelli per la pianificazione dei trasporti e dell’uso del suolo. Tali modelli sono molto usati e particolarmente efficaci nell’analizzare l’impatto dell’uso del suolo sulla mobilità e sulla domanda di trasporto. Sono anche utilizzati per indagare le caratteristiche urbane delle aree origine o destinazione degli spostamenti. Tuttavia, la loro complessità ne impedisce l’utilizzo nel processo di progettazione.

XII

permettendo di fare previsioni sulle possibilità di movimento che interessano una data configurazione spaziale. Collegando in maniera diretta l’ambiente costruito con le persone che lo utilizzano, sarebbe possibile spiegare determinati aspetti sociali attraverso il loro manifestarsi in determinate circostanze, o in determinati luoghi. È questo, in breve, il potenziale della sintassi spaziale. La sintassi spaziale è un insieme di teorie e modelli analitici, come

l'analisi assiale, l'analisi del segmento e l'analisi del grafo visivo, che ha alla base due proposizioni fondamentali. La prima è che lo spazio è intrinseco all'attività umana, plasmato in modo da riflettere l'interazione diretta tra spazio e persone: considerandoli intrinsecamente collegati, analizzare lo spazio, aiuta a comprendere la società. La seconda proposizione è che lo spazio è un'entità configurazionale, definita come rapporti simultaneamente esistenti tra le forme costruite (B. Hillier 1996). Ricerche ed applicazioni citate nel corso del testo hanno dimostrato l’esistenza di una forte relazione tra la configurazione urbana diversi aspetti della città, come il movimento pedonale, gli spostamenti veicolari, la locazione di attrattori, le destinazioni d’uso del suolo e la segregazione sociale. Per quel che concerne le tecniche di rappresentazione e analisi, queste sono state sviluppate da Hillier, Hanson, ed altri ricercatori, basandosi principalmente su concetti fondamentali come il movimento, la percezione visiva e l’occupazione degli spazi, che collegano direttamente spazio e persone. Le analisi utilizzano semplici attributi geometrici come linee di vista e di movimento per creare una rete, che è poi trasformata in un modello di relazioni – un grafo – per determinare il ruolo di ogni spazio all’interno dell’intera configurazione, o nelle sue parti. L'output dell'analisi è poi indicato da una gamma di colori dal rosso al blu scuro.

XIII

Una tipologia di analisi sintattica è la descrizione della rete di spazi pubblici attraverso una serie di linee assiali, che rappresentano le linee di vista e movimento. Questa rappresentazione permette di creare un modello della configurazione urbana che corrisponde direttamente con la percezione (visibilità) e l’attraversamento (movimento) delle persone (Figure 4.12). Le linee assiali possono essere trattate come entità continue, oppure possono essere suddivise in segmenti. Le relazioni tra ogni segmento e tutti gli altri sono calcolate da un software3 , utilizzando varie definizioni di distanza, come distanza metrica (cammino minimo), distanza topologica (cambi direzionali) e distanza angolare (spostamento angolare). La prima analisi è chiamata analisi assiale, la seconda analisi segmentale

angolare. Traducendo la rete di linee in un grafo rappresentante le relazioni topologiche fra gli assi, si analizzano i collegamenti di ogni spazio con tutti gli altri compresi nella configurazione. L'analisi può essere basata sulla profondità tra gli spazi, che è una misura di potenzialità come destinazione, ossia accessibilità, o basata sulla possibilità che lo spazio sia utilizzato da viaggi attraverso la configurazione urbana, che è una misura di potenzialità di at-

traversamento o permeabilità. La prima misura è chiamata integrazione, la seconda scelta (Hillier e Iida 2005). Infine, le analisi possono essere eseguite per l’intera configurazione urbana o parti di essa. Nell’analisi a scala globale, si considera ogni rapporto possibile nel sistema (da e verso qualsiasi luogo), mentre nell'analisi a scala locale il processo è limitato a un determinato raggio, che può essere topologico (fino a un certo numero di cambi di direzione da ogni linea), angolare (fino ad un certo grado di variazione angolare di ogni

DepthmapX. http://varoudis.github.io/depthmapX/

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segmento), o metrico (fino a una distanza metrica definita da ciascun segmento). Dopo aver introdotto le teorie e le analisi fondamentali della sintassi spaziale, queste sono applicate a scala globale sul caso studio di Torino, e a scala locale considerando l’area di Porta Nuova compresa tra il Centro cittadino ed i quartieri Crocetta e San Salvario. L’analisi assiale a scala urbana rivela come assi quali Corso Vittorio Emanuele II, Corso Regina Margherita, Corso Trapani (che poi diventa Corso Lecce e Corso Potenza) e Corso Galileo Ferraris siano particolarmente accessibili sia a scala globale (Figure 4.27) che a scala locale (Figure 4.29). Sono messi in risalto i principali accessi alla città, quali Corso Giulio Cesare, Corso Unione Sovietica e Corso Francia. L’area est della città risulta poco accessibile, essendo costruita sulla collina e divisa dal fiume Po. Scarsa accessibilità è anche rilevata all’estremo nord della città, nel complesso residenziale della Falchera. A proposito dell’area di Porta Nuova, gli assi maggiormente accessibili sono principalmente distribuiti tra Centro e Crocetta: Corso Vittorio, Corso Duca degli Abruzzi; Corso Galileo Ferraris; Corso Filippo Turati. San Salvario sembra essere leggermente meno accessibile a scala urbana. Ciò nonostante, Crocetta non presenta un’alta concentrazione di negozi sui suoi assi più accessibili, se non in Corso Peschiera e Corso Vittorio Emanuele II. San Salvario invece ha le due vie con la più alta concentrazione di negozi sui suoi due assi più accessibili: Via Nizza con 518 negozi e Via Madama Cristina con 397 negozi. È infatti evidenziato come la correlazione tra attività commerciali e integrazione sia più alta considerando l’accessibilità locale (Figure 4.32) piuttosto che l’accessibilità a scala urbana (Figure 4.30). Di conseguenza, un eventuale progetto per la ricucitura del tessuto urbano nell’area di Porta Nuova dovrebbe prevedere un’asse forte in direzione nord-sud, ben connesso con Centro e XV

Crocetta, con particolare attenzione alle connessioni con San Salvario, causa la minore accessibilità a livello urbano di quest’ultimo e l’alta presenza di attività commerciali altamente accessibili a scala locale. Tale asse potrebbe estendersi fino a toccare il complesso edilizio di Via Arquata, inglobando Via Mario Pagano e aumentandone l’accessibilità. L’analisi a segmenti a scala urbana mostra come il concetto di centralità non sia da intendersi come un dato statico, bensì dinamico nel tempo e a diverse scale. Per quel che concerne l’accessibilità, i pattern d’integrazione a scale locali evidenziano centralità nelle aree nord-ovest della città, mentre la zona di Porta Nuova non presenta particolari centralità. I pattern d’integrazione a scale più ampie evidenziano l’interazione tra Piazza Statuto e Corso Vittorio nella definizione dell’accessibilità del centro cittadino. Corso Vittorio è anche responsabile dell’alto grado di accessibilità in Crocetta (Figure 4.33). Per quel che riguarda la permeabilità, i pattern di attraversamento a scale locali evidenziano che l’area di Porta Nuova, non avendo sufficienti collegamenti tra Crocetta e San Salvario, soffre una mancanza di permeabilità, limitata al ponte di Corso Sommeiller e alla sezione di Corso Vittorio Emanuele II tra Porta Nuova e Piazza Carlo Felice. I pattern di permeabilità ad alte scale mostrano che Corso Vittorio Emanuele II ha il più alto potenziale di attraversamento della città, mentre il ponte di Corso Sommeiller perde d’importanza (Figure 4.34). Nel caso di un intervento nell’area di Porta Nuova quindi, si raccomanda la creazione di un sistema di spazi pubblici connesso, tramite l’asse sopra indicato in direzione nordsud, a Corso Vittorio Emanuele II, per favorire l’emergenza di centralità locali ben accessibili a scala urbana. Per incrementare il potenziale di attraversamento dell’area a scala urbana, potrebbe essere utile il potenziamento di Corso Sommeiller che attraversando i due XVI

quartieri e connette a un asse di livello gerarchico superiore. Connessioni aggiuntive con San Salvario al livello del terreno favorirebbero il bilanciamento degli attraversamenti est-ovest che ora gravano per la maggior parte su Corso Vittorio, andando al contempo a migliorare la permeabilità di entrambi i quartieri. L’analisi a segmenti su scala locale mostra che i pattern di accessibilità locale si spostano da Crocetta e San Salvario verso il Centro, poi verso Corso Vittorio Emanuele II, dal quale l’accessibilità a scale maggiori si ridistribuisce lungo gli assi che attraversano Crocetta e San Salvario (Figure 4.38). I pattern di permeabilità invece mettono in risalto il segmento di Corso Vittorio Emanuele II tra Porta

Nuova

Piazza

Carlo

Felice

(Figure

4.39).

consegue

che

un’eventuale proposta di riqualificazione dell’area dovrebbe prevedere altre assialità accessibili a livello locale, in direzione estovest tra San Salvario e Crocetta. Le estensioni di Corso Stati Uniti e Corso Guglielmo Marconi potrebbero essere le più adatte al caso, per la diretta connessione delle sedi del Politecnico e per l’attuale estensione di Corso Stati Uniti fino a Corso Castelfidardo, che consente facile accesso alla stazione ferroviaria di Porta Susa. Inoltre, per favorire l’emergenza di centralità locali che sfruttino il potenziale di Crocetta e San Salvario, il sistema di spazi pubblici e l’asse nord-sud sopra menzionato siano dovrebbero essere collegati al Centro e a Corso Sommeiller, passando per Corso Vittorio Emanuele II. A tale riguardo, la definizione dell’asse nord-sud potrebbe riguardare l’estensione di Via Lagrange verso il limite sud dell’area considerata fino a integrare il complesso edilizio di Via Arquata. Lungo quest’asse si potrebbero formare attraversamenti con diversa importanza che connettano Corso Galileo Ferraris, corso Duca degli Abruzzi e Corso Re Umberto I con Via Madama Cristina, Via Nizza e Via Ormea. È consigliato a questo proposito, il collegamento tra Corso GuXVII

glielmo Marconi e Via Pastrengo, come tra Via Berhollet e Corso Stati Uniti. Concluse le analisi primarie della sintassi spaziale, è introdotta l’analisi del grafo di visibilità (VGA). Tale grafo è creato formando una griglia di punti, a una distanza scelta dal progettista, che sono connessi qualora intercorra una relazione di intervisibilità (Figure 4.46). Su questo grafo sono calcolate misure di accessibilità visiva globali e locali. Una misura globale è l’integrazione visiva, che riflette la facilità di vedere – e dunque di spostarsi verso – un determinato luogo, tenendo in considerazione tutti gli altri luoghi presenti nella configurazione urbana analizzata. Una misura locale è la connettività, che calcola l’area visibile da ogni luogo. L’analisi di visibilità dell’area di Porta Nuova è stata eseguita considerando gli spazi occupati dalle ferrovie e dalla stazione come se fossero vuoti. Ciò ha permesso di valutare in che modo la configurazione urbana circostante possa influenzare lo spazio che dev’essere rimodellato, andando a costituire dei potenziali di accessibilità visiva locali e globali, che possono essere sfruttati in fase di progetto per la giustificazione di scelte progettuali, e possono essere fonte d’ispirazione per la proposta di soluzioni altrimenti non considerate. Alcuni esempi possono essere la mappa della visibilità locale (Figure 4.47), che presenta tracce indicanti la possibile connessione tra Corso Filippo Turati, Via Nizza e Corso Vittorio Emanuele II, come anche tra Corso Guglielmo Marconi e via Pastrengo. Oppure la mappa della compattezza (Figure 4.49), che identifica zone adatte alla creazione di spazi pubblici tra Via Donizetti, Via Nizza e Via Morgari. Sono state anche create mappe che identificano aree con alto potenziale di controllo visivo (Figure 4.50)!e aree altamente controllabili! (Figure 4.51). La mappa dell’integrazione visiva (Figure 4.52) indica la prevalenza degli attraversamenti trasversali dell’area, con particoXVIII

lare attenzione a Corso Stati Uniti e Via Berthollet, e l’estensione di via Lagrange fino al limite sud dell’area, con la formazione di aree molto accessibili che possono suggerire la locazione di eventuali spazi pubblici, per esempio tra corso Guglielmo Marconi e via Pastrengo, o tra via Magellano, corso Dante Alighieri e via Gaetano Donizetti. Un’ultima mappa d’interesse mostra i valori di intelligibili-

tà (Figure 4.54), che identifica zone che offrono un alto apporto alla comprensione della configurazione urbana partendo da via Lagrange in direzione Corso Filippo Turati, fino al limite con Corso Dante Alighieri. Le analisi del grafo di visibilità possono dunque essere interpretate, in fase di progettazione dell’area riguardante Porta Nuova, per introdurre una possibile estensione di Corso Filippo Turati e via Nizza, seguendo le relative assialità. Oppure, per la definizione di spazi pubblici in accordo con le aree di maggiore compattezza, integrazione e intelligibilità, o per la predisposizione di eventuali scuole, asili e parchi in accordo con le aree di maggior controllo e controllabilità. Come ultima analisi dell’area in questione è proposta una simulazione con un modello ad agenti. Tale modello, utilizza i dati ricavati dal

grafo

dell’ambiente

visibilità circostante

per agli

permettere

una

sorta

“agenti”,

che

sono

visone

rilasciati

all’interno della configurazione urbana, e la esplorano liberamente. I ricercatori Turner e Penn, idearono tale modello con l’obiettivo di esplorare in che modo i meccanismi decisionali a livello individuale andassero a formare i comportamenti osservati a livello di popolazione. Il modello in questione si distingue rispetto ad altri modelli, come per esempio i LUTM, per tre motivi: in primo luogo agli agenti non sono assegnate origini e destinazioni, ma si muovono liberamente nello spazio; in secondo luogo, questo modello non è calibrato su dati reali, gli agenti infatti ridefiniscono la propria destinazione in XIX

maniera continua in base ai parametri scelti dal progettista; in terzo luogo, non è assegnato loro nessun percorso predefinito. In altre parole, seguendo le scoperte della sintassi spaziale, questo modello ad agenti è un tentativo di capire fino a che punto la configurazione urbana, da sola, possa influenzare il movimento pedonale, avendo regole basate solamente sulla configurazione spaziale in questione. Dando agli agenti solamente la possibilità di vedere, i ricercatori furono condotti verso un modello di comportamento pedonale che porta gli agenti in fase di esplorazione a muoversi nella direzione che garantisce la maggior possibilità di spostamenti successivi. Tenendo presente queste considerazioni, e sapendo che un modello completo per la simulazione del movimento pedonale non può prescindere dall’inclusione di fattori socioeconomici, che hanno un ruolo decisivo nella scelta del percorso e nell’esplorazione di una configurazione urbana, l’area attorno alla stazione Porta Nuova è stata analizzata con l’unico scopo di valutare in che modo la configurazione urbana esistente può influenzare il movimento verso l’area lasciata libera dalla stazione. Le analisi sono state condotte con agenti aventi un campo visivo di approssimativamente 170°, cui è permesso esplorare l’ambiente circostante camminando al massimo tre chilometri, e ricalcolando la propria destinazione ogni 30 metri. Gli agenti sono stati rilasciati gli inizialmente da punti casuali scelti dal software (Figure 4.60 e Figure 4.61), poi sono stati fatti partire da Piazzale Duca d’Aosta (Figure 4.62 e Figure 4.63), Piazza Madama Cristina (Figure 4.64 e Figure 4.65) e Piazza Castello (Figure 4.66 e Figure 4.67), per un totale di quattro simulazioni che hanno permesso di rilevare differenze tra i movimenti in partenza dai tre quartieri coinvolti. Da queste analisi è emerso come la configurazione urbana esistente favorisca l’accesso all’area d’interesse principalmente attraverso Corso Vittorio, Corso Stati Uniti, Corso Dica d’Aosta, Corso Guglielmo Marconi, Via Claudio Luigi Berthollet e via Lagrange, confermando la XX

necessità di un’attenta connessione di questi assi per garantire una buona accessibilità dell’area. È anche confermata la necessità di prestare particolare attenzione alle connessioni con San Salvario, essendo questo quartiere il meno “esplorato” data la presente configurazione urbana. Si rileva infine che nonostante tale simulazione permetta al progettista di avere un riscontro quasi immediato sulle conseguenze di una proposta progettuale in merito al movimento pedonale, i risultati di tali analisi sono relativi solo all’influenza delle modifiche spaziali, e necessitano più ampie considerazioni di natura socioeconomica per essere utilizzate nella presentazione di una proposta progettuale. Come ultimo capitolo di questa tesi è illustrato il lavoro svolto presso il Massachusetts Institute of Technology, che affronta in maniera mirata il problema dell’accessibilità veicolare in ambiente urbano. È presentato il progetto DriveWave, ideato e sviluppato dal

Senseable City Laboratory per dimostrare in via sperimentale la gestione autonoma dell’attraversamento di un incrocio stradale da parte di veicoli autoguidati. Tale modello è confrontato con un convenzionale sistema di regolazione semaforica del traffico, permettendo di

evidenziarne

relativi

vantaggi

termini

dell’inquinamento atmosferico e della congestione stradale.

XXI

riduzione

2 INTRODUCTION 2.1 SMART CITIES In the past decade there has been a lot of discussion regarding new approaches to understanding and forecasting cities forms and functions. This debate is still ongoing today, and its arguments are oriented toward cities performances in terms of energy consumption, quality of life and services provided. The discussion gave birth to the concept of Smart Cities, that since the firsts definitions from Giffinger et al. (2007) and Hollands (2008) suffers languages and objectives diversification, given the multidisciplinary nature of the argument. Nevertheless, all these definitions imply a combination of coordinated actions, which aim to make the city more sustainable. But what does sustainable means when it comes to cities? As we anticipated above, first and foremost we look at sustainability from the environmental energy point of view, through choices and technologies that allow energy saving and which permit the use of renewable energy both at home as in the streets. Then we might think of sustainability from a functional perspective, ensuring the quality of urban services in response to user requests and developing adaptability. Sustainability is also measured in terms of quality of life, starting from the development of social participation toward a sense of community, and in terms of incomes derived from the development of new services. Lastly, sustainability is understood as the ability of city to plan with the environment and to give a flexible response to environmental emergencies such as those resulting from human activities (Annunziato 2012). To connect all these dimensions, extensive use of Information and Communication Technology (ICT) is required, and this gave birth to the adjective smart (Annunziato 2012). 22

As a matter of fact, urban performances nowadays depend not only on the city’s physical capital – the endowment of hard infrastructure – but also on the intellectual and social capital, namely the availability and quality of knowledge communication and social infrastructure. It ha been shown (Berry e Glaeser 2005) that the most rapid urban growth rates has been achieved in cities where a high share of educated labor force is available. Because not all cities are equally successful in investing in human capital, an educated labor force is spatially clustering over time. This tendency for cities to diverge in terms of human capital has attracted the attention of researcher and policy makers. It turns out that some cities, which were in the past better endowed with a skilled labor force, have managed to attract more skilled labor, whereas competing cities failed to do so. The concept of smart city has been introduced as a strategic device to encompass modern urban production factors in a common framework, and to highlight the growing importance of social and environmental capital in profiling the competitiveness of cities. Hence, a city can be defined as “smart” when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic development and a high quality of life, with a wise management of natural resources, through participatory action and engagement (Caragliu, Nijkamp e Del Bo 2009). Following the above-mentioned criteria, smart cities can be identified and ranked along six main axes or dimensions, which connect with traditional regional and neoclassical theories of urban growth and development. In particular, the axes are based on theories of regional competitiveness, transport and ICT economics, natural resources, human and social capital, quality of life, and participation of citizens in the governance of cities (Giffinger, et al. 2007). What differentiate the smart city approach from the past are the willingness 23

and the ability to capture in a single framework many aspects of the city that until now have been addressed separately. The city is thought of as a set of interconnected networks, such as the transportation network, the power grid, the buildings network, the network of social relations and so on. The integration of such networks in a coordinated design is the one that makes possible new services that were impossible in the past decade and opens the possibility of progressive transformation of the city.

2.2 A DIFFERENT WAY TO LOOK AT THE CITY Among the factors that shed new lights on how cities are being interpreted, evaluated, criticized and also planned, there are a series of theories, some well established, other gaining ground from the early 1980s. These theories are, in some ways, questioning some aspect of the current approach to urban and even architectural design. In his recent book â&#x20AC;&#x153;The new science of citiesâ&#x20AC;? Michael Batty pointed out how, throughout recorded history, the urban environment has been studied as a place whose form and structure can be represented as models, maps and pictures of locations. The author highlighted how this physical representation has framed our understanding of cities in terms of how we might manipulate urban activities. He claimed in fact, that location encapsulates urban activities, but does not reveal the relations and interactions between populations, which represent the essence of a city. If it is true that by the end of this century most people will be living in cities, is time we change our focus from location to interaction. If we approach cities by dealing merely with patterns of location, we might miss the point of why cities exist in the firs place. As Michael Batty suggest, we need a new methodology for understanding cities, that is 24

“[…] Built on concepts of why people come together to trade and exchange commodities and ideas, to realize social contacts, and to procreate; in short, to relate. Interactions, hence networks, are therefore considerably more important to our understanding and planning of cities than are locations.” (Batty, The new science of cities 2013, 15). As Batty suggests, this does not mean disregarding locations, for these are intimately related to interactions and networks. But if we agree that the patterns characterizing urban growth and form emerge as consequences of interactions, flows of energy, and information, then ideas about the shape of cities, about urban sprawl, about the integrity of neighborhood and so on, which have been used in the past in illustrating density and accessibility, follow naturally from this approach. As a matter of fact, the author reports Chadwick (1971) observation that early in the twentieth centuries, the approach and the ideas about urban planning moved toward more systematic social science approaches, which favored the collision between city studies and systems approach. Systems are defined as organized entities that are composed of elements and their interactions. It follows that hierarchical organization from the bottom up is essential for systems that evolves, and is the way nature and society develop robust and resilient structures. As soon as this notion of hierarchy became fashionable in thinking about cities and their neighborhoods, it was relaxed to reflect the notion that such strict subdivision could only be a simplification. Indeed, a famous paper entitled “A City is Not a

Tree” (Alexander 1965) argued that the kind of variety and diversity that was the essence of cities, as articulated by Jacobs (1961), was being destroyed by the implementation of city plans that imposed such 25

a rigid hierarchy through, for example, zoning. This way of thinking did not consider any sense in which systems might change, because when systems theory was first applied to cities, it was widely assumed that articulating the city system in terms of some long-term equilibrium was the appropriate response. Nevertheless, Batty emphasizes, cities might appear in equilibrium because of the huge lag between the speed at which the built environment changes and the way human behaviors change. He observed that the problem of a systems theory of cities is that it tends to underestimate external inputs. In the last fifty years in fact, has been realized that the notion of systems freely adjusting to changed conditions was never valid. If we agree to analyze the urban environment starting from the conception of an open system, existing in a volatile environment, then the idea of a top-down order is no longer appropriate. As the concept of openness become significant and the idea that systems in equilibrium are the exception rather than the rule, the idea that systems grew from the bottom-up gain ground. As Batty states:

â&#x20AC;&#x153;The complexity sciences, which change the focus from highly organized,

top-down

conceptions

much

organically

structured, bottom-up types of systems, grew from these concerns and now provide a framework that is considerably richer and more appropriate for the city systemsâ&#x20AC;? (Batty, The new science of cities 2013, 61). In human systems, decisions about organization and behavior are made at all scales, from the local and routine to the global and strategic, but they tend to be made by individuals and in this sense are highly decentralized. Articulating systems as structures that grow from the bottom up puts dynamic into account and leads to the notion that patterns and structure at the most local scale, where in26

dividuals operate, â&#x20AC;&#x153;emergeâ&#x20AC;? from these actions. Emergence, which involves novelty and innovation, is the watchword of this new view of systems. It captures the notion that a system such as a city cannot be planned from the top-down but emerge organically.

2.3 THE NEEDS AND CHALLENGES OF ANALYTICAL METHODS From the point of view of urban planners and architects the cooccurrence of the above mentioned theories and criteria, along with the increasing requirements of energetic efficiency, economic viability and level of comfort, among other requirements, have raised the bar in terms of the overall quality of their design proposals. In order to match the clientâ&#x20AC;&#x2122;s expectations, being a private, a group of stakeholders or a municipality, the designers increasingly need models and means of analyze their projects on a quantitative level to reduce the risk of failure and test their proposals against the required criteria. As highlighted by Karimi (2012), the challenge of using analytical methods in urban design begin with questions such as what type of analysis should be used, or how they should be applied. There are various analytical tools and models, such as transport models and planning models that have not been developed specifically for urban design, but have been used in the disciplines that are associated with urban design. Recently, with the advancement of computer programs, new techniques of rendering and 3D modeling have emerged that are mainly used in representation of design, but sometimes are also used to analyze specific aspect of the design. Among the most technical development in this field, perhaps the invention of Geographical Information Systems (GIS) has had the most direct influence on analytical approaches in urban planning. The capability of overlaying layers upon layers of geo-referenced data and the ability to analyze these layers quantitatively has turned GIS into a 27

powerful tool in urban planning. The primary problem with the implementation of these analytical techniques into the design process is the lack of an urban theory that could link physical aspects of the urban system with its functional, social and behavioral aspects. This theoretical shortfall, firstly highlighted by Hillier (1996), creates a gap between the analysis of things and how their manipulation in design could impact people. Karimi also cite Bryan Lawson’s opinion (2005), regarding the act of design, that see it inherently a process. A process, Lawson explains, which is normally considered as a continuous action, operation or series of changes that take place in a continuous manner, seems to be very relevant to any design activity. Furthermore, if we look at design as a process it seems natural that it starts with an initiation phase – a project brief, a request or some sort of undefined needs – and ends up with an outcome, as a plan or an object. In other words, design is a purposeful process that starts with some sort of objectives, well-defined or ill-defined, and ends up with an outcome that responds to them. From this point of view, it is clear that the design process involves some degree of problem solving or solution making: if we seek to respond to some objectives to produce a result through a series of actions, we have to think of different ways of achieving the results and responding to the challenges involved in each approach. It follows that design cannot be an entirely logical or discursive process, and that some form of intuition, creativity and novelty, which are not entirely governed by logical or scientific discourses, can be identified in some parts of the design process. During this process, designer are frequently involved in a ‘conjecture-test’ operation, which is predominantly based on a cycle of creating design concepts and testing them against certain criteria. Accepting that the design is not an entirely logical process give rise to the 28

question whether the design is an entirely intuitive process, where intuition is considered as a form of knowledge created by instinctive feelings as opposed to deductive knowledge, or it can be informed at any stages by non-intuitive actions, such as reasoning or analysis. It is in fact very difficult to argue that logical thinking cannot play a role in any part of the design process. Accepting the design as a purposeful process of problem solving inevitably leads to conceding that some degree of rational thinking and reasoning has to be applied throughout the process. As Lawson (2005) suggests, a design process needs to reflect on itself and assess whether the output of each stage respond adequately to the objectives of design, even if this reflection appears as an implicit form of reasoning. Designers with the task to design a project, at architectural scale as at urban scale, need to identify a number of questions that are either given to them directly, or arise from their own understanding of the tasks. Then they need to develop design ideas that would in their parts, or entirely respond to those questions. The important issue in this process is that the solutions have to be somehow evaluated against a series of criteria that are introduced internally or externally. A part of this evaluation takes place during the ideageneration stage, which is a conjecture-test cycle, which leads to an initial option generation and option testing. In a design process, conjectures are normally tested intuitively and the designers come up with their own judgment of whether or not a design conjecture would work. However when the design ideas are shaped, a more rigorous evaluation is needed to determine whether or not the design idea could potentially become the right design solution for the project. The conjecture-test cycle does not treat design as a linear process, but present it instead as a cycle of design generation and design development, where these two main stages are distinct but feed into 29

each other. The design cycle normally starts with an acknowledgement of what is intended to be achieved eventually, and at the end of the design development phase a design output appears, that needs to fulfill at least partially the requirements of the brief.

Conjecture

GOALS

DESIGN IDEAS

OUTPUT

Test

FIGURE 2.1 CONJECTURE-TEST CYCLE IN CREATION OF DESIGN IDEAS. MODIFIED FROM (OSTERWALDER, PIGNEUR E CLARK 2010).

Following Karimi (2012), what it is important is to understand whether any form of analytical investigation could be applied to any part of the design process, and if so where it should be applied to make a meaningful contribution. The author points to Blakey’s (1850) definition of the term analysis, as the process of dividing a complex entity into its constituent components, study each component in detail and bring them back together to form a better understanding of it. On the other hand, design is inherently a complex issue comprised of different components and facets. In principle the design process can be divided into components to be investigated separately and then be synthesized within the general framework of the design. In this sense analysis is an advantageous method when there is a need to build more rigor in the study of design components and evaluating them against certain criteria: even before generating any ideas, the analysis can provide the designers with the information that they might not be able to obtain intuitively. Furthermore, during the conjecture-test process, the ‘test’ part of the process could be enhanced by an analysis of the conjecture. It is conceivable that only intuition could be applied at this stage, but human intuition is limited in many ways and a pure intuitive test could be inaccurate and biased. Also after the formation of design ideas through a conjecturetest cycle, the design ideas could be tested more systematically using the same analytical techniques that are applied at the beginning of the process. The analysis of the design ideas would determine whether they respond adequately to the objectives of the design and whether they work as intended by designers. By applying analytical methods, a more reliable evaluation of the design ideas is expected, as it is not the mind of one individual that determines whether or not the design ideas would work, but there is a method that could be repeated and applied by others to get the same results. Following Karimi’s argument, two extra stages can be implemented be32

fore and after the idea-generation, or design development phases: in the beginning of the design process a set of analytical investigations, or a baseline analysis, is produced before the generation of any ideas or solutions. The baseline analysis aims to clarify the brief, the context limitations, particularities and other issues that are relevant to design. The design solutions are produced after the digestion of the analytical study, as well as the wider issues (social, economical, political) that exist and are relevant to the design. Once the design options are shaped, analytical tools are used to evaluate them. More than being just a rejection-approval filter, this phase could critically determine what aspects of the design options might not work. During the design development phase, where the design ideas are taken forward, analytical methods could still be used to assess specific aspects of the design. This could be achieved either by the analytical method that have been developed at the earlier stages or methods that are developed specifically to deal with certain aspects of the design. When dealing with architectural or urban design process, Karimi argues that four important characteristics seem to be more relevant than other, and thus should be met by the analytical method in order to be implemented within the design process. First, the analytical approach to be used in design has to be a spatial one, because both urban and architectural design are about creating and shaping spaces, and therefore the analytical approach chosen should deal directly with this important aspect of the design. Second, the spatial analytical approach should be able to link directly space with people and users, because urban and architectural design are about shaping space for the people, and if we analyze the space in isolation from how it would influence the life of people we just produce an abstract representation of the space. Third, the analytical ap33

proach has to be able to deal with different scales, because both urban systems and architectural projects manifest themselves in many scales, each one with different characteristic, but that are in continuous interaction with each other. Finally, a spatial analytic model has to be able to investigate a system as a whole or in its parts, because the parts are explored and perceived differently from each other and the entire system, but on the other hand the whole is made of its parts and it is influenced by these when it grows of transform. It is here argued that space syntax, a set of theories linking space and society and a set of techniques for analyzing spatial configuration, could provide such a means, along with two other analysis methods, Visibility Graph Analysis and a particular version of Agent

Based Modeling. The latter two analysis, that were built upon space syntax theories, serve both to extend its applications, taking account respectively of the visual perception of the environment and the forecasting of pedestrian movement, and also to reinforce its theoretical premises. However, before getting to the central question about what space syntax is and how it might be applied within the design process, a little digression on flows and networks is required, since both themes serve as a background to the argument.

4 MODELS AND ANALYSIS 4.1 NOTES ON FLOWS AND NETWORKS According to Lambiotte et al. (2011) the notion of flows and networks provides a dual conception that places each of these on different sides of the same coin. Flows have much more to do with processes across spatial and temporal scales, and reveal dynamics that drives the functions of the city in the short term and transforms it over the long term. Networks are generally the physical containers whose capacity constrains flows of materials, people, or ideas, and their physicality existing in real Euclidean space. If we take into account the flows of people and the network of the streets, it seems arguable that both concepts might account for, if not all, much of the functioning of a city. Also, both notions seems to be on the background of space syntax theories, given that theories as natural

movement (Hillier, Penn, et al. 1993) link together movement of people and the topology of the street network, and that much of the measures of space syntax analysis derives from network analysis. Given these premises, in what follows are exposed some opinions about how flows analysis and network analysis might be helpful to the designer who want to approach the design process from a broader perspective, taking as example the city of Turin.

4.1.1 FLOWS ANALYSIS It is hereby argued that some notion about how to handle and interpret the concept of flows is useful in understanding the dynamic of an urban system. From the perspective of the urban designer, having access to information pertaining the human movement can provide valuable insight to se up an overall picture that might support early design decisions. It is clear, and this is true for the majority of 36

the analyses, that the more precise and accurate are the data and the simulations, the less margin of error one might expect. Furthermore, when dealing with urban movement, transportation engineers are by definition the professionals who have the whole baggage of knowledge and techniques to manage such matters. Knowing this, is not the purpose of this work to deliver an exhaustive review of transportation models, rather to accommodate part of this very broad knowledge into visual information that could be useful to designers. To do this, we will follow Michael Batty’s notation (2013). As observed by the author, flows are summations or integrations of what happens at locations: these can be depicted as forces whose summation or integration generates potentials that pertain to what goes on in locations. Cities, manifest an essential tension in terms of flow patterns – pulling people, material and information into their cores (centralization) and pushing these same activities out to their edges in symmetric fashion (decentralization). Batty suggests to begin the analysis of urban flows by classifying sets of places in terms of origins and destinations. The volume of activity at the start of a flow is the origin !! , which are defined for a set i = !1, 2, … , !, where ! is the total number of such places. The volume of flow that is destined for a place is !! , where !! = !1, 2, … , !, and where !!is the total number of destinations. If happen to be ! = ! the number of origins and destinations is defined as !. We can then define a set of flows between origins and destinations as !!" . The most basic accounting relations can be stated as !!" !

!! = ! !

That is the amount of outgoing flow from an origin !! , and 37

(4.1)

!!" !

!! = !

(4.2)

Which is the amount of incoming flow into a destination !! . In both cases the total sum of flows ! is the summation that links flows to origins and to destinations as ! =!

!!" = !

!! !

!! = !

(4.3)

According to Batty (2013) the most common method of visualizing flows is achieved by plotting directional vectors from origins to destinations or vice versa, called desire lines in transportation modeling, with the flow volume set equal to the width of the vector. The problem with this approach is that as the number of origins and destinations increases, our ability to discriminate pattern from such flow maps becomes gradually more difficult. For this reason with the aim of having a general picture of the mobility in Turin, we restrict our analysis to the movement between neighborhoods, and we decided to map the volume of flows to both width and color of the vectors. We used public available data from the 2008 IMQ survey4, conducted by the AMM (Agenzia per la Mobilità Metropolitana), for commuters who use public and private transportation to travel, daily, between 91 sample zones in the province of Turin. The original dataset includes data for:

•

Turin (23 sample zones)

•

Immediate hinterland, called “Cintura” (31 sample zones)

•

Others aggregate communes (37 sample zones).

http://www.mtm.torino.it/it/dati-statistiche/matrici-origine-destinazione-imq-2008

The data is composed by total number of trips occurred between each two locations, calculated both with return to the origins and without it. For the purpose of this analysis we used the data including the return to the origins, and we built an origin-destination matrix merging the trips occurred by public and private means of transportation. In Figure 4.1 using QGis software we locate the centroid of each shape that represent a neighborhood and we plotted the gross

flow magnitude, that is the summation of the incoming and the outgoing flow between each two locations.

FIGURE 4.1 DIFFERENCE BETWEEN (A) TWO WAY FLOW, (B) GROSS FLOW, (C) NET FLOW

According to the analysis we have a total magnitude of flow equal to 1037962.65, distributed between 276 flow lines. The maximum flow value on a single path is 22710, whereas the minimum is 118 and the average is 3774.41. The five vectors that represent higher interaction between two neighborhoods are in order: Centro-San Donato, Centro-Crocetta,

Centro-Pozzo Strada, Centro-Cenisia, Centro-Borgata Vittoria. This suggests hat the city, in terms of general movements, is fundamentally a monocentric system gravitating around the city center, as can be seen from the picture, where all the main flow vectors are directed in there. Looking carefully though, some subcenters can be found, such as the one revolving around Lingotto and San Donato.

FIGURE 4.2 AGGREGATE FLOW BY VOLUME BETWEEN NEIGHBORHOODS OF TURIN. COLOR AND LINE THICKNESS MAPPED TO FLOW VOLUMES. DATA FROM THE 2008 IMQ SURVEY, SHAPE FILE COURTESY OF AGENZIA PER LA MOBILITÀ METROPOLITANA E REGIONALE

Looking at the total incoming flows and the total outgoing flows for each neighborhood, we realized that, taking into account the return to the origins for each trip, the movements are almost symmetric. In fact one of the findings of the 2008 IMQ report5 was that the mobility is essentially symmetrical with respect to the origin and destination: for every output movement from home corresponds, in most cases, one reverse movement performed later in time, to return home. Indeed the report shows that the movements not bounded by a relation exitreturn home are about 6% of the total. It is for this reason that, in order to highlight the asymmetries due to the different role of mobility generators and attractors, the AMM also provides the dataset that excludes the movements back to origins. We therefore used this dataset to compare the total incoming flows and the total outgoing flows for each neighborhoods, showing the differences between the most attractive ones, which have highest incoming flows, and the ones that act as a provider of movement, with highest outgoing flows. The exploration of this dataset allows us to classify the first five attractors of movement: City Center, San Donato, Nizza Millefonti, Cro-

cetta and Aurora. On the other hand, the first five provider of movement are: San Donato, Borgata Vittoria, Mirafiori Nord, Pozzo Strada and Parella.

http://www.mtm.torino.it/it/dati-statistiche/imq2008.pdf

FIGURE 4.3 OUTGOING FLOWS AND INCOMING FLOWS FOR EACH NEIGHBORHOOD OF TURIN.

To analyze this dataset also we used another methods illustrated by Batty (2013), that involves producing a weighted average of flows in the form of potential, which is then visualized as a vector whose direction is given by the average of all flow directions around the zone in question and whose magnitude is generated using the size of the flows. To construct this for origin zones ! and destination zones !, we first scale the flows !!" and express them as a proportion !!" of the total flow ! as !!" = !

!!" , !

!!" = 1! !

(4.4)

The coordinates of zones !, !! , !! , and !, !! , !! , define the direction of flow, and we then produce a weighted average flow for each origin in terms of coordinate displacements: ∆!! = !

!!" !! − ! !! !!"#!∆!! = !

!!" !! − ! !! !!

(4.5)

!!" !! − ! !! !

(4.6)

and for destinations as !!" !! − ! !! !!"#!!∆!! =

∆!! = ! !

Our new vectors define directions of average flow for each origin and destination as !! = ! !! , !! , !! + ∆!! , !! + ∆!! !!

(4.7)

!! , !! , !! + ∆!! , !! + ∆!! !!

(4.8)

and !! =

These vectors are called interaction winds by Tobler (1976). The great advantage of compressing flow information into this form is that the various asymmetries in the system are revealed. Therefore we looked at both interaction winds from origins, and into destinations. Figure 4.4 shows the weighted average flows from each origin. The neighborhoods seem to have outgoing flow with average direction pointing toward the city center. Some exceptions may be the following neighborhoods: • •

San Salvario, which points more toward Crocetta or San Donato; Crocetta, pointing toward San Donato;

•

Aurora, pointing toward Cenisia;

•

Barriera di Milano, pointing toward San Donato.

•

Cenisia, pointing toward Crocetta or San Salvario;

•

City Center, pointing southwest.

This led to think that on average there might be greater attraction potentials in the south-southwest of the city center. Also, if we look at the first five providers of movement listed above: •

San Donato points toward Crocetta and San Salvario;;

•

Borgata Vittoria also seems to point toward Crocetta, and it need to cross San Donato, Aurora and the center;

•

Mirafiori Nord is pointing toward the center but has to cross Santa Rita and Crocetta;

•

Pozzo Strada is also pointing toward the city center and Crocetta, but passing through Cenisia;

•

Parella is pointing toward the center, and has to cross San Donato and Censia.

These too seem to have an average direction pointing toward the immediate south of the city center. 44

FIGURE

4.4

DIRECTIONAL

FLOWS

FROM

ORIGINS

DESTINATIONS.

LINE

LENGTH

MAPPED

WEIGHTED AVERAGE VOLUME. DATA FROM 2008 IMQ SURVEY, SHAPE FILE COURTESY OF AGENZIA PER LA MOBILITÀ METROPOLITANA E REGIONALE

If we now look at the destinations, with the aim of knowing from which direction comes the flow directed into a neighborhood, on average, we are presented with the pattern shown in Figure 4.5. In contrast with the previous image, the average vectors does not show an average origin of movement located in the city center or in its immediate soutwest. Instead, here we can see that the majority of the neighborhoods of Turin have incoming flow that comes from the central-west area. If we look at the five main attractors of movements in fact we can note the following: •

The city center has the major incoming magnitude flow, and it comes on average from Pozzo Strada and Cenisia;

•

San Donato has incoming flow mainly coming from south, where there are neighborhoods like Cenisia, Crocetta and Santa Rita;

•

Nizza Millefonti has incoming flows from the center-west area, where there are Crocetta, Cenisia, San Donato;

•

Crocetta has incoming flow from the west, or from Cenisia, San Donato and Parella;

•

Aurora also have mainly incoming flow from the west area.

FIGURE

4.5

DIRECTIONAL

FLOWS

FROM

DESTINATIONS

ORIGINS.

LINE

LENGTH

MAPPED

WEIGHTED AVERAGE VOLUME. DATA FROM THE 2008 IMQ SURVEY, SHAPE FILE COURTESY OF AGENZIA PER LA MOBILITÀ METROPOLITANA E REGIONALE

These firsts analysis allows us to have an overall understanding about the urban movement that happen within the city: we now know what are the neighborhoods that have the majority of movement between them, which are the major attractors and providers of movement, and on average where the outgoing and incoming movement point to. It emerges in fact, that at urban level, the central, western and southern parts are the most “active” in terms of movement. Because these inferences are based on the 2008 IMQ survey, we can say that these are good indexes of daily movement patterns based on origin and destination matrixes. But What if we are willing to explore the poten-

tial to interact of certain spaces, without being bounded by real movement data? One example might be to use a model of spatial interaction based on Newton’s law of universal gravitation. Newton’s law articulates flow as a force between the volumes of activity of any origin ! and destination !. Newton’s law dictates that the force is equal to the product of mass and acceleration, with respect to two bodies defined by their size – population in our case – !! and !! and their deterrence effect. This law can be stated as !!

! = ! !!" = !!!! !! !!" !

(4.9)

Where !!" is the flow between locations ! and !, !!" is a measure of deterrence such as distance between the two locations, ! is a scaling constant and ! is a parameter that in the pure scaling case is 2, implying an inverse square law of distance. This model is symmetric if the distance matrix !!" is symmetric, meaning that !!" = !!" , ∀!" . According to Batty (2013), this is a good analogy to the way population interacts, in fact, Wilson (1970) defined four variants of spatial interaction models based on Newton’s law. These models constrain the predicted flows to external locational information, however for the 48

purpose of this introduction, we will focus on the simplest one, the

unconstrained model. In this model the overall activity is constrained by the total interactions !; ! =!

!!" = !

!! !

!! = !

(4.10)

the unconstrained model, as stated in equation Error! Reference source not found., can therefore be rewritten as !!

!!" =

!! !!!! !! !!"

!! !! !!"

=! !

! !! !! !!"

(4.11)

This includes the constant !, which ensure that the overall constraints on total flows ! are met. If the distance matrix is symmetric, then the model is symmetric in its flows and in the activities originating at each origin node and destined for each destination node. Applying the unconstrained spatial interaction model to the 23 sample zones of Turin, we can visualize the interaction between the various neighborhoods on the basis of their population6 with the deterrent effect of the distances calculated from the centroids of each neighborhood. In Figure 4.6 we show the results of this calculation that highlights the interactions between the neighborhood Aurora and Barriera di Milano, followed by Santa Rita â&#x20AC;&#x201C; Mirafiori Nord and

Lingotto â&#x20AC;&#x201C; Nizza Millefonti. Knowing that the model is symmetric in its distances !!" and in the activities at origins and destinations, we can appreciate the differences from the real system showed above in Figure 4.1. The fact that the unconstrained gravity model in its

data from the website of the city of Turin:

http://www.comune.torino.it/statistica/dati/territ.htm

basic form is symmetric as long been regarded as a problem, because most flow matrices that are observed in real spatial systems are asymmetric. In fact, Tobler (1976) suggests two strategies for dealing with asymmetries, but we wonâ&#x20AC;&#x2122;t dive in the specifications of these analyses because an exhaustive explanation of flow analysis is not the core of this work.

FIGURE 4.6 UNCOSTRAINED SPATIAL INTERACTION MODEL. POPULATION DATA FROM 2008 ISTAT SURVEY. SHAPE FILE COURTESY OF AGENZIA PER LA MOBILITÀ METROPOLITANA E REGIONALE

Nevertheless, it is argued that this model might be useful to professionals who are dealing with particular types of project, especially the one dealing with the regeneration of an urban area. This assumption it is made on the basis of what this model reveals, that is the pattern of interaction potentials between neighborhoods that might exists, but differs from the real situation. For example, the spatial interaction model presented in Figure 4.6, highlight that the two neighborhoods Crocetta and San Salvario have indeed a relatively high interaction potential between them, that is not revealed by the real movement data. Why this happen? It might be argued that one of the reasons of such difference lie, among other factors, on the particular

infrastructure

configuration,

which

encourages

certain

movements and discourages others. As a matter of fact, in between these two neighborhoods there is the train station Porta Nuova. In this case, the reconstitution of the urban grid in place of the train station – and the relative rails – might reconnect two areas of the city that have a sort of “natural inclination” to be connected, giving rise to new centralities that might benefit both the neighborhoods and the whole city. Of course, there are more powerful frameworks for generating such models: the most widely used functions that describes variety in complex systems is the measure of information – entropy – developed by Shannon (1948), which measures the degree of uncertainty in the probabilities of different components that make up the system in general. Also, if the aim is to simulate movement in a large-scale urban system one might think about the nowadays availability of powerful Land-Use and Transportation Models (LUTM). However our goal at this point is not to embrace complex urban dynamic processes, rather, to propose an instrument that might support the decision process in the early design stage of a project. 52

4.1.2 NETWORKS ANALYSIS In the last chapter we analyzed peopleâ&#x20AC;&#x2122;s movement data and we showed a model based on Newtonâ&#x20AC;&#x2122;s law of gravitational attraction to visualize the interaction potential between neighborhoods of Turin. In both cases we disregarded how these movement might be embedded in space: we assumed that flows could exist between any two locations, and we connected each origin and destination with straight lines. While this is understandable when analyzing flows, because what is of interest is how they constitute activity at locations, when analyzing networks the focus shift toward how locations are connected rather than on the volume of activity that get into (or out from) the locations (Batty 2013). For this very reason of being concerned about how connections between elements are made, when dealing with urban environment we cannot assume that connections can exist between any two locations. Also, in cities there are many networks that might be of interest, ranging from the more physical and visible, the street network, to more ethereal ones, like the social networks. The street network is precisely the one we are interested in because, being it composed by the street grid, it carries the movement of people within the urban environment. Following what Batty explains in his book (2013), here it is illustrated a generic approach to network analysis that enables us to introduce the basis of the argument and the principal measures that will afterward be applied in space syntax analysis. In order to illustrate the shift from flows analysis to networks analysis, we will still use the flow matrix !!" derived from the IMQ survey, however what we want to emphasize now is the pattern of con-

nection that this data reveal, and not the flow. For this reason, we will represent the neighborhood as nodes around a circle, connecting them if there is any movement in between. Such representation allows 53

to emphasize the emergent structure of connections caused by real movement. In fact, in Figure 4.7 we represented, to the left column, the flows between the 23 neighborhoods in Turin, built on symmetric flows !!" !!" = ! !!" + ! !!" !

(4.12)

Whereas to the right, we represented the pattern of connection caused by these flows. In both cases we have imposed different thresholds on the representations.

Flow Graph

Binary Connection MIRAFIORI SUD CAVORETTO

MIRAFIORI SUD CAVORETTO

CENTRO

MADONNA DEL PILONE

CENTRO

MADONNA DEL PILONE

SAN SALVARIO

REGIO PARCO

SAN SALVARIO

REGIO PARCO

CROCETTA

CROCETTA FALCHERA

FALCHERA

SAN PAOLO

SAN PAOLO BARRIERA DI MILANO

BARRIERA DI MILANO

CENISIA

CENISIA BORGATA VITTORIA

BORGATA VITTORIA

SAN DONATO

SAN DONATO MADONNA DI CAMPAGNA

MADONNA DI CAMPAGNA

AURORA

AURORA VANCHIGLIA

VALLETTE VANCHIGLIA

VALLETTE

NIZZA MILLEFONTI

PARELLA PARELLA

NIZZA MILLEFONTI

LINGOTTO

POZZO STRADA

MIRAFIORI NORD

MIRAFIORI SUD

MIRAFIORI SUD CAVORETTO

CENTRO

MADONNA DEL PILONE

REGIO PARCO

BARRIERA DI MILANO

SAN DONATO

MADONNA DI CAMPAGNA

AURORA

VANCHIGLIA

PARELLA

AURORA

VANCHIGLIA

VALLETTE

NIZZA MILLEFONTI

PARELLA

LINGOTTO

MIRAFIORI NORD

CENISIA

BORGATA VITTORIA

SAN DONATO

VALLETTE

SAN PAOLO

BARRIERA DI MILANO

CENISIA

BORGATA VITTORIA

CROCETTA

FALCHERA

SAN PAOLO

MADONNA DI CAMPAGNA

SAN SALVARIO

REGIO PARCO

CROCETTA

FALCHERA

CENTRO

MADONNA DEL PILONE

SAN SALVARIO

POZZO STRADA

SANTA RITA

Completely connected C(Ψ) =0.996 Ψ=0

SANTA RITA

CAVORETTO

LINGOTTO

POZZO STRADA

LINGOTTO

POZZO STRADA

SANTA RITA

NIZZA MILLEFONTI

MIRAFIORI NORD

SANTA RITA

Strongly connected C(Ψ) =0.545 Ψ = 2000 MIRAFIORI SUD

MIRAFIORI SUD CAVORETTO

CAVORETTO

CENTRO

MADONNA DEL PILONE

SAN SALVARIO

REGIO PARCO

BARRIERA DI MILANO

VANCHIGLIA

NIZZA MILLEFONTI

LINGOTTO

POZZO STRADA

MIRAFIORI NORD

SANTA RITA

SAN DONATO

MADONNA DI CAMPAGNA

AURORA

PARELLA

CENISIA

BORGATA VITTORIA

SAN DONATO

VALLETTE

SAN PAOLO

BARRIERA DI MILANO

CENISIA

BORGATA VITTORIA

CROCETTA

FALCHERA

SAN PAOLO

MADONNA DI CAMPAGNA

SAN SALVARIO

REGIO PARCO

CROCETTA

FALCHERA

CENTRO

MADONNA DEL PILONE

AURORA

VANCHIGLIA

VALLETTE

C Weakly connected C(Ψ) =0.245 Ψ = 4756

PARELLA

NIZZA MILLEFONTI

LINGOTTO

POZZO STRADA

MIRAFIORI NORD

SANTA RITA

FIGURE 4.7 FLOW GRAPH AND RELATIVE BINARY CONNECTION OF THE 23 NEIGHBORHOODS OF TURIN. DATA FROM 2008 IMQ SURVEY. IMAGE MODIFIED FROM BATTY (2013)

In Figure 4.7a we plotted the complete flow matrix, in Figure 4.7b only flow volumes greater than 2,000 and in Figure 4.7c only flow volumes greater than 4,756. Even if the graphs are derived from flow data (the width of the links in the images to the left is proportional to the flow volume), the binary graphs that underlie these flows are plots of connections between the nodes, which we show to the right. This way of represent the network allows to highlight the structure of connections, that defines if a system is more or less connected. Graphs can be classified as completely connected, strongly connected,

weakly connected and disconnected (Harary, Norman e Cartwright 1965). A completely connected graph is one in which every node is connected every other node directly. In a strongly connected graph, a node is connected to every other node directly or indirectly, that is through intermediate nodes. A weakly connected graph is one where all nodes are connected, but it is not possible to find a direct link from every node to every node. In a disconnected graph, at least one node cannot reach another, breaking the graph into two or more subgraphs. As Batty highlights (2013), looking at interactions with attention to the connections, raises the question of how well connected the structure we are examining is. This allows to introduce an index of connectivity that give some sense of how graphs change as they become less dense. This is done by counting the links that exist in the graph and form the ratio with the total possible links. Following Battyâ&#x20AC;&#x2122;s notation, which define a graph generically as !(!, !) with nodes ! and links !, if we set the flow threshold at !, then we can define a binary relation on the graph as !!" = !

1, !"!!!" > !!, ! â&#x2030; ! ! 0, !"â&#x201E;&#x17D;!"#$%!

The connectivity !(!) is then defined as 56

(4.13)

!(!) = !

! !!"

! !â&#x2C6;&#x2019;1

(4.14)

where we are not counting the self-flows !!! ; hence, !!! = 0, â&#x2C6;&#x20AC;!. The maximum number of links is the sum of all the other possible links between the nodes, which depends on the number of nodes !. This measure, !(!), varies between 0 and 1, with 1 occurring when there are connections everywhere, and 0 occurring when there are no connections and the system is entirely disconnected. Connectivity is therefore the ratio of the positive binary links defined above the threshold !, to the total number of possible links. What emerge from this first analysis is that if we regard the data of the real movements that take place within Turin from the point of view of the network it creates, the city appears as a completely connected system, one in which a person can potentially move from everywhere to everywhere. As we increase the threshold, disregarding the movements below a certain volume, the connectivity of the system is reduced, but in spite of this many movements are still allowed that, for going from a neighborhood to another, pass through the city center or through the neighborhoods San Donato, Crocetta and Aurora. We also found that the neighborhood most susceptible to the increasing of the threshold is Regio Parco, meaning that is the one with both lower incoming flows and lower outgoing flows, and it is in fact the first neighborhood that would be completely disconnected if the threshold is increased over 4,756. Another interesting thing is that the city hardly become a disconnected system: as we increase the threshold, instead of creating two or more separate subgraphs, the city tends to completely isolate single neighborhoods and preserve its connections to the city center.

Two other graph measures that are concerned with the nodes and will be useful in subsequent analyses are the number of links destined for a node, that is the in-degree of a node !! , and the number of links originating from a node, that is the out-degree !! . The in-degree and out-degree of a node could be seen as measures of “local accessibility” to nodes, because these only take account of direct links to the nodes in question. In the case of binary graphs, these measures of degree are a count of the number of links into or out of any node as !! =

!!" !

!! =

!!"

(4.15)

Given these two basic measures, we can now focus on the type of networks we are interested in, the street networks. These are defined

spatial networks, because their nodes and links are embedded in space: nodes are intersections, and streets are links. A common property of street network is called planarity, meaning that these networks can be drawn on a plane such that no edges cross each other (Clark e Holton 1991). However, for networks such as streets, planarity tends to be an idealization because roads sometimes do not intersect but cross through tunnels and bridges. The total distance ! ! in such graphs can be defined as !!" ∝ !!

! ! = ∀!,! !!!"!!!!" !!

(4.16)

Where !!" is the distance between node !! and !! and the link !!" = 1. Figure 4.8 shows the street network of Turin, derived from a dataset built by the CSI Piemonte (Consorzio per il Sistema Informativo). The 58

dataset is composed by two â&#x20AC;&#x153;shape filesâ&#x20AC;?: in one it is stored the information regarding the location of the street intersections, represented as nodes, in the other it is stored information about each street segment, represented as links.

FIGURE 4.8 THE STREET NETWORK IN TURIN

From this, we note the following key properties of the graph. There are 14,204 nodes and 21,034 links, that leads to a very low average connectivity of the entire graph from equation (4.14) as 0.000104, which implies an average degree (noting the graph is symmetric and in- and out-degrees are the same) of NC( ) = 2.962.

FIGURE 4.9 DEGREE DISTRIBUTION OF LINKS IN THE TURIN STREET NETWORK

The distribution of degrees has a maximum number of links equal to 8, of which there are two nodes: one is the intersection between Via

Francesco Cigna, Via Cuneo and other minor streets, the other is Piazzale Regina Margherita, in which converge Corso Regina Margherita, Corso Tortona and other minor streets, all connected with the bridge Ponte Regina Margherita. The distribution of degrees from 1 to 7 links follows as [1.242, 2.265, 6.711, 3.791, 176, 16]. The total distance in the graph is the number of edges E, which from equation Error! Reference source not found. is D = 21,034. If we use actual distances

computed from the map itself, then this total is 1,731 kilometers, and we can compute the average real distance per segment as 81 meters. Having introduced the concept of spatial networks and planar graphs, and the essential measures of connectivity, in-degree and out-degree we can illustrate the measures that we are really interested in, that will also be used in space syntax. These are measures of â&#x20AC;&#x153;global accessibilityâ&#x20AC;? of the nodes, which in network analysis are usually defined as centrality. As seen above, local accessibility measures such as in-degree and out-degree are a simple count of the direct links of a node. Instead, if we consider all the links in the network, the measures for each node would reflect the total distances from all nodes to the node in question. The inverse of these measures is what produce an index of accessibility. A measure related to this is called closeness centrality, used in social network theory (Jackson 2010) and is defined as: !!

!! = !!!!! = !

!!"

(4.17)

Where ! is a constant set as the number of nodes less one, !! represents the total adjacent distances and !!" is measured as the shortest route from ! to!!. This is the measure solely for out-degrees since the in-degree measure follows directly. From the CSI street network dataset, we have extracted and rebuilt the topology of the network using Gephi software 7 , an interactive visualization and exploration platform for networks and complex systems, which allow us to measure the closeness centrality of the

http://gephi.github.io

street network of Turin, showed in Figure 4.10. Given the fact that the closeness centrality index, as stated above, measures the average distance from a given starting node to all other nodes in the network, it follows that this measure will highlights the most central nodes in the network as the ones that are closer, on average, to all other nodes, and are therefore the ones with the higher accessibility. Even if it might appear a measure of little interest, when it will be applied to streets instead of being applied to their intersections, as in space syntax framework, it will reveal one of the most interesting patterns of the street network, which is the level of in-

tegration of the street.

Closeness Centrality High

Low

FIGURE 4.10 CLOSENESS CENTRALITY OF THE TURIN STREET NETWORK

The last centrality measure we will introduce is betweenness cen-

trality !! , which measures how often a node appears on shortest paths between nodes in the network. Freeman (1979) defines it as !! = !

!!"# ! !!"

(4.18)

where !!"# is the number of shortest routes from node ! to ! but passing through node !. Also this measure will be applied, within space syntax framework, to streets rather than to intersections, and it will reveals which streets will be the most used when we take into account movement from every street in the city, to every other streets. This measure will be called choice.

Betweenness Centrality High

Low

FIGURE 4.11 BETWEENNESS CENTRALITY OF THE TURIN STREET NETWORK

This brief introduction about flow and network analysis allowed us to familiarize with the concept of flows and network, and to evaluate their potentials and their deficiencies. On one hand, flow analysis might be interesting and useful to professionals like architects and urban planners because it allows to have a generalized idea about urban movements and interaction potentials. However, flow analysis does not consider any way in which the built environment may affect urban dynamics, and therefore the effects of interventions dealing with spatial entities can hardly be assessed. On the other hand, network analysis, when applied to nodes of spatial networks such as the street networks, with a certain degree of planarity, does not seem to provide meaningful information. Luckily, a family of analyses that, dealing directly with built environment, can provide useful insight about urban dynamics, through the use of measures derived from network analysis, does exist. Space

syntax, in fact, is a corpus of theories and quantitative analyses that is able to forecast the presence of people in certain areas of the city, investigating the topological structure that underlie a spatial configuration.

4.2 SPACE SYNTAX Space syntax can be described as a set of theories linking space and society and a set of techniques to analyse spatial configuration (B. Hillier 1996). The corpus of both theoretical background and empirical application was developed by Hillier, Hanson, Peponis, Turner and Penn, among others researcher, at the University College of London between the late 1980s and the early 2000, and it is increasingly being adopted in either academic and practice projects. The aim of space syntax is to describe the logic of society through its manifestation in spatial systems: in this view, the way spaces are put together â&#x20AC;&#x201C; through their configuration â&#x20AC;&#x201C; relates directly with how people perceive, move through and use spatial systems of all kind, ranging from small domestic spaces to large-scale urban settlements (B. Hillier, Space is the Machine: A Configurational Theory of Architecture 1996). In other words, the research tended toward the developing of an understanding of the way that spatial design and social function were related. Generally speaking, space syntax is an overarching concept and a set of specific theories, such as the theory of order and struc-

ture (Hanson 1989), natural movement (Hillier, Penn, et al. 1993), centrality as a process (B. Hillier 2001) and movement economy (B. Hillier 1996). Furthermore, there are analytical models and tools, such as axial analysis (Hillier e Hanson 1984), visual graph analysis (Turner 2003), and angular

segment analysis (Hillier e Iida 2005), which are direct products of the main theoretical paradigm and its theoretical proposition. Karimi (2012) highlights that the core concept of space syntax can be explained through two fundamental propositions. The first proposition is that space is intrinsic to human activity, not a background to it. Space is shaped in ways that it reflects the direct interaction between space and people, and through this the space we create â&#x20AC;&#x201C; the 68

built environment â&#x20AC;&#x201C; becomes humanised. An important implication of considering space and society as inherently corresponding entities is that by analysing one we create a deep understanding of the other. Analysis of the society is admittedly a much more difficult task, as it involves dealing with the intricacies of humans and lack of tangible and measurable components or features (Bernard 2000). On the contrary, analysis of space is a much more achievable task. The second core proposition of space syntax is that space is fundamentally a configurational entity (B. Hillier 1996). Configuration, simply defined as simultaneously existing relations, is about the composition of the built form from the parts that are in a unique relationship with each other. Both of these propositions are further explored in the subsequent section. The research has shown that there is a strong relationship between spatial configuration and how people move through the city (Hillier, Penn, et al. 1993). The spatial configuration is also associated closely with other important issues in the city, such as: the pattern of vehicular movement, cognition and wayfinding, location of prominent urban elements, land uses, social segregation and crime. As there is a direct relationship between spatial configuration and urban functions, analysis of the spatial configuration provides a powerful tool for designing, shaping, maintaining and altering urban functions. On the basis of this assumption, a series of representation and modelling techniques has been developed for analysing spatial configuration. These techniques are primarily based on fundamental concepts, such as movement, visual perception and human occupation, which link physical space with people directly (Karimi 2012).

FIGURE 4.12 THE MOST FUNDAMENTAL, YET SIMPLE, ATTRIBUTES OF THE SPACE IS USED TO CREATE SPACE SYNTAX MODELS. IMAGE FROM KARIMI (A CONFIGURATIONAL APPROACH TO ANALYTICAL URBAN DESIGN: ‘SPACE SYNTAX’ METHODOLOGY 2012).

The models use simple geometrical attributes, such as line of sight and movement or visual fields, to create a network. This network is then turned into a pattern of relationships, or a graph representation, which can be analysed quantitatively to determine the relative role that each space plays in the configuration of the system, as a whole, or in its parts. The output of the analysis is usually shown by a range of colours from dark red to dark blue.

FIGURE 4.13 AXIAL MAP OF LONDON, UK. FROM PENN AND TURNER (2002).

A very important type of syntactic analysis for urban studies is the description of the network of public spaces by a series of axial

lines, which represent the longest lines of sight and movement. This is an efficient representation of the spatial configuration described by a network of lines that can be analysed easily. The advantage of this model is that it creates an uncomplicated model of the spatial configuration that corresponds directly with how the configuration is perceived (visibility) and navigated through (movement) by people. The direct association between how space is configured and how people use it creates an analysis that could be used and interpreted directly in the design process. The lines in the spatial configuration could be treated as continuous entities, or they can be decomposed into segments. The relationship between each segment and all other segments is calculated by 71

computer software, using various methods, such as metric distances (how far to travel), topological distances (how many changes of direction) and angular distances (what degree of angular shift). The second type of analysis is called an axial analysis and the third, which has been developed more recently, is called segmental angular

analysis (Hillier e Iida 2005). By translating the network of lines into a graph that represent the topological relationships between lines, a quantitative analysis of the system is performed by calculating how each space is connected with the other spaces in the system. The analysis can be based on the relative depth (or shallowness) of spaces from each other, which is a measure of ‘proximity’ or ‘to-movement’, or based on the possibility of being used by journeys throughout the system, which is a measure of ‘betweenness’ or ‘through movement’. The former measure of analysis in space syntax terminology is called integration and the latter is called choice (Hillier e Iida 2005). Each of these measures explains certain aspects of the urban structure and is used in connection with specific questions that have to be answered in an urban study. The analysis can be performed for the entire system (the global network), or parts of it (the local network). In the global scale of analysis, it is taken into account every possible relationship in the system (from anywhere to anywhere), whereas in the analysis at local scale the process is restricted to a certain local catchment – a ra-

dius – which could be topological (up to a certain number of changes of direction from each line), or angular (up to a certain degree of angular change from each segment), or metric (up to a defined metric distance from each segment)

FIGURE 4.14 ANGULAR SEGMENT ANALYSIS OF THE CITY OF JEDDAH, SAUDI ARABIA. ANALYSIS AT LOCAL LEVEL (A) PICKS UP LOCALLY DISTINCT AREAS, BUT IN THE CITYWIDE ANALYSIS (B) A TOTALLY DIFFERENT PATTERN EMERGES. FROM KARIMI (2012) .

The local and global analyses are very useful method for looking at different scales of a spatial system, but they could also be used to define how an entire system is understood by the perception of its parts. The congruence between local and global spatial configuration determines how intelligible the system is to the people who navigate through it (Hanson 1989). The intelligibility of the network in another set of analytical metrics that could be used in the process of urban design. Analysis of different urban systems shows a remarkable degree of consistency in results. In most cities, the spatial structure is normally a

“Foreground network of linked centres at all scales, set into a network of largely residential space” (B. Hillier, Space is the Machine: A Configurational Theory of Architecture 1996). These centers range from very local centers, where you find very local functions, to major centers of large cities, where a specialized system of high permeability routes and smaller urban blocks facili73

tate a more complex urban system. The research also shows that the structure of the grid correlates consistently with the pattern of pedestrian and vehicular movement and other issues such as the distribution of land uses and social behavior. We will introduce the argument by first giving an overview of the main theoretical framework that build the foundation of space syntax, and therefore introducing the analyses considered along with an example of application to the city of Turin.

4.2.1 SPACE SYNTAX THEORIES 4.2.1.1 R ELATIONS ,

CONFIGURATIONS , AND SPACE

Hillier pointed out that, because they were are concerned about the relations that happen in a spatial configuration, some specifications about the terms relation, configuration and space are required. Regarding relations, as Hillier highlights (1996) these seems to exist “objectively” but we cannot point directly to the relation in the way that we can do to other entities. Therefore the author suggests we may accept what Russel (1912) argues, that

“The relation, like the terms it relates, is not dependent on thought, but belongs to the independent world which thought apprehends, but does not create. […] We must then accept that a relation is neither in space nor in time, neither material nor mental, yet it is something” (B. Russell 1912, 153-154). That said, taking in account any pair of elements in a complex, we can refer to terms like adjacency or permeability to define a simple relation. Given our interest in the relations that happen within the space, how can we define the space itself? First and foremost, as Hillier suggests (1996), space is vacancy rather that thing, and its bodily nature cannot be taken for granted in the way we think we can take objects for granted. Secondly, related spaces cannot be seen all at once, but require movement from one to other in order to experience the whole. Regarding the bodily nature of space, the author suggests that we might agree that the dominant view of space in western culture has been one we might call the “Galilean-Cartesian”, derived from a scheme of reasoning first set out by Descartes (1911). He argued that the primary properties of physical object are their “exten75

sionsâ&#x20AC;?, their properties like length, width and breadth. Because these extensions can be measured independently from human agency, extensions can be seen as the objective properties of things. That said it is easy to see the extension also as the primary property of the space within which objects sit, also because when we take the object away from its space its extension is still present as an attribute of space. From this point of view then, space is generalized extension, or extension without object. Nevertheless, Hillier argues that following this reasoning space comes to be seen as the general abstract framework of extension against which the properties of object are defined, or a metric background to the material object that occupy space. Once we see space in this way we are doomed not to understand how it plays a role in human affairs. Hence, the author suggests that culturally and socially space is not simply the inert background of our material existence, but it is a key aspect of how societies and cultures are constituted in the real world and, through this constitution, structured for us as objective realities. According to this view, space is more than a neutral framework for social and cultural forms: it is built into those very forms. Therefore human behaviour does not simply happen in space, but it has its own spatial form. Beyond this, for what concerns related spaces, Hillier argues that we might expect the relation between people and space to be found at the level of the configuration of space rather than the individual space. Consequently, the author suggests that we might need to think about space as a configuration, since it is as configuration that it has its most powerful and independent effects on the way buildings and built environments are formed and how they function for their purposes. With this in mind, what is the meaning of â&#x20AC;&#x153;configurationâ&#x20AC;?? Hillier (1996) point out that if we define spatial relations as existing when 76

there is any type of link between two spaces, like adjacency or permeability, then a configuration exists when relations between two spaces are changed according to how we relate one or other, or both, to at least one other space. Therefore a configuration is a set of interdependent relations in which each is determined by its relation to all the others. According to this definition, a spatial configuration consists of elements (buildings, roads, etc.) and the relations between them. Consequently, as the author emphasizes

“the techniques of configurational analysis made it possible to bring the “pattern aspect” of things in architecture and urban design into the light of days, giving quantitative expression to the idea that is “how things are put together” that matters” (B. Hillier, Space is the Machine: A Configurational Theory of Architecture 1996). As Hillier explains, architectural and urban design can be seen as fundamentally configurational in that the way parts are put together to form the whole is more important than any of the parts taken in isolation. He also suggests that the designer is in effect a configurational thinker, because the object of architectural and urban attention is precisely the configurational idea. This of course does not mean that the designer does not think of object, but that at the same time he or she thinks of configuration.

4.2.1.2 C ONFIGURATIONAL

ANALYSIS AND UNIVERSAL DISTANCES

In his book Bill Hillier (1996) highlight how, despite built environments appear to us as a collection of buildings, and as such subject to physical laws, this is not all it they are. In fact in terms of spatial and formal organisation, built environments are also configurational entities, whose forms are not given by natural laws. He pointed out how if we wish to consider built environments as organised systems, then their primary nature is configurational, because 77

is through spatial configuration that the social purposes for which the built environment is created are expressed. We can highlight some of the basic effect of configurational behaviours by using some examples directly from the book:

FIGURE 4.15 EXAMPLES OF RELATIONS, CONFIGURATIONS, AND J-GRAPHS (B. HILLIER, SPACE IS THE MACHINE: A CONFIGURATIONAL THEORY OF ARCHITECTURE 1996).

In Figure 4.15 we can see in the first row some set of simple relations between two elements, in the second row the same set of relations involving a third elements, thus defining different configurations, and in the third row a device called the j-graph, or justified graph. As Hillier explains, in Figure 4.15i, a and b are two cubes standing on a surface. In Figure 4.15ii the relation of a and b is symmetrical in that a being the neighbour of b implies that b is the neighbour of a. In Figure 4.15iii b now appears to be “above” a, and the relation of being above, unlike that of being “neighbour of” is not symmetrical but asymmetrical: b being above a implies that a is not above b. In 3.1iiii we have the same set of relations represented 78

in the pictures above, but this time taking into consideration a third element c. As we can see, in Figure 4.15ii the relation of both a and b taken separately, to the third object c, is also symmetrical, as is their relation to each other. This is to say that a and b are symmetrical to each other as well as with respect to c. This is a configurational statement, since it describes a simple spatial relation in terms of at least a third. What happens to the relation in Figure 4.15iii is that although a and b remains symmetrical with respect to each other, they are no longer symmetrical with respect to c. The difference between Figure 4.15ii and Figure 4.15iii is then a configurational difference. The situation is clarified by the justified graphs in which nodes represent the elements and relations are represented as links. The nodes are aligned above a root – a circle with a cross inscribed – according to their depth from the root, meaning the number of “steps” they need to take to arrive at the root node. The number beside each node represents the sum of depth from that node to the other nodes in the graph. For example, in Figure 4.15v a has total depth 3, because it is one step away from the root node plus two steps away from b. In Figure 4.15vi a has total depth 2 because it does not needs to pass through the root node to access b, therefore it is one step away from

c, the root node, and one step away from b. In Figure 4.15v a and b are each independently connected as neighbours to c, while in Figure 4.15vi the relation of neighbour between a and b is added. In Figure 4.15vii the relation between b and c is broken, creating a “two deep” relation between b and c. The total depth (td) of Figure 4.15v and vii is therefore 8, while that of Figure 4.15vi is 6. We might say then, that the distribution of total depths and their overall sum describe at least some configurational characteristics of these composite objects. 79

In Figure 4.16, also from Hillierâ&#x20AC;&#x2122;s book, we show a series of figures composed of cells with total depth for each cell to all others inscribed, and the sums of these total depths for each figure below the figure. These are all composed by seven cells plus an eighth which is joined to the right progressively more centrally.

FIGURE 4.16 THE EFFECT OF MOVING ONE CELL IN THE CONFIGURATION AS A WHOLE AND ON OTHER INDIVIDUAL

CELLS

(B. HILLIER, SPACE IS THE MACHINE: A CONFIGURATIONAL THEORY OF

ARCHITECTURE 1996).

There are two effects from changing the position of this single element. First the total depth values and their distribution all change. Second, the sums of total depth for each figure change, reducing from left to right as the eighth element move to a more central location. This illustrates two key principles of configurational analysis. First, changing one element in a configuration can change the configurational properties of many others, and perhaps of all others in a complex; second, the overall characteristic of a complex can be changed by changing a single element, meaning that changes do not cancel out their relations to different elements and leave the overall properties invariant.

Hillier highlights how the distributions of depths are the most fundamental idea in quantifying the configuration properties of spatial complexes. However he notes how the original definition from Buckley and Harary (1990) – that of the sum of distances from a node to each other node in the graph – was very substantially affected by the number of nodes in the graph. Therefore he proposed a normalization formula in (Hillier e Hanson, The Social Logic of Space 1984, 108) which eliminates the bias due to the number of nodes in the graph, thus enabling the assignation of numerical values that express total depth independently of the size of the graph. These normalized values can be referred as the degree of integration of an element in a complex, and it has proved to be fundamental in the empirical study of spatial configuration because, as Hillier underline, its simplicity conceals a very fundamental theoretical property: that is essentially a generalisation of the idea of distance. Our common concept of distance is that of a specific number of metric units between one point and another, and the author suggest that we can call this “specific distance”. Total depth sums all specific distances from a node to all others, thus we may think of it as a “universal distance” from that node. If specific distance is about the metric properties of shapes and complexes, universal distances seem to be the key to configurational properties. Universal distance seems to be a generalisation of the idea of depth that permits configuration to become the central focus of analysis.

4.2.1.3 N ATURAL

MOVEMENT , FROM

H ILLIER

ET AL .

(1993)

In a paper written by Hillier et al. (Natural movement: or, configuration and attraction in urban pedestrian movement 1993) the professor and the researches brought to light a new ‘configurational’ paradigm in which a primary property of the form of the urban grid is to privilege certain spaces over others for movement. It suggested that the configuration of the 81

urban grid itself is the main generator of patterns of movement; retail land uses are then located to take advantage of the opportunities offered by the passing trade and may act as multipliers on the basic pattern of natural movement generated by the grid configuration. In the above-mentioned paper, the authors highlighted how the spatial configuration can have effects on movement that are independent of attractors. In fact the attraction theory of pedestrian movement8 said little about the way in which the spatial elements through which people move (streets, squares, and so on) are linked together – the spatial configuration – to form some kind of global pattern. Using schematic examples and testing the theory against real pedestrian data, they showed that the spatial configuration have effects on both through movement, if the layout is considered as a system of possible routes, and on to-movement, if the layout is considered as a system of origins and destinations.

FIGURE 4.17 EFFECTS OF CONFIGURATION ON THROUGH MOVEMENT: (A) THE MORE CENTRAL SEGMENTS OF THE ‘MAIN STREET’ ARE LIKELY TO BE THE BEST USED, AND THE PERIPHERAL SEGMENTS THE LEAST. (B) THE TWO MOST CENTRAL VERTICAL ELEMENTS, ONE ABOVE AND ONE BELOW THE ‘MAIN STREET’, WOULD BE ON MORE SHORTEST ROUTES THAN MORE PERIPHERAL VERTICAL ELEMENTS. MODIFIED FROM HILLIER ET AL. (NATURAL MOVEMENT: OR, CONFIGURATION AND ATTRACTION IN URBAN PEDESTRIAN MOVEMENT 1993)

In the attraction theory movement is seen as being to and from built forms with differing degree of attraction, and design is seen as coping with the local consequences of that attraction (Hillier, Penn, et al. 1993)

FIGURE 4.18 EFFECTS OF CONFIGURATION ON TO-MOVEMENT: THE ‘CENTRAL SQUARE’ (A) AND CENTRAL ELEMENTS (B) OFFER A METRICALLY OR TOPOLOGICALLY MORE ACCESSIBLE DESTINATION THAN THE OTHER SPACES IN THE LAYOUT. IMAGE FROM HILLIER ET

AL.

(NATURAL MOVEMENT: OR, CONFIGURATION AND ATTRACTION IN URBAN

PEDESTRIAN MOVEMENT 1993)

It follow that, the authors argued, if configurational priorities are both present in urban grids and important enough to have significant effect on movement patterns, as an urban system evolved, the distribution of built-form attractors might itself be influenced by these priorities. Spaces which the grid configuration prioritized for through-movement might for that reason already have been selected as a good locations for ‘passing trade’ land uses. Similarly, topologically or metrically accessible locations may have been preselected for types of land use where this was a useful asset. Therefore in a situation where movement, configuration, and attraction were all in agreement there would be logical reasons for preferring configuration as the primary ‘cause’ of movement. The presence of attractors can influence the presence of people, but it cannot influence the fixed configurational parameters that describe its spatial location. Similarly configuration may affect movement, but configurational parameters cannot be affected by it. If strong agreement it is found among all three, then the authors suggests that it must either be the result of chance or the result of configuration having influenced both the pattern of movement and the distribution of attractors. Because movement generated by the grid configuration is so basic, they suggest it should be identified with the term natural movement. 83

FIGURE 4.19 A IS ATTRACTION, C IS CONFIGURATION, M IS MOVEMENT. ATTRACTORS AND MOVEMENT CAN INFLUENCE EACH OTHER, BUT THESE CANNOT INFLUECE THE CONFIGURATION. IMAGE FROM HILLIER ET AL. (NATURAL MOVEMENT: OR, CONFIGURATION AND ATTRACTION IN URBAN PEDESTRIAN MOVEMENT 1993)

Of course, as Hillier et al. suggest, this is not to say that natural movement in not a culturally variable phenomenon. On the contrary, it takes different forms in different cultures, reflecting the different spatial logics of the urban grid. Urban grids are cultural products because they create, through natural movement, encounter fields, with different structures. What is invariant about natural movement is the logic that links spatial configuration to movement. The key element in this relation is that natural movement is a global property of a configuration in that it responds to configurational parameters that relate each spatial element to every other element in a system that may be several kilometers in diameter. This is to say that the fundamental proposition of natural movement is that movement in an urban grid is determined, other things being equal, by the distribution of the configurational quantity called integration. Consequently the theory of natural movement shows that movement is

fundamentally a morphological issue in urbanism, a functional product of the intrinsic nature of the grid, not a special aspect of it.

4.2.3 SPACE SYNTAX ANALYSIS 4.2.3.1 A XIAL A NALYSIS Following Hillier’s reasoning (Hillier, Penn, et al. 1993), the grid of a city may be defined as the system of public access created by the way in which buildings are aggregated and aligned. For clarity we might represent an urban grid by reversing usual conventions and showing the space as black and built ‘islands’ as white.

FIGURE 4.20 AEREAL VIEW OF A PART OF TURIN, TO THE RIGHT A CONVENTIONAL MAP HIGHLIGHTING THE BUILDINGS IN BLACK, TO THE RIGHT A MAP OF THE GRID HIGHLIGHTING THE ROADS IN BLACK

Most of the grids are deformed grids, characterized by apparent irregularity, rather than ideal grids, characterized by geometric regularity. The deformed grid is different first in that the shaping and alignment of islands break the continuity of lines of sight and access across the grid; and second, in that spaces vary in width as one passes along lines. More formally, the authors suggest, it might be 86

said

that

two

types

deformity

have

been

introduced:

one-

dimensional, or axial, deformity by breaking up of lines of sight and access; and two-dimensional, or convex, deformity by variations in the width of spaces. Urban grids are also by definition continuous, and this creates a non-trivial difficulty: in the words of the authors

â&#x20AC;&#x153;How is the urban grid to be represented as a set of discrete elements to make configurational analysis possible?â&#x20AC;? (Hillier, Penn, et al. 1993, 33) One answer might be to use the traditional street network representation illustrated in Figure 4.8, where intersections are nodes, and streets are the links between them.

FIGURE 4.21 PART OF THE STREET NETWORK OF TURIN REPRESENTED WITH THE ENVIRONMENT AND THE SAME STREET NETWORK REPRESENTED AS A GRAPH WHERE NODES ARE THE INTERSECTIONS AND EDGES ARE THE STREETS

The problems with this representation is that it makes grids look too similar to each other, and few interesting consequences, theoretical or empirical, seem to follow from this representation (Hillier, Penn, et al. 1993). As a consequence Hillier and Hanson (The Social Logic of Space 1984) have come to the formulation of an axial map of an urban grid, as the one shown in the upper image in Figure 4.22: it consists of the longest and fewest straight lines that can be drawn through the spaces of the grid so that the grid is covered. These lines are called

axial lines and can be thought of as lines of sight or unobstructed movement. Subsequently, starting from the axial map, an axial graph is constructed, that consists on the graph in which the lines of the axial map are the nodes and the intersections of the lines are the edges, as the one shown in the lower image in Figure 4.22. The axial graph is constructed in order to calculate topological relations between axial lines, that is, what line intersects which other lines. It follows that the relations between them cannot be embedded in Euclidean space as it was in the case of the traditional street network representation, instead these have to be distance in the topological rather than the Euclidean sense. On the axial graph there are a number of measures that can now be used to describe configurational properties of the grid. The simplest are those describing the local properties of a node in the graph (that is, an axial line) like con-

nectivity, control value and others. Other measures take into account the relations between each space and the whole system: among the most important are choice and integration.

FIGURE 4.22 PART OF THE AXIAL MAP OF TURIN REPRESENTED WITH THE ENVIRONMENT, AND THE SAME AXIAL MAP REPRESENTED AS THE AXIAL GRAPH WHERE NODES ARE THE AXIAL LINES AND EDGES ARE THE INTERSECTIONS.

As said above, space syntax research has found that spatial configuration alone explains a substantial proportion of the variance between aggregate human movement rates in different locations of urban space. Although it seems possible to explain how people move on the basis of these analyses, the question of why people move this way has seemed more problematic. One possible explanation for the predictive power of the method is that the way people understand their environment and decide on movement behaviors is somehow embedded in space syntax analysis. From this point of view, Alan Penn (2001) suggests that ‘cognitive space’, defined as the space which supports our understanding of configurations more extensive than our current visual field, is not a metric space, but topological in nature. He made this assumption on the basis of several studies that investigated the way humans move about in different spaces. Dongkuk Chang’s doctoral research examined pedestrian movement behavior in two multi-level urban complexes, the Barbican and the South Bank Centre in London. A key observation of his research was that maintaining a straight line appeared to be preferred to maintaining a correctly oriented trajectory toward the final destination, with deviations along shorter lines taking place later in the trip to bring one to the destination (Chang 1998). Young Ook Kim’s doctoral research was aimed to understanding the relationship between configuration, cognition and behavior. He conducted a study of the Hampstead Garden Suburb area of North London in which he combined observations of spatial behavior, questionnaire interviews, including a sketch mapping exercise with a sample of 76 local residents, and space syntax analysis of the spatial configuration of the area embedded in its surroundings as well as of the sketch maps. Strong correlation were found between axial integration in resident sketch maps and axial integration of the real map, confirming that although the sketch maps were often distorted and partial, in syntactic terms they repre91

sented global features of the real world (Kim 1999). Ruth Conroy’s doctoral research used a head mounted display to immerse experimental subjects in a series of virtual models of real and experimental urban areas. She shows that linearity is strongly conserved, with subject usually following long lines of sight with pauses in configurationally ‘integrated’ locations offering strategic visual properties, long lines of sight and large isovist areas. Furthermore, this axial map approach has proven quite unexpectedly successful in generating not only models for predicting urban movement, but also strong theoretical results on urban structure. In response to the questions

“How can so much of the geometric and metric complexity of urban space be discounted, and so much weight put on a simple line representation? Why should topological rather than metric measures then be chosen?” (B. Hillier, The hidden geometry of deformed grids: or, why space syntax works, when it looks as though it shouldn't 1999) Bill Hillier proposed that the axial analysis does not ignore the geometric properties of space but internalizes them into the axial map, and it is precisely because it does so that it is able to pick up the non-local properties of spaces that are critical to the movement dynamics through which a city evolves its essential structures. He presents this argument firstly by analyzing axial maps of cities from a geometrical point of view, and showing that these axial maps manifest consistent ways of relating geometric variables such as line length and angle of intersection. Then Hillier shows that the emergent global structures of cities seems to have pervasive geometric properties which combine aspects of both orthogonal and radial grids, two of the prime ‘rationalist’ geometric notions by which designers have sought to create ideal cities. In spite of this appar92

ently pervasive geometry, it is then explained how space syntax seems to account for the spatial and functional dynamics of cities without reference to geometry. He used as example an axial map of a part of London analyzed and shaded from dark to light according to the ‘local integration’ values of each line calculated by summing depth only for lines that intersect the root line, and those which intersect these. Measuring the angle formed by these incident and parallel lines, it was found that a high proportion is either near right-angle connection of very obtuse angles. Moreover it was found that highly obtuse angles of incidence were associated with longer lines and near right angles with shorter lines. The same analysis was carried out for a city that at first seems as axially different as possible from London, the Iranian city of Hamedan, where the same type of relation between line lengths and angles of incidence was found. The precise geometric parameters of line length and angles of incidence are set differently, but the general process of ‘geometric construction’ is in these respects strikingly similar.

FIGURE 4.23 ABOVE, AXIAL MAP SHOWING THE LOGARITHM OF LOCAL INTEGRATION IN LONDON. BELOW, AXIAL MAP SHOWING THE LOGARITH OF GLOBAL INTEGRATION OF HAMEDAN. IMAGES FROM HILLIER (THE HIDDEN GEOMETRY OF DEFORMED GRIDS: OR, WHY SPACE SYNTAX WORKS, WHEN IT LOOKS AS THOUGH IT SHOULDN'T 1999).

Another finding concerned the fact that it was found an unexpected degree of order in the axial maps not only at the level of the geometric construction. It also appears at the global level. In fact, considering the whole visual pattern formed by the most integrated lines in the axial analysis, in both cases it was composed of two dominant elements: on one hand, the obtuse angle sequences form radial routes from more central to more peripheral areas, on the other hand, a more grid-like central area, at one more orthogonal and smaller in block scale, to some part of which most radials connect. Again, Hillier highlights how such order was brought to light through axial analysis, which takes no account of geometrical variables. After having proved how axial map embed geometrical properties of the space, Hillier shows how it seems to be the mere existence of relation between elements, without considering such matter of angles or lengths, that captures dynamic processes by which evolving space structure influences movement, leading to effects on land-use pattern and, through multiplier effects back on movement, to further elaboration of space structure. He drew a notional street grid shown in Figure 4.24(a), made up of a main horizontal street, a secondary vertical axis, and some other back streets. He assumed the grid to be loaded everywhere with buildings that both generate and attract movement, and that the movement tended to take the simplest available routes. On the basis of these prerequisites, he speculated that more routed would tend to pass through the main horizontal street. Hillier reinforced the power of the grid structure to influence events by focusing on two different streets of the grid, Figure 4.24(b) and (c) and drawing the respective j-graphs, Figure 4.24(d) and (e). These show not only that the shallower the graph is to the â&#x20AC;&#x2DC;rootâ&#x20AC;&#x2122;, the more probable it is that a trip of n-line segments will include a 95

segment of the root line, but also that the shallower the j-graph is to its root space, the more accessible that root space is as a destination from all other spaces. He concluded that it was this combination of accessibility and potential permeability â&#x20AC;&#x201C; that is, of to movement and through movement â&#x20AC;&#x201C; that is expressed by the various measures of integration. Furthermore, he argued, in shading the notional grid from dark to light according to integration, as in Figure 4.24(f), it is represented the potential of the different grid elements for both accessibility and movement. Seen this way, the relation between grid structure and movement seems to be related entirely to syntax, as captured in the j-graph, and has little to do with geometry. In fact, if the grid is deformed geometrically without changing its topology, as in Figure 4.24(g), no difference is made to the analysis. If however the connection of lines is changed, as in Figure 4.24(h) and (j), then the whole distribution of integration changes.

FIGURE 4.24 NOTIONAL STREET GRID. IMAGE FROM HILLIER (THE HIDDEN GEOMETRY OF DEFORMED GRIDS: OR, WHY SPACE SYNTAX WORKS, WHEN IT LOOKS AS THOUGH IT SHOULDN'T 1999).

4.2.3.2 A NGULAR S EGMENT A NALYSIS The studies carried out on axial maps of various cities revealed that in order to understand patterns of accessibility and movement in their finer structure, it was necessary to break the axial lines at their intersections. This because even if the axial analysis is performed considering only local radiuses â&#x20AC;&#x201C; for example only axial lines that are two step away from the one analyzed â&#x20AC;&#x201C; the integration values are assigned to the entire axial line. Consequently, more localized patterns of integration, which might occur between smaller portion of the streets are not highlighted. It was therefore decided to work on the creation and the analysis of a segment map, a disaggregated line-network representation that, starting from the existing axial map, represent the street network subdividing the axial lines into segments between intersections. An angular segment analy-

sis was therefore introduced, which performed over the segment map, could records the sum of the angles turned from the starting segment to any other segment within the system (Alasdair Turner 2007). Figure 4.25(a) is an example of an axial map with three axial lines, and its graph representation is shown in Figure 4.25(b). Figure 4.25(c) shows how the axial map is disaggregated at intersections to form a segment network, and Figure 4.25(d) shows its graph representation. To construct the graph, each line segment is represented as a node in the graph and links between nodes are intersections, in a similar way to which the axial graph is constructed.

FIGURE 4.25 LINE-NETWORK MODEL AND ITS DISAGGREGATED MODEL WITH GRAPH REPRESENTATION FOR EACH MODEL. IMAGE FROM HILLIER AND IIDA (NETWORK EFFECTS AND PSYCHOLOGICAL EFFECTS: A THEORY OF URBAN MOVEMENT 2005)

The distance cost between two line segments is measured by taking a ‘shortest’ path from one to the other, so the cost of travel between ! and ! can be given as !(! − !!) ! + !!(!), while the cost between ! and!! can be !(!) ! + !!(!! − !). As Turner highlights (2004) by breaking the axial lines into segments we run into the segment problem, that is concerned with increasing resolution: if you divide an axial map into segments, a previously integrated long line turns into a segregated mass of shorter lines, as you must take a step each time to pass from one segment to another along the length of the line. Angular segment analysis was introduced to overcome this problem: be99

cause there is no angular turn to another segment that leads straight on, there is no associated cost, and thus a path that continues in the current direction is by definition continuous across the junction. Furthermore, as in axial analysis, radius measures can be used to avoid edge effect or to observe a local phenomenon. Rather than calculate the graph measure from a segment to all other segments, the measure is calculated from one segment to all other segments within a certain metric radius. Angular segment analysis was then compared to two other distance definitions between adjacent segments, which reflect different conjectures in the literature as to how distance is conceptualized in human navigation, and all of these were tested against real movement data to see which one would correlates best. Paths between all segments and all others were then assessed in terms of least angle

paths, least length and fewest turns. Least length paths are the shortest metric distances, fewest turns paths the least number of direction changes, and least angle paths the smallest accumulated totals of angular change on paths, between all pairs of nodes. The real movement data was sampled in four areas of London (Barnsbury, Clerkenwell, South Kensington and Knightsbridge) on which earlier studies had established dense vehicular and pedestrian movement flows at the segment level throughout the working day for a total of 356 observation â&#x20AC;&#x2DC;gatesâ&#x20AC;&#x2122;. The street network was then analyzed using the closeness and betweenness measures. Translating the numerical results of the analysis into images of the network, with segments colored in bands of value for each measure, from red for least distance through to dark blue for most, Figure 4.26 shows that the different interpretations give different pictures of the network, some slight, others more substantial. The upper row of figures shows

100

closeness (a: geometric, b: topological, c: metric) and the figures at the bottom betweenness (d: geometric, e: topological, f: metric).

FIGURE 4.26 DISAGGREGATED LINE NETWORK MODELS OF BARNSBURY AREA IN LONDON, EACH COLOURED UP BY A DIFFERENT MEASURE. IMAGE FROM HILLIER AND IIDA (NETWORK EFFECTS AND PSYCHOLOGICAL EFFECTS: A THEORY OF URBAN MOVEMENT 2005)

In the majority of the correlations against real movement data, least angle correlations were found to provide the best results. In the remaining cases fewest turns was best, but in each case only marginally better than least angle. In no case was a metrically based measure best, and in no case was a least angle measure worst.

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4.2.5 SPACE SYNTAX APPLICATION TO REGENERATE URBAN AREAS In order to illustrate how space syntax analyses may be embedded in the early design stage, what follows illustrate an application for a project of urban regeneration in Turin. The software used to draw the axial maps and to perform the relevant analysis is DepthmapX9 developed by Alasdair Turner and maintained by Tasos Varoudis at UCLâ&#x20AC;&#x2122;s Space Syntax group, which offers a wide range of analyses and measures. 1. The firsts step is to perform an axial analysis at a citywide scale, in order to provide an overall picture about the integration value of all the streets that compose the urban street network. We will consider the global integration and the integration at radius 7 and radius 3. 2.

The axial map is therefore broken up in its segments between intersections, in order to perform an angular segment analysis, and investigate the street network at a higher resolution. Also the angular segment analysis is carried out at a citywide scale and at different radii, calculating both integration values and also choice values.

3. After this citywide analysis, the attention shift on the zone of interest, the area of the train station called Porta Nuova. Both an axial analysis and a segment analysis are executed at this scale, to reveal more localized patterns of axial and segment integration at the neighborhood scale. With the aim of having an overall picture regarding the integration level of the urban area, in order to spot the relevant axes that might occur within the project site and to have a broad picture of

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the city structure, we have drawn the axial map of the city within its municipal boundaries. Over the axial map of the city, we have performed a global axial analysis, a local axial analysis at radius 3, and a local axial analysis at radius 7 (also called radius-radius). The reasons for having a global analysis is that, according to the theory of natural movement, movement along a particular street within the city is mainly influenced by its position in the larger-scale urban grid, and we must include enough of the whole urban grid in the analysis to ensure that each line in the project area is embedded in all the urban structure that may influence its movement. We cannot then do better than to begin with the whole of an urban system, in order to ensure that the project area is sufficiently well embedded. In Figure 4.27 we show the global integration analysis, which reveal the most global structure of Turin, highlighting the historic center as the most integrated part of the city. In particular, an inspection of the numerical values reveals which are the most globally integrated streets. The first five streets in order from the main integrated are Corso Vittorio Emanuele II, Corso Regina Margherita, Corso

Trapani (that then becomes Corso Lecce and afterward Corso Potenza), Corso Galileo Ferraris and Via Bertola (that becomes Via Principe Amedeo). The analysis also successfully picks up the main accesses to the city: to the north we have Corso Giulio Cesare, that subsequently becomes the highway to Milan; to the south it highlights Corso Gio-

vanni Agnelli, that becomes Corso Unione Sovietica and connects to the ring road; from north-west to east we find Corso Regina, that traverse the whole city and connects to the north ring road; finally to the west it is highlighted Corso Francia, that it also is connected to the ring road. The global integration analysis also recognize the east area of Turin as less integrated: it is in fact the part of the 103

city which is built on the hill, on the east riverside, and has few streets coming down to the city. Ultimately, Falchera, an housing estate to the north, it is also identified as poorly integrated within the urban structure: it is indeed an area whose, since its conception as working class dwelling units, after the Second World War, has been conceived as an enclosed borough separated from the city, and its reconnection to the city is argument of constant debate.

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FIGURE 4.27 TURIN, GLOBAL INTEGRATION

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Figure 4.28 illustrate the radius 7 integration (or radius-radius), where the integration analysis is set at the mean depth of the whole system from the main integrator, which in this case is 6.66. This means that the integration analysis is restricted to those lines which lay 7 steps depth away from the line for which integration is calculated. As Hillier suggests (1996) the effect of setting the radius of analysis at that of the main integrator is that each line is analyzed at the same radius, which is at the same time the maximum radius possible without differences in radius between lines. The effect of a radius-radius analysis is to maximize the globality of the analysis without inducing â&#x20AC;&#x2DC;edge effectâ&#x20AC;&#x2122;, that is, the tendency for the edges of spatial system to be different from interior area because they are close to the edge. As it is shown by the figure, this reduces quite drastically the amount of highly integrated lines, especially within the center of the city. In spite of this, the five most integrated streets remain almost the same with minor adjustments: the first one is still Corso Vittorio Emanuele II, Corso Trapani is now at the second place, while Corso Regina Margherita is at the third place, Via

Giuseppe Garibaldi (that becomes Via Cibrario) take the place of Corso Galileo Ferraris, which is now at the fifth place, while Via Bertola goes at the tenth position. Also the four most integrated accesses to the city that we listed above, along with the residential neighborhood Falchera, are confirmed.

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FIGURE 4.28 TURIN, RADIUS 7 INTEGRATION

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Figure 4.29 shows the integration analysis at radius 3, where the integration analysis is restricted to those lines that lays three steps depth away from the line for which integration is calculated. According to Hillier (1996), the radius 3 integration analysis produces another highly informative map, which highlights a much more localized structure, including most local shopping streets, while it also confirm the most integrated streets and accesses to the city. The fact that these streets are not only the strongest global integrator in Turin as a whole, but also the stronger local integrator, give rise to the assumption that their relevance is not confined at the urban scale, indeed, with particular emphasis on Corso Vittorio Emanuele II, these streets play a central role at the local level, being the main attractor of movement in their immediate surround. The reason why a spatial analysis can give such a cross-scale functional picture is due to the influence that natural movement â&#x20AC;&#x201C; the tendency of the structure of the grid itself to be the main influence on the pattern of movement â&#x20AC;&#x201C; has on the evolution of the urban pattern and its distribution of land uses.

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FIGURE 4.29 TURIN, RADIUS 3 INTEGRATION

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A good example might be the distribution of retails: we can think of commercial activities as attractors of urban movement, and then located on the most integrated streets. But as argued above, even if the presence of shops attract people, it cannot change the integration value of a line, since it is purely a spatial measure. We might think that the shops were deliberately located on integrating lines, because these are the lines that naturally carry the most movement. From this point of view, we can argue that the relation between the urban grid and the natural movement influences the location of the shops. It might seems common sense for a retailer to locate his shop where people are going to be anyway, and therefore it is conceivable that the structure of the urban grid influences at least some land uses. However what is being claimed by Hillier goes beyond this: he suggests that there is an underlying principle which, other things being equal, relates grid structure to movement pattern not only on the main streets in and out of a city, but also in its fine structure, and through this gives rise to a multiplicity of inter-relationships between grid structure, land uses and densities. To test this assumption and confirm the theory on movement economy (B. Hillier 1996), we explored the relationship between the location of the retails and the integration values of the relative streets. From the site of the municipality of Turin10 we downloaded a QGis project file of the city containing, among other things, information about the location and the type of commercial activities within the municipal boundaries. We have therefore matched each store to the nearest axial line, and we have measured the correlation between integration values of the axial lines at different radii and the number of

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stores found on those axial lines. In general we found that there is a positive trend at citywide level, meaning that the more a street is integrated, the higher the concentration of shops found on that street. Indeed the higher count of shops is always found on very well integrated streets, and never on poorly integrated ones. However, when the number of retails per street is correlated against global integration values the correlation coefficient is low, while it increase gradually when the count of shops on axial lines is correlated with more localized measures of integration. The results are shown in the following images, along with the scatterplot representing the correlations between the two values, and some basic statistics.

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FIGURE 4.30 SHOPS IN TURIN: CORRELATION WITH GLOBAL INTEGRATION

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In Figure 4.30 we show all the retails in Turin represented as dots in their current location, and colored according to the global integration value of the nearest axial line. As we can see from the picture, despite having the majority of shops identified as laying on very well integrated streets, the distribution of shops per streets does not correlates much with global integration value, with an R2 of 0,26. This does not means that there is not a relation between the position of the shops and the integration value, in fact there are not so many shops colored in blue, that would suggest these shops were located on poorly integrated streets. It means that the count of shops found on different streets, does not have a good correlation with the global integration value. This might be due to the edge effect we mentioned above, among other factors. On the other hand, it is shown that at least there is some relation between the two values.

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FIGURE 4.31 SHOPS IN TURIN: CORRELATION WITH RADIUS 7 INTEGRATION

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In Figure 4.31 we show a similar picture, but this time the shops are colored according to the integration at radius 7 of the nearest street. If we reduce the radius of the integration analysis to a radius 7, and measure the count of retails per streets against the integration value at this radius, the correlation slightly increase. This reveals that, as a matter of facts, the edge effect of the global integration was playing a role in determining the correlation coefficient. Nonetheless the R2 that is now almost 0.30, it is still a poor value. Lets reasoning on why: these global values are an index of accessibility potentials measured taking into consideration respectively all the lines in the system, and those that lies 7 step-depths away from the line observed. This means that the lines that are the most integrated at these levels are the most reachable at a citywide scale. We might assume that exists a certain amount of people that, traveling by car, is willing to traverse the city to go shopping in particular places, eventually taking advantage of some offers or promotions. At the same time, we may argue that the amount of people that traverse the city to go shopping with public or private means of transportation, is on a daily basis lower than the amount of people which go shopping in retails at a walkable distance from their home or place of work. This assumption can be reinforced by the fact that Turin, despite being a capital, and one of the bigger cities in Italy, is still limited in its dimensions compared to other European cities, and therefore has a high degree of walkability. In addition to this, is common across different building typology in Turin to have the ground floor dedicated to commercial activities. On the basis of these suppositions, we might find a better correlation between the number of retails per street and the local integrators, in other words those streets that have the higher accessibility potential on a local level, and are reachable by foot from the immediate vicinities. In Figure 4.32 we show the map of the retails colored according 115

to the radius 2 integration value of the nearest axial line, and the relative

scattergram.

Measuring

the

integration

values

the

streets at radius 2, and correlating it with the distribution of commercial activities, we obtain a coefficient R2 of almost 0.49. Looking carefully to the picture we can distinguish Via Nizza, that is the street with the higher number of shops distributed along its length, precisely 518, and Via Madama Cristina, with 397 shops. Both of these are in fact strong local integrators for the neighborhood San Sal-

vario, and they offer a high variety of retails.

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FIGURE 4.32 SHOPS IN TURIN: CORRELATION WITH RADIUS 2 INTEGRATION

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Given these results, long from being able to affirm that local integration values are the only factor determining the distribution of retails in the street network, we can acknowledge that among the various circumstances that influence the position of commercial activities, the local integration value of the streets plays an important role. Now that we have a broad picture of the city and we have confirmed, to the limits imposed by this work, that axial analysis is capable to provide valuable insight that link the spatial configuration of the street network to the presence of people and therefore to the distribution of commercial activities, we can move to angular segment analysis. As previously mentioned, segment analysis increase the definition of the axial analysis by breaking down axial lines in their constituting segments. This is done because axial analysis extends the value of the integration on the overall length of the axial lines, without distinguishing particular portion of the lines that might have different integration values. On the contrary, segment analysis allows us to analyze the street network in its finer structure, revealing cluster of integration that were not visible using axial analysis. By this means, we are able to identify local centers using integration analysis at different metrical radii. Indeed, while axial local integration is based on topological radii (2 or more step-depth from the line in consideration), segment analysis is thought to be more meaningful if radiuses are defined using a metrical definition of distance. That being said, we constructed a segment map from the axial map generated earlier, and we carried out our Angular Segment Analysis to reveal patterns of integration and

choice. Before illustrate in depth the result of the analyses, it is worth to repeat the difference between the two measures of centrality, because 118

this is central for the correct interpretation of the results. While

integration measure how central a street (or segment) is in terms of to-movement, that is how accessible a street is as a destination, the measure of choice, indicate how accessible a street is in terms of

through-movement, which is how often a street would be crossed in journeys from every street to every other street, and can be thought as a measure of permeability.

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ANGULAR SEGMENT ANALYSIS: INTEGRATION

FIGURE 4.33 ANGULAR SEGMENT ANALYSIS, INTEGRATION VALUE AT VARIOUS RADII.

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The integration at radius 250 m highlights a number of local centers distributed around the city, mainly in the center, in the north part and in the west area, while it appears that in the south and in the east there is a lack of local centralities. The east area as we noted above is built over the hill, but the analysis successfully highlights the areas nearby the bridges: in fact it picks up Ponte Regina

Margherita, Ponte Vittorio Emanuele I and Piazza Gran Madre di Dio, Ponte Umberto I and Piazza Crimea, and Ponte Isabella. It also spotted the streets surrounding Parco Europa. The southern part of the city instead, accommodates the industrial area property of FIAT, one of the larger shopping centers – Lingotto – and one of the three train station of the city. As we can see, here the presence of these large urban components prevents the formation of local centers. In the north sector there are a number of very well integrated areas along

Corso Palermo, between Largo Palermo and Via Volpiano. There appears to be a peak of integration in Piazza Bottesini, at the crossing of

Via Sesia, Via Malone, Via Saverio Mercadante and Via Paisello. In our opinion that square does not carries such a higher relevance compared to the other local centers found, and we think that the importance given to it by the analysis is being biased by either a redundant line present in the axial map or in the segmentation process. Anyhow, this has not prevented to highlight other important places. In fact, even if at citywide scale the area called Falchera might not be well integrated, it appears to have some sort of autonomy at this scale, according to the design intention of its planner. Other relevant areas accentuate in the north are Piazza Sofia and a section of Via Stradella. Moving toward the city center, we can see an emphasis on the area between Piazza della Repubblica and Piazza

Castello, also known as “Quadrilatero Romano”, which is one of the oldest areas of the city, and also on Piazza Statuto and Piazza Car-

lo Emanuele II. In the neighborhood Crocetta, Piazzale Duca D’aosta, 121

in front of the Politecnico di Torino, is highlighted. Whereas in the neighborhood San Salvario emerge Via Principe Tommaso, between Via

Baretti and Via Valperga Caluso, and a portion of Via Madama Cristina between Via Gaetano Donizetti and Via Ugo Foscolo. To the west Piazza Sabotino, crossed by Corso Peschiera and Via Dante di Nanni is indeed an important local center for the neighborhoods Censia and

San Paolo. What it is really interesting, in our opinion, is to watch these pattern of centralities evolving and merging with each other as the radius of the integration analysis increases. At radius 500 those very local centralities merges together to reveal four main centers: a section of Corso Palermo in the north, the upper area of Piazza Ri-

sorgimento on the north-west, Piazza Sabotino in the south-west and the part of Via Milano which crosses Via Giuseppe Garibaldi. Also,

Falchera and a section of Strada di San Mauro keep their local independence at this scale, but these areas gradually loose their importance if the radius is increased to 750 m and then to 1000 m. At this point, the most integrated areas appear to be the section of

Corso Ferrucci that crosses Piazza Bernini and Via Cibrario. Once crossed the threshold of radius 1000 m we can better see the effect of the angular segment analysis, that is to weight the segments which form straight lines as if they were single entities. As a matter of fact, a series of longer integrated lines appear, that progressively increase their length and adjust their position according to the boundaries considered by the radius. Moving from radius 1000 m to 1250 m and then 1500 m, it is possible to appreciate the increasing relevance of Corso Francia and Via Garibaldi, both joining Piazza

Statuto at their ends, as well as Corso Racconigi and Corso Ferrucci. Going further, from radius 2000 m to 4000 m, Corso Vittorio Emanuele

II, Corso Inghilterra and Via San Francesco Dâ&#x20AC;&#x2122;Assisi gain importance, 122

and the overall pattern of centrality at this scale seems to gravitate around Piazza Statuto. Finally, increasing the radius from 4000 m to 10000 m has the effect of readjusting the pattern of integration between the most integrated axes that we showed in the axial analysis. In fact, the distribution of integration at radius 10000 m shows a high degree of similarity with that of the axial map. Having evaluated the different roles that streets play at various scales of analysis we came to the following conclusions. At some very local dimensions – below radius 1000 m – the city appears as a set of interconnected centralities. As we broaden our view and consider greater extensions – up to radius 5000 m – the streets of Turin seems to have a higher degree of integration in the area between the city center and Piazza Statuto, with Corso Vittorio Emanuele II being the southern most integrated street. Increasing further the area of analysis, up to its municipal boundaries, a series of north-south axes (Corso Inghilterra, Corso Castelfidardo, Via Sacchi, Via Madama

Cristina and others) are responsible for augmenting the integration of neighborhood such as Crocetta and San Salvario. In fact, these two neighborhoods have a good degree of local integration, which is being lost gradually extending the boundaries of the measurements. From this considerations we can assume that the presence of the train station Porta Nuova, in between these two, is limiting the possibility of these neighborhoods to become an important centrality at higher scale, and consequently it prevents a better integration with the city center. Furthermore, we are led to suppose that a better integration of these two regions might lead not only to local advantages, but also benefits the city as a whole.

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ANGULAR SEGMENT ANALYSIS: CHOICE

FIGURE 4.34 ANGLULAR SEGMENT ANALYSIS, CHOICE VALUE AT VARIOUS RADII.

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The last results we will present at citywide scale, concern the measure of choice. The emergent patterns and the progressive shifts of centralities revealed are somehow similar to the ones highlighted by the integration value. However, as can be seen in Figure 4.34, instead of growing and expanding to cover the whole urban area, these tend to shrink as the radius of analysis increase, highlighting those streets that have greater potential of being used in shortest path within the system. At local scales the distribution of centralities confirms local centers in the central, northern and western areas, whereas the eastern and the southern parts of the city suffers the same lacks of centralities evidenced above. Instead, larger radii of analyses put emphasis on longer axes that cross the city and link together the various neighborhoods. In fact, local radii 250 m and 500 m emphasize clusters composed by dense grids that seem to be bounded by longer axes. This might suggests that at this level shortest path are better accommodated with this street morphology. What is interesting is that the neighborhoods themselves appears relatively well defined and enclosed within their limits, as it is the case for San

salvario, Crocetta, San Paolo, Cenisia, San Donato, Aurora, Vanchiglia, Barriera di Milano and others. Radius 750 m gives major relevance to the portion of Corso Vittorio Emanuele II that is in front of the station Porta Nuova, and this might be because that segment is frequently crossed by shortest path going from the city center to both

Crocetta and San Salvario and the other way around. In turn, the streets within these two neighborhoods loose part of their relevance as a suggestion that the attention is shifting toward longer movements. The same can be said for the bridge at the end of Via Rossini, which connects the city center with the neighborhood Aurora. A progressive increase of the radius from 750 m, to 2000 m starts to delineate major axes of movement, with particular attention to the bridges connecting Crocetta and San Salvario, and to those bridges that con125

nect the city with its extension over the hill. This might means that the potential of possible journeys crossing these bridges are relatively higher compared to others in the system. Going from radius 2500 m to 3000 m and then to 4000 m, Corso Vittorio Emanuele II is being highlighted as the most used, followed by Corso Racconigi, a curved avenue crossing the neighborhoods around the city center,

Corso Lecce (that becomes Corso Trapani and then Corso Siracura), Corso Sommellier (then Corso Peschiera) and Corso Francia. Ultimately, from radius 5000 m up until 10000 m and the global choice, Corso Rac-

conigi loose importance leaving its place to Corso Regina Margherita. The analysis at this scale reveals two main east-west axis that are, in order of importance, Corso Vittorio Emanuele II and Corso Re-

gina Margherita. Corso Inghilterra, Corso Duca degli Abruzzi (then Corso Agnelli), Corso Giulio Cesare, Via XX Settembre (then Via Sacchi and Corso Unione Sovietica) and Via Madama Cristina form the main axes used for north-south journeys. On the east side of the river, at the foot of the hill the avenue Corso Casale, which then become Corso

Moncalieri, is also being used by part of the journey at this scale. It is also interesting to see how the path formed by Corso Grosseto,

Corso Lecce, Corso Trapani and Corso Siracusa, which connects northern and southern parts of the city, avoiding the center, is indicated as intensively used. Given the results of these analyses, we may find ourselves in agreement with what has been said by Hillier (1999), concerning some of the more commons street network morphologies he explored. In fact it emerges that the urban morphology of Turin tend to maximize movement efficiency at local scale, exploiting the property of orthogonal grid to minimize mean trip length from all origins to all the destination. On the other hand, at larger scales where movement is from edge to center or from a specific origin to a specific destination, 126

the need to minimize mean trip length requires an essentially linear form that takes the form of longer uninterrupted axes. With particular attention to the areas of our interest, we pointed out how at very local scales the two neighborhoods Crocetta and San Salvario form well-integrated clusters. Nevertheless, enlarging the area considered by the analysis up to radius 2000 m, causes the bridges connecting the two regions and a section of Corso Vittorio Emanuele II to be heavily highlighted in terms of potential for through-

movements, and subsequently to gradually being discarded, with the exception of the latter. This might suggest that, as we noted above in the section on flows, the two neighborhoods have indeed a relatively high interaction potential, that is partially being absorbed by the bridges of Corso Sommellier and Corso Dante, other that Corso Vitto-

rio Emanuele II. However, the current spatial configuration prevents movement at greater scales to pass through these neighborhoods, giving priority to Corso Vittorio Emanuele II. This appears to reinforce our earlier interpretation of the integration analyses, leading to the conclusions that both movement with destination and passing through the two areas, at medium to large-scales, are being biased by the presence of the railway station. Lastly, the choice analysis at increasing radii also suggest that, being Corso Vittorio Emanuele II the preferred route for journeys that could otherwise cross these two neighborhood, this have effects also at a citywide scale in terms of congestion. This would be avoided if the city had a third westeast axis, capable of withstanding both medium scale movement between neighborhoods and large-scale urban movement.

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At this point we can restrict the boundaries of the analyses to a smaller area that involve the train station Porta Nuova. The area considered merge together three neighborhoods: the city center, San

Salvario and Crocetta. In Figure 4.35 are represented the boundaries of the area of interest in red, the blocks aggregates building blocks in white, the parks in green and the station and its respective railway in orange. Having the aerial photo of the city in the layer beneath allows understanding the level of accuracy we have used to draw the axial map. The cartography that defines the position and the boundaries of the city blocks, the parks, and the other elements, is part of the same geo-referenced map used to locate the shops, that was downloaded from the site of the municipality of Turin11.

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FIGURE 4.35 AEREAL VIEW OF THE RAILWAY PORTA NUOVA AND ITS SURROUNDING.

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An axial map of this restricted region it was drawn and analyzed, revealing the pattern of integration shown in Figure 4.36. The axial map highlights Corso Vittorio Emanuele II, Via Bertola (then Via

Principe Amedeo) and Via Cernaia (then Via Santa Teresa and Via Maria Vittoria) as the more integrated, followed by Corso Galileo Ferraris, Corso Umberto I, Via Madama Cristina and Via Alfieri (Via Giolitti). The analysis also picks up the low integrated housing estate on the lower left, comprised within Corso Rosselli, Via Arquata and Via

Rapallo. As we can see, the analysis maintain an overall integration pattern which is very similar to the one revealed before, when we did the axial analysis at citywide scale with radius 3, even if there are some obvious differences due to the restricted boundaries. This is an important factor, as it confirms the consistency of the analysis at different scales. However, with these boundaries we can also confirm that the more integrated streets tend to be distributed toward the city center on a west-to-east alignment, and are followed by northsouth axes that connect the center with the two neighborhoods Cro-

cetta and San Salvario. These two neighborhoods appears in fact to have the majority of poorly integrated lines.

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FIGURE 4.36 GLOBAL INTEGRATION OF THE AREA SURROUNDING THE RAILWAY STATION PORTA NUOVA.

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If we assume that the area currently occupied by the train station and its relative railways is given as an open space, ready to be planned, and we replicate the same analysis, the axial map created can be interpreted as a suggestion that tend toward the most integrated solution (Figure 4.37). The newly created map is clearly biased by the large open space left by the train station, for this reason there is a high variety of well-integrated lines that cross this area. This happen because, given how this map is created, it try to draw the longest lines, which have the higher number of intersection possible and that cover the majority of the spaces available. However there are some useful considerations that we can do at this stage. Firstly, it appears how the distribution of integrated streets is now more evenly distributed between the center and the two neighborhoods. In fact, two new local axes might be created, connecting Corso

Stati Uniti with Via Berthollet and Corso Duca Dâ&#x20AC;&#x2122;Aosta (Via Pastrengo) with Corso G. Marconi. These in fact are now far more easily reachable from all the streets considered within these boundaries and, according to the theory of movement economy (B. Hillier 1996), might attract a greater number of commercial activities. Also, interestingly enough,

Corso Duca Dâ&#x20AC;&#x2122;Aosta and Corso G. Marconi are two avenues that start respectively in front of the School of Engineering, in Crocretta, and in front of the School of Architecture, in San Salvario. Both schools are part of the Polytechnic of Turin and it is possible to argue that, being important attractor at a citywide scale, these might be responsible for much of the incoming and outgoing movement within the two neighborhoods. Having a well-integrated axis that connects the two schools might improve the local mobility between the two neighborhoods, and the reachability of the two avenues from other regions. Secondly, it is possible to see how Via Accademia delle Sci-

enze, which starts from Piazza Castello and then becomes Via Lagrance, could be extended all the way down to Via Mario Pagano and 132

reach Via Giacomo Zino Zini connecting, and therefore integrating, the housing estate around Via Arquata and the neighboring Lingotto. This axis would also acts as the major means of connecting Crocetta and San Salvario to the city center. Lastly, the extension of Corso

Filippo Turati (Corso Unione Sovietica) up until Via Lagrange, crossing Corso Vittorio Emanuele II, might be an interesting option to evaluate, as we are aware of the importance of both avenues at larger scales.

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FIGURE 4.37 GLOBAL INTEGRATION OF THE AREA WITHOUT THE RAILWAY STATION PORTA NUOVA.

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We can now increase the resolution also at this scale, and break down the axial map into its segments, as we did for the axial map of the city, in order to investigate the patterns of integration and choice at the neighborhood scale. Both metrics are analyzed at subsequent metrical radii, from 250 m to 5000 m with increasing steps of 250 m. The integration patterns, illustrated in Figure 4.38, can be read from left to right and top to bottom. An overall view of the sequence formed by the patterns of integration at various radii, reveals that at lower scale – 250 m – each neighborhood has its own integrated core. These are Largo 4 Marzo, in the city center, Piazza dalla Chiesa in Crocetta and the intersection between Via Madama Cristina and Via

Belfiore in San Salvario. Progressively increasing the radius of the analyses – 500, 750 and 1000 m – causes at first the vanishing of the integration core in San Salvario, followed by almost all the integrated streets in Crocetta. At the same time, in the city center, the most accessible area slowly moves from the area comprised between

Via Pietro Micca, Via San Franceso D’Assisi and Via Garibaldi, to the intersection between Via Pietro Micca and Via Cernaia, where there is

Piazza Solferino. Further extension of the radius – 1000 to 2000 m – causes the progressive shift of the integration core of the area from the center toward Corso Vittorio Emanuele II and its perpendicular streets, Corso Galileo Ferraris, Corso Re Umberto I, Via Massena and

Via Sacchi. These streets appear to extend well-integrated axes into both Crocetta and the city center. At the same time the bridge connecting Via Valperga Caluso to Corso Sommeiller is indicated as highly integrated – 1250 m to global radius. It is just above radius 2000 m that the extension of the integration core along the length of Corso Vittorio Emanuele II finally reaches Via Madama Cristina and Via Ormea. Above this radius San Salvario gradually returns to 135

be well-integrated thanks to these streets and their joining with Via

Valperga Caluso. The analysis of the emergent integration patterns, which raise, merge and shift, within the boundaries defined by the area considered, allows the following considerations. In accordance with what was found earlier, accounting for increasingly greater area when determining the degree of accessibility, causes the neighborhoods Crocetta and San Salvario to loose their centralities in favor of the city center. It is only when we consider areas of influence greater than 1250 meters, that Corso Vittorio Emanuele II begin to be relevant – and prominent – to the accessibility of the area analyzed. Exactly after this happens, in fact, the two neighborhoods adjacent to the city center increase their reachability at higher scales. This is further reinforced by the fact that above this threshold (1250 m) the only other well-integrated segments are those traversing the bridge that connect Corso Sommeiller and Via Valperga Caluso. It is therefore arguable that the reconstitution of the urban grid would increase the local accessibility – below 1250 m – of both Crocetta and

San Salvario, reinforcing the presence of their centralities at this scale. Additionally this is likely to improve the livability of highly segregated areas, such as the one surrounding Via Arquata.

136

ANGULAR SEGMENT ANALYSIS: INTEGRATION

FIGURE 4.38 ANGULAR SEGMENT ANALYSIS, INTEGRATION OF THE AREA AT VARIOUS RADII.

137

With regard to the choice patterns, also analyzed at the same subsequent radii as the integration ones, these reveal since very local areas of influence the prioritization of the segment in front of Por-

ta Nuova. The latter remain constantly highlighted up until the global choice of the area, emphasizing that it is frequently traversed both by shorter and longer journeys. In fact, this is true for the choice measure at all radii, with the exception of the choice pattern at radius 250 m, where the extremely local area of influence coupled with the segmentation process does not seems to provide meaningful results. According to what previously said, the bridge of

Corso Sommeiller begins to be heavily used for journeys from 1250 m and below 3250 m. For travels longer than this, the section mostly used remain the one between Porta Nuova and Piazza Carlo felice. It is interesting to note how, above this threshold, regularly traversed streets are identified in the city center between Corso Vittorio

Emanuele II and Via Bertola, but also include Corso Galileo Ferraris, Corso Re Umberto I, Corso Sommeiller, Via Valperga Caluso and Via Ormea. When considering movements within 1000 and 2000 meters, San Salvario happens to be relatively less traversed compared to Crocetta and the city center, but considering longer journeys, Via Madama

Cristina and Via Ormea, along with Via Nizza and Via Saluzzo are the most traversed streets in the neighborhood.

138

ANGULAR SEGMENT ANALYSIS: CHOICE

FIGURE 4.39 ANGULAR SEGMENT ANALYSIS, CHOICE OF THE AREA AT VARIOUS RADII.

139

4.3 FROM SPACE SYNTAX TO VISIBILITY GRAPH ANALYSIS The original concept behind Visibility Graph Analysis (VGA), developed by Alasdair Turner and Alan Penn at University College of London between the late 1990 and the early 2000, starts from two strands of thought. On one hand there was the theory of space syntax developed by Hillier and Hanson, which we exploited in its basic concepts in the previous chapter. On the other hand there was isovist analy-

sis, independently developed by Benedikt (1979). He created maps of properties of the visual field at points within plans of buildings, and then he drew contours of equal visual area within the plan and called the resulting map an isovist field. Benedikt believed that these maps would give an insight on how people navigate the building. Since closely packed contours would indicate rapidly changing visual field, he reasoned that these would indicate decision points within the building (Turner, Doxa, et al. 2001). Since Benedikt had theorized that isovist fields would correspond to movement patterns of people and Hillier et al have shown that lines through the spaces â&#x20AC;&#x201C; axial lines â&#x20AC;&#x201C; does correspond with movement pattern within space, it was decided to combine isovist fields with space syntax to provide a measure of how well integrated isovists themselves are within a plan of an environment (Turner 2004) (Turner e Penn 1999). The methodology was later formalized more simply as Visibility Graph Analysis in Turner et al. (2001). In VGA, a grid of points is overlaid to the plan; a graph is then made of the points, where each point is connected to every other point that it can see. The visual integration of a point is based on the number of visual steps it takes to get from that point to any other point within the system. The idea was that all possible locations 140

within the built environment would be categorized by their visual relationships to other occupiable spaces through a continuous map (Turner, Depthmap 4 A Researcher's Handbook 2004). As Turner says, whether or not VGA succeeds in its aims is open to debate, in fact a recent study has proven that for people movement, another method based on space syntax seems to correlate better with pedestrian count rates in cities (Turner, Analysing the visual dynamics of spatial morphology 2003). Regardless of the outcome of the debate, as Turner highlights, there does appear to be a promising avenue of research for modeling people movement that uses the visibility graph at its core. In fact in the next chapter we will exploit an agent-based analysis with an underlying visibility graph proposed by Penn and Turner (2002), where â&#x20AC;&#x153;agentsâ&#x20AC;? are released into a plan of the environment and navigate using the visibility information directly available to them through the visibility graph. The first stage of a VGA is to define the grid of point locations for the analysis, thus setting grid spacing for the analysis.

141

FIGURE 4.40 IMAGE OF THE GRID UNDERLYING THE VISIBILITY GRAPH ANALYSIS

The point locations to be analyzed are situated at the very center of each grid squares. Once set up the point locations, the visibility graph is constructed connecting each point location to every other point location that it currently see. The graph itself it is not visualized during actual analyses because, as Turner notes (2004), this 142

is typically highly connected and near meaningless, but it does show the connectivity of each node as a result from the constructed graph. The potential of VGA lies on the assumptions we are able to make on the basis of its measures. Once the grid of point locations is defined and the visibility graph gets constructed, we can in fact calculate different isovist fields and various measures derived from space syntax theories like integration, connectivity, control and controllability, among others.

4.3.1 ISOVISTS AND ISOVIST FIELDS As Turner et al. (2001) highlight, the concept of an isovist has ad a long story in both architecture and geography, as well as mathematics. Tandy appears to have been the originator of the term itself: he presents isovists as a method of

“Taking away from the [architectural or landscape] site a permanent record of what would otherwise be dependent on either memory or upon an unwieldly number of annotated photographs”. (Tandy 1967) The appeal of the concept is that isovists are an intuitive and attractive way of thinking about a spatial environment because they provide a description of the space “from inside”, from the point of view of individuals, as they perceive it, interact with it and move through it. In spite of Tandy’s description being very inspiring, we believe Benedikt’s definition may better suits our needs of a graphical explanation, which depicts an isovists as

“The set of all points visible from a given vantage point in space and with respect to an environment” (Benedikt 1979, 47)

143

FIGURE 4.41 ISOVISTS FROM THREE DIFFERENT POINTS. FROM BENEDIKT (1979)

The author also introduce a set of analytic measurements of isovist properties to be applied to achieve quantitative descriptions of a spatial environment, and he notes that in order to quantify a whole configuration, more than a single isovist is required, suggesting that the way in which we experience a space is related to the interplay of isovists.

144

FIGURE 4.42 ANALOG PRODUCTION OF ISOVISTS ALONG A PATH BY POINT-SOURCE ILLUMINATION OF A MODEL. FROM BENEDIKT (1979)

145

FIGURE 4.43 PHYSICAL ISOVISTS ALONG A GIVEN PATH STACKED ONE ABOVE THE OTHER. FROM BENEDIKT (1979)

This leads him to formulate an isovist field of his measurements, which record a single isovist property for all locations in a configuration by using contours to plot the way those features vary through the space. To sum up, we might say that the isovist from a point location is the polygon which contains all the visible area from one particular location, while an isovist field is a map where we calculate isovist properties for each point of a grid in the open space of a configuration, and then we interpolate each one of these.

146

FIGURE 4.44 EXAMPLES OF ISOVISTS FIELDS FOR THREE (TWO-DIMENSIONAL) SIMPLE ENVIRONMENTS: A FREE STANDING WALL, THE END OF A LONG WALL, AND A FREE-STANDING CUBE. FROM BENEDIKT (1979)

The isovist properties that are available for calculations are the following: â&#x20AC;˘

Isovist area is the area of the isovist polygon from the point. It is a measure of how much space can be seen from a point, and conversely, how much space a point can be seen from. Calculated on a visibility graph it highlights points with equal degree of visibility. 147

•

Isovist perimeter measure how much environmental surface can be seen from a point. Calculated on a graph it shows points that can see the same amount of surface.

•

Isovist compactness is the ratio of the area to the perimeter squared. It is normalized so a perfect circle has compactness 1, and a long thin isovist has compactness close to 0. It highlights compact spaces that may be attractive to socialize and meet each other.

•

Isovist minimal radial is a measure of the minimum length of the optic ray used to generate an isovist field. This means that it might be used to highlight local visual centers.

•

Isovist occlusivity measures the length of the polygonal edges which are adjacent to space which cannot be seen from the generating point, that is, the edges of the isovist that are not solid wall. The length of hidden edges indicates the depth to which environmental surfaces are partially covering each other as seen from a point. Calculated on a graph it emphasizes points that have equal amount of occluded surfaces.

These are of interest to architects and urban design in that they offer a way of addressing the relationship between the viewer and their immediate environment. However, as highlight by Turner and Penn (1999), all the measures Benedikt proposes are locally defined, and are independent of the state of the field in other location. In other words, despite their sensitiveness to the shape of spaces, isovists provide essentially local properties of configuration, while they miss the visual relationship between the current location and the whole spatial environment. On the other hand, the lesson of space syntax research suggests that it is the global properties of spatial configuration that are important in determining the functional consequences of design. Hence Turner et Al. (2001), propose a method that 148

embraces how visual characteristics at locations are related, and allows global relational measures to be developed which are attributable to each viewer location, but which are essentially relational. They show how a set of isovists can be used to generate a graph of mutual visibility between location, and they also demonstrate that this graph can be constructed without reference to isovists, invoking the more general concept of a visibility graph.

4.3.2 FROM ISOVISTS TO VISIBILITY GRAPHS As Turner et al. (2001) pointed out, constructing an isovist graph of a spatial environment involves two distinct set of interrelated decision. First they had to select an appropriate set of isovists to form the vertices of the graph. Second, given a particular set of isovists, they needed to determine which relations between them are significant to form edges in the graph. The most obvious approach was to generate isovists at points defined by a grid and to consider the relation that occurs when two isovists polygons intersect with one another. Turner et al. (2001) distinguished between a first-order relationship, which occurs when two isovists intersect and their generating locations are mutually visible, and a second-order relationship,

that

occurs

taking

visibility

step

from

one

isovist-

generating location to an intervening location and then a step onto the next isovist-generating location.

149

FIGURE 4.45 (A) FIRST-ORDER AND (B) SECOND-ORDER VISIBILITY RELATIONSHIPS BETWEEN ISOVISTS (TURNER, DOXA, ET AL. 2001).

Once done that, Turner et al. (2001) realize that in order to determine these two relationships they did not need to invoke isovists at all, because both of these information were embedded in a first order visibility graph. Therefore they decided to concentrate the analysis only on the first order visibility graph, for which an illustrative example is shown in Figure 4.46. Following Turner, in mathematical terms, a graph consists of two sets: the set of the vertices, labeled V and the set of the edge connections joining pairs of vertices labeled E. This information is summarized by writing the graph as the pair of these sets: G(V, E). In the case of a visibility graph, the vertices represent the set of generating locations to be considered: ! = !! , !! , !! , â&#x20AC;Ś , !! !

150

(4.19)

FIGURE 4.46 AN EXAMPLE OF A FIRST-ORDER VISIBILITY GRAPH, SHOWING THE PATTERN OF CONNECTIONS FOR A SIMPLE CONFIGURATION (TURNER, DOXA, ET AL. 2001).

The edges are pair of mutually visible points, so they denoted the edge joining !! and !! , that is !! , !! , as !!" , thus forming the set ! = !!" , !!" , … , !!" !!ℎ!"!!!!" ⇔ ! !!" !

(4.20)

In a representation on a two-dimensional plane, the graph edges are undirected, that is, if !! can see !! , then !! can see !! (Turner, Doxa, et al. 2001). Using the visibility graph, Turner et Al. (2001) extended both isovist and current graph-based analyses of architectural space to form a new methodology for the investigation of configurational relationships. Ultimately, they show how visibility graph properties may be related to manifestations of spatial perception, such as way finding, movement, and space use. These measures are the core of VGA and once the grid of point locations is defined and the visibility graph is constructed, we can run the VGA based on visibility distances or metric distances. 151

4.3.3 ANALYZING THE VISIBILITY GRAPH With the visibility analysis it is possible to make a global analysis or a local analysis of the graph. The global measures are constructed using information from all the nodes in the graph, thus preparing shortest paths from each node to all other nodes. The local measures are based on the relationship between each node and the nodes directly connected to it, therefore these are constructed using information from the immediate neighborhood of each vertex in the graph. The global measures of the graph include mean depth, integration, entropy, and relativized entropy, and are described as follow: •

Mean depth, as described by Turner et al. (2001) is calculated for each node by summing shortest paths from each node to every other node in the graph and then dividing by the number of nodes in the graph. It has parallels with Hillier and Hanson’s approach, in that they quantify the visual accessibility of spaces, whereas this measure quantifies the visual accessibility of every location in the spatial system, thus showing how visually connected a vertex is to all other vertices in the system. Hence the measure introduced extends the Hillier and Hanson method to continuous space and enables the resulting locations within a space to be mapped across that space.

•

Integration, much like in Space Syntax analysis, is a normalized version of the mean depth that has been found to correlate well with pedestrian movement.

•

Entropy as described by Turner (2001) is a measure of the distribution of locations in terms of their visual depth from a node. It can be expressed using Shannon’s formula of uncertainty, as 152

!!"#

â&#x2C6;&#x2019;!! log !! !

!! =

(4.21)

!!!

where !!"# is the maximum depth from vertex !! and !! is the frequency of point depth d from the vertex. Calculating the entropy can give an insight into how ordered the system is from a location. For example, if a doorway is connected to a main street then there is a marked disorder in the point depths from the point of view of the doorway: at depth 1 there are only a few locations visible from the doorway, then at depth 2 there are many locations from the street, and then order contained within further depths will depend on how the street is integrated within its environment. So if many locations are visually close to a node, the visual depth from that node is asymmetric and the entropy is low. If the visual depth is more evenly distributed, the entropy is higher. Entropy appeals to a tentative model of people occupation of a system, in that the entropy corresponds to how easy it is to traverse to a certain depth within the system (low disorder is easy, high disorder is hard). It also remedies the problem that VGA integration is heavily biased towards large open areas. In axial integration, because the system is dimensionless, large open areas do not unduly weight the values of the lines; that is, the large areas only weight the values by their increased connections, not through their area. By contrast, in VGA integration the measure approximates a mean of distance times area. Hence, by using a topological measure such as entropy we eliminate the area dependence of the measure, and instead concentrate on the visual accessibility of a point from all other points.

153

For global measures there is also an option to set a radius that, if set to an integer number, will restrict the global analysis to path length up to that number. So for example, with radius 2, the mean depth is calculated by summing the length of the paths to all nodes one visual step away, and the length of the paths to all nodes one step from there. The total is then divided by the number of nodes encountered to give the mean depth. The local measures are cluster coefficient, control, and controllability: •

Clustering coefficient is defined as the number of edges between all the vertices in the neighborhood of the generating vertex, divided by the total number of possible connections with that neighborhood size. In mathematical form, it is defined as !! =

! Γ! ! !! !! − 1

(4.22)

where ! Γ! is the set of edges in the neighbourhood of !! , and !! is the neighborhood size for a vertex. It is a measure introduced by Watts and Strogatz (1998) to help assess whether or not a graph is a small world12. Further consideration by Turner et al. (2001) reveals that the clustering coefficient gives a measure of the proportion of intervisible space within the visibility neighborhood of a point. They highlight how it might indicates how much of an observer’s visual field will be retained or lost as he or she moves away from that point. In fact if the

A small world is one that has tightly clustered groups of friends, but surprisingly low mean depth, that is, the number of steps to get from one person to any other person through links of mutual friends is surprisingly low.

154

neighborhood of a point approximates a convex polygon, then the clustering coefficient is high and moving from that location in any direction will not cause any great loss of visual information. However, at a junction with multidirectional visual fields, the clustering coefficient will be low as moving from that location will involve loss of part of the currently visible area. Turner and his colleagues have gone beyond this, noting how, because movement in some sense involves making decisions about which parts of one’s current visual information to leave behind, the clustering coefficient is potentially related to the decision-making process in way finding and navigation, and it might marks out key decision points within complex configurations. Further, they said, if we regard vertices in the graph as potentially occupied by people, clustering coefficient values might indicate the potential for perceivable co-presence in a space and therefore the potential to form groups to interact. In the end, the clustering coefficient appears to highlights what might be perceived as the most “private” spaces of a spatial configuration, and how the visual information is changing within systems, dictating, perhaps, the way a journey is perceived and where the decision points come within it. •

Control is a measure coming from Hillier and Hanson (The Social Logic of Space 1984) that picks out visually dominant areas. It is calculated by summing the reciprocals of the neighborhood sizes adjoining the vertex, as !! = !! ∈! !!

1 ! !!

(4.23)

Each location is first assigned an index of how much it can see, the reciprocal of its connectivity. Then, for each point, these indices are summed for all the locations it can see. 155

Therefore if a location has a large visual field will pick up a lot of points to sum, so initially it might seems controlling. However, if the locations it can see also have large visual fields, they will contribute very little to the value of control. So, in order to be controlling, a point must see a large number of spaces, but these spaces should each see relatively little. â&#x20AC;˘

Controllability is a measure proposed by Turner (2001) that picks out areas that may be easily visually dominated. For a location it is the ratio of the total number of nodes up to radius 2 to the connectivity. Therefore on a controllable point, the area of visual field is small compared to the area viewable on the points it connects to.

156

4.3.5 VGA APPLICATION TO REGENERATE URBAN AREA In order to see how the VGA may be applied within the early design process, we can illustrate the case study carried out for the same area we analyzed earlier, that is the railway station Porta Nuova and its surrounding in Turin. However, in this case we analyzed only the area without the train station and its railways. Doing this, the VGA measures allow to understand to what extent the built environment influences the “non-built”, through the creation of areas with different “potentials”. This may suggest design solutions that might not be considered with a merely intuitive approach. In fact, in this work we are limiting ourselves to register the invisible qualities and potentials that an unplanned area reveals through adjoining configurational properties, and we will not propose design solutions that accentuate one or another quality. We will start by looking at three isovist properties of the visibility graph above mentioned, which we found to be meaningful for our purpose: isovist area, occlusivity and compactness. Therefore we will proceed with two other local measure introduced by Hillier and Turner, control and controllability. Lastly, we will look at two global measures of integration and entropy. This will allow us to gain useful insight both at local and global level. As a note, in both cases we have used a grid spacing of 10 meters, in order to allow a level of detail “fine enough” to discern meaningful differences.

157

FIGURE 4.47 MAP OF POINTS WITH EQUAL VISIBLE AREA.

158

In Figure 4.59 we show the result of the isovist area property calculated over the grid of points. For each point it is calculated how much area it can currently see, on the basis of the underlying grid spacing. In red are shown points with high visible area, in blue points with low visible area. The availability of a very wide openspace, as the one left by the train station and its railways, make it obvious to guess that in that area there will be the higher concentration of visible area. Nonetheless, if we pay attention to the subtle details in the shading of red, orange and yellow, we can make some interesting thoughts. In fact, the highest values are not evenly distributed along the longitudinal axis of the open space, but rather seems to emphasize the extension of some streets. An example may be Corso Unione Sovietica, which at the joining with Via Sacchi is presented with a sequence of highly visible points that prolong its path until Corso Vittorio Emanuele II. The same is true for Via Nizza, where points with high visibility extend its length toward the westeast axis. The joining of these two “virtual extensions” generate a cluster of high visibility between Via Claudio Luigi Berthollet and

Via S. Pio V, which are both “connected” respectively toward Via Magenta and Corso Stati Uniti. On the contrary, the same relevance is not given to the “prolongation” of Via Bernerdino Galliari and Via

Assietta. This causes the area with high visibility in between Via Claudio Luigi Berthollet and Via S. Pio V to be susceptible of various interpretations. For example, it might become an important junction or, if the area will be developed as a park or as a car-free zone, a local square. Another connection that is suggested “links” togheter

Corso Guglielmo Marconi and Via Pastrengo, and we already mentioned the improvements that the connection between the School of Engineering and the School of Architecture would provide at local scale. Also in this case the dimension of the cluster of highly visible points may suggest the future presence of a square. The bigger area in red 159

between the end of Via Governolo and Via Antonio Rosmini can hardly suggest some intervention proposal, but later in the chapter, the visualization of other metrics will overcome this problem. Ultimately, the “connection” of Corso Dante Alighieri and Corso Rosselli, where there is now the bridge connecting Crocetta and San Salvario, is also an area highly visible at “ground level”.

160

FIGURE 4.48 MAP OF POINTS WITH HIGH OCCLUSIVITY.

161

Figure 4.48 shows the map of the isovist occlusivity measure. This illustrates to what degree the local built environment causes long occlusion edges from a given point of view. The fact that the overall pattern is similar to the one of the isovist area, with a cluster of high occlusivity slightly shifted from Piazza Carlo Felice toward

Via Nizza, tells us that while this area has high visibility for what concern the boundary of the preexisting train station, it also has an high degree of occlusion in its proximity to San Salvario, between

Corso Vittorio Emanuele II and Corso Guglielmo Marconi. West to east axes are also highlighted, to the extent that in an open area like this one, relatively little surfaces defining the boundaries of buildings can cause elongated occluded edges. On the other hand, this measure does not present the large cluster in the center of the area that was present in the isovist area measures. Instead a long series of points with high occlusivity connect Via Sacchi and Via Frugaro-

lo, Crossing Via Arquata. This are likely caused by the buildings positioned at the end of Corso Filippo Turati. Areas with high occlusivity might be read as places where the surrounding configuration causes uncertainty and insecurity. On the other hand, these might also be thought as areas that carries a high potential for that sense of discovery and surprise, which might also increase the quality of the built environment. Despite the interest in this field, the degrees to which areas with high occluded edges may cause different perceptions is not covered in this work, nor are proposed solutions that might favor one or the other. Going further, Figure 4.49 shows the analysis of the isovist compact-

ness. Compact spaces may be areas where the degree of perceived enclosure can attract people to socialize and meet each other. In fact most of the regions highlighted in the map are either dead-end or very small streets. As one might guess, the open space left by the 162

train station present a low degree of compactness, which is accentuate in the area previously highlighted with high occlusivity. Relatively higher values are measured nearby Via Sacchi, between Via

Legnano and Corso Vittorio Emanuele II, where the area shows an acceptable value of compactness and therefore a potential to become an â&#x20AC;&#x153;urban meeting placeâ&#x20AC;?. The same it is true for the seemingly triangular-shaped clusters nearby San Salvario, between Via Giovanni Argen-

tero (Via Gaetano Donizetti), Via Nizza and Via Oddino Morgari, that are bigger enough to become some sort of park which could be extended toward the end of the considered area, following the boundary of the area highlighted in green.

163

FIGURE 4.49 MAP OF ISOVIST COMPACTNESS.

164

The last two local measures are control and controllability, defined respectively by Hiller and Turner, and we present these together because are highly related one to the other. Control, illustrated in Figure 4.50, highlights visually dominant areas that have a large visual field, and are able to see places which in turn have a small visual field. This highlights the first three consecutive path that join the west-east streets, creating a good level of control as well as some cluster of relatively high control values between them, shifted toward San Salvario. It does the same for the other two paths below Via Pastrengo, but more on the side of Crocetta. There appears to be a lack of high control values in the large open-area between

Via Federico Campana and Corso Dante Alighieri. The control values are also high, even if proportionally less, for west-east axis in the southern part of the open-area left by the train station, with particularly attention to the extension of Corso Dante Alighieri.

165

FIGURE 4.50 MAP OF VISUAL CONTOL.

166

On the other hand Figure 4.51 shows the pattern of controllability, which aims to highlights areas that might be visually dominated. These are areas whose visual field is small compared to the one of the places it can see. Accordingly to the map of visual control, this highlights the areas in between the “virtual extension” of the westeast axes as likely to be controlled, with some decreasing in values nearby Via Nizza and Via Gaetano Donizetti. The overall pattern is less defined in the southern part of the area, but it shows high degree of controllability. Considerations about matters such as control and controllability of certain areas are important for what concern the perception of safety within the neighborhood. Nonetheless, the theories on how to handle this from the point of view of spatial configuration are somewhat divergent. Jacobs thinks that permeable mixed-use environments, in which strangers passing through spaces, coupled with inhabitants occupying them, create some sort of ‘eyes on the street’ natural policing mechanism that inhibits crime (Jacobs 1961). On the other hand, Newman argues that having too many people in spaces creates exactly the anonymity that criminals need to access their victims, and so diminish the ability of residents to control their own environment. From this point o view, crime can then be expected to be less in low density, single use environments with restricted access to strangers, where inhabitants can recognize strangers as intruders and challenge them (Newman 1972). It is possible, as argued by Hillier (2009), that both are right about certain aspect and wrong about others, and that both sets of commonsense intuitions need to be seen as part of a more complex model which incorporates their underlying ideas. It is argued that measure such as control and controllability might be extremely useful in such situations.

167

FIGURE 4.51 MAP OF VISUAL CONTROLLABILITY.

168

Now that we know how the visible built environment influences the non-built area considered at a local level, it is important to understand its implication at global scale. Here we use the term global to indicate that, for every point of the analysis, are evaluated its relations with all other points in the system, as suggested by space syntax theories. By doing this, we are aware of both the â&#x20AC;&#x153;edge effectâ&#x20AC;? that persist on the boundary of the area, and the consistency of the space syntax measures at different scale. The measure of visual integration it is shown in Figure 4.52. As we now from previous definition, integration measure how easy it is to reach a given point from all other points in the system. This means that the pattern of visual integration allows understanding the

visual accessibility of a certain area at global level, and might have consequences onto movement patterns. This analysis gives clear priority, in terms of accessibility, to all the connection between the street in Crocetta and San Salvario, with proportionally less integration values for what concern the connection between Via Assietta and Via Bernardino Galliari. On the other hand, high values of integration are found between Corso Stati Uniti and Via Berthollet, as well as between Corso Duca Dâ&#x20AC;&#x2122;Aosta (Via Pastrengo) and Corso Gugliel-

mo Marconi. This seems to coincide with what emerged in the isovist area analysis, although we are now dealing with global accessibility. Therefore this might suggest that these connections have both local and global importance. The main difference between the map of visual integration values and the map of isovist area values is found on the connection of Corso Unione Sovietica and Corso Vittorio

Emanuele II. In fact, this is not presented as a highly integrated path in the integration analysis. In contrast, what seems to hold together all the integrated paths is the extension of Via Giuseppe

Luigi Lagrange up to the limit of the area considered by the analy169

sis. On the southern area of the railway, this path forms larger clusters of integration at the intersection with others west-east paths, and a similar extension is highlighted for Via Sacchi. This might suggests that although Corso Unione Sovietica and Corso Vitto-

rio Emanuele II have a high relationship in terms of local visible area, with regard to global integration their relationship is not persistent. The last interesting point concerning this map is the presence of a highly integrated path that connects Corso Sommeiller and Corso Raffaello. This can lead to the conclusion that at â&#x20AC;&#x153;ground levelâ&#x20AC;?, not considering the bridge toward Via Valperga Caluso, the joining of these two streets might be of more relevance than the one present nowadays.

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FIGURE 4.52 MAP OF VISUAL INTEGRATION.

171

Another global perspective about the area is given by looking at the values of entropy. As described above, calculating the entropy can give information about how “ordered” a certain space is from a location. From a given point of view, a space can be perceived highly ordered if the distribution of location is evenly distributed around it (high entropy). That space might therefore be perceived as easily traversed. On the contrary, if the distribution of location is uneven, from the point of view of the observer, the system is perceived as disordered (low entropy) and might be perceived as less traversable. The map that shows the values of entropy is shown in Figure 4.53, where we can immediately note that it is very similar to the map of the integration values. In fact it shows mostly the same paths connecting the two side of the area, but now the relation with the space in between these is inverted. Indeed, where the paths connecting the end points of the streets were evidenced as being more integrated than the space in between them, now the latter are those having higher entropy values, with respect to the paths themselves. This means that within those regions, the unplanned area and its surrounding appears as a relatively ordered space. On contrary, when we approach any of these “extended streets”, the same area appear less ordered from a visual perspective, because on one side we have a relatively ordered distribution of locations, and on the other side the street introduces many more locations that alter the overall distribution presented to the observer.

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FIGURE 4.53 MAP OF ENTROPY.

173

An interesting method to evaluate accessibility is illustrated by Hillier, using both global and local variables to estimate the degree of intelligibility of a certain urban configuration. Hillier (1996) argues that the intelligibility of spaces at ground level cannot be experienced all at once, but requires the observer to move around the system, collecting information and building up an overall picture piece by piece. In fact, Hillier suggests that from the point of view of the observer, the understanding of an entire spatial configuration might be built putting together all the information collected at a local scale. Therefore, the more the local information is able to provide meaningful details about the global configuration, the more intelligible it become. On these bases, it is possible to achieve an index of intelligibility by correlating a local measure with a global measure. The local measure can be the connectivity, a local property that can be seen locally from each space, because wherever one is in the space can see how many connected spaces there are. The global measure can be the integration, that is a global property that cannot be seen from a space, because it sums up the depth of a space from all others, most of which cannot be seen from that space. To sum up, the index of intelligibility can be defined as the degree to which what we see from the spaces that make up the system is a good guide to what we cannot see, that is the integration of each space into the configuration as a whole. An intelligible system tends to be one where well-connected spaces are also well integrated. That said, we show the map of intelligibility of the area in Figure 4.54. This reveal us that inside the area to be planned, there is a path starting from Via Lagrange and moving toward Corso Filippo Turati and Corso Dante Alighieri that, if followed by a person, would better help to orientate him or herself.

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FIGURE 4.54 MAP OF INTELLIGIBILITY.

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4.4 FROM VISIBILITY GRAPH ANALYSIS TO AGENT BASED MODELS As Cheng highlights (2012), the history of the agent-based models can be traced back to as early as the 1940s when the first prototypical

cellular automata was invented, simulating grid cells interaction with their immediate neighbors by on-off state switches (Neumann 1951). Computer simulation of agents was revolutionized by Reynolds (1987) by introducing individual perception, intelligence and behavior to his Boids agents, and therefore allowing emergent pattern based on a large group of constituent units to be simulated. Despite its long history, it is only until the 1990s the agent-modeling paradigm has become both computationally and conceptually mature to be employed as a feasible simulation tool, and sparked interest from the social science and the urban analysis community. Agent-based models simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena in a system as a whole (Gustafsson e

Sternad 2010). To describe the concept of agent there is no single universally accepted definition: the existing descriptions range from as simple as “just something that perceives and acts” (Russell e Norvig 1955), to as elaborate and rigorous as “Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed” (Maes 1995). 176

Nevertheless, a definition of agents, as what it is and what it does, will avoid confusion and suffice the purpose of this work. It is therefore presented below using the words of Jennings et al.:

“An agent is a computer system, situated in some environment, that is capable of flexible autonomous action in order to meet its design objectives” (Jennings, Sycara e Wooldridge 1998). As Cheng highlights (2012), this definition emphasizes on the two central properties of agents that have been commonly agreed on by people working in relates areas: autonomy and social ability. Being autonomous means that an agent must be able to operate, carry out instructions and make decisions without direct intervention of others, and have control over their actions and internal state; being social means that an agent is part of a community, being able to interact with other agents in order to complete their own tasks and to help others with their activities. Bonabeu (2002) has captured the most essential advantages of Agent based models over conventional modeling paradigms in three statements: •

ABM captures emergent phenomena;

•

ABM provides a natural description of a system;

•

ABM is flexible

Firstly, in contrast to traditional aggregate models, ABM frames a system from the bottom up, by studying the behaviors of its constituent units – the agents. By definition, the autonomous and social features of agents allow complex, nonlinear interactions between them to be modeled, which will lead to collective behaviors and emergent phenomena such as self-organization. Secondly, the ontological correspondence between the computer agents in the model and real world 177

actors makes it easy and evident to represent actors and the environment and their relationship. Thirdly, ABM can be defined within any given system environment with the complexity of agents tuned freely.

4.4.1 PEDESTRIAN MODELING According to Penn and Turner (2002) understanding the way in which pedestrians move around the environment is important for predicting congestion, for evacuation planning, pedestrian traffic and crowd control, as well as assessment of the social and economic function of buildings and urban layout. Two main approaches have been developed to address the issue of pedestrian movement analysis, modeling and simulation. These might be defined simply as ‘configurational analysis’ – to cover methods based on representing and quantifying aspects of the spatial configuration of the environment within which movement takes place – and ‘pedestrian simulation’ – to cover methods that seek to represent the individual pedestrian or the pedestrian population. Helbing et al. (2001) categorize approaches to pedestrian simulation into two groups: large-scale urban simulation and low-level building micro-simulation. For urban modeling, pedestrian movement may be incorporated into Land-Use and Transportation Models (LUTMs), while small-scale urban and building micro-simulation models are constructed for fire evacuation and crowding situations. At both level of resolution, the researcher explicitly chooses the origindestination pairs for agents, albeit at very different scales. In LUTMs, the movement patterns are calibrated using empirical data, while at the micro-simulation level ideal paths to the destination are generally chosen. As stated by Penn and Turner (2002), the missing link appears to be an intermediate level of model or simulation where the origin-destination pairs are not pre-programmed, and where there is neither calibration nor a predefined path rationale. Fur178

thermore, they argue that, given the promising correlations between space syntax configurational analyses and pedestrian movement patterns and co-presence in space, since co-presence is a prerequisite for both communication and transaction in socio-economic life, the spatial configuration itself plays a critical role in determining this â&#x20AC;&#x201C; even though until recently it has been unclear how mechanisms at the level of the individual might give rise to these population level effects. Consequently, Penn and Turner decided to design an agent-based model simulation for pedestrian movement which incorporates space syntax, in view of the need to investigate individual level mechanism that might give rise to the observed population level behaviors. Their ABM was developed to approach the problem of how individual route choice decisions are made in order to model possible decision making processes at the level of the individual, and validate observed population level behaviors. Within their conceptualization of the model, Turner and Penn integrate also Gibsonâ&#x20AC;&#x2122;s ecological theory of perception (1979), to build a model in which the agent and its environment are conjoined by a set of affordances so the agent perceives the contents of the environment directly and uses the affordances within it to guide its action without reference to superior representational models. In order to apply Gibsonâ&#x20AC;&#x2122;s approach to an agent based model of human movement, the agents should have a vision-based mental model of the environment, but for large number of agents this becomes extremely computationally expensive. Knowing this, the researchers (Turner e Penn 2002) developed a software architecture for their model that looks at the environment as the provider of possibilities, assuming that the possibility of exploring the walkable surface of a given spatial layout would be enough for a human to do exactly that. Thus, in contrast to other pedestrian models in which path evaluation is based on cali179

bration from observed data or on deterministic route-choice mechanisms, their model is also an attempt to find out to what extent it is possible to use configuration alone to explain movement, having rules based solely on spatial configuration, without recourse neither to learned paths nor to calibration. Of course, as Turner and Penn highlight (2002), socioeconomic factors do affect human behavior at a fundamental level, therefore when social science turns to agent-based modeling, it seems natural that at least some component should be based on the concept of the rational being. Consequently they propose to firstly add the ability to see, to investigate this ability as an ecological phenomenon leading to natural movement, and then return to socioeconomic factors to provide the constraining framework for the models. In fact, solely by adding the ability to see, they were led to an intuitively attractive model of human-pedestrian behavior: that the human moves in a direction that provides him or her the potential for possible further movement. Gibson calls such interaction between human and environment natural vision:

“When no constraints are put on the visual system, we look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision…” (Gibson 1979, 1) Consequently Turner and Penn defined the conditions for natural

movement as “we look around” and “we go from one vista to another”, while natural interaction was associated with behaviors like “we walk up to it” and “we move around it”. This distinction is important in that for natural movement an agent does not even require the ability to recognize an ‘object’ as distinct from ‘environment’: the agent merely has to recognize that there is an environment which 180

may be explored in order to move. Moreover, they continue, there already exists a theory of natural movement from Hillier et al. (1993) that show how the majority of human-pedestrian movement occurs along lines of sight, and that the more ‘integrated’ (in terms of connection to other lines of sight) a line is, the more movement exists along it. Hence if human movement – the socioeconomic framework and physical constraints being equal – is generated by the configuration, it follows that the relationship of configuration (Hillier et al.) to surface (Gibson) is direct: these are equivalent in terms of the ability to provide a possibility to walk or explore.

4.4.2 EVA: EXOSOMATIC VISUAL ARCHITECTURE In order to implement natural movement in their ABM, Turner and Penn needed to firstly introduce a visual architecture for the agents, then use it as a basis for encoding affordance-based rules. Thence, they conceptualize an exosomatic visual architecture (EVA) that operates on a simple concept of affordance, whereby the natural reaction of an individual in response to the availability of space is to walk toward it. The EVA is based on a pre-processed grid visibility graph that calculates which points, within a predetermined grid, are able to see which other points. The set of visible locations for each point are stored in a lookup table, and thus the visibility graph can be used by the agents to access information about the viewable area for each point. In addition, information can be attached to the nodes of the graph describing attributes of the visible nodes, such as space syntax measures of the configuration properties of the graph at each node. The visible locations are split into thirty-two angular bins, as shown in the picture to the right in Figure 4.55 (Turner e Penn 2002), which then can be used to construct approximate fields of view from any location. To obtain the field of view, the agent’s heading is rounded to the nearest bin and its position is 181

rounded to the nearest visibility-graph location. The standard field of view, shown in the picture to the left in Figure 4.55, is a subset of 170째 from the original 360째 isovist as this corresponds to the approximate occlusion of human vision. Once the field of vision has been defined, to define movement an agent simply chooses a destination that lies within the field at random, walks toward it for a set number of grid space and then repeats the decision making process. Longer site lines will contain a larger number of visible spaces so the random draw of a destination means that an agent is probabilistically more likely to choose its next step in line with the visual continuity of space, as shown in Figure 4.56.

170

FIGURE 4.55 VISIBLE LOCATIONS ON THE VISIBILITY GRAPH GRID ARE SPLIT BETWEEN 32 ANGULAR BINS. IMAGE FROM TURNER AND PENN (2002)

182

FIGURE 4.56 THE CURRENT LOCATION IS ROUNDED TO THE NEAREST GRID POINT ON THE VISIBILITY GRAPH, AND CURRENT HEADING IS ROUNDED TO THE NEAREST BIN. IMAGE FROM TURNER AND PENN (2002)

Because, as highlighted above, the information is stored outside the individual agent but into the nodes that constitute the locations analyzed, it is accessible to all the agents in the system, they decided to call the architecture exosomatic (out of the body). The key factor here is that by using visibility graph and computing metrics of that graph, including those used in the space syntax approach, the lookup table encodes not only object locations, but also information about the accessibility structure of the environment. This means that in effect the agents can infer the affordances of the environment, or at least information on the global spatial relations of different locations visible from their current position in the environment. This allows rules governing agent movement not only to read local information from the lookup table, but also to use that to fulfill global intentions. This property converts the lookup table architecture from a method for representing agent perception to one for representing a form of agent memory. Perception is entirely local and a lookup table that gives information only about what can be 183

seen directly, essentially only replace perception. However, in this system the visibility graph allow the lookup table to do three additional things. Firstly, it can store extended local, telling the agent about spaces within their field of view with high potential for further movement. Secondly, it can store global information – for example the global mean depth of all locations visible from the agent’s point of view. Thirdly, it allows the entire graph to be traversed, and so for the computation of rational routes to remote locations. On the basis of this visual architecture, they encoded the rules of affordance by mean of a hypothesis:

“When engaging in natural movement, a human will simply guide him or herself by moving toward further available walkable surface. The existence of walkable surface will be determined via the most easily accessed sense, typically his or her visual field” (Turner e Penn, Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment 2002, 480) Such a formulation allows them a concise agent implementation: the agent merely has to choose a location to walk through a stochastic process for it to be engaging in natural movement, and then moving towards it for a number of steps before redefining the destination. This appears to be in contrast to Gibson’s approach, as he clearly denies that the concept of space in the Cartesian sense is of relevance to animal perception. However Turner and Penn (2002) clarified that their agents are not in fact ‘seeing’ space, but are basing their decisions on the availability of a destination – a point on a surface within the environment – that affords them the possibility of a further destination. Furthermore the agents are also given a physical presence in that, rounding the position to the nearest grid location, no two agents can exist in the same location. The image below illus184

trates the full agent-decision process, taken from Turner and Penn (2002).

FIGURE 4.57 (A) THE COMPLETE EVA (EXOSOMATIC VISUAL ARCHITECTURE) AGENT-DECISION PROCESS (TURNER E PENN, ENCODING NATURAL MOVEMENT AS AN AGENT-BASED SYSTEM: AN INVESTIGATION INTO HUMAN PEDESTRIAN BEHAVIOUR IN THE BUILT ENVIRONMENT 2002).

The ABM was tested against real pedestrian movements sampled in various interior architectural configurations as well as in exterior urban plans. In the Tate Britain Gallery, in London, Turner and Penn (2002) found that the best combination of parameters â&#x20AC;&#x201C; grid resolution at 0.75 m, a mean of 3 steps before the selection of the next destina185

tion and a field of view of 170° – gives a correlation coefficient of !! = 0.76, resulting in a better performance than visibility graph analysis alone.

FIGURE 4.58 (A) TRAILS LEFT BY AGENTS WALKING THROUGH THE TATE BRITAIN GALLERY. AS EACH AGENT STEPS ON A GRID SQUARE IT INCREMENTS A COUNTER. BLACK AREAS HAVE LOW COUNTS AND WHITE AREAS HAVE HIGH COUNTS. (B) ACTUAL MOVEMENT TRACES FOR 19 PEOPLE FOLLOWED FOR THE FIRST TEN MINUTES OF THEIR VISIT TO THE GALLERY (REPRODUCED FROM HILLIER ET AL, 1996, PAGE 15). IMAGE FROM TURNERD AND PENN (TURNER E PENN, ENCODING NATURAL MOVEMENT AS AN AGENTBASED SYSTEM: AN INVESTIGATION INTO HUMAN PEDESTRIAN BEHAVIOUR IN THE BUILT ENVIRONMENT 2002, 484)

In another study of shopper behavior in a large London department store, the researchers found a correlation coefficient of !! = 0.56 between agent movement and observed shopper movement rates.

186

FIGURE 4.59 TO THE LEFT OBSERVED MOVEMENT TRAILS FOR SHOPPERS ON A SATURDAY IN THE â&#x20AC;&#x2DC;FOOD HALLSâ&#x20AC;&#x2122; AREA OF THE STORE. TO THE RIGHT AGENT MOVEMENT DENSITY AFTER 10 000 AGENT MOVES. (PENN E TURNER, SPACE SYNTAX BASED AGENT MODEL 2002)

In the applications at urban scale, the ABM was tested against human movement in a section of the City of London within a 1.5 km x 1.5 km area and a visibility graph produced at 3 m resolution. The parameters of the agents were the same as the ones used in the Tate Gallery in London, and the peak correlation coefficient among the iterations of the test was !! = 0.67. This lower correlation coefficient in relation to the Tate Gallery application may be due to a less controlled environment where entrance and exit are unconstrained, or it can be related to the fact that agents appears to congregate in larger spaces, as their direct perception leads them toward open areas. That said, it appears that the hypothesis is confirmed: because the EVA system rule-base weights higher viewable area in a certain direction with higher probability of that direction being chosen, EVA agents have a higher chance of moving in the direction of higher walkable surface, and those agents are the most successful at reproducing aggregate human-pedestrian behavior. The aim of the ABM developed by Turner and Penn, on the light of the previously mentioned studies, can therefore be framed within the 187

corpus of applications which support the theory developed by Hillier, that sees the spatial configuration of a given environment as the principal cause affecting the way people move around. We can say that because, in their ABM, the only movement strategy possible is dependent on the configuration of space.

188

4.4.4 ABM APPLICATION TO REGENERATE URBAN AREA Once completed the Visibility Graph Analysis, the data can be stored and used to inform the Agent Based Model about the characteristic of the environment. It was decided to run the simulation without taking into account the area of the railway station Porta Nuov,a in order to investigate how the agents would behave, knowing that they will most likely congregate in the wide empty area left by the station. This might allow to understand which are the streets most used to arrive in the area, that need to be carefully connected with others. During the pedestrian movement simulation in the area, agents were released in the environment and allowed to navigate the configuration. The grid used to perform the VGA is the same used by agents to move around. In this case a grid space of 10 meters was chosen, in order to accurately cover the overall area, and to avoid unnecessary finer details. The ABM runs for 500,000 time-steps, releasing the agents each 10 time-step, The field of view of such agents was 170Â°, approximating human field of view. Each agent was able to recalculate its destination after 3 grid-steps, meaning each 30 meters, and were allowed to explore the configuration for a total of 1000 time-steps, meaning for a maximum walked distance of 3 Kilometers. The grid is then colored from blue to red to indicate the frequency with which the agents are cross that point. Additionally, the trails of the first 50 agents were recorded, and it is shown after the associated analysis. In the figure are shown the results of such analysis. As we noted above, the exploration was carried out for the current state of the built environment and without the train station. Agents were first released from any location, then from Piazza Castello, Piazza Vitto-

rio, Piazzale Duca dâ&#x20AC;&#x2122;Aosta and Piazza Madama Cristina.

189

In Figure 4.60 and Figure 4.61 are shown respectively the pedestrian movements simulation and the traces left by 50 agents randomly released in the environment. As predicted, agents tend to congregate in big open spaces, allowing us to make some consideration about how they get there. Firstly we can note how the most traversed streets are in the neighborhood Crocetta: Corso Duca degli Abruzzi, Corso

Galileo Ferraris and Corso Re Umberto I. In San Salvario relatively well-traversed streets are Via Madama Cristina, Corso Guglielmo Mar-

coni and Via Claudio Luigi Berthollet, whereas in the City Center good amount of movement is found along Via Giuseppe Garibaldi, Via

Po, Via Roma, Via Lagrange and Corso Giacomo Matteotti. We can say that in this case, the streets most traversed to arrive in the area left by the station are Corso Vittorio Emanuele II, Corso Stati Uniti,

Corso Duca dâ&#x20AC;&#x2122;Aosta, Corso Guglielmo Marconi, Via Lagrange and Via XX Settembre.

190

FIGURE 4.60. PEDESTRIAN MOVEMEMENT SIMULATION, RANDOMLY RELEASED AGENTS.

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FIGURE 4.61. TRACES LEFT BY 50 AGENTS RANDOMLY RELEASED IN THE ENVIRONMENT.

192

In Figure 4.62 and Figure 4.63 agents with the same settings were released from Piazzale Duca dâ&#x20AC;&#x2122;Aosta. It is possible to see how Corso Du-

ca degli Abruzzi and Corso Galileo Ferraris are the most traversed streets, while Corso Vittorio Emanuele II, Corso Stati Uniti and Corso

Duca dâ&#x20AC;&#x2122;Aosta are the most used to arrive in the area to be planned. Agents do not arrive in San Salvario, and barely explores the center. In contrast, if agents are released from Piazza Madama Cristina (Figure 4.64 and Figure 4.65) some traces of exploration of Crocetta are highlighted through Corso Vittorio Emanuele II and Corso Stati Uniti, and extend along Corso Re Umberto I, Corso Galileo Ferraris and Corso

Duca degli Abruzzi. From San Salvario, the most used street to arrive in the area of the train station are Via Bernerdino Galliari, Via

Claudio Luigi Berthollet, Corso Guglielmo Marconi and Corso Dante Alighieri. Lastly, agents were released from Piazza Castello, in the City Center (Figure 4.66 and Figure 4.67), and the analysis highlight via Lagrange and via Roma as the most traversed. Starting from there, a good amount of movement can be found within Crocetta, through its main streets mentioned above, whereas San Salvario appears to be less reachable, and only through Via Madama Cristina.

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FIGURE 4.62. PEDESTRIAN MOVEMEMENT SIMULATION, AGENTS RELEASED FROM PIAZZALE DUCA D’AOSTA.

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FIGURE 4.63. TRACES LEFT BY 50 AGENTS RELEASED FROM PIAZZALE DUCA D’AOSTA.

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FIGURE 4.64. PEDESTRIAN MOVEMEMENT SIMULATION, AGENTS RELEASED FROM PIAZZA MADAMA.

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FIGURE 4.65. TRACES LEFT BY 50 AGENTS RELEASED BY PIAZZA MADAMA.

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FIGURE 4.66. PEDESTRIAN MOVEMEMENT SIMULATION, AGENTS RELEASED FROM PIAZZA CASTELLO.

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FIGURE 4.67 TRACES LEFT BY 50 AGENTS RELEASED BY PIAZZA CASTELLO.

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From the results of this last analysis of the area, it is possible to draw the following conclusion. As mentioned above, a complete model of pedestrian movement cannot avoid to incorporate socio-economical factors, that are relevant in determining individual human behaviors when dealing with route choice and exploration of the built environment. That said, this ABM implementation was not created to provide a complete model of pedestrian decision-making process, instead, it was built to explore to what extent the configuration alone could be thought as the main factor influencing pedestrian movement. From this premises, we can say that the urban configuration considered tends to privilege the neighborhood Crocetta and its connections with the City Center, leaving San Salvario less explored. Interestingly enough, when agents start from Crocetta they do not cross the area left by the train station to arrive in San Salvario, whereas the opposite is not true. This might lead to conclude that a design proposal for the area should pay particular attention to the incoming

movement toward this neighborhood. Furthermore, these analyses confirm the most used streets and the connections suggested above, including the extension of Via Lagrange, the connection between Corso

Stati Uniti and Via Berthollet and between Corso Duca dâ&#x20AC;&#x2122;Aosta and Corso Guglielmo Marconi. It is also possible to note how the housing estate

around

Via

Arquata

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good

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incorporated in the urban fabric, increasing its accessibility if the extension of Via Lagrange incorporate Via Mario Pagano.

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4.5 MOBILITY IN THE CITY OF THE FUTURE: SMART INTERSECTION

MANAGEMENT AT MIT SENSEABLE CITY LAB

When dealing with urban accessibility, one important factor to take account for is vehicular movement and its influence on urban sustainability. If it is true that pedestrian accessibility can be linked with most aspects that involve the functioning of cities, as their social vitality and economic activity, we might be able to say the same about vehicular accessibility and its consequences on pollution and traffic. This last chapter does not want to be an extensive overview of urban vehicular accessibility; rather it provides a specific point of view for a specific case. Through a review of the work carried out at the MIT Senseable City Lab, the aim of this chapter is to illustrate the benefits that an Intersection Management System might have on urban vehicular accessibility, taking into consideration the possible introduction into the market of self-driving vehicles. The questions being addressed by the laboratory is in fact how to allow autonomous vehicles to safely navigate the city, and how to handle the flow of self-driving cars crossing the intersections in such a way to avoid collisions, decrease the waiting time and reduce the pollution.

4.5.1 AUTONOMOUS VEHICLES Nowadays we all are familiar vehicles that have some sort of sensors embedded. Think for example to the proximity detection that allows one to unlock the alarm of his or her car while approaching the car, without using the key. Also, more recently, integrated systems with built in cameras, ultrasound sensors, and packages of hardware and software allows people to take their hands off the steering wheel, and let their cars to park autonomously. Moreover, the costs for the 201

implementation of technologies such as LIDAR (Light Detection and Ranging) systems, that allow the detection of streets and obstacles in 3D, are gradually diminishing.

FIGURE

4.68

IMAGE

TAKEN

FROM

LIDAR

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MOUNTED

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ROOF

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FROM

HTTP://WWW.TECHHIVE.COM/ARTICLE/2043878/DRIVERLESS-CARS-YIELD-TO-REALITY-ITS-A-LONG-ROADAHEAD.HTML

The increasingly demand for the implementation of such systems and the improvement rate of their performances make it clear the roadmap for such new technologies. In fact, during the 2013 International Consumer Electronic Show (CES), Audi proposed its first autonomous vehicle: a car that is able to drive and park itself without the intervention of the driver. Despite Audiâ&#x20AC;&#x2122;s engineers teams forecast that for the technology to be ready, there would need at least ten years, all the major automobile manufacturers are working toward the integration of such systems into their fleets. Long from being a distant dreams for the technology and automotive enthusiasts, autonomous driving is now a reality, and its developments are constant and rap-

202

id. According to an IHS report13, after the 2050 all vehicles on the market might be self-driving.

FIGURE 4.69 AUDI A7 PILOTED DRIVING CONCEPT.

The development of these new technologies carries with it a series of questions that need to be answered. Firsts among others, the issues about pollution, traffic and safety are the ones that might concern the work urban planners, from the point of view of a “smart development” of urban areas. These are the questions that are being addressed by the Senseable City Laboratory, at the Massachusetts Institute

technology.

article

for

the

site

www.project-

syndicate.org, Carlo Ratti – the director of the Senseable City Lab – and Matthew Claudel, highlight how autonomous cars might have a

http://press.ihs.com/press-release/automotive/self-driving-cars-moving-industrysdrivers-seat 14

http://fourtitude.com/news/Audi_News_1/audi-a7-sportback-piloted-driving-concept-

arrives-las-vegas-following-560-mile-drive/ 15 http://www.project-syndicate.org/commentary/carlo-ratti-and-matthew-claudelforesee-a-world-in-which-self-driving-cars-reconfigure-urban-life

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positive impact on urban life, since they may be employed both as private and public means of transportation. The same car could be used by an individual for its journey to work during the morning and then, rather than staying put in the parking lot, it could reach and â&#x20AC;&#x153;driveâ&#x20AC;? someone else in the neighborhood. In the same article is cited another publication by the Massachusetts Institute of Technologyâ&#x20AC;&#x2122;s SMART Future Mobility team, revealing that the mobility demand of a city like Singapore could be met using just the 30 of its existing vehicles. Furthermore, other researchers in the same group suggest that this number could be cut by another 40

if passengers

traveling similar routes at the same time were willing to share a vehicle. These assumptions outline a city in which everyone could travel with just one-fifth of the number of cars in use today. Such reductions in car numbers would dramatically lower the cost of our mobility infrastructure and the embodied energy associated with building and maintaining it. Fewer cars may also mean shorter travel times, less congestion, and a smaller environmental impact.

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4.5.2 DRIVEWAVE: SMART INTERSECTION MANAGEMENT In order to allow self-driving vehicles to safely navigate the city, the deployment of more intelligent transportation systems is required. Real-time data planning and smart routing, which allow one to choose the less congested path, or the faster route toward the destination, are already a reality. Instead, autonomous cars will require other innovations: from optimization of road capacity to intersection management. As a matter of fact, exactly these issues are being tackled at the MIT’s Senseable City Lab. The project is called

DriveWave, and it is aimed to experimentally prove that it is possible to manage intersections crossed by autonomous vehicles, where the flow of cars is handled in such a way to avoid collisions, decrease the waiting time and reduce the pollution. In particular, the research seeks to answer the following questions: what is the impact of widespread use of self-driving vehicles on urban mobility? What are their benefits on travel time? What are their paybacks in terms of emissions?

FIGURE 4.70 A PHOTO OF THE DRIVEWAVE INSTALLATION AT THE EXHIBITION “WAVE”, THAT TOOK PLACE IN PARIS FROM THE 10TH OF SEPTEMBER TO THE 5

205

OF OCTOBER

What follow is a resume of the work done by the Senseable City Laboratory, which includes a brief explanation of the underlying principles of the project. The principal aim is to estimate the benefits of self-driving vehicles at a city scale, where the intersections are consider as black boxes managed by an “intersection manager”. The latter grants the access of the vehicle to the intersection on the basis of trajectory compatibility and safety constraints. It is assumed that at the city wide level a network of “intersection managers” would regulates city traffic, but at the moment the simulation is restricted on a single intersection where four roads converge, each one having a double carriageway with two lines. This kind of intersection is simultaneously among the most common within the urban environment, and complex enough to provide a reliable mix of trajectories. A real example of such intersection is illustrated in the image below, representing the junction between Massachusetts Avenue and Columbus Avenue, in Boston, and the modeled intersection for the simulation.

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FIGURE 4.71 INTESECTION BETWEEN MASSACHUSETTS AVENUE AND COLUMBUS AVENUE IN BOSTON. BELOW, THE MODEL OF THE INTERSECTION USED IN THE SIMULATION PLATFORM. COURTESY OF MIT SENSEABLE CITY LAB.

The project for a “smart intersection management” that take account of the advent of self-driving cars technology raises the question if the actual traffic light systems are still efficient. To evaluate the “smart intersection management” two important index are measured: the capacity of the intersection, or the number of vehicles per unit of time served by the intersection, and the delay, that is the ratio 207

between the average delay and the free flow travel time experienced by vehicles. The former is a system metric that city planner wants to maximize, while the former is an individual metric that driver wants to minimize. The access to intersection is based on an incompatibility network and safety constraints, which are softer for vehicles with compatible trajectories, and harder for vehicles with incompatible trajectories.

FIGURE 4.72 SCHEME OF THE POSSIBLE TRAJECTORIES WITHIN THE INTERSECTION. COURTESY OF MIT SENSEABLE CITY LAB.

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The following image illustrates a partial view of the incompatibility network, where the trajectories are connected if are mutually exclusives.

TWS

TWL

TSL

TWR

TNR

TNS

TES

TNL

TSR

TEL

TSS

FIGURE 4.73 A PARTIAL VIEW OF THE INCOMPATIBILITY NETWORK. COURTESY OF MIT SENSEABLE CITY LAB.

An example of two incompatibles trajectories is shown in the image below:

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saN (v N )

Pin V1 Pout saE (v E )

l V1

FIGURE 4.74 AN EXAMPLE OF TWO INCOMPATIBLES TRAJECTORIES. COURTESY OF MIT SENSEABLE CITY LAB.

The Intersection Management Algorithm is called IM-FCFS (Intersection Manager-First Come First Serve), and is based on the principle that vehicles send an intersection access requests when entering the road, and these requests are processed on a First-Come-First-Served basis. To be able to process the request from a given vehicle !, the intersection management algorithm computes the earliest possible intersection access time !"! based on preceding vehicles on same lane, and considers vehicles with incompatible trajectory against !. It therefore calculates the earliest possible time window !! , after time !"! , where ! can be accommodated satisfying hard safety constraints. 210

FIGURE 4.75 SCHEMATIC WITH THE MAIN VARIABLES OF THE IM_FCFS ALGORITHM. COURTESY OF MIT SENSEABLE CITY LAB.

The Intersection Management Engine is built around three main classes of parameters, which are car parameters, road parameters and

traffic parameters. Each car has a length of 5 m, can accelerate at a speed of 3 m/s and decelerate at 5 m/s. Its straight speed is 55 Km/h, the right turn speed is 25 Km/h and the left turn speed is 50 km/h. The roads considered have a left lane for each direction, and the parameters are the road length and width, respectively 300 m and 5 m, whereas the speed limit allowed is 70 Km/h. Finally, the traffic parameters are the number of vehicles approaching the intersection, measured in terms of car per second, and the trajectory mix, that is the repartition of vehicle trajectories.

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FIGURE 4.76 SCHEMATIC OF THE MAIN PARAMETERS OF THE INTERSECTION MANAGEMENT SIMULATOR. COURTESY OF MIT SENSEABLE CITY LAB.

For what concern the trajectory mix, it was build analyzing the real GPS data from taxis in Singapore, collected by the Senseable City Laboratory for an earlier study, in the case of a congested intersection. This was divided into four regions, and the road taken by taxis was used to deduce the trajectory used.

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FIGURE 4.77 REAL LIFE TRAJECTORY MIX. COURTESY OF MIT SENSEABLE CITY LAB.

An evaluation of the simulator was run considering the Singaporean example and a uniform trajectory mix, comparing the Intersection Management system against the actual traffic light system using the above mentioned parameters. In the case of the Singaporean example, it was found a negligible delay for the Intersection Management system below 0.5 cars per second, and that the road its saturated at a rate of 0.7 cars per second in the case of the traffic light, and at a rate of 0.9 cars per second in the case of the IM. For what concern the uniform trajectory mix, it was considered a minimum delay of 10 seconds for the traffic light, and it was found that for the Intersec213

tion Management system there was a negligible delay below 0.8 cars per second. In this case, the road it is saturated at a rate of 1.2 cars per second for the traffic light, and at a rate of 1.6 cars per second for the Intersection Management system. Therefore, in both cases it seems that the implementation of such system would perform better than a classic traffic light.

FIGURE 4.78 REAL LIFE TRAJECTORY MIX. COURTESY OF MIT SENSEABLE CITY LAB.

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4.5.3 DRIVEWAVE: THE INSTALLATION On the basis of the previous results, it was decided to build an interactive installation, to visualize the output of the Intersection Management System in terms of flows of cars crossing the intersection, and to compare it with a simultaneously running traffic light system. The purpose was to enable people to interact with both scenarios, increasing and reducing the amount of cars approaching the intersection from each road, and introducing pedestrians that cross the streets using buttons and knobs. The installation was built using two large LCD monitor to allow the two systems to be viewed by the user, and a smaller screen to display and compare the performances of the two intersections. In fact, along with the visualization of car flows, in order to raise the awareness of the users about the question of urban pollution and traffic congestion, a data visualization regarding emissions and delay was embedded in the installation. The visualization platform was built using Processing 16 , a Javabased open source programming language specifically designed for visual and interactive installations. To build the User Interface and the relative code it was used an Arduino board17, an open-source electronics platform based on hardware and software. This allows the users to be engaged with the installation and receive instant feedback concerning the performances of the two systems. The visualization platform was then embedded within a 3D model of the intersection between Massachusetts Avenue and Columbus Avenue, which was built using transparent Plexiglas.

https://processing.org

http://www.arduino.cc

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FIGURE 4.79 DATA VISUALIZATION AND USER INTERFACE. COURTESY OF MIT SENSEABLE CITY LAB.

FIGURE 4.80 DETAILS OF THE 3D PLEXIGLAS MODEL. COURTESY OF MIT SENSEABLE CITY LAB.

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The installation was exposed in Paris, from the 10th of September to the 5th of October at the Wave Exhibition, and in Pisa, from 9th to 12th October, at the Internet Festival. Further development of the project are being conducted at the Senseable City Laboratory, in order to extend the Smart Intersection Management System over a network of intersections and prove that the system can be implemented at citywide scale.

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FIGURE 4.81 THE DRIVEWAVE INSTALLATION EXPOSED IN PARIS. COURTESY OF MIT SENSEABLE CITY LAB.

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6 CONCLUSIONS As a way to conclude this thesis, it is thought to drive the attention onto the results of the analyses that have been done so far. To do so, here it is presented a series of guidelines for the reconstitution of the urban grid in the area of Porta Nuova. These guidelines are meant to assist the design process of any new development proposal for the area, that would include, along with other important objectives, the willing to exploit the accessibility and permeability potentials of the area, in order to create a new vibrant center that

connect to the surrounding urban environment, rather than compete against it. 1. The reconstitution of the urban fabric should include a strong axis in north-south direction, well connected with the City Center and Crocetta, with particular attention to connections with San Sal-

vario, to improve its accessibility at urban scale and exploit the presence of commercial activities higly accessible at local scale. A proposal might consider the extension of Via Lagrange to connect with Via Mario Pagano, in order to improve the accessibility of the housing estate around Via Arquata. 2. to increase the permeability of the area at urban scale, it would be convenient to reinforce an east-west axis, which passes through Crocetta and San Salvario, and connects to an higher level axis. An example could be the reinforcement of Corso Sommeiller through additional connections at ground level with San Salvario. This would facilitate the balancing of the east-west crossings that now use mostly Corso Vittorio Emanuele II. 3. To create a network of locally integrated axes, higher priority should be given to the connection between Corso Galileo Ferraris,

Corso Duca degli Abruzzi and Corso Umberto I, with Via Madama Cristi219

na, Via Nizza and Via Ormea. To this regard, are suggested the connections between Corso Guglielmo Marconi and Via Pastrengo, and between

Via Berhollet and Corso Stati Uniti. These may be the most suitable for the direct connection between the School of Engineering and the School of Architecture, and the fast access to the train station Porta

Susa through Corso Stati Uniti and Corso Castelfidardo. 4. To ensure the emergence of local centrality exploiting the potential of Crocetta and San Salvario, it is recommended the creation of a system of public spaces resulting from the intersections between the east-west axes and the extension of Via Lagrange that would be connected through the latter to Corso Vittorio Emanuele II and to Corso Sommeiller, increasing the accessibility also at urban scale. 5. The proposed configuration might include, if necessary, a further extension of Corso Filippo Turati toward Piazza Carlo Felice; 6. The definition of other public spaces could be made in accordance with the areas of greatest compactness, integration and intelligibility, where necessary; 7. The definition of land uses that might include primary school, kindergarten, hospital or other similar functions could be done accordingly with the areas of greatest control and controllability. Of course, these guidelines are not meant to be exhaustive, and do not provide all the necessary information that would lead to a successful design proposal. In fact, further considerations need to be done, that should consider energetic sustainability, economic viability and historical preservation, among other factors, to allow a comprehensive understanding of the area considered. Lastly, it is important to highlight that the creation of a design proposal that aims to successfully meet different objectives, cannot be based only on analyses, but it needs to be supported by a thorough 220

knowledge of the site and, possibly, of the people who use, and will use, the spaces that are going to be created or modified

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9 RINGRAZIAMENTI Desidero innanzitutto ringraziare il Professor Roberto Pagani, relatore di questa tesi, per la grande disponibilità e cortesia dimostratemi, e per tutto l’aiuto fornito durante la stesura. Inoltre, ringrazio sentitamente il Professor Carlo Ratti per avermi accolto al Senseable City Lab, e avermi dato la possibilità di vivere un’esperienza di grande valore accademico. Intendo poi ringraziare Lorenzo Savio, per i preziosi consigli che mi hanno aiutato a mettere in ordine i pensieri. Un sentito ringraziamento va anche a Enzo Bason, dell’AMM, e a Laura Annibaletto, del CSI Piemonte, per avermi fornito file e dati indispensabili per la realizzazione della tesi. Un grazie di cuore ai miei amici, tutti, per ogni momento passato assieme. Infine, vorrei ringraziare i miei genitori per l’immancabile fiducia, e mio fratello, per le risate capaci di alleggerire le lunghe notti di lavoro.

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