Learning from Informal Settlements by parimala1988

LEARNING FROM INFORMAL SETTLEMENTS Research Work by Parimala Venkatesh Guided by Professor Urs Hirschberg Technical University at Graz

“the boundaries of the individual unit extended well beyond the unit itself, into the lane and beyond the networks of lanes that shared a common infrastructural ecology” Vyjayanthi Venuturupalli Rao Harvard Design Magazine Issue

#41

Introduction to Research Problem Informal settlements are comparable to autonomous organisms that adapt it’s built environment to encapsulate socio-cultural, economic and political factors influencing its inhabitants over time. But how are informal settlements formed? How do they address the changes in environment? What role does it play in its sense of community? Understanding Informal Settlements can shed insight on how human-habitat connectivity can derive a thriving community that evolves with time. Jane Jacobs in her book ‘The death and life of Great American Cities’, addresses the vitality of user-driven dense communities. She compares straight-jacketed developers’ notion of dense informal communities to the likes of a layman’s perception of the rocket engine – “chaos if they are seen without comprehension”. So, how does one begin to unravel this multi-layered built form? Can documenting/ mapping Informal Settlements begin to decipher the system of chaos?

Research Question To answer this question, this study is set on the learning from Mumbai’s Informal Settlements. In the year 2050, Mumbai is said to become the largest city with slum population equating that of New York and London put together. With alarming rates of urbanization, the need to understand the inner workings of dense settlements has never been more crucial. Current methods of mapping informal settlements involve drawing tedious settlement layouts and select typical unit plans. This documentation process is met with hostility from intimidating groups with political connections who place themselves above the law. This the direct result of land appropriation in the name of top-down slum rehabilitation proposals by greedy politicians and real estate developments. Aside from the bureaucratic hurdles, slum documentation efforts present linear understanding of the spatial characteristics with little to no connection of intangible influences shaping it. Current tools of mapping the anthropological connections with the built environment, done using traditional door-to-door survey, causes subjective results that become obsolete quickly. With current limitations of physically mapping Informal Settlements arises the question, ‘Is there a remote/objective method to map, document and generate informal settlements swiftly?

Research Design & Methods This study follows a qualitative approach by analyzing collected data for generating a programming code (in Python language) to create an urban layout of the selected Informal Settlement. The research methods followed can be broadly sub categorized as – (1) Site Selection, (2) Data Collection & Analysis, (3) Retro-Mapping, (4) Slum Generator. 1. Site Selection The research setting is that of ‘Kumbharwada’, a pottery settlement in Dharavi, Mumbai. The purpose of this selection was two-fold. Firstly, the occupation driven ecology of the slum that encompasses the intertwined idea of community and commerce. The analysis and generation of an occupation driven settlement can help verify research methods in similar settlements. And secondly, the plethora of existing information on the slum’s history, unit layouts and visual imagery. 2. Data Collection & Analysis The first step was to curate existing information to handful of sources. Since, informal settlements are in a constant state of flux, referring to several sources at once without site verification can mislead analysis. Since, this research was conducted remotely, site verification was not a possibility. Therefore, to develop the programming code only two sources of data were selected so that a clear layout of the slum is given at that given point in time.

KUMBHARWADA

Source: 4th year Students at Kamla Raheja Vidyanishi Institute of Architecture

LOCATION

Dharavi, MUMBAI

AGE OF SETTLEMENT

More than 100 years

AREA OF SETTLEMENT (HECTARES)

5.1

NO. OF UNITS (ESTIMATED)

2125

POPULATION (ESTIMATED)

9452

DENSITY (PERSONS/HECTARE)

1853

UNIT SIZE (SQUARE METERS)

Between 40 and 120

WATER SUPPLY

Individual House Connection

SEWERAGE DISPOSAL

Individual Toilet & Sewer Line + Public toilet

OCCUPANCY DIMENSION

Municipal Land. Occupants were given vacant land Tenure to undertake their enterprise. They built their houses and also sometimes extended them for renting purposes

CULTURAL DIMENSION

The population belongs to one ethnic community specializing in making earthen pots. The entire area is a pottery industry with many small entrepreneur families.

BUILT-FORM DIMENSION

Built form is connected to enterprise of pottery long row houses with internal network of streets and open spaces used for making pottery

Fig 1 : Typical Unit & Cluster layout by CRIT Publication, ‘0.Slum Typologies & Beyond’

As per this criterion, the information gathered is credited to CRIT Publication (Mumbai) that collected and produced historical analysis, typical unit layouts (Fig1), census of occupation for select of slums for the State of Maharashtra government. The methods of documenting the slums was to send student teams to various slums to measure, map and produce architectural drawings. This was done with personal connections with select inhabitants of the slum.

et z.n urb w. ww Z RB yU Pb MA e: So urc

Fig 2 : Khumbharwada Map by URBZ (www.urbz.net)

Aside from the data collected from here, the slum map (Fig2) of Khumbharwada produced by URBZ (a research collaboration specialized in participatory planning and design), showed the interplay of various unit typologies (Jhopads) forming clusters (Nagars) and ultimately the entire slum map (Jhopadpatti).

Typical Unit (Given)

Interior Layout

Fig 3 : Analysis of Typical Unit

Function Distribution

re 5. Grid analysis of live/work unit

With these two data as point of departure, began the shape rule analysis of typical unit typology to inform and derive similar grammar in generating typologies other than the typical unit. This process began with spatial analysis of typical unit and that information helped derive similar spatial characteristics of other units. Function Rearranged

Commercial Typology

Storage/Kiln Typology

Residential Typology

Live-Work Typology

Residential Typology

l b

l * b = 24 sq.mts. live/ cook

store

: 2.5 : 1.5

make/ live

: 2.5 : 1.5

l:b= 8:3 12 : 2

live/ sleep/ cook

l:b= 4:4 5:3 6 : 2.5

occupation

occupation live/sleep/ cook

Live vs. Work (Day)

occupation

make

3.0 : 1.0

live/sleep/ cook

Live vs. Work

occupation live/sleep/cook

Live vs. Work (Night)

personal

communal

Private vs. Public

personal

communal

occupation

live/ cook

make

2.0 - 2.5

live/ cook

make

2.5 - 3.5

Fig 5 : Derivation of other typologies using shape rule

1 : 1 :

make/ live

3.0 - 4.0

sell

l * b = 15 sq.mts.

occupation

Private vs. Public

Commercial Typology

Storage/Kiln Typology

b l * b = 10 sq.mts.

l * b = 7-15 sq.mts. 3.0 3.0

store

3.0 3.0

work

l:b= 3:3 2:5

l:b= 3:3 2:5 3:5

occupation

Live vs. Work

communal

Private vs. Public

The typical unit presented in CRIT Publication is a linear unit which with triple function – (1) Living/Sleeping/Cooking, (2) Pot-Making/Pot Storage, and (3) Pot Selling space. Understanding the spatial rationality of this one typology, lends itself towards defining other typologies (Fig 5) with single or double functionality. Impact of these various typologies can be seen in python code development of slum cluster subdivision (explained later).

Fig 6 : Tracing routes from Google Earth Imagery A. SLUM BOUNDARY B. PRIMARY ROUTES

C. SECONDARY ROUTES

C. TERTIARY ROUTES

3. Retro-Mapping (Python Code Development) After analyzing the Unit Typologies, the next step required generating an overall map of the slum with circulations that will incorporate an array of units. This involved analyzing Google Earth Imagery and tracing circulatory routes. Examining the shadows of tenements on google maps revealed the width of routes. Cross-checking this information by URBZ Slum Map (Fig 2) the routes were subdivided into three categories – Primary, Secondary and Tertiary. Below (Fig 7) reveal the logic behind these observations. Being a potter community, the Primary Routes (7-15 mts) had unit entries and shop fronts with sporadically located community kilns and storage. The secondary routes (3-5 mts), again with mixed access to units and shops had no kilns/storage tents. And finally, the tertiary routes (1-3 mts) mostly had access to storage units or shop-only typology.

Fig 7 : Analyzing and deriving route typology and widths

Primary

Secondary

Tertiary

ROUTE TYPOLOGY

Primary

Secondary

Tertiary

7 - 15 mts

3 - 5 mts

1 - 3 mts

The routes mapped give subsections of the slums. These subsections will be referred as ‘Clusters’ (Nagars). These clusters are further sub-divided into individual units/ jhopads. The python code for this cluster subdivision was based on analyzing URBZ’s slum map (Fig 2) while observing the ratio & areas of various unit typologies.

primary

slum boundary

secondary

CLUSTER SUBDIVISION

As seen in the above image (Fig 8), after the route mapping, each section is separately analyzed by the python code. This code analyzes the cluster boundary into simple quadrangle and redraws it as a rectangle. After this step, the rectangle is divided into 3-5mt wide sections perpendicular to the longest edge. The divisions on the shorted edges are further divided in similar manner. After this, the units in the middle of the cluster are put through a ratio corresponding to variations in typology to further divide it in two units. This is done to accommodate the various unit typologies that both single and double points of access.

Fig 9 : Route Typology affecting Unit offsets

Primary Roads : 7 - 15 mts

Secondary Roads : 3 - 5 mts

After subdividing the cluster, each unit boundary is generated. This unit boundaries are further analyzed by their proximity to an access route. Depending on this factor, the unit boundaries are offset toward their centroid (Fig 9). This offset parameter is automatically set by the route typology. For example, a primary route proximity will trigger an offset from 3.5mts - 7.5mts (50% of route widths).

Tertiary Roads : 1 - 3 mts

This random offset will start to convert the single line routes traced using google earth imagery to double line with varying width simulated with the logic of the informal settlements (Fig 10).

Fig 10 : Impact of route types on Units

The python code here retro maps the slums following the logic of actual slum growth. By that logic, first the users erect their tenements and then the routes develop . Therefore, analyzing the route typologies plays a crucial role in the development of the Python programming code for the slum generator.

I - I

I - II

II - II

II - III

III - III

6 7

Fig 11 : Generated Unit Typologies

These rules are converted into python definitions for each unit type. This definition needs a list of four coordinates to generate a complete unit layout with interior block definitions (Figure 7). These rules are converted into python definitions for each unit type. This definition needs a list of four coordinates to generate a complete unit layout with interior block definitions (Figure 7). 6 6 These rules are converted into python definitions for each unit type. This definition needs 7 7 a list of four coordinates to generate a complete unit layout with interior block definitions (Figure 7). 8

14 15

Figure 7. Generating unit interior layouts using Rhino-Python.

16 16 Kumbharwada’s16slum map – route networks 16 To integrate the unit definitions to form are traced by analyzing roof skiagraphy in satellite images (Google Earth). This analysis 17 17 17 Figure 7. Generating unit interior using Rhino-Python. often needs site verification as theselayouts tenements tend to have staggered upper levels as 16 shown in typical section by CRIT. Since this research slum was map conducted the 18 18 Kumbharwada’s 18 To integrate the unit definitions to form – routeremotely, networks are circulatory routes were with field data inimages ‘The study Dharavi Structure’[sic] by traced by analyzing roofverified skiagraphy in satellite (Google Earth). This analysis Figure 7. Generating unit interior using Rhino-Python. the and ofasKamala Raheja Vidyanidhi Institute for Architecture oftenfaculty needs sitestudents’02 verification theselayouts tenements tend to have staggered upper levelsand as After offsetting the unit boundaries based on the parameter of the Environmental (KRIVIA). shown in typical section by CRIT. Since this research slum was map conducted To integrate theStudies unit definitions to form Kumbharwada’s – routeremotely, networks the are route typologies, the python code runs categorizes each unit as circulatory routes were verified with field data in ‘The study Dharavi Structure’[sic] by traced by analyzing roof skiagraphy in satellite images (Google Earth). This analysis per area. This categorization then helps identify the typology of the The traced routes are categorized as – primary, secondary and tertiary. The primary the faculty and students’02 of Kamala Raheja Vidyanidhi Institute for Architecture and often needs site verification as these tenements tend to have staggered upper levels as unit that will generate in the given boundary. Before this step, for routes are 20-50 feet wide with access to live/work, residential, and kilns/storage Environmental Studies (KRIVIA). shown in typical section by CRIT. Since this research was conducted remotely, the the code to work seamlessly, each unit boundary gets converted to typologies. The secondary routes are 10-15 feet wide with mixed access to residential and circulatory routes were verified with field data in ‘The study Dharavi Structure’[sic] by four coordinates. These coordinates are then subjected to piece of commercial unit typologies. And finally, tertiarysecondary routes areand to be 3-10 feet with The traced routes are categorized as – the primary, tertiary. Thewide primary the faculty and students’02 of Kamala Raheja Vidyanidhi Institute for Architecture and python code called ‘functions’ – a uniquely written for each typology access to storage typologies (Figure 8).residential, and kilns/storage routes arecommercial 20-50 feetandwide withunitaccess to live/work, Environmental Studies (KRIVIA). (Fig 11). Depending on the typology assigned, the code will generate typologies. The secondary routes are 10-15 feet wide with mixed access to residential and walls, windows, doors that mimic the typologies studies (Fig 5). commercial unit typologies. And finally, tertiarysecondary routes areand to be 3-10 feet with The traced routes are categorized as – the primary, tertiary. Thewide primary access storage typologies (Figure 8).residential, and kilns/storage routes to arecommercial 20-50 feetandwide withunitaccess to live/work, typologies. The secondary routes are 10-15 feet wide with mixed access to residential and commercial unit typologies. And finally, the tertiary routes are to be 3-10 feet wide with

17 18

CLUSTER GENERATION

Step 1: Cluster Generation, each cluster here is a region with line segments assigned with layers – Primary, Secondary & Tertiary

Primary

Secondary

Tertiary

CLUSTER DIVISION

Step 2: Each cluster is then processed with code that will subdivide perpendicular to the long side. The end subdivisions are further divided similarly (logic shown in Fig 8).

UNIT BOUNDARIES (ROADS)

Step 3: After subdivision of cluster, each subdivided region is then offset internally as per the proximity of road hierarchy. (Explained in Fig 9)

TYPOLOGY (AREA)

Step 4: The offset unit boundaries are processed with python code that categorizes them depending on its area.

Live-Work

Shop/Store

To Split

T YPOLOGY (DATA)

Step 5: In the previous step, the unit categorization done by the code does not consider historical data. Therefore, the map is reprocessed with new parameters that improve accuracy of the map.

Live-Work

Shop/Store

To Split

Res/Store

UNIT SUB-DIVISION

Step 6: The units that span across the clusters which are not mandatory live-work typology based on historical data are recategorized for subdivision.

Live-Work

To Split

Shop/Store

Res/Store

UNIT SUB-DIVISION

Step 7: The subdivided units are then recategorized by ratio of occupancy suggested by CRIT’s research. At this point, the all the units have been categorized as per unit typology and this is then run through the unit python functions to generate the entire slum map.

Live-Work

Shop/Store Residence To Split Store Res/Store

GENERATED SLUM

TOTAL UNITS : 1318 N

LIVE-WORK UNITS

437

RESIDENCE UNITS

541

SHOPS / STORES

379

START

Identify Settlements to for Retro-Mapping Unit Layout?

Collect Data and Documentation

Yes

Identify Shape Grammars & Derive Variations

Yes

Identify Cluster Rules, Road Hierarchy & Cluster Division Patterns

Cluster Layout?

Slum Mapping using Google Earth Image

Closed Clusters?

Yes

Apply Cluster division

Generate Unit Boundaries

Offset Unit Boundaries as per Road Hierarchy

Categorize as per Unit Sizes

Guesstimate Roads

RETRO-MAPPED SLUM LAYOUT

Type A

Type B

Figure 2. Algorithmic process for initial slum map

Research Analysis/Findings Repeat until results are met

With current limitations of mapping and learning from Informal Settlements, this study explores computational analysis of existing data and google earth images via slow-modelling process.

Analyze Unit Sizes & Areas

Yes

Does it Match with Data?

This process goes step-by-step in reverse order. In doing so, the study can generate semi-accurate quick maps that reveal the slum layout swiftly.

Further research needs to be conducted in terms of verifying the slum map with actual site. This will ensure the imperfections in the Python code can be rectified. Additionally, similar process needs toofberules conducted with other similar slums. can possible Figure 3. Cross-Fertilization for Kumbharwada’s initial slumThis map reveal possible commonality in slum patterns. Adaptive Mapping using Slow Modeling Methodology

Type C

Apply Unit Typologies

The inputs in python code can further be toggled with to create other slum maps or hybrid maps, giving us quick methods to visualize slum intricacies.