Smart City Infrastructure Index by Husna Begum M R

PLAN9075 URBAN DATA AND SCIENCE OF CITIES

DESIGNING URBAN PERFORMANCE INDICATORS

SMART CITY INFRASTRUCTURE INDEX DI WEN DWEN9655 450036206

HUSNA BEGUM M R HUSN2107 500241114

J O C E LY N F R A N C I S JFRA4343 510414128

PRASOON DHOUNDIYAL PDHO6793 490542495

TA B L E

O F

C O N T E N T S

TABLE OF CONTENTS

WHAT ’S INSIDE?

01 INTRODUCTION

Page 03

02 INDICATORS

03 INDICATOR

TABLE

05 RANKING

Page 05

04 DEMONSTRATING

A N A LY S I S

Page 11

06 POLICY

REFLECTION

APPLICATIONS Page 04

Page 07

Page 16

INTRODUCTION

SMART CITY INDEX

IMPROVING FEEDBACK LOOP

With growing population and rapid urbanization, increasing complexity of the urban ecosystem has led to an unorganized urban environment with an emerging series of urban issues (Sujata, Saksham, Tanvi &Shreya, 2016, p.902-903). An idea of using technology to solve urban issues in a smarter way formed the concept of ‘Smart City’ (Sujata, et al. 2016, p.903).

‘Smart city’ has beeen defined as a city that use “information and communication technology” to “monitor and integrate conditions of all of its critical infrastructures'' to “enhance its liveability, walkability and sustainability” (Smart Cities Council 2014; US Office Technical and Scientific Information, (as cited in Eremia, Toma, Sanduleac, 2017, p.14).

Australia as a leading country has implemented various smart city projects in its 21 largest cities, including Greater Sydney (Commonwealth of Australia 2017, p.1; p.13). However, the indicators that, monitors the city’s progress towards smartness is insufficent to provide direct feedback.

In order to monitor the smartness of cities in Australia, the Commonwealth of Australia has created a performance framework with 30 performance indicators, along with an official dashboard for public access (Commonwealth of Australia 2017, p.6; p.7). As shown in the image, the dashboard conveys very limited information and insights, i.e., on the use of information and technology in the city’s infrastructure (refer to figure 1). For instance, as in Figure 1 “infrastructure indicator” discusses on output like job accessibility, but fails to provide feedback on how to further improve accessibilty.

In addition, the lack of clear direction on integrating smart city concept in Greater Sydney Plan (GSC, 2016), varying degree of response is observed by different Local Government Areas (LGAs), leading to lack of integrated development which is vital to efficiency of the smart city project

Considering the gaps of the current smart city indexes and its policy context, the proposed “smart infrastructure index”, aims to provide direct feedback on the infrastructure gap that supports in building smart cities. Furthermore, it is applied to LGAs accross Greater Sydney to understand the degree of smart city concept integrated within each administrative boundary.

Figure 1. Infrastructure indicator graph of Sydney (The Department of Infrastructure, Transport, Regional Development and Communications, 2021)

#SMART

CITY 03

indicators and sub indicators

INDICATORS The proposed “Smart Infrastructure Index” aims to measure a tangible progress and informs the smartness degree of LGAs. A report prepared by KPMG and Public Sector Network (2019) shows that the priorities of smart infrastructure projects amongst the Australian councils, were targeted to improve the platform, smart physical communications network, mobility, environmental, parking, digital signage/wayfinding, lighting, Wi-Fi, and security areas.

Considering the real world implementation of the project for smart cities, an indicator with a family of sub-indicators were created. The indicator aims to measures the distribution of Mobility, Connectivity, Traffic Monitoring, Environmental Monitoring infrastructures that use information and communication technology to govern the critical infrastructures in the urban settings.

Smart Connectivity Infrastructure

Free public Wi-Fi hotspots across Greater Sydney

#2 Smart Mobility Infrastructure

E-vehicle charging station, car parks with real time data, & train stations that has Departure Indicator and Wi-Fi across Greater Sydney

Smart Traffic Monitoring Infrastructure

ive traffic camera, speed cameras, red light speed cameras and Traffic monitoring stations across Greater Sydney

The proposed smart city infrastructure index has been used to quantify the level of LGA’s ‘smartness’ in all four proposed domains. Leveraging tables, graphs, and maps, the Index illustrates the present performance of Greater Sydney Metropolitan Region and hopes to provide insights that can assist the future policy directions pertaining to making LGA’s smart.

Futhermore, the Index and its family of sub-indicators has been analysed to understand the infrastructure gap per capita. The indicator(s) has been explored to understand the spatial distribution with respect to SEIFA and understand the spatial equity of the infrastructure distribution.

# 4 Smart Environmental Monitoring Infrastructure

Weather, Air Quality, GPS, Water Quality, Water Level, Rainfall, Meteorology Monitoring Stations across Greater Sydney.

#INDICA-

TORS

INDICATOR(S) TABLE

SMART CITY INFRASTRUCTURE INDEX

SMART CONNECTIVITY INFRASTRUCTURE INDEX

SMART MOBILITY INFRASTRUCTURE INDEX

SMART TRAFFIC MONITORING INFRASTRUCTURE INDEX

SMART ENVIRONMENTAL MONITORING INFRASTRUCTURE INDEX

DESCRIPTION

It measures the relative distribution of smart connectivity, monitoring, mobility, traffic control infrastructures, across Greater Sydney.

This index is obtained by combining the values of the sub-indicators.

It measures the relative distribution of Free public Wi-Fi hotspots across Greater Sydney

It measures the relative distribution of Smart Mobility Infrastructure which includes E-vehicle charging station, car parks with real time data, and train stations that has Departure Indicator and Wi-Fi across Greater Sydney

It measures the relative distribution of Smart Traffic Monitoring Infrastructure which includes Live traffic camera, speed cameras, red light speed cameras and Traffic monitoring stations across Greater Sydney

It measures the relative distribution of environmental monitoring systems, which includes Weather, Air Quality, GPS, Water Quality, Water Level, Rainfall, Meteorology Monitoring Stations across Greater Sydney.

RATIONALE

Existing Smart City Index discusses the performance of cities but do not give exact measure on the availability of smart infrastructure that supports that performance. Smart Infrastructure Index, would give a direct indication on the infrastructure gap in the regions.

Connectivity and synergy between components is essential in building smart cities. Free public Wi-Fi is a gateway to connect large masses to the smart network and promote equitable benefits of Smart City projects (Cambium Networks, 2018).

Increasing the patronage for green transport modes is esstential for building sustainbale cities. The availability of infrastructure that supports green transport and use of technology to improve reliability would increase patronage of green transport systems making cities smarter.

Productivity is a key dimension to build sustainable cities (UN SDG, 2016). The contribution of a reliable transport systems to productivity cannot be undermined. Traffic Monitoring Systems uses technology to improve reliabiliity and saftey of the transport systems, making the city smart.

nly free Wi-Fi hotspots in Public Spaces, Recreation, Beaches, Stations are included, and not the Wi-Fi hotspots within private businesses.

A train station having facilities, with potential for upgradation with technology are not included

Among the wide range of smart mobility services like MaaS Autonomous System etc, only unsr centric components are chosen TfNSW Open Data Hub

This index is a relative measure of concentration and not an absolute measure

The type of infrastructures are relatively very low in number as they cater to large regions and hence, making the dataset unbalanced and biased .

PARENT INDICATOR SUB-INDICATORS

LIMITATIONS

This index is a relative measure of concentration of smart infrastructure and not an absolute measure

The type of smart infrastructures are not

extensive databases.

DATA SOURCE

As mentioned in all sub indicator

Australian Bureau of Statistics (2016) (ASGS shape file for LGAs, Population Count in LGA in Greater Sydney)

pen Street Map (Free Wi-Fi in Public Spaces, Libraries & Transport)

Data Mining from (goget, 2018) (Free Wi-Fi in Public Spaces)

TfNSW Open Data Hub (Live traffic

Sustainable Development goals emphasize on resilience as a key for building sustainable cities (UN SDG, 2016). Environmental Monitioring Systems are fundamental infrastructure to build resilience by creating robust warning systems befor calamities. UN

The data source does not mention whether the station is active, decommissioned or redundant.

pen Street Map

camera, Speed cameras, Red light speed cameras)

pen Street Map (Traffic Monitoring

Station)

INDICATOR(S) TABLE PARENT INDICATOR SUB-INDICATORS

SOURCE DATA GEOGRA P HY / COUNTRY METHOD OF DATA COLLECTION

METHOD OF DATA ANALYSIS

DATA UPDATES FREQUENCY

UNIT

SMART CITY INFRASTRUCTURE INDEX LG

As of Greater Sydney

Part of location (Lat. & long.) of the POI is scrapped from Open Street Map using the OSMnx library in python. Part of location (Lat. & long.) of the POI is scrapped from TfNSW Open Data Hub. Total population & SEIFA Scores of LGAs is taken from ABS Table builder. Smart City Infrastructure Index = (Connectivity Infrastructure Index+Monitoring Infrastructure Index+ Mobility infrastructure Index+Traffic infrastructure Index)/4 Australian Bureau of Statistics

= [once in five years]

Open Street Map

= [Real-Time Daily Updates]

NSW transport open Data

= [Yearly]

Numbers

SMART CONNECTIVITY INFRASTRUCTURE INDEX

SMART MOBILITY INFRASTRUCTURE INDEX

SMART TRAFFIC MONITORING INFRASTRUCTURE INDEX

SMART ENVIRONMENTAL MONITORING INFRASTRUCTURE INDEX

As mentioned in the parent indicator

Smart Connectivity Infrastructure Index = [(Number of Smart Connectivity POIs in a LGA)/(No of all Smart Connectivity POIs in all LGAs)]/ [(Number of all smart POIs in a LGA)/(No of all POIs in Sydney)]

Smart Mobility Infrastructure Index = [(Number of Smart Mobility POIs in a LGA)/(No of all Smart Mobility POIs in all LGAs)]/ [(Number of all smart POIs in a LGA)/(No of all POIs in Sydney)]

Smart Traffic Control Infrastructure Index =[(Number of Smart Traffic POIs in a LGA)/(No of all Smart Traffic POIs in all LGAs)]/ [(Number of all smart POIs in a LGA)/(No of all POIs in Sydney)]

Smart Monitoring Infrastructure Index = [(Number of Smart Monitoring POIs in a LGA)/(No of all Smart Monitoring POIs in all LGAs)]/ [(Number of all smart POIs in a LGA)/(No of all POIs in Sydney)]

As mentioned in the parent indicator

Numbers

DEMONSTRATING APPLICATIONS

DEMONSTRATING

APPLICATIONS The smart infrastructure index is developed by measuring the relative distribution of the smart infrastructure across Greater Sydney

STEP 1 – DATA MINING Open Street Map: The POIs (points of interests) for Smart Connectivity

Key Value Pairs

and Smart Environmental Monitoring index were extracted from Open

Connectivity:

Street Map (using key-value pairs) through OSMnx library in Python.

wlan(internet_access)

Among the three elements of OSMnx data ‘nodes, ways and relations’,

Environmental Monitoring:

the extracted information had ‘ways’ and ‘nodes’. The ways which has

water_level(monitoring), weather(monitoring),

polygon information data has been converted into nodes equivalent. The

air_quality(monitoring), gps(monitoring),

scrapped data frame contained location information (latitude and

groundwater(monitoring), rainfall(monitoring),

longitude).

meteorology(monitoring)

TfNSW Open Data Hub: The POIs (points of interests) for Smart Mobility and Smart Traffic Monitoring index were extracted from Open Data Hub available in the .CSV format and Car Park location static data from APIs.

Figure 2: Python Libraries Applied

DEMONSTRATING APPLICATIONS

STEP 2 – ABSOLUTE SPATIAL DISTRIBUTION OF SMART INFRASTRUCTURES The extracted POIs were clustered into 4 sub-indicators and plotted spatially in different hues to visualise its spatial distribution. Figure 3 shows the concentration of POIs in centre, and extension of mobility POIs over transport network at varying degree of proximity continutiy.

Pacific Highway

Smart Mobility Characteristics on Road Network

Greater Western Highway - Continuous for long

stretch from centre, and present Close Proximity.

Pacific Highway - Continuous for short distance

from centre, but breaks at short distance

Prince Highway - Continuous for short distance

with large gaps between POIs

Hume Highway - Discontinuous network of POIS

on the Highway.

Greater Western Highway

Hume Highway

Connectivity

Mobility

Traffic Monitoring

Environmental Monitoring

Prince Highway

Figure 3: Distribution of POIs across Greater Sydney, overlayed with road networks

DEMONSTRATING APPLICATIONS

STEP 2 – ABSOLUTE SPATIAL DISTRIBUTION OF SMART INFRASTRUCTURES

STEP 3 – RELATIVE DISTRIBUTION OF POIS USING SMART INFRASTRUCTURE INDEX

Figure 4 gives the scaled count of POIs in various LGAs. Min_Max Scalar is used to scale data between 0 and 1. The boundaries of LGA has been plotted through the GeoPandas library using the ASGS 2016 LGA Shapefile. The geodata was projected to EPSG : 3857 projections using PyProj library.

The smart Infrastructure Index developed explains the relative distribution of smart Infrastructures over different LGAs of Greater Sydney. The smart index is computed using a family of sub-indicators as mentioned in the Indicator(s) Table.

Smart Connectivity Infrastructure

Smart Mobility Infrastructure

Infrastructure Smart Smart Infrastructure Index

The computation of sub-indicators are calculated using the reasoning of “Location Quotient”. The formula below explains the sub-indicators generically;

ABSOLUTE SPATIAL DISTRIBUTION OF SMART INFRASTRUCTURES

Connectivity : Highly concentrated at the city

centre with complete absence in regional areas

Mobility : Compared to other POIs, it is spread

evenly across Greater Sydney with

concentration at centre and radial expansion

across transport route

Traffic Monitoring : Concentrated at the centre

with highest concentration at City of Sydney,

Canterbury-Bankstown and Ku-ring-gai.

Environmental Monitoring : Concentrated in the

Regional areas with highest at Ku-ring-gai.

Smart Traffic Monitoring Infrastructure

Smart Environment Monitoring Infrastructure

Figure 4: Absolute Spatial Distribution of Smart Infrastructures

The average of all sub-indicators after normalising their values from [0,1] is the resulting Smart Infrastructure Index.

Normalising of sub-indicators is neccessary to bring them all into one scale for taking average. It was done using the min-max scalar from sklearn library.

d e m o n s t r at i o n a n a ly s i s

The min-max scalar transforms the values in the range of [0,1] using the formula as shown below x = sub-indicator value of LGA

min(x) = Least value of sub-indicator among all LGAs

min(x) = Highest value of sub-indicator among all LGAs

Smart Connectivity Index

Smart Mobility Index

Observing Figure 5, which shows the relative distribution of Smart Infrastructures within all LGAs of Greater Sydney, it can be concluded that Smart Connectivity Infrastructure is highly concentrated in inner suburbs and is completely absent in outer suburbs. This can be attributed to difference in provison of free Wi-fi by different councils.

Smart Mobility Infrastructure is distributed towards outer suburbs and not city centre, it can be attributed to the high number of e-charging stations on major highways as shown previously in figure 3. Smart Traffic Monitoring Infrastructure is interestingly not concentrated in city centre, but is concentrated at few inner suburbs. Also, a clear seperation is observed between north and south west Sydney.

Smart Infrastructure Index Overall Index

SmartTraffic TrafficMonitoring Index Smart Index

Figure 5: Relative Distribution of POIs using the developed Index

Smart Environmental Monitoring Smart Monitoring Index Index

Smart Environmental Monitoring Infrastructure is again not concentrated in city centre, but unlike traffic monitoring index is concentrated at one location in North Sydney and present in some LGAs in Western Sydney. Overall, the Smart Infrastructure Index shows a relatively high concentration of Smart Infrastructures in inner city and outer western regions. The types of sub-indicators that made up Smart Index has distinct characteristis of either being in Inner (Connectivity and Traffic Monitoring Index) or Outer Suburbs (Environmental Monitoring and Mobility Index).

S T E P 4 – A N A LY S I S O F T H E DEVELOPED INDICATOR LAG Analysis: The developed indicators shows the relative distribution of smart infrastructure, but does not explain whether the infrastructure provided is sufficient for the population. The Lag Analysis attempts to find correlation between the Index(s) and population, to provide insights on infrastructure gap per capita.

SEIFA Analysis: An insight on equitable distribution of the infrastructure across Greater Sydney is derived by analysisng the developed index(s) with the SEIFA IRSD Score.

Shannons Entropy Analysis: It measures the amount of information gained to find a particular type of infrastructure in a given region. The analysis is done to understand the urban form of Greater Sydney with respect to the smart infrastructure distribution.

r a n k i n g a n a ly s i s

RANKING

ANALYSIS

Smart Index Ranking Smart Mobility Index Smart Traffic Monitoring Index

3.5

Smart Environmetal Monitoring Index Smart Connectivity Index Smart Infrastructure Index SEIFA Score

Observations 3.0

2.5

2.0

1.5 1.0

0.5

0.0 Bay s Blu Black ide ( e M tow A) oun n ( tai C) Bur ns ( Cam wo C) pbe od C l a l tow md (A) Can n ( en ( ter bur Can C) (N A) Cen y-Ba ada SW tra nks Bay ) l Co tow (A ast n (A ) Cum (C) (N ) ber SW lan ) Geo Fair d (A) rge fiel Haw s Riv d (C) kes er (A b ) Hor ury (C Hun nsb ) ter y (A Inn s Hill ) e Ku- r We (A) ring st ( Lan -gai A) e C (A) Liv ove ( erp A) ool M N o Nor orth sm (C) the Sy an ( rn B dne A) eac y (A Pa hes ) rra (A ma ) tt Pe a (C) Ran nrith dw (C) ick Sut Stra Ryde (C) he rlan thfiel (C) d S d (A hir ) e Th e H Sydn (A) ills ey S (C Wa hire ( ) Wil verle A) Win lou y (A gec ghb ) arr y (C Wo ibee ) llon (A) dill y (A )

Bayside Council has the highest Smart Infrastructure Index, which is contributed largely by connectivity and traffic monitoring index

Connectivity and Monitoring Infrastructures have sparse distribution.

Smart Traffic Monitoring Infrastructure is spread across all LGAs

Smart Mobility Index is higher in outer suburbs like Willoughby LGA and differs a lot in other LGAs.

Figure 6: Stack Bar graph of Smart Infrastructure Index(s) for all LGAs in Greater Sydney

L A G A N A LY S I S

LAG ANALYSIS smart index vs population density Smart Connectivity Index of a LGA

Smart Infrastructure Count of a LGA

Smart Mobility Index

Population Density of a LGA

Mobility Index vs Population Density

Traffic Monitoring Index vs Population Density

Smart Traffic Monitoring Index Smart Environmental Monitoring Index Smart Connectivity Index Smart Infrastructure Index SEIFA Score

Smart Traffic Monitoring Index of a LGA

SEIFA Score

Smart Infrastructure Index

Smart Connectivity Index

Smart Environmental Monitoring Index

Smart Traffic Monitoring Index

Smart Infrastructure Count

Population Proportion Smart Mobility Index

Smart Mobility Index of a LGA

Population Density of a LGA

Environment Monitoring Index vs Population Density

Smart Infrastructure Index vs Population Density

Figure 8: Correlation Heat Map

Smart Infrastructure Index

The correlation heatmap (Figure 8) indicates the strength of the relation between different indexes as well as population and count of POIs. Surprisingly, population and smart POIs almost negligible correlation inferring that poi’s are not distributed as per population density. Our indexes are not correlated with SIEFA rankings indicating that even though various socio-economic factors are considered while calculating SIEFA index, smart infrastructure avaialbility is left out of consideration.

Connectivity Index vs Population Density

Population Density of a LGA

Smart Environmental Monitoring Index

The scatterplot indicates whether the availability of Smart Infrastructure respondes to the population of the area. In addition, the change in the hue of the scatterplot indicates the count of all smart infrastructures in LGA, which when compared with the index (y-axis) gives an insight on the proportion of relavant infrastructure (Figure 7).

Infrastructure Counts vs Population Density

Population Density of a LGA

Figure 7: Understanding Infrastructure Lag for the Population

L A G A N A LY S I S

LAG ANALYSIS smart index vs population density

Smart Infrastructure Index D

opulation roportion

opulation ensity of a LGA

Figure 7: Understanding infrastructure lag for the population

Smart Connectivity Index

The Index value is extremely low in most of the LGAs, indicating a

complete lack of free Wi-Fi hotspots accross Greater Sydney

The proportion of Connectivity Infrastructure compared to total is low, indication less contribution to overall smart index (Green spots in Figure 7)

Very low negative correlation (Fig 8) between Index and population is observed.

Smart Mobility Index

The index value is relatively high showing availability of Smart Mobility

Infrastructure. Also, the proportion of mobility infrastructure

compared to others is high (Green spots in Figure 7), indicating a greater contribution to overall smart infrastructure index.

Very low negative correlation with population density. Although Smart Connectivity and Smart Mobility Infrastructures are directly utilised by people, none of the indexs show correlation with population, indicating significant lack of the infrastructures

S IFA Score

Smart Infrastructure Index

Smart Connectivity Index

opulation ensity of a LGA

S IFA Score

Smart nvironmental Monitoring Index

Smart Traffic Monitoring Index of a LGA opulation ensity of a LGA

Smart Infrastructure Index

Smart nvironmental Monitoring Index

Smart Connectivity Index

Smart Traffic Monitoring Index

opulation ensity of a LGA

Smart Mobility Index

Smart Traffic Monitoring Index Smart nvironmental Monitoring Index

Smart Infrastructure Count

opulation ensity of a LGA

Smart Mobility Index of a LGA

Smart Infrastructure Count of a LGA

Smart Connectivity Index of a LGA

Smart Mobility Index

Figure 8: Correlation Heat Map

Smart Traffic Monitoring Index The index value is relatively high showing availability of Smart Traffic

Infrastructure in the region. Also, in some LGAs the proportion of Traffic

infrastructure compared to others is high (Green spots in Figure 7), indicating a greater contribution to overall smart index.

The index has very low positive correlation with population density

Smart nvironmental Monitoring Index E

Some LGAs have distinctly high Index, while most have extremely low values.

Index has a very low negative correlation with population of the area, however, it can be argued that this infrastructure is not directly serving to local LGA population, but rather cater to larger regions.

SEIFA Vs Smart Mobility Index No correlation between SEIFA and Mobility Index has been observed. However, a significant cluster is present in regions of higher SEIFA Scores SEIFA Vs Smart Traffic Monitoring Index No correlation between SEIFA and Traffic Monitoring Index is observed, however a significant cluster of LGAs are found with high SEIFA score SEIFA Vs Environmental Monitoring Index Irrespective of the SEIFA score, most of the LGAs has low Index, indicating an overall absence of the infrastrcuture in most of the LGAs

In the few LGAs that has Environmental Monitoring Infrastructure, increase in Index with increasing SEIFA scores is observed

Smart Mobility Index Smart Traffic Monitoring Index

SEIFA Vs Smart Connectivity Index Irrespective of the SEIFA score, most of the LGAs has low Index, indicating an overall absence of the infrastructure in most of the LGAs

In the few LGAs that has Smart Connectivity is generally on higher SEIFA areas, except for a couple of outliers

Smart Environmental Monitoring Index

SEIFA IRSD index measures the relative measure of disadvantage considering various socio-economic dimensions. The analysis is done after normalising the SEIFA scores [0,1], taken from ABS Table Builder*.

Smart Connectivty Index

The developed Smart Infrastructure Index and its family of sub-indicators have been analysed against SEIFA IRSD Index using scatterplots as shown in Figure 9.

SEIFA Score

SEIFA ANALYSIS

Smart Infrastructure Count

S E I FA A N A LY S I S

Smart Infrastructure Count

*The SEIFA score of the recently amalgamated Bayside Council is not available in ABS table builder. Hence, the data of Botany Bay and Rockdale Council has been averaged for Bayside Council Score

Smart Mobility Index

Smart Traffic Monitoring Index

Smart Environmental Monitoring Index

Figure 9: IRSD SEIFA vs Indexes

Smart Connectivty Index

SEIFA Score

SHANNON’S

ENTROPY

A N A LY S I S

SHANNON’s ENTROPY

ANALYSIS

Figure 10.1: Entropy Analysis of Smart

Figure 10.2: Entropy Analysis of Smart

Figure 10.3: Entropy Analysis of Traffic

Figure 10.4: Entropy Analysis of

Connectivity Infrastructure

Mobility Infrastructure

Monitoring Infrastructure

Environmental Monitoring Infrastructure

Shannon’s Entropy: Although, the smart infrastructure is spread across all LGAs, the probability in inner suburbs is higher than other areas.

In information theory, Shannon’s entropy quantifies the amount of information contained in a variable.

Given a discrete random variable X, with possible outcomes

The probability to locate connectivity infrastructure exhibits

{x1,...,xn}, which occur with probability P(x1),..., P(xn), the entropy X

monocentrcity, with almost NIL values in outer suburbs and

is formally defined as:

concentration at centre.

The Mobility Infrastructure is dispersed across Greater Sydney.

Where

denotes the sum over the variable's possible values, the

choice of base varying between different applications. Base 2 gives The dispersion of Traffic Monitoring Infrastructure shows polycentricity with three centres in the inner and north suburbs.

the unit of ‘bits’ or "shannons". Intuitively, one way to understand the concept of "amount of information" in a variable is to tie it to how difficult or easy it is to guess that information without having

The dispersion of Environmental Monitoring Infrastructure shows monocentric characteristics,but the centre is not the usual centre (Harbour City).

Figure 10: Entropy Analysis of Smart Infrastructure

to look at the variable (Vajapeyam, 2014). The p(i) taken to compute the entropy is the proportion of relavant POI in an LGA to total count of all POIs in that LGA.

POLICY REFLECTION AND FUTURE DIRECTION

POLICY

ANALYSIS

The aim of the smart infrastructure index to understand the current infrastructure gap and the spatial equity in distribution, that help inform policies. The following insights has been gained from the proposed index through various analysis.

Smart Connectivity Index

The availability of Free Wifi hotspots is NIL in almost all outer suburbs of Greater Sydney. Also, the few inner suburbs that has Smart Connectivity Infrastructure, are mostly in regions with higher SEIFA Scores (Figure 8).

Although, this infrastructure is directly used by people, no correlation with population density of the region was observed.

Smart Mobility Index

Most of the Mobility Infrastructure is found in outer suburbs along the major highways.

No correlation with SEIFA score has been observed, but it is to be observed that although the index values is low, a significant cluster of infrastructure is in regions with higher SEIFA

Smart Traffic Monitoring Index

Most of the infrastructure is concentrated in 3 inner & north suburbs and dispered in LGAs to the north.

No correlation with SEIFA score and population has been observed.

Contributes significantly to Smart Infrastructure Index

Smart Environmental Monitoring Index

The infrastructure is highly concentrated in one LGA at North Sydney and few in Western Sydney, other areas almost have NIL, this can be attributed to the characteristic of this infrastructure, which severs the region and locally only.

When suburbs with NIL infrastructure is removed from the dataset, a positive correlation between SEIFA and Index is observed

Smart Infrastructure Index

The probability to find smart infrastructure is in inner LGAs or around transport routes.

No correlation between population and Index is shown, this may be due to the mixture of population serving and region serving sub-indicators that contributes to this index

Regions with last 20% of SEIFA scores , do not have any smart infrastructures (Figure 9) Although, the overall spatial distribution of the smart infrastructures depends upon its type, the concentration of the smart infrastructures are absent in regions of lower SEIFA index.

A significant infrastructure gap is observed in the region, with clear disconnect from the population residing

Greater Sydney has to drastically improve the smart infrastructure provision to build smart cities Future Direction: Although the indicator gives general idea on the spatial distribution, each sub-indicator has different range of users. A detail study on accessibility of each infrastructure by the population can be done.

In this report, only the facilities that are integrated with technology has been considered as smart infrastructure. However, a study on Degree of Smartness could be done, by considering infrastructures that has the potential for upgradation.

PROCESS REFLECTION

REFERENCE LISTS

PROCESS

REFLECTION Data is highly skewed. Mobility and traffic related poi’s outweigh monitoring and connectivity related poi’s by larger degree. Hence, min_max scalar is used to scale data at each step of analysis so as to avoid bias in the data.

Figure 11: Distribution of Raw Dataset

Monitoring and Connectivity related poi's are very few in number. Of course, monitoring related POIs such as GPS or weather monitoring stations are located at few places as they cater monitoring services to large areas. Connectivity related poi's that provide free public internet services are very low in number indicating that Greater Metropolitan Sydney region is still devoid of free public internet on streets.

Location Quotient (LQ) was chosen as our index for gauging ‘smartness’ of a LGA. LQ does not show much affinity with the population, hence scatterplots were made to understand the correlation. Not much correlation is found between indexes and population.

LQ based index is not akin to SEIFA indicator inferring that variables indicating ‘smartness’ may not be considered while calculating SEIFA rankings.

Australian Government (2020). Smart Cities and Suburbs. Retrieved from https://www.infrastructure.gov.au/cities/smart-cities/collaboration-platfor m/index.aspx

Cambium Networks. (2018). Wireless Connectivity Solutions for Smart Cities. Cambium Networks.

Commonwealth of Australia (2017). Smart Cities Plan. Retrieved from https://www.infrastructure.gov.au/cities/national-cities-performance-fram ework/files/National_Cities_Performance_Framework_Final_Report.pdf

Eremia, M., Toma, L. & Sanduleac, M. (2017). The Smart City Concept in the 21st Century, Procedia Engineering, 181 (2017), 12-19. doi: 10.1016/j.proeng.2017.02.357

goget. (2018). Free Wifi Sydney | The Ultimate Free Internet Guide for 2018. Retrieved from goget: https://www.goget.com.au/blog/free-wifi-sydney/

KPMG and Public Sector Network (2019). Smart Cities: A Snapshot of Australia in 2019. Retrieve from https://home.kpmg/au/en/home/insights/2019/12/smart-cities-snapshotaustralia-2019.html

Sujata, J., Saksham, S., Tanvi, G. & Shreya. (2016). Developing Smart Cities: An Integrated Framework. Procedia Computer Science, 93 (2016), 902 – 909. doi: 10.1016/j.procs.2016.07.258

UN SDG. (2016). UN Sustainable Development Goals. Retrieved from Goal 11: Make cities inclusive, safe, resilient and sustainable: https://www.un.org/sustainabledevelopment/cities/

Vajapeyam S. Understanding Shannon’s entropy metric for information. 2014. arXiv preprint arXiv:1405.2061

ATTACHED AS APPENDIX ARE THE WORKING FILES (PROGRAM AND RAWDatA)