School Planning in Smart Cities:

The role of Data, Technology & Systems Dr Simone Z Leao City Futures Research Centre, UNSW, Sydney, Australia Presented at School Planning, Design & Construction Conference 2016, Sydney.

https://cityfutures.be.unsw.edu.au/

https://cityfutures.be.unsw.edu.au/research/city-analytics/

https://cityfutures.be.unsw.edu.au/cityviz/

Data Information Knowledge Planning & Decision

Volume Velocity Variety Veracity Value

Analytics Integration Visualisation

Research, Teaching & Capacity building

Partnerships in Real-world projects

City Futures Research centre / Data Analytics Groups Scope of work:

Data evaluation Data cleaning Data visualisation

https://cityfutures.be.unsw.edu.au/cityviz/strata-schemes-sydney/

Longitudinal official data

https://cityfutures.be.unsw.edu.au/cityviz/million-dollar-sales/

https://cityfutures.be.unsw.edu.au/cityviz/cycling-sydney/

Crowdsourcing

Social Media Twits about “Overcrowded Schools” in the last week.

Smart Systems, sensors

30 min city concept

Participatory GIS Interactive/multi-stakeholder MapTable

May 2016

Built with ArcMap Model Builder.

Modelling for better understanding processes Modelling for scenarios’ assessment

Agent-based model of urban renewal driven by economic feasibility of development and constrained by planning controls

Sensitivity analysis: Construction cost (Significant effect)

Default +10% +20%

Step 100

Virtual Reality Immersive visualisation and assessment of built environments

Kensington Town Centre Urban Renewal

Winner of the 2016 K2K International Urban Design Competition

Projects associated to School Planning

UNSW & NSW DAC:

Can new big-data assist in a better modelling of a synthetic population & Travel Diaries for planning purposes?

Challenges: • Data is varied and fragmented, difficult to integrate; • Still difficult to access fine scale data from owners/custodians; • Models describing behaviour are not fully developed yet; • Future projections are still heavily based on past trends.

Can SP-TD be used to evaluate future demand of population on urban infrastructure?

Challenges: • Self-correcting models with high frequency of data input are required

to better understand behaviour and trend/cycle changes;

Synthetic population 2011

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Socio-demographics and location

Travel Diary (2011)

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Trips’ characteristics

Home

Bus stop

Bus stop

School P

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Travel diaries of a synthetic population

Not public yet

Synthetic population to explore possible futures driven by spatial strategies

Comparison of Scenarios overall growth and allocation

The percentage of primary students walking to school in Sydney region have declined from 60% in 1971 to 25% in 2003, while drop-off by private car have increased from 20% to 65% during the same period. (van der Ploeg et al, 2008, Preventive Medicine)

Walkability to Schools: urban health

Walkability to Schools: urban health

How can we increase walkability to primary schools in Sydney? Methods • Mapping walkability to schools. • Better understanding of school selection process by families. • Better understanding selection of mode of transport to school.

Challenges • Some required data is not available (possible crowdsourcing).

https://cityfutures.be.unsw.edu.au/cityviz/k2k-walkability-indicators/

Not public yet

It is not only a matter of proximity ….

Summary • New high resolution data in space and time and the ubiquity

of advanced computing brings great opportunities to infrastructure planning;

• They also bring challenges associated to their 5Vs;

• Data and technology are instrumental by themselves, requiring new integrative systems to put them into service of better planning.

Thanks!

Simone Z Leao, s.zarpelonleao@unsw.edu.au