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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
…..
Ho
use
ho
ld
Ind
ivid
ual
s
Socio-demographics and location
Travel Diary (2011)
Ind
ivid
ual
s
Ho
use
ho
ld
Trips’ characteristics
Home
Bus stop
Bus stop
School P
P
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, [email protected]