Abstract
This chapter proposes and tests an innovative method to integrate a spatial scenario-based approach with a synthetic population evolution model and a mobility assignment model. Results are applied to assess future housing demand by dwelling type, and to identify future hubs for daily trips associated to an urban growth scenario in a case study . Main contributions of this research include the generation of urban population data at fine spatial and temporal scales and the inclusion of spatial planning into a synthetic population evolution model. Although current limitations associated to model assumptions and data availability are identified, the model is built in a platform flexible to adapt to new parameters and datasets. Big data is envisioned as a potential source of future improvements, through better and more frequent characterisation of population behaviour and interdependencies between demographics, land use and transportation.
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The authors wish to acknowledge the support from the Data Analytics Centre, NSW Government Department of Finance, Services and Innovation for funding this research.
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Leao, S.Z., Huynh, N., Taylor, A., Pettit, C., Perez, P. (2017). Evolution of a Synthetic Population and Its Daily Mobility Patterns Under Spatial Strategies for Urban Growth. In: Geertman, S., Allan, A., Pettit, C., Stillwell, J. (eds) Planning Support Science for Smarter Urban Futures. CUPUM 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-57819-4_22
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