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Developing a Flexible Framework for Spatiotemporal Population Modeling

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  • David Martin
  • Samantha Cockings
  • Samuel Leung

Abstract

This article proposes a general framework for modeling population distributions in space and time. This is particularly pertinent to a growing range of applications that require spatiotemporal specificity; for example, to inform planning of emergency response to hazards. Following a review of attempts to construct time-specific representations of population, we identify the importance of assembling an underlying data model at the highest resolution in each of the spatial, temporal, and attribute domains. This model can then be interrogated at any required intersection of these domains. We argue that such an approach is necessary to moderate the effects of what we term the modifiable spatiotemporal unit problem in which even detailed spatial data might be inadequate to support time-sensitive analyses. We present an initial implementation of the framework for a case study of Southampton, United Kingdom, using bespoke software (SurfaceBuilder247). We demonstrate the generation of spatial population distributions for multiple reference times using currently available data sources. The article concludes by setting out key research areas including the enhancement and validation of spatiotemporal population methods and models.

Suggested Citation

  • David Martin & Samantha Cockings & Samuel Leung, 2015. "Developing a Flexible Framework for Spatiotemporal Population Modeling," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(4), pages 754-772, July.
  • Handle: RePEc:taf:raagxx:v:105:y:2015:i:4:p:754-772
    DOI: 10.1080/00045608.2015.1022089
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    Cited by:

    1. Rains, Tim & Longley, Paul, 2021. "The provenance of loyalty card data for urban and retail analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    2. Malleson, Nick & Andresen, Martin A., 2016. "Exploring the impact of ambient population measures on London crime hotspots," Journal of Criminal Justice, Elsevier, vol. 46(C), pages 52-63.

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