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Modelling population density over time: how spatial distance matters

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  • Ilenia Epifani
  • Rosella Nicolini

Abstract

Modelling population density over time: how spatial distance matters. Regional Studies. This study provides an empirical application of the Bayesian approach for modelling the evolution of population density distribution across time. It focuses on the case of Massachusetts by tracking changes in the importance of spatial distance from Boston concerning citizens’ choices of residence according to data for 1880–90 and 1930–2010. By adopting a Bayesian strategy, results show that Boston reinforced its attractiveness until the 1960s, when the city's accessibility no longer represented the unique determinant of population density distribution. Referring to selected historical evidence, a few possible interpretations are presented to endorse these results.

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  • Ilenia Epifani & Rosella Nicolini, 2017. "Modelling population density over time: how spatial distance matters," Regional Studies, Taylor & Francis Journals, vol. 51(4), pages 602-615, April.
    • Handle: RePEc:taf:regstd:v:51:y:2017:i:4:p:602-615
    DOI: 10.1080/00343404.2015.1110237
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    1. Daniel S. A. Carvalho & Gervásio F. Santos, 2022. "Transport and density of population groups in the urban area of the city of Salvador," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(6), pages 234-253, December.

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