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Population distribution over time: modelling local spatial dependence with a CAR process

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  • Ilenia Epifani
  • Chiara Ghiringhelli
  • Rosella Nicolini

Abstract

The effectiveness of local spatial dependence in shaping the population density distribution is investigated. Individual location preferences are modelled by considering the status-related features of a given spatial unit and its neighbours as well as local random spatial dependence. The novelty is framing such a dependence through conditionally autoregressive (CAR) census random effects that are added to a spatially lagged explanatory variable X (SLX) setting. The results not only confirm that controlling for the spatial dimension is relevant but also indicate that local spatial dependence warrants consideration when determining the population distribution of recent decades. In this respect, the framework turns out to be useful for the analysis of microdata in which individual relationships (in a same spatial unit) enforce local spatial dependence.

Suggested Citation

  • Ilenia Epifani & Chiara Ghiringhelli & Rosella Nicolini, 2020. "Population distribution over time: modelling local spatial dependence with a CAR process," Spatial Economic Analysis, Taylor & Francis Journals, vol. 15(2), pages 120-144, April.
    • Handle: RePEc:taf:specan:v:15:y:2020:i:2:p:120-144
    DOI: 10.1080/17421772.2020.1708442
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    Cited by:

    1. Albert Esteve & Coro Chasco & Antonio López-Gay, 2022. "Modeling Local Variations in Intermarriage," Mathematics, MDPI, vol. 10(7), pages 1-18, March.

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