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

Ilenia Epifani, Chiara Ghiringhelli, Rosella Nicolini*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


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.

Original languageAmerican English
Pages (from-to)120-144
Number of pages25
JournalSpatial Economic Analysis
Issue number2
Publication statusPublished - 2 Apr 2020


  • hierarchical Bayesian spatio-temporal model
  • population density distribution
  • spatial conditionally autoregressive (CAR) model
  • spatial interaction


Dive into the research topics of 'Population distribution over time: modelling local spatial dependence with a CAR process'. Together they form a unique fingerprint.

Cite this