TY - JOUR
T1 - Population distribution over time
T2 - modelling local spatial dependence with a CAR process
AU - Epifani, Ilenia
AU - Ghiringhelli, Chiara
AU - Nicolini, Rosella
N1 - The authors are grateful to the editor, Paul Elhorst, and two anonymous reviewers for insightful suggestions, as well as to the participants at both NARSC 2016 (Portland, OR, USA) and XII SEA 2018 (Vienna, Austria) for interesting comments. The authors also acknowledge the information provided by officers at CIS Mass-State, NAco and the Massachusetts Archives. All errors are the authors? own responsibility.
PY - 2020/1/20
Y1 - 2020/1/20
N2 - 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.
AB - 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.
KW - Hierarchical Bayesian spatio-temporal model
KW - Population density distribution
KW - Spatial conditionally autoregressive (CAR) model
KW - Spatial interaction
UR - http://www.scopus.com/inward/record.url?scp=85078439902&partnerID=8YFLogxK
U2 - 10.1080/17421772.2020.1708442
DO - 10.1080/17421772.2020.1708442
M3 - Artículo
AN - SCOPUS:85078439902
SN - 1742-1772
VL - 15
SP - 120
EP - 144
JO - Spatial Economic Analysis
JF - Spatial Economic Analysis
IS - 2
ER -