TY - JOUR
T1 - Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
AU - Tavares, Joana Paulo
AU - Costa, Ana Cristina
N1 - Tavares, J. P., & Costa, A. C. (2021). Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal. ISPRS International Journal of Geo-Information, 10(11), 1-18. [731]. https://doi.org/10.3390/ijgi10110731
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Many researchers have unraveled innovative ways of examining geographic information to better understand the determinants of crime, thus contributing to an improved understanding of the phenomenon. Property crimes represent more than half of the crimes reported in Portugal. This study investigates the spatial distribution of crimes against property in mainland Portugal with the primary goal of determining which demographic and socioeconomic factors may be associated with crime incidence in each municipality. For this purpose, Geographic Information System (GIS) tools were used to analyze spatial patterns, and different Poisson-based regression models were investigated, namely global models, local Geographically Weighted Poisson Regression (GWPR) models, and semi-parametric GWPR models. The GWPR model with eight independent variables outperformed the others. Its independent variables were the young resident population, retention and dropout rates in basic education, gross enrollment rate, conventional dwellings, Guaranteed Minimum Income and Social Integration Benefit, purchasing power per capita, unemployment rate, and foreign population. The model presents a better fit in the metropolitan areas of Lisbon and Porto and their neighboring municipalities. The association of each independent variable with crime varies significantly across municipalities. Consequently, these particularities should be considered in the design of policies to reduce the rate of property crimes.
AB - Many researchers have unraveled innovative ways of examining geographic information to better understand the determinants of crime, thus contributing to an improved understanding of the phenomenon. Property crimes represent more than half of the crimes reported in Portugal. This study investigates the spatial distribution of crimes against property in mainland Portugal with the primary goal of determining which demographic and socioeconomic factors may be associated with crime incidence in each municipality. For this purpose, Geographic Information System (GIS) tools were used to analyze spatial patterns, and different Poisson-based regression models were investigated, namely global models, local Geographically Weighted Poisson Regression (GWPR) models, and semi-parametric GWPR models. The GWPR model with eight independent variables outperformed the others. Its independent variables were the young resident population, retention and dropout rates in basic education, gross enrollment rate, conventional dwellings, Guaranteed Minimum Income and Social Integration Benefit, purchasing power per capita, unemployment rate, and foreign population. The model presents a better fit in the metropolitan areas of Lisbon and Porto and their neighboring municipalities. The association of each independent variable with crime varies significantly across municipalities. Consequently, these particularities should be considered in the design of policies to reduce the rate of property crimes.
KW - Crime concentration and hot spot analysis
KW - Spatial regression analysis
KW - Geographic crime analysis
KW - Geographically Weighted Poisson Regression
KW - Spatial heterogeneity
KW - Portugal
UR - http://www.scopus.com/inward/record.url?scp=85118939589&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000725121100001
U2 - 10.3390/ijgi10110731
DO - 10.3390/ijgi10110731
M3 - Article
SN - 2220-9964
VL - 10
SP - 1
EP - 18
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 11
M1 - 731
ER -