Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

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 t...

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Autores principales:	Joana Paulo Tavares, Ana Cristina Costa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	crime concentration and hot spot analysis spatial regression analysis geographic crime analysis Geographically Weighted Poisson Regression spatial heterogeneity Portugal Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/22c522474c2d4e7694e4eed6cd8141b8
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spelling	oai:doaj.org-article:22c522474c2d4e7694e4eed6cd8141b82021-11-25T17:52:48ZSpatial Modeling and Analysis of the Determinants of Property Crime in Portugal10.3390/ijgi101107312220-9964https://doaj.org/article/22c522474c2d4e7694e4eed6cd8141b82021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/731https://doaj.org/toc/2220-9964Many 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.Joana Paulo TavaresAna Cristina CostaMDPI AGarticlecrime concentration and hot spot analysisspatial regression analysisgeographic crime analysisGeographically Weighted Poisson Regressionspatial heterogeneityPortugalGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 731, p 731 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	crime concentration and hot spot analysis spatial regression analysis geographic crime analysis Geographically Weighted Poisson Regression spatial heterogeneity Portugal Geography (General) G1-922
spellingShingle	crime concentration and hot spot analysis spatial regression analysis geographic crime analysis Geographically Weighted Poisson Regression spatial heterogeneity Portugal Geography (General) G1-922 Joana Paulo Tavares Ana Cristina Costa Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
description	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.
format	article
author	Joana Paulo Tavares Ana Cristina Costa
author_facet	Joana Paulo Tavares Ana Cristina Costa
author_sort	Joana Paulo Tavares
title	Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
title_short	Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
title_full	Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
title_fullStr	Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
title_full_unstemmed	Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
title_sort	spatial modeling and analysis of the determinants of property crime in portugal
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/22c522474c2d4e7694e4eed6cd8141b8
work_keys_str_mv	AT joanapaulotavares spatialmodelingandanalysisofthedeterminantsofpropertycrimeinportugal AT anacristinacosta spatialmodelingandanalysisofthedeterminantsofpropertycrimeinportugal
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Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

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