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

Joana Paulo Tavares,Ana Cristina Costa

doi:10.3390/ijgi10110731

Abstract

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.

Highlights

Crime rates have been declining in Western countries since the 1990s, property crimes [1]
The best-fit model was the Geographically Weighted Poisson Regression model with eight independent variables (GWPR model 8), which assessed the association between property crimes and young age groups, social inequality, poverty and income inequality, and residential instability at the municipality level
The model showed greater explanatory power in the metropolitan regions of Lisbon and Porto, as well as in southern municipalities, where high crime rates are clustered. The model made it possible to capture the existence of spatial non-stationarity in property crime data, as evidenced by the spatial distribution of the local coefficients of each independent variable (Figure 5)

Summary

Introduction

Crime rates have been declining in Western countries since the 1990s, property crimes [1]. Portugal has one of the lowest crime rates among Western countries. According to the Annual Internal Security Report [3], property crimes constitute the largest share of total crimes committed in Portugal, representing 51.4% of all reported crimes in 2019. There is a paucity of studies on the geography of crime for Portugal, most likely due to the lack of open data with finer spatial resolution, such as point, neighborhood, or parish data. Despite the availability of socioeconomic and demographic data having increased in the last decade, only a few recent publications analyzed spatial crime patterns or addressed the relationship between crime and environmental characteristics in Portugal

Objectives

Results

Conclusion