# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
###### 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.  
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between 2004 and 2012, using spatial autoregressive models, and concluded that low population density has a significant positive impact on property crimes. In an exploratory study, [PERSON] and [PERSON] and [PERSON] (2015) applied a GWR model to crime incidence data in 86 municipalities of the north of Portugal in 2015. The results indicate that poverty, measured by 'beneficiaries of the Social Integration Income' has a higher impact on rural regions than on more densely urbanized municipalities. [PERSON] (2016) used the Risk Terrain Modeling methodology ([PERSON], 2016) to identify areas where residential burglary is most likely to occur in the Police Division of Loures city located in Lisbon's metropolitan region. The risk factors used in the model are consistent with the literature, and the results show that the location of fueling stations, educational establishments, food and beverage businesses, and bus stops are associated with areas that are vulnerable to residential burglaries.  
This study aims to contribute to improved knowledge on the relationship between property crime and the demographic and socioeconomic characteristics of each municipality of mainland Portugal. Poisson-based regression models are used for this purpose, and spatial effects in crime rates are also investigated.  
The geographical analysis of different factors associated with property crimes in Portugal enables a better understanding of this phenomenon. [PERSON] et al. [PERSON] et al. (2018) stressed the importance of developing demographic and crime scenarios in Portugal, and disclosed a dashboard for data visualization to support location-allocation scenarios for security forces. Our findings can be useful not only for subsequent crime studies in Portugal, but also for police authorities and city councils in the decision-making process and planning of actions adjusted to regional characteristics in crime prevention.