# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
## 2 Literature on Geography of Crime Analysis  
The occurrence of criminal activity is neither randomly nor uniformly distributed in space ([PERSON] et al., 2018), and crime patterns vary according to the type of crime ([PERSON] et al., 2018). The routine activity theory ([PERSON] et al., 2018) and the crime pattern theory ([PERSON] et al., 2018) do not seek to explain crime geography based on the characteristics of criminals but rather through the specific environment and context in which crimes occur. The routine activity theory asserts that motivated offenders, desirable targets, and the lack of effective security are three important crime determinants. This theory supported many studies on residential burglary ([PERSON] et al., 2018). The rational choice theory and the crime pattern theory also underline the importance of places of crime. [PERSON] et al. [PERSON] et al. (2018) propose a framework to extend these environmental criminology theories and reviews their theoretical background. The theory of social disorganization ([PERSON] et al., 2018) highlighted three structural factors on the geography of crime, namely social and economic deprivation, ethnic heterogeneity, and population turnover, which have been later considered in several studies (e.g., ([PERSON] et al., 2018; [PERSON] et al., 2018)). [PERSON] (1978), and [PERSON] and [PERSON] (1979), advocated that family instability or disruption also leads to social disorganization, thus increasing crime and delinquency in urban environments. [PERSON] et al. [PERSON] et al. (2018) and [PERSON] et al. [PERSON] et al. (2018) review and expand the research on neighborhood and poverty effects on urban crime. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) propose an extension of theoretical and methodological approaches for explaining the concentration of crime in urban environments by combining elements of the routine activities and social disorganization theories. Valuable reviews of the various theoretical contributions to these theories, which inform our analysis on the geography of property crimes in Portugal at the municipality level, are offered by [PERSON] and [PERSON] (2016), [PERSON] (2018), and [PERSON] and [PERSON] (2018). The relationship between environmental characteristics and crime has been widely investigated using Geographic Information Systems (e.g., ([PERSON], 2018)), data mining techniques ([PERSON], 2018), and multivariate statistical analysis (e.g., ([PERSON], 2018; [PERSON], 2018)). Spatial dependence and spatial heterogeneity violate the assumptions underlying classical regression models, and thus, these spatial effects should be addressed when modeling geographic data. In crime analysis, conventional spatial regression models dealing with spatial autocorrelation are the spatial lag and spatial error models ([PERSON], 1996; [PERSON], 1996). A less common approach to deal with it is the Cliff-Ord spatial autoregressive model with spatial autoregressive disturbances [41]. Geographically Weighted Regression (GWR) was proposed by [PERSON] et al. [42] to deal with spatial heterogeneity. It is difficult to model both spatial effects at the same time [43], but GWR diminishes the problem of spatially autocorrelated error terms [42], and it is increasingly being used in crime analysis (e.g., [44; 45; 46]). Poisson or Negative Binominal regression models are more suitable for event counts [47; 48], and thus, the geographically Weighted Poisson Regression (GWPR) and the Geographically Weighted Negative Binomial Regression (GWNBR) models have been applied to address spatial effects in crime rates [49; 50; 51]. The key dimensions underlying the spatial variability of crimes against property are primarily supported by the previously mentioned crime theories (e.g., [52; 53]). The literature also highlights complex relationships between income inequality and property crime, and conflicting results on the relationships between unemployment rates/low-income factors and burglary rates [41].