# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
## 4 Results and Discussion
### Spatial Analyses of the Coefficients  
The coefficients of the best-fit model (GWPR model 8) show the existence of non-stationarity through different spatial patterns of the local coefficients of each independent variable. In Figure 5, these coefficients are shown for municipalities where the pseudo \(t\)-values are high (\(|t|>1.96\)), indicating that local relationships with property crimes occurrence may be significant at the 5% level [66]. There are several local coefficients varying from negative to positive values. [PERSON] et al. [49] argue that such a pattern might be explained by not considering overdispersion in GWPR. The spatial patterns of local coefficients may also be influenced by the spatial aggregation level of the data, which might not capture the heterogeneity of the phenomena properly. The coefficients of the young resident population are negative in northern municipalities and are positive in the south and in the metropolitan region of Lisbon (Figure 5), where they are higher in Sintra, Cascais, and Oeiras, which are municipalities with high property crime rates. Based on the routine activity theory, it was expected that this variable would have a positive impact on crime [68] because young age groups are often affected by low income and unemployment, thus generating conflicts and inequalities, and making them prone to opportunities that can generate increased criminal occurrences. Retention and dropout rates in basic education have a positive association with property crimes in most of the study region (Figure 5). This result was expected from the theory of social disorganization [51], considering the complementarity between dropping out of school, disadvantageous education attainment, and the practice of criminal activities. This variable has a higher positive impact in the northwest municipalities, particularly in Valencia, Moncao, and Vila Nova de Cerveira, located in the Viana do Castelo district. The spatial pattern of conventional dwellings is similar to that of population density. Therefore, municipalities located in coastal regions (west and south) have a larger density of conventional dwellings than those located in rural regions. This variable shows positive  
Figure 5: Local coefficients of the best-fit model (GWPR model 8). coefficients in most of the study region (Figure 5), particularly in the rural municipalities of the northeast such as Penalva do Castelo, Aguiar da Beira, and Fornos de Algodres. These results may be explained by a higher number of vacant houses (i.e., not being used for all or part of the year) in the rural interior [69], thus making them attractive targets for burglary [70]. The variable 'Guaranteed Minimum Income and Social Integration Benefit' measures poverty, and 'purchasing power per capita' characterizes income inequality. The former shows a positive relationship with property crime in many municipalities (Figure 5), particularly in Lisbon and its neighboring municipalities (especially in Sesimbra, Seixal, and Barreiro in the Setubal district) as well as in the northwest. Contrariwise, a few municipalities in the south show small negative coefficients. Purchasing power per capita also has positive coefficients in most administrative units, but their spatial pattern is very different from those of income from government assistance. The former variable has a higher positive association in Porto and its neighboring municipalities. Moreover, purchasing power per capita has a negative relationship with property crime in a few municipalities in the northeast. The relationship between poverty, income inequality, and crime rates is complex. Hence, it is a controversial issue that has been debated at length in the literature (e.g., [71; 72; 73; 74]). The conclusions from different studies often seem contradictory as they depend on the type of crime, the spatial resolution of the analyses, and the methodological and empirical approach. [PERSON] and [PERSON] [41] analyzed the impact of income inequality within and across census block groups in three USA cities.