# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
## 4 Results and Discussion
### Model Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model No.** & **Predictors** & **AICc of the Global Model** & **AICc of the GWPR Model** & **Percent of Deviance Explained of the Global Model** & **Percent of Deviance GWPR Model** \\ \hline
1 & All 9 variables & 6328.360 & 2937.661 & 0.861976 & 0.941470 \\ \hline
8 (best) &
\begin{tabular}{c} 8 variables: all except ‘Monthly \\ remuneration of employees’ \\ \end{tabular} & 6326.444 & 2938.292 & 0.861971 & 0.940991 \\ \hline
6 & 8 variables: all except’Gross enrollment rate’ & 6545.357 & 2999.848 & 0.857180 & 0.939530 \\ \hline
7 & 8 variables: all except & 6326.214 & 3045.044 & 0.861976 & 0.938852 \\ \hline
5 &
\begin{tabular}{c} 7 variables: all except \\ ‘Conventional dwellings’ and ‘Monthly \\ remuneration of employees’ \\ \end{tabular} & 6324.318 & 3062.525 & 0.861970 & 0.937960 \\ \hline
3 & 8 variables: all except ‘Young resident \\ \hline
9 &
\begin{tabular}{c} 6 variables: all except ‘Young resident \\ population’, ‘Retention and dropout rates in basic education’, and ‘Monthly \\ remuneration of employees’ \\ \end{tabular} & 6372.742 & 3092.361 & 0.860957 & 0.937563 \\ \hline
11 &
\begin{tabular}{c} 6 variables: all except ‘Young resident \\ population’, ‘Retention and dropout rates in basic education’, ‘Monthly \\ remuneration of employees’ \\ \end{tabular} & 6413.493 & 3264.871 & 0.859973 & 0.932586 \\ \hline
10 &
\begin{tabular}{c} 5 variables: all except ‘Gross enrollment \\ rate’, ‘Conventional dwellings’,’ \\ remuneration of employees’ \\ \end{tabular} & 6749.296 & 3349.604 & 0.852578 & 0.930480 \\ \hline
10 &
\begin{tabular}{c} 5 variables: all except ‘Retention and \\ dropout rates in basic education’, ‘Gross enrollment rate’,’Unemployment rate’, and ‘Monthly \\ remuneration of employees’ \\ \end{tabular} & 6821.190 & 3465.591 & 0.851005 & 0.927567 \\ \hline
4 &
\begin{tabular}{c} 7 variables: all except ‘Young resident \\ population’ and ‘Purchasing power \\ per capita’ \\ \end{tabular} & 8219.041 & 3661.203 & 0.820509 & 0.924551 \\ \hline
2 &
\begin{tabular}{c} 8 variables: all except ‘Purchasing power \\ per capita’ \\ \end{tabular} & 8152.876 & 3669.091 & 0.822004 & 0.924930 \\ \hline
15 &
\begin{tabular}{c} 7 variables: all except ‘Guaranteed \\ Minimum Income and Social Integration \\ Benefit’ and ‘Unemployment rate’ \\ \end{tabular} & 7575.738 & 3695.206 & 0.834586 & 0.923893 \\ \hline
14 &
\begin{tabular}{c} 7 variables: all except ‘Conventional \\ dwellings’ and ‘Purchasing power \\ per capita’ \\ \end{tabular} & 8243.499 & 3867.498 & 0.819974 & 0.920283 \\ \hline
12 &
\begin{tabular}{c} 7 variables: all except ‘Retention and \\ dropout rates in basic education’ and \\ ‘Guaranteed Minimum Income and Social Integration Benefit’ \\ \end{tabular} & 7529.236 & 3900.690 & 0.835604 & 0.919182 \\ \hline
13 &
\begin{tabular}{c} 7 variables: all except ‘Gross enrollment rate’ \\ and ‘Purchasing power per capita’ \\ \end{tabular} & 9165.079 & 3906.035 & 0.799808 & 0.919152 \\ \hline
17 &
\begin{tabular}{c} 7 variables: all except ‘Monthly \\ remuneration of employees’ and \\ ‘Purchasing power per capita’ \\ \end{tabular} & 9178.405 & 4185.598 & 0.799516 & 0.913153 \\ \hline
16 &
\begin{tabular}{c} 7 variables: all except ‘Foreign population’ \\ and ‘Purchasing power per capita’ \\ \end{tabular} & 14071.495 & 4692.504 & 0.692443 & 0.902055 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Goodness-of-fit measurements for global and local Poisson regression models. The Local Percent of Deviance Explained map (Figure 4) shows that the GWPR model 8 has greater explanatory power in the municipalities of the metropolitan regions of Lisbon and Porto. On the other hand, the model fit is lower in the municipalities of Vila Pouca de Aguiar, Vila Real, Santa Marta de Penaguiao, Fundao, Idanha-a-Nova, Castelo Branco, Vila Velha de Rodao, Marinha Grande, Alcobaca, Nazare, Porto de Mos, and Batalha.