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>The analysis relationship of poverty, unemployment and population with the rates of crime using geographically weighted regression (GWR) in Riau province
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The analysis relationship of poverty, unemployment and population with the rates of crime using geographically weighted regression (GWR) in Riau province
In the last decades, crime has become a major issue against local decision-makers in the Pekanbaru city region. The incidence of criminality in a region depends not only on local socio-economic conditions, but also on the ones in nearby regions, due to significant population mobility. Understanding the relationship between crime and its surrounding environment can reveal possible strategies that can reduce crime in a neighborhood. Techniques account for such territorial correlations, such as use of spatial weights that capture the influence of each region upon its neighbors. Among the spatial methods, the geographically weighted regression (GWR) is a valuable instrument that allows estimating local coefficients, specific to each location, thus providing useful information for appropriate policy design at the regional level. In this context we employed a criminality GWR model in an attempt to find the local determinants, both economic and demographic, that explain the spatial distribution of criminal offenses in Pekanbaru city region. The results indicated that the incidence of this phenomenon in Pekanbaru is linked to factors largely acknowledged in the literature, such as poverty, unemployment, and population. Thirty novelty brought about by the GWR model compared to previous research is that it also revealed important spatial variations in the impacts of the variables and indicated which region is more vulnerable to specific factors. From a modeling perspective the GWR model represents a better fit than the classic OLS model, in addition to capturing the spatial variation in coefficients’ estimation.
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