Information and Knowledge Management using ArcGIS ModelBuilder

Paulo João, Jorge Ferreira and José Martins
Looking at the crime rate with the local population, it is possible to observe the emergence of new
parishes with a higher crime rates however, the maximum crime rate is much lower when compared
to the representation only by the resident population.
Finally, it was created a spatial index based on crime incidence using spatial statistical tools. This
type of operation should be performed at a micro scale of analysis, statistically equal to subsection
(Martins, 2010), constituting the highest level of disaggregation corresponding to the block in urban
terms (Geirinhas, 2001).
This index was calculated using a Geographic Weighted Regression (GWR) using spatial context of
dynamic Gaussian Kernel. This operation uses the square root or log of the variables that are
selected for analysis by smoothing the absolute values and approximating the curve of normal
distribution in parabolic form, reducing disparities in the distribution (Longley et. al., 2005).
The result is expressed in standard deviation units, which is repeatedly used as an index of risk
(Harries, 1999). GWR provides a local model of the variable or process you are trying to understand
by fitting a regression equation to every feature in the dataset. GWR constructs a separate equation
for every feature in the dataset incorporating the dependent and explanatory variables of features
falling within the bandwidth of each target feature.
The shape and extent of the bandwidth is dependent on user input for the Kernel type, Bandwidth
method, Distance and Number of neighbors parameters The dependent variable for the GWR index
consisted on the criminal occurrences and as an independent variable it was used as the sum of the
resident population with the mobility of population. It should be noted that this type of analysis can be
a part of numerous independent variables of socio-economic development in order to identify a more
realistic index. For studies with similar scales to parishes it would be appropriate but for larger areas it
would be a difficult task for socio-economic values. It is also important to mention that this type of
procedure (if used alone without performing a normalization of the variables) may generate incorrect
analysis results (Harries, 1999) due to the fact that there are high levels of occurrence in areas with
low population and this happens also with a cartographic representation of crime rates.
Figure 8: Spatial index of criminal incidence per 1000 inhabitants (actual and planning)
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