Information and Knowledge Management using ArcGIS ModelBuilder

4.1 Crime hot-spots
Paulo João, Jorge Ferreira and José Martins
This is the most common method used in criminal representation. It assumes that past crime locations
will persist into the future, however the actual results of this method depends on the time period under
review, usually this robust method only produces good results when applied to short time series.
An interesting feature in the detection of hot-spots is its persistence and coincidence over time as
shown by Anselin (2000), the hot-spots reflect high levels of crime initially moderate, but over time,
usually, this crime will change to more violent types of crimes (e.g. acts of vandalism to crimes of
theft).
Therefore, should be contained and controlled in time to prevent more serious incidents to people and
property in the geographic area covered by the hot-spot.
According to Eck, J. et al. (2005), this method assumes that they must map the locations and not
criminal occurrences thus understand why certain settings are more easily criminal occurrences while
others appear to inhibit these same events. Ainsworth (2001:88) refers that "A crime hot-spot is
usually understood as a location or small area with boundaries clearly identified where there is a
concentration of criminal incidents, which exceed the normal for this area, the term can also be used
to describe locations that showed an increase in crime a given period of time".
4.2 Descriptive statistics for crime analyses
One of the most important tasks associated with crime analysis is to know data and its particularities.
This data is the baseline for an intelligent analysis, however, very little of this data are accurately
collected or has the necessary detail.
Understanding the different types of data and their definitions is important because some types of
analysis have been designed for particular types of data and may be inappropriate.
After preprocessing and geocoding all the information, data was analyzed and the dataset consisted
in 35549 police records, distributed by 8 variables, according with the fallowing coding:
Table 2: Data coding process
Variable Min Max
Day 1 31
Month 1 12
Hour 0 23
Minute 0 59
Type of crime 1 6
Subtype of crime 1 34
Classification 1 112
Parish 1 53
According to the Persons’ coefficient correlation matrix, there was no significant correlation between
the variables which let us to identify the variables as independent observations.
Table 3: Person correlation matrix
Day Month Hour Minute Type Subtype Classification Parish
Day 1
Month -0,011 1
Hour 0,008 0,003 1
Minute 0,007 0,000 -0,019 1
Type 0,050 0,016 0,013 -0,020 1
Subtype 0,004 0,002 0,003 -0,004 0,020 1
Classification -0,014 0,009 0,010 -0,009 -0,008 0,000 1
Parish -0,002 0,005 0,007 -0,013 0,012 0,020 0,032 1
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