Moreover, different bandwidth values are highlighted by KDE plots in order to determine hotspot distribution.
Therefore this paper shall compare the plot types and consider essential differences between the plot types, with particular reference to the potential advantages and disadvantages of both as regards the interrelationship between crime mapping and crime response strategy.
The Choropleth plot valuates aggregate data of key regions such as suburban areas. The Choropleth plot further measures the points within the region, which is signified on a 2-dimensional map on a graded colour chart. The colour graduation is characteristically red, increasing and decreasing in strength to highlight crime hotspots.
A significant advantage of the Choropleth plot is that it is user friendly and is considered more accurate in representing numerical data pertaining to crime in the highlighted areas. However, a central problem is the structuring of “areas” under the Choropleth plot as certain areas will inherently be more populated and can create disproportionate data regarding the level of crime in a particular area. Nevertheless, such issues can be remedied by the implementation of fitting denominator with prime examples including area or population.
The Choropleth map below demonstrates varying area distributions of robbery and burglary and do not suggest any pattern of crime activity in any particular area. Additionally, the area structuring problems referred to above highlight the point that the colour chart may distort the actual nature of crime issues in the areas covered by the Choropleth plot.
There are distinct parallels between Grid Maps and Choropleths however the significant difference is the use of grid spacing to avoid the structural problems referred to above. Indeed, a comparison with the Grid maps highlights the misrepresentation of crime hotspots in the