Crime mapping - Essay Example

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The objective of this paper is to highlight hotspot mapping of crime by undertaking a comparative analysis of various crime mapping techniques; namely, choropleth, grid and kernel density estimation plotting (KDE). In simple terms, plots are formulated as a result of data input…
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Crime mapping
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1. Introduction The objective of this paper is to highlight hotspot mapping of crime by undertaking a comparative analysis of various crime mapping techniques; namely, choropleth, grid and kernel density estimation plotting (KDE). In simple terms, plots are formulated as a result of data input charting robbery and burglary statistics in a specific area which is further split into regional subdivided plots.
The creation of chloropleth plots is rooted in what are termed as “tracts”, which represent census regions. Alternatively, the grid and KDE plots share similar grid measurements with the KDE dimension going beyond the grid plot in terms of processing detail. 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.
2. Choropleth Maps
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.
3. Grid Maps
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 larger areas covered by the Choropleth maps.
4. KDE Maps
KDP maps also use a grid pattern similar to the grid maps however is more scientific in incorporating mathematical formulas and algorithms to address distance points between criminal activity. As a result, the areas covered mirror the grid squares. Whilst the distribution patterns are similar to the grid maps, the bandwidth of the KDE plot is the central difference particularly with regard to surface temperature. Read More
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