Identifying Crime Hot Spots

The growth of intelligence-led policing has placed a greater emphasis on the effective identification of crime hot spots as well as the choice of the crime reduction or detection strategy identified to combat a problem. Crime hot spots have become strategically important locations for policing surveillance and functioning in many locations, as they enable an operational commander to focus resources into the areas of highest need. The chapter outlines the techniques used to identify the spatial and temporal components of crime hot spots, and utilizes these methods to identify three broad categories of temporal hot spots and three broad categories of spatial hot spots. Real examples show how through spatial interpolation, inverse distance, weight, age, and kriging, combined within the regression analysis, yield operational results for appropriate crime prevention or detection strategy.

Spatial Interpolation and GIS

Spatial analysis is the process of using spatial information to extract new information and meaning from the original data. The interpolation process uses points with known values to estimate values at other unknown points. GIS uses spatial analysis tools for calculating feature statistics and carrying out geoprocessing activities as data interpolation. This tool is immensely applicable in research for terrain analysis, slope profiling, and hydrological modeling (modeling the movement of water over and in the earth). In wildlife management, it can be used for analytical functions dealing with wildlife point locations and their relationship to the environment. In the field of climatology, spatial analysis is used for making precipitation (rainfall) maps for a country; to estimate the temperature variations at different locations without recorded data by using the known temperature readings from nearby w'eather stations. Such interpolated surfaces are often known as statistical surfaces. This tool can be used to predict unknown values for any geographic point data, such as chemical concentrations, noise levels, and snow accumulation; water table and population density are other similar types. Large data collection sometimes seem to be a herculean task that is cost intensive and cumbersome as well. In such cases, data collection is usually conducted only in a limited number of selected point locations. Later, with the help of software, spatial interpolation of these points is used to create a raster surface w'ith estimates made for all raster cells (Fig. 4.1).

In order to create a continuous map for digital elevation from elevation points measured with a GPS device, a suitable interpolation method has to be used to

Spatial Distribution Map of Haryana (India) Showing Depth to Water Level

FIGURE 4.1 Spatial Distribution Map of Haryana (India) Showing Depth to Water Level.

optimally estimate the values at those locations where no samples or measurements were taken. The results of such interpolation analysis can then be used for analyses that cover the whole area for modeling.

The use of spatial interpolation in crime studies is very impactful. The crime locations, their frequency of occurrences, and geographical understanding of a region. together lay emphasis on its analysis. Spatial distribution of incidents include many analyses such as the center of minimum distance, standard deviational ellipse, and the convex hull and directional mean.

Standard deviational ellipse involves a common way of measuring the trend for a set of points or areas to calculate the standard distance separately in the x and у directions. These two measures define the axes of an ellipse encompassing the distribution of features in a region. The ellipse is referred to as the standard deviational ellipse, since it calculates the standard deviation of the x coordinates and у coordinates from the mean center to define the axes of the ellipse. The ellipse allows you to observe if the distribution of features is elongated and hence has a particular orientation (Fig. 4.2).

Distribution Trend with Standard Deviational Ellipse

FIGURE 4.2 Distribution Trend with Standard Deviational Ellipse.

The investigator can interpret a sense of orientation by drawing the features on a map, calculating the standard deviational ellipse to make the trend clear. One can also calculate the standard deviational ellipse using either the locations of the features or using the locations influenced by an attribute value associated with the features. The latter is termed a “weighted standard deviational ellipse.” The potential application of this tool is for mapping the distributional trend for a set of crimes and help identifying its relationship to a particular physical feature (a string of bars or restaurants, a particular boulevard, and so on).

It can also be used for mapping groundwater well samples for a particular contaminant and might indicate how the toxin is spreading and, consequently, may be useful in deploying mitigation strategies. It helps compare the size, shape, and overlap of ellipses for various racial or ethnic groups and may provide insights regarding racial or ethnic segregation. Plotting ellipses for a disease outbreak over time may be used to model its spread effectively, using this tool.

Convex hull in geometry represents a closure of a shape that contains the convex. It can be better understood as encircling the crime incidence in a closet and analyzing it (Fig. 4.3).

Convex Hull

FIGURE 4.3 Convex Hull.

Directional Mean

FIGURE 4.4 Directional Mean.

Directional mean is something which is related to or indicates the direction in which someone is moving. In particular it indicates the progression direction, which is very helpful for police patrolling and tracking culprits (Fig. 4.4).

The linear directional mean tool is used to calculate the trend of either the direction or orientation of line features by calculating the average angle of the lines. This statistic is used to evaluate auto theft data that contains information on the location of occurrence from where each vehicle was taken and where it was eventually recovered. The linear directional mean tool facilitates highlighting the recurring patterns that can suggest an underlying infrastructure supporting car thefts in the region. Similar analysis has been used to study data on missing or abducted children in a region.

When crime distributions are compared to other features in the landscape, similarities or relationships often become evident. The most common way for measuring the trend for points or areas is to calculate the standard distances separately in x and у directions. These two measures define the axis of an ellipse encompassing the distribution of features. The ellipse is referred to as the standard deviational ellipse since the method calculates the standard deviation of the x-coordinates and у-coordinates from the mean center to define the axis of the ellipse. This ellipse shows if the distribution of features is elongated, it has a particular orientation.

In some cases, crime events grouped by police beat and evaluated using the standard deviational elipse tool may indicate that, in some police beats, crime activities were evenly distributed throughout the beat, thus the ellipse resembles a circle. Whereas, in other cases, crime activities tend to follow some road networks and crime incidents in these police beats highlight that orientation (Fig. 4.5).

 
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