Different Kinds of Maps Generated by GIS
Up to this point, we have been discussing theories in this chapter. These theories will help you understand more clearly why geography, place, and hot spots are important. You can see that to understand the problems of crime, each form of crime occurrence requires its own form of mapping. For instance, victim maps would be different from hot streets or place maps. Various maps are needed so that the police command can make appropriate decisions as to what action to take to deal with the particular type of crime concern. The following are the different kinds of maps that crime analysts can generate, explaining the unique way each depicts hot spots.
A dot map can show a particular place, a store or gas station, for instance, where crime problems have occurred. The specific address of the relevant place of a crime occurrence helps to distinguish between addresses that have had crime problems versus those that have not. A dot map or a dot distribution map is simply a map that uses dots or other symbols to represent the presence, quantity, or value of a phenomenon or thing in a specific area, such as a crime or crime victimization. In a dot distribution map, the size of the dots may be scaled in proportion to the intensity of the problem. For example, if two or more robberies occurred at the same convenience store, the dot may be larger than the dot depicting a convenience store experiencing only one robbery.
Line maps are used when the hot spots are along streets. If, for example, robberies and holdups occur in the streets leading up to a stadium where sports and entertainment events are held, a dot map would be less relevant than a line map. The crimes do not occur at the stadium, but along routes leading to and from the venue. Some of the streets may not have parking, and so few people would be walking to and from the stadium on those streets. But knowing which streets have crime and which ones don’t would be important for the police in terms of addressing the problems.
Ellipse and Choropleth Maps
These maps are used when the hot spots cover broader areas and include neighborhoods. Ellipse and choropleth maps imply that the areas within the designated hot spots share the same risk level, so a specific street or a certain address within that district is less relevant (Eck et al., 2005). Ellipses are common in physics, astronomy, and engineering. For example, the orbit of each planet in the solar system is an ellipse. One of the earliest crime mapping software applications that became widely available to practitioners for crime analysis was Spatial and Temporal Analysis of Crime (STAC). STAC is not a GIS, but instead acts as an aid to persons who already have a GIS or desktop mapping capability. STAC is a spatial tool to find and examine hot spot areas within the study area. In concise terms, this means that STAC first finds the densest concentration of points on the map (hot clusters), and then fits a “standard deviational ellipse” to each one. The ellipses themselves indicate through their size and alignment the nature of the underlying crime clusters.
Examples of the use of STAC include a study of how to reduce incidents in Detroit’s infamous Devil’s Night period (Martin, Barnes, and Britt, 1998). An advantage of STAC is that it derives hot spots without relying on defined boundaries such as census units or police administrative boundaries. However, a limitation is that crime hot spots do not naturally form into convenient ellipses; thus, STAC hot spots do not represent the actual spatial distribution of crime and can sometimes be misleading (Eck et al., 2005).
Choropleth mapping or geographic boundary thematic mapping is a way of representing spatial distributions of crime events.The boundary areas that are used for this type of thematic mapping are usually arbitrarily defined for administrative purposes, since they can be police beats, census blocks, wards, or districts. Offenses as points on a map can be aggregated to these geographic unit areas and are then shaded in accordance with the number of crimes that fall within them. This map allows for quick determination as to which areas have a high incidence of crime, and allows further diagnosis of the problem by “zooming in” on those areas (Eck et al., 2005). Areas are shaded according to their data values, by either rate or frequency.
Isoline maps are, by definition, maps with lines that join points of equal value. Physical geography often uses isoline maps as isobars to show barometric pressure or isotherms to show temperature, but the form most likely to be used in crime analysis is the isopleth (equal crowd), in which data for areas, such as crimes per neighborhood or population density, are calculated and used as control points to determine where the isolines will be drawn.
Grid Thematic Mapping
In order to combat the problems associated with different sizes and shapes of geographical regions, uniform grids (or quadrats) can be drawn in a GIS as a layer over the study area and thematically shaded. Therefore, all areas used for thematic shading are of consistent dimensions and are comparable, assisting the quick and easy identification of hot spots. This approach does have some limitations; the usage of grids still restricts how the hot spots can be displayed. Spatial detail within and across each quadrat is correspondingly lost because the crime events have to conform to one specific quadrat, which can then lead to inaccurate interpretation by the map user. Additionally, they often have a “blocky” appearance, which is related to grid cell size (Eck et al., 2005).
Kernel Density Estimation
Kernel density estimation (KDE) is regarded as the most suitable spatial analysis technique for visualizing crime data (Chainey, Reid, and Stuart, 2002; Chainey and Ratcliffe, 2005; Eck et al., 2005). It is an increasingly popular method due to its growing availability, the perceived accuracy of hot spot identification, and the aesthetic look of the resulting map in comparison with other techniques (Eck et al., 2005). Offenses are aggregated within a user-specified search radius, and a continuous surface that represents the density or volume of crime events across the desired area is calculated. A smooth surface map is produced, showing the variation of the crime density across the study area, with no need to conform to geometric shapes such as ellipses. It is the most visually impressive and has the capability of identifying hot spots through a statistically robust methodology (Chainey et al., 2002; Chainey and Ratcliffe, 2005; Eck et al., 2005).
Polygon mapping is the cartographic display of regularly or irregularly shaped polygons and their attributes.Typically, this capability includes shading, symbology, and numeric labeling, as well as other map cosmetic functions for generating alphanumeric labeling of polygons. Polygons are multisided but closed figures that may indicate a large geographical area or an area as small as a building.
Hot streets are slightly more challenging to demonstrate with most commonly used GISs. This is because they do not easily allow users to depict hot streets (Eck et al., 2005). Alternatively, analysts can plot crime incident locations and match them to street layouts. Another method to identify hot streets would be to join the points file to the street segments file of the map (for those unfamiliar with joins, the process essentially aggregates the count of incidents that occur within a street segment). If geocoding is based on a street file, there will be no issues. If geocoding is based on addresses, there will be an issue, as many points will not overlay the street segments and hence will not join properly. One option is to set the join for an approximate distance between the point and the street segment. This may cause the points to be assigned to a street segment that is not part of the actual street address. Careful review of these results is suggested. Once the points and street segments are joined, there will be a count of points per street segment. Analysts can display the hot streets by line width or color of the street segments. This type of analysis assists in narrowing the size of a targeted neighborhood or area (Gwinn, Bruce, Cooper, and Hick, 2008). Finally, recall that this technique is limited in that the boundaries are argued to be artificial and rarely reflect actual criminal patterns.