Types of map information
Chapter 1: Context and Concepts

Maps can provide a rich variety of information, including but not limited to location, distance, and direction as well as pattern for maps displaying point or area data. Each type of data means different things to different users.

  • Location is arguably the most important of all the types of information to be represented on, or gleaned from, a map from the perspective of a crime analyst. Where things have happened, or may happen in the future, is the most sought after and potentially useful piece of information because it has so many implications for investigators and for the allocation of patrol and community resources, in addition to utility in the realms of planning and politics.

  • Distance is not much use as an abstract piece of information. It comes to life when translated into some kind of relationship: How far did the victim live from the place where she was robbed? What is the maximum distance police cars can travel within a specific urban environment to provide acceptable response times? How far could a suspect have gone in a particular time period?

  • Direction is most useful when considered in conjunction with distance, although it is not typically an important piece of map information in crime analysis unless it relates to other relevant processes or conditions. It is generally used in a broadly descriptive context, such as "the hot spot of burglaries is spreading to the west," or "serial robberies are moving southeast," or "the east side is becoming a high-crime area." In the example shown in figure 1.12, serial robbers were found to have a tendency to migrate south from Baltimore along the major highways.

    Figure 12

  • Pattern is an especially useful concept in crime analysis, as so much of what crime analysts do involves describing or analyzing the pattern of crime occurrences. Pattern can be a powerful investigative tool because the way points are arranged may tell us something about the process driving that arrangement. Patterns are usually classified as random, uniform, clustered, or dispersed. In a random arrangement, points are just as likely to be at any place on the map as at any other. Points are distributed haphazardly around the map. A uniform distribution has points that are equally spaced. Alternatively, we can say that in a uniform distribution the distance between neighboring points is maximized. In a clustered pattern, points are clumped together with substantial empty areas.

It is tempting to assume that the nonrandom distributions (uniform and clustered) automatically mean that some interesting underlying process is at work, providing useful information about crime. This may or may not be true. For example, burglaries show up in a cluster, suggesting a hot spot. But further investigation shows that the cluster corresponds to a neighborhood with a dense population, so the high frequency is no more than an expression of the geography of risk. The terms describing the types of patterns are subject to some semantic confusion. For example, what does dispersed mean? A dispersed pattern could be random or uniform. Is "less dispersed" the same as "clustered"? Various indexes have been developed to measure regularity, randomness, and dispersion. For additional reading, see, for example, Taylor (1977, chapter 4) and Hammond and McCullagh (1974, chapter 2).

Line data (for networks, for example) and both discrete and continuous areal distributions are additional types of data.

  • Line data. Linear features or processes can be abstracted on maps. The Minard map (see figure 1.5) did this by symbolizing the flow of troops to Moscow and back. The street maps used to map crime also contain line data that show points on streets, indicating the linear arrangement of incidents. A map connecting places where vehicles are stolen to the places where they are recovered is prepared with lines connecting the places. Or a line map might connect victim addresses to suspect addresses. Traffic flow can be shown with lines proportional in thickness to the flow (again like Minard's map in figure 1.5).

  • Discrete distributions. When point data are combined within unit areas such as precincts, patrol areas, census tracts, or neighborhoods, each area is separated from the others; it is "discrete." Mapping by discrete areas can be used to reorganize the point data into a context that may have more meaning for a specific purpose. For example, commanders may want to see the distribution of drug offenses by patrol beat to decide how workloads should be assigned. This could be measured by density, which expresses how often something happens within an area. A common application is population per square mile, for example. Density also is used increasingly frequently to describe crimes, and population density data can provide additional explanation of the risk or rate concepts in public forums. Graphic representations of this general type, mapping data by administrative or political areas, are known as choropleth13 maps. (A more detailed explanation is given in chapter 2.)

  • Continuous distributions are used less in crime analysis than discrete distributions, but they can be useful and are finding more frequent application in conjunction with, for example, commonly used software such as ArcView Spatial Analyst. Continuous distributions are phenomena found in nature, such as the shape of the land (topography), temperature, and atmospheric pressure. All places on Earth (above and below sea level) have topography and temperature, and all places above sea level (and a few on dry land below sea level) have atmospheric pressure. Thus it makes no sense to represent these conditions on maps divided into areas such as counties or cities, except for reference purposes.

    What does this have to do with crime mapping? It is sometimes useful to assume that crime can be represented as a continuous distribution to prepare a generalized statistical surface representing crime density. This can provide a "smoother" picture of crime that can be enhanced by adding three-dimensional effects (figures 1.13 and 1.14). This implies an acceptance of the tradeoff between loss of detailed locational information from the point map and the smoothed, generalized picture provided by the quasi-continuous presentation. This smoothed version may have the advantage of better legibility than the original detail. Another advantage of a surface smoothed across jurisdictions is that it may vividly illustrate that political boundaries have little or no meaning for criminals.

Figure 1.13

Figure 1.14

Chapter 1: Context and Concepts
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Mapping Crime: Principle and Practice, by Keith Harries, Ph.D., December 1999