Data, maps, and patterns |
Chapter 1: Context and Concepts
The stage is set. We have a truckload of data, computers, software, and printers. Provided our data have been gathered in a form that permits computer mapping (or have been converted to such a format;
see chapter 4), we are at least technically ready. It should be noted at this point that computer mapping is not "plug and play" but is more akin to word processingthe data have to be entered and sentences (read "maps") composed. Similarly, we will not be able to do computer mapping until we have data in a format that the program can understand. Also, many choices will have to be made. Automated mapping is automated only up to a point. (When you put your car on cruise control, someone still has to steer!)
Before we plug it all in and start mapping, it might be helpful to pause and think about the reasons underlying the patterns we observe and map. Those patterns must be generated by specific conditions and processes, and theories can be employed to help us understand them.
Each type of crime tends to be influenced by different conditions. For example, shoplifting is the outcome of circumstances different from those that produce homicide. Even within crime types, there are qualitative differences in circumstances. For example, a drug-related homicide may be the result of a conflict over turf or unpaid debts, whereas a
domestic homicide may be the product of long-simmering animosities between
Crimes may have distinctive geographic patterns for two underlying reasons that often overlap:
- First, crimes must have victims, and those victims (or their property) have definite geographic coordinates at
any given moment, although these coordinates can shift, as in the case
- Second, some areas in cities, suburbs, or rural areas have persistently high rates of crime, so that for certain neighborhoods there is a rather permanent expectation that crime is a major social problem. For example, Lander's (1954) research on Baltimore revealed high rates of juvenile delinquency to the east and west of downtown from 1939 to 1942. Some 60 years later, the pattern has changed a little but is basically the same.
A broad-based discussion of the causes of crime is beyond the scope of this guide, and the reader is referred to the substantial literature of criminology for guidance. However, we can consider issues with more obvious geographic implications, and we can do this by moving along a continuum of scale from the macro to the micro level.
On the macro scale, interpreted here to mean national or regional, geographic variation is apparent for some crimes, notably homicide, in the United States. Although the pattern has decayed somewhat in recent decades, a stream of research has addressed what has been called by some the southern violence construct (SVC), the tendency for high rates of homicide to be concentrated in the South. Similar regional variations are seen in other countries. In India, for example, high homicide rates are seen in the densely populated northern states.
On the intermediate scale, we see variations in crime rates among cities, although much of the apparent variation can be explained by boundary effects. In other words, "underbounded" central cities (where the urbanized area spills over beyond the city limits) tend to have high rates, since their territories exclude low-crime suburbs. Conversely, "overbounded" cities (such as Oklahoma City) tend to have low rates. However, by no means are all explanations of rate variation
necessarily found in boundary anomalies. Cities differ in social structure, traditions, mores, the strength of various social institutions, and other conditions relevant to potential criminality. These include economic conditions, the impact of gangs, and gun and drug trafficking. The wide variation in homicide rates among cities is represented by figure 1.15, which also reinforces the point that similar numbers of incidents may yield vastly different rates. (For an interesting example of interurban comparisons examining multiple factors, including gangs, guns, and drugs, see Lattimore et al., 1997.)
On the micro or intraurban scale, a broad array of environmental factors must be taken into account if crime patterns are to be understood. Arguably, the most important general principle is usually known as distance decay. This is a process that results from another behavioral axiom, the principle of least effort, suggesting that people usually exert the minimum effort possible to complete tasks of any kind. Distance decay (see also chapter 6) is the geographic expression of the principle of least effort. As shown in figure 1.16, the relationship between the number
of trips and distance is represented by a line showing that people take many short trips but few long ones. This principle has been observed to apply to a broad range of behaviors, including shopping, health care, recreation, social visiting, journeying to work, migration, and last but not least, journeying to crime. It is possible to create families of distance-decay curves to represent different classes of movement behavior. For example, shopping trips can be divided into convenience and comparison types. Convenience shopping is characterized by many very short trips because most people will get items such as milk and bread from the closest possible source. Comparison shopping occurs when buyers need big ticket items such as appliances, cars, houses, and college educations. Longer trips in search of more expensive goods and services are thought to be worthwhile because price savings produced by better deals will pay for the longer distances traveled, at least in theory.
Similar reasoning might be applied to criminal movements, although not enough data have been published to permit much in the way of generalization. The pioneering work of Frisbie et al. (1977), in Minneapolis, showed that more than 50 percent of residential burglary suspects traveled less than half a mile from their homes to their targets. Commercial burglars went somewhat farther, with some 50 percent of incidents occurring within 0.8 miles. Stranger-to-stranger assaults had a wider range, with the cumulative 50-percent threshold not accounted for until a radius of about 1.2 miles from the offenders' homes. Commercial robbers also reached the cumulative 50 percent of incidents at about 1.2 miles. However, a larger proportion of the commercial robbers traveled longer distances compared with those who committed the other crimes, presumably to locate suitable targets and also to avoid the recognition that may come with robbing the corner store. Travel distances tend to reflect population density and other characteristics of the physical environment (such as the geography of opportunities)15, so it is unlikely that distance-decay curves for crimes could ever apply universally. Nevertheless, the concept of distance decay is still a useful one, even if curves for specific crimes cannot be calibrated very accurately.
Although journeys to crime vary among crime types and with the demographic characteristics of offenders, targets or
victims tend to be chosen around the offender's home, place of work, or other often-visited locations. If your home is burglarized, the chances are that the burglar
is a not-too-distant neighbor. The long-established prevalence of violence among intimates is further confirmation of the idea that most interactionsincluding negative onesoccur at short range.
Distance decay is a useful general concept, but a detailed understanding of the fine points of local crime patterns demands detailed local knowledge. Where are the neighborhoods that are experiencing the greatest social stress? What are the patterns of mobility of the population? Who are the movers and shakers in the drug and gang scenes, and do their movements affect crime patterns? What changes are going on?