Perhaps the most basic analytical task in using geographic information systems is the process of selecting and displaying scientific information. As shown in figure 4.6, when objects, in this case aggravated assault cases, are selected or "highlighted" on the map with the select tool, their corresponding database records are highlighted in color or with a symbol located next to the record. If the analyst wants to bring together the selected records at the top of the table for easier recognition and manipulation, they can be "promoted" (in ArcView, for example) using the appropriate button.

Mapping time

A more sophisticated process for selecting information uses various criteria and is referred to as "filtering" or "querying." Sometimes, you will see Structured Query Language (SQL), which is a specialized programming tool for asking questions of databases. If the conditions you set are satisfied, then certain cases are included or retained in a new data set, as a subset of the main data. The new file can then be saved, mapped, or manipulated in any way. For example, the condition "Time is greater than or equal to 1500 hours and time is less than or equal to 2300 hours" (written in computerese as time>=1500 & time <=2300) would select all cases in the 8-hour shift between 3 and 11 p.m. Many variations are possible on the "mapping time" theme. In figure 4.7, maps of domestic disputes in Charlotte-Mecklenburg, North Carolina, show change over a decade. The units of analysis were the central points, or centroids (see the section "Centroid display" later in this chapter) of 537 response areas. Idrisi software (see appendix) was used to generate the two surfaces based on the square roots¹ of the data values for each response area, resulting in a clear picture of substantial growth over the time period. In 1984, domestic disputes occurred mainly in the north and west, as shown in green. By 1993, calls had increased in number and geographic coverage, particularly on the east side and in the southwest.

Time geography is viewed through a different lens as shown in figure 4.8. Using methods similar to those used to produce figure 4.7, seven daily maps were constructed, followed by an eighth map to denote the day of the week with the highest frequency of calls for each of the 537 response areas. Maps such as those in figures 4.7 and 4.8 could be used to allocate resources and to coordinate domestic violence prevention efforts.

Mapping space

Because crimes usually affect some neighborhoods more than others, maps may focus on certain beats, posts, patrol areas, communities, census tracts, neighborhoods, or other units. What is the geography of crimes in terms of council districts? Could this information be used by the police department to anticipate political firestorms? Attention may not be confined to such official areas, but may involve informal or ad hoc areas, such as a 500-yard radius around a drug market, bus stop, or automatic teller machine, for temporary investigative purposes. Provided that the boundary files for the official areas are in the computer, queries can be addressed to them. For example, you could compute the rate of vandalism incidents per 1,000 housing units, per unit of the general population, or per unit of the male youth population. A few alternative base maps are shown in figure 4.9.

Mapping incident types and modus operandi

Conditions, or filters, can be used to refine searches at any level the analyst chooses. For example, the most obvious filter would isolate all crimes of a specific type. However, filtering can isolate crimes by time of day, by neighborhood, and by modus operandi (MO). Conditions can be set to specify all the desired criteria, possibly resulting in the isolation of a cluster of incidents that could be linked to the same perpetrator. In figure 4.10, for example, rape has been selected. These incidents are shown without identifying victims by using a large symbol to make the location of each somewhat vague. The analyst must trade some precision to accommodate the overriding need to protect victims.

Mapping attributes of victims and suspects

Mapping by characteristics of victims, suspects, or both can also be useful and is easily accomplished. For example, where have females been assaulted? Is there evidence that a cluster of burglaries has occurred at homes occupied by elderly persons?

Mapping other recorded characteristics

Possibilities for filtering are unlimited. You can set as many time, space, victim, suspect, MO, or other filters as you wish, given data availability. For example, where are the armed robberies occurring? What is the pattern of robberies at gunpoint within a 1-mile radius of drug markets? Where are juvenile offenders victimizing elderly persons at gunpoint during hours of darkness? Are spousal assaults randomly distributed, or are they clustered?

The only limitation is the availability of geocoded or geocodable information in the database. A potentially useful map might reflect the relationship between persons on probation or parole and the types of crimes they have committed. For example, a rash of robberies occurs in a neighborhood: Where are the robbery probationers and parolees in that area? Do the MOs match up?