Step 17: Know how hot spots develop
Analysts often examine hot spots by use of geography alone. This can often be a useful starting point, but to reduce or eliminate the hot spot you must look deeper to understand why it is a hot spot. We focus on developing an understanding of the processes that create hot spots. Later, in Steps 23 and 55, we examine how to analyze and map hot spots without letting your mapping software call the shots.
Step 19: Research your problem
Other police agencies might already have dealt with the problem you are tackling or researchers might have studied it. You could save a lot of time by finding out how they analyzed it and what they did, in particular which responses seemed to be effective and which not. Studying the efforts of others can provide you with useful hypotheses to test on your problem (Step 20).
Step 20: Formulate hypotheses
Whenever we confront some new and perplexing crime pattern we form hypotheses about its causes, often based on incomplete information. Experience and theory are good sources of hypotheses. You should (1) clearly state your hypotheses, (2) not be wedded to them, and (3) use data to objectively test them. Expect all hypotheses to be altered or discarded once relevant data have been examined because no hypothesis is completely right.
Step 18: Learn if the 80-20 rule applies
A very important principle of crime prevention is that crime is highly concentrated on particular people, places, and things. This suggests that focusing resources where crime is concentrated will yield the greatest preventive benefits. These concentrations (dealt with in more detail in later steps) have attracted labels that are becoming well known to most crime analysts:
Step 21: Collect your own data
In the course of your routine analytic work, you probably use mainly crime incident and arrest data, but for problem-oriented projects, you will need to use a much wider array of data. For example, calls-for-service data could give you a better handle on the amount of drug dealing at troublesome locations than arrest data.
Step 24: Know when to use high-definition maps
Conventional software is of little use when mapping crime in a downtown area, a college campus, a public housing project, or any site with many large buildings. This is because most buildings, however large, have only one street address, and crimes occurring anywhere in the building are assigned to that address.
Step 23: Diagnose your hot spot
When mapping crime, it is helpful to distinguish between acute and chronic hot spots (Step 17). Acute hot spots show abnormal spikes in crime, which may decline naturally, while chronic hot spots have persistently higher crime levels than other areas and are unlikely to decline unless something is done. There are three basic forms of chronic hot spots, each of them linked to particular theories and types of responses.
Step 22: Examine your data distributions
After collecting your data you need to know what it is telling you. Suppose you collected incidents of assaults on taxi drivers. Are assaults concentrated among a very few drivers? Are the assaults concentrated on some days of the week or times of day?
Step 26: Take account of long-term change
Is your problem getting worse or better? Does it fluctuate regularly or randomly? To answer these questions you need to study your problem by graphing either the number of events or a rate against time. A rate is typically the number of crime or disorder events divided by the number of targets at risk (Step 27).
The time course of a problem can be divided into three basic components:
Step 25: Pay attention to daily and weekly rhythms
Cycles of activities have tremendous influences on problems. The ebb and flow of vehicles caused by commuting and shopping rhythms, for example, changes the number of targets and guardians in parking facilities. This, in turn, influences when vehicle thefts and break-ins are most frequent. Robberies of drunken revelers may be more likely around bar closing time on Fridays and Saturdays, because the number of targets is higher.