Prospective crime mapping in operational context Final report

hence available nearby targets plentiful. Considerations of land use provide the mostplausible accounts of such area variation in pattern. Other possibilities exist.Predicting the futureHaving demonstrated that the risk of burglary is indeed communicable in the East Midlands,the next task was to determine whether it is sufficiently predictable to suggest that theproduction of predictive mapping software would aid operational policing. Thus, research wasconducted to compare the effectiveness of:1. Promap;2. what the police are currently doing;3. a simple variant of retrospective hotspotting; and4. what might be expected if areas were prioritised for crime reductive attentionrandomly.At the time of the research, none of the areas currently used retrospective mappingtechniques per se. Instead, they generated pin maps on a fortnightly basis using two weeks’data. This simply involved generating a map which shows the locations of all burglaries thatoccurred within the previous two weeks. On the basis of these maps, operational resourcesmay be deployed. This is similar to retrospective hotspot mapping in that both methodssummarise historic data, and hence in what follows the authors will use retrospectivehotspotting as a proxy for what the analysts currently do. However, it should be noted thatproblems with pin mapping are well documented (e.g. Chainey & Ratcliffe, 2005) and thus theretrospective approach used here as an analogue is significantly superior to what the analystswere doing.Item 2 in the list above has thus been eliminated, being a sub-optimal type of retrospectivehotspotting. As is intuitively clear, random allocation of resources will be less efficient thaneither prospective or retrospective hotspotting, so the choice of method comes down toprospective or retrospective. While necessarily technical, the remainder of this section shouldbe read at least in a cursory way by the interested practitioner, since it identifies thecomputational differences between the two primary contending approaches.The technique most commonly used to identify hotspots involves the generation of a twodimensionallattice to represent the area of interest. As shown in Figure 2.2, a twodimensionallattice (Fig. 2.2(2)) is overlain upon a study area (Fig. 2.2(1)). This comprises aseries of (x*y) cells, each with identical proportions. The challenge of delineating a hotspotlies in the derivation of a set of values, one per cell, that reflects the intensity of crime risk ateach location. Thus, a methodology and mathematical algorithm is required that can generaterisk intensity values for every cell. One technique commonly used to do this is called the‘moving window’. Here, a circle with a predetermined radius (referred to as the bandwidth) isdrawn from the midpoint of each cell (Fig. 2.2(3)), and each of the events that falls within thecircle is used to generate the risk intensity value for that cell. The risk intensity value for eachcell is determined by the number of crime events (in Figure 2.2(1), four for the cellconsidered) that occurred within the circle and how far away they are located from themidpoint of the cell. Those closest to the midpoint are typically assigned a greater weightingthan those further away. To illustrate the method, three of the cells in Figure 2.2(3) have beenshaded to indicate the intensity of risk at those locations. Those shaded darkest exhibit thehighest risks. An example of a hotspot (quartic) function (Bailey & Gatrell, 1995) is describedby equation (1):223 ⎛ d ⎞( ) = 1 ⎟2 ⎜ −iλτs ∑(1)2di≤τπτ ⎝ τ ⎠Where, λτ(s)= risk intensity value for cell s τ = bandwidthd i = distance of each point (i) within the bandwidth from the centroid of the cell18