IDENTIFYING REPEAT VICTIMIZATION WITH GIS

Abstract

Until recently most attempts at identifying repeat victimization locations have focused on searching address fields in police records. Problems with inaccurate data entry and variation in addressformat make this method fraught with difficulty and time consuming to correct. This study of burglary suggests that a standard GIS package, searching geo-referenced crime locations can dramatically improve the time and accuracy of identifying repeats. The research presented here appears generally consistent with other published work in that the period of highest risk is immediately after an initial burglary. The study covers a longer period than usual. Two years' worth of data raises issues regarding definitions of repeat victimization. The benefit to crime prevention of identifying repeat victimization has been widely recognized (Anderson etal. 1995; Ellingworth etal. 1995; Farrell and Pease 1993), but the process of accurately distinguishing the repeat locations has always been difficult. While the under-reporting of crime to the police is also well documented (Hough and Lewis 1989; Mayhew et al. 1993; Tilley 1995), it remains a reality that police recorded crime data are still one of the best sources of information on local crime distribution in this country. Computerized systems for recording police crime data have been setup within forces but usually the extraction of data pertinent to the geographical crime distribution and the identification of repeat victims is not a priority. A number of articles highlight the difficulties posed by police data in identification of repeat victims (Read and Oldfield 1995; Sampson and Phillips 1995). Crime data tend to be recorded for statistical measurement, and not specifically designed for the identification of repeat victimizations (Ellingworth et al 1995). A graphic example of the difficulties posed by police crime recording systems is provided by David Anderson ( 1995). The study was geographical only in confining the search parameters to a certain police divisional area, and the identification of repeat victims to home addresses. These examples all show the problems of trying to identify a unique location from a number of text fields within a crime data system. If search criteria are tied to beat boundaries and other forms of containment then the problems of identifying repeats increase. Address complexity increases the geo-referencing problem. For example, Nottinghamshire Constabulary's Crime Recording Interim System (CRIS) has separate fields for building name, building number, floor number, sub unit number, sub unit name, sub street name, street name. With such a variety of options it is hardly surprising that different operators on different shifts occasionally record a complex address in fields different from those of their colleagues. Nottinghamshire Constabulary has attempted to get round this problem by improving the address field data entry dialog on CRIS. The system will only accept addresses that it recognizes, and then places the