Kate J. Bowers

The Burglary as Clue to the Future The Beginnings of Prospective Hot-Spotting

Predicting when and where crimes are likely to occur is crucial for prioritizing police resources. Prior victimization is an excellent predictor of risk. Repeat victimization, when it occurs, tends to occur swiftly after an initial incident. The predictive power of prior victimization is greater than that of other analysed variables (see Budd 1999). Self-evidently, prior victimization yields no prediction about properties as yet unvictimized. This article, using data about the crime of domestic burglary, contends that research should seek to realize the predictive potential to be gained from both pre-and post-victimization factors. One of the advantages of crime reduction through the prevention of repeats is that it offers a constant (and, it is hoped, declining) rate of events that trigger preventive action, and hence a natural pace for preventive work. In that spirit, postvictimization prevention should, as well as targeting the victimized home, also protect other properties that are similar with respect to the dimensions used by burglars in target selection. The central purpose of the research here reported is to identify the ways in which it is prudent to allocate crime reduction resources in the wake of an offence and across time and location relative to the burgled home. We analysed police-recorded crime burglary data for the county of Merseyside. Using statistical techniques developed to study the transmission of disease, we first confirmed that burglaries do cluster in space and time. The operational payoff of this result is that a residential burglary flags the elevated risk of further residential burglaries in the near future (1-2 months) and in close proximity (up to 300-400 metres) to the victimized home. Put simply, the burglary event should trigger preventive action that is not restricted to the burgled home. The data enable prospective burglary hot-spotting.

Measuring the Geographical Displacement and Diffusion of Benefit Effects of Crime Prevention Activity

The displacement of crime is an important criminological phenomenon. However, while there has been theoretical discussion of this issue in the research literature, there has been little in the way of either standardized empirical work that investigates the incidence of displacement or in the development of techniques that can be used to measure it. In the current paper we discuss a new technique, the weighted displacement quotient (WDQ), that was developed to measure the geographical displacement of crime. A critical feature of the rationale is that displacement can only be attributed to crime prevention activity if crime is reduced in the target area considered. Thus, the WDQ not only measures what occurs in a buffer (displacement) zone but also relates changes in this area to those in the target area. Part of the appeal of the measure is that it can be used either with aggregate or disaggregate crime data and for any geographical boundary selected, provided the appropriate data are available. In addition to detecting displacement, when detailed data are available, the technique can also be used to identify where the effect was most prominent. The WDQ can equally be used to measure the diffusion of benefit of any crime prevention activity. A series of examples are presented for illustration purposes.

Domestic Burglary Repeats and Space-Time Clusters: The Dimensions of Risk

Predicting when and where crimes are likely to occur is crucial for prioritizing police resources. In a companion paper (Johnson and Bowers 2004), we showed that burglaries clustered within 1-2 months and up to 300-400 metres of a prior burglary, i.e. a domestic burglary could profitably trigger time- and space-limited resource deployment around the burglary. Research is here presented which refines the priorities that should be given in such deployment. It demonstrates that (a) whereas repeat victimization proper tended to occur in more deprived areas, space-time clustering was more evident in affluent areas, (b) houses next to a burgled home were at a substantially heightened risk relative to those located further away, particularly within one week of an initial burglary, (c) properties located on the same side of the street as a burgled house were at significantly greater risk compared with those opposite, even when corrections are made for differences in linear distances between homes, (d) houses with probably identical layouts (e.g. houses two, four, six, etc., doors away from a burgled property) were slightly more at risk than those with the reverse layout, but these differences are too slight to inform crime reduction practice. Taken together, these patterns may be used to prioritize attention within the two months after, and up to 400 metres from, a prior burglary identified by Johnson and Bowers (2004) as encompassing homes at elevated burglary risk.

Exploring links between crime and disadvantage in north-west England: an analysis using geographical information systems

This paper reports some of the findings from a two-year study into crime and disadvantage on Merseyside in north-west England. Particular attention is paid to how a GIS has been used in conjunction with crime pattern analysis software to explore relations between crime and the distribution of different types of disadvantaged, middle income and affluent residential neighbourhood. The GIS has also been used to examine crime incidents in relation to the distribution of residential properties, community facilities, administrative boundaries and the street network. Discussion is focused on the utility of combining disaggregate information with aggregate statistics in crime pattern analysis.

Mapping and Analysing Crime Data: Lessons from Research and Practice

1. Introduction Part I: Crime Mapping and Research 2. Methods for Automating the Geographical Analysis of Crime Incident Data 3. GIS and the Journey to Crime: An Analysis of Patterns in South Yorkshire 4. Crime, Repeat Victimisation and GIS Part II: Local Authority Applications 5. Combating Crime Through Partnership: Examples of Crime and Disorder Mapping Solutions in London, UK 6. A GIS-linked Database for Monitoring Repeat Domestic Burglary Part III: GIS in the Police and Emergency Services 7. Mapping Out Hazardous Space for Police Work 8. GIS for Spatial Analysis of Fire Incidence: Identification of Social, Economic and Environmental Risk Indicators Part IV: International Perspectives 9. Tools in the Spatial Analysis of Crime 10. The Evolution of Crime Mapping in the United States: From the Descriptive to the Analytic Part V: Practical Considerations: What Can We Expect of GIS? 11. What to do About it?: Lets turn off our minds and GIS 12. Decision Support in Crime Prevention: Data Analysis, Policy Evaluation and GIS Applications GIS in the police and Emergency Services International Perspectives Future Directions

A comparison of methods for temporal analysis of aoristic crime

ObjectivesTo test the accuracy of various methods previously proposed (and one new method) to estimate offence times where the actual time of the event is not known.MethodsFor 303 thefts of pedal cycles from railway stations, the actual offence time was determined from closed-circuit television and the resulting temporal distribution compared against commonly-used estimated distributions using circular statistics and analysis of residuals.ResultsAoristic analysis and allocation of a random time to each offence allow accurate estimation of peak offence times. Commonly-used deterministic methods were found to be inaccurate and to produce misleading results.ConclusionsIt is important that analysts use the most accurate methods for temporal distribution approximation to ensure any resource decisions made on the basis of peak times are reliable.

Predictive Mapping of Crime by ProMap: Accuracy, Units of Analysis, and the Environmental Backcloth

This chapter concerns the forecasting of crime locations using burglary as an example. An overview of research concerned with when and where burglaries occur is provided, with an initial focus on patterns of risk at the individual household level. Of central importance is evidence that as well as being geographically concentrated (at a range of geographic scales), burglary clusters in space and time more than would be expected if patterns of crime were simply the result of some places being more attractive to offenders than others. One theoretical framework regarding offender spatial decision making is discussed and consideration given to how features of the urban environment which affect the accessibility of places (e.g., road networks or social barriers) might shape patterns of offending. A simple mathematical model informed by the research discussed is then presented and tested as to its accuracy in the prediction of burglary locations. The model is tested against chance expectation and popular methods of crime hot-spotting extant and found to outperform both. Consideration of the importance of different units of analysis is a recurrent theme throughout the chapter, whether this concerns the intended policy purpose of crime forecasts made, the spatial resolution of different types of data analyzed, or the attention given to the dimension of time – a unit of analysis often overlooked in this type of work. The chapter concludes with a discussion of means of developing the approach described, combining it with others, and using it, inter alia, to optimize police patrol routes.

Theft in Price-Volatile Markets: On the Relationship between Copper Price and Copper Theft

Recently, against a backdrop of general reductions in acquisitive crime, increases have been observed in the frequency of metal theft offences. This is generally attributed to increases in metal prices in response to global demand exceeding supply. The main objective of this article was to examine the relationship between the price of copper and levels of copper theft, focusing specifically on copper cable theft from the British railway network. Results indicated a significant positive correlation between lagged increases in copper price and copper cable theft. No support was found for rival hypotheses concerning U.K. unemployment levels and the general popularity of theft as crime type. An ancillary aim was to explore offender modus operandi over time, which is discussed in terms of its implications for preventing copper cable theft. The authors finish with a discussion of theft of other commodities in price-volatile markets.

A Stab in the Dark?: A Research Note on Temporal Patterns of Street Robbery

Objectives: Test the influence of darkness in the street robbery crime event alongside temperature. Methods: Negative binomial regression models tested darkness and temperature as predictors of street robbery. Units of analysis were four 6-hr time intervals in two U.K. study areas that have different levels of darkness and variations of temperature throughout the year. Results: Darkness is a key factor related to robbery events in both study areas. Traversing from full daylight to full darkness increased the predicted volume of robbery by a multiple of 2.6 in London and 1.2 in Glasgow. Temperature was significant only in the London study area. Interaction terms did not enhance the predictive power of the models. Conclusion: Darkness is an important driving factor in seasonal variation of street robbery. A further implication of the research is that time of the day patterns are crucial to understanding seasonal trends in crime data.

Crime on bus routes: an evaluation of a safer travel initiative

This paper reports the main findings of an evaluation of an intensive four‐week policing operation along a single bus corridor, aimed at reducing the extent of crime along the bus route. The evaluation, which adopts a mixture of quantitative evaluation techniques, demonstrates that the operation was successful both in increasing officer arrest rates (up to four times for the officers who worked on the scheme), and also in reducing crime levels for particular crime types, namely assault and theft from vehicle, up to 400m from the route. A conceptual discussion is provided as to how to measure the effectiveness of an operation with no geographically predefined action area and to define the relationship between action areas and displacement or diffusion zones. Consequently, this evaluation examines both the influence of the scheme within a predefined distance from the route, and also proposes a method for determining the likely range of influence of the scheme in terms of physical distance.