4 Crime Concentration - Burglary If we look at the distribution of burglary we find that if we consider the areas most at risk, about 40% of burglaries occur in about 20% of the area. As we look at smaller areas, in this case street segments, we see that it is even more clustered.Johnson, S.D. (2010). A Brief History of the Analysis of Crime Concentration. European Journal of Applied Mathematics, 21,
6 Is Victimization Risk Time-Stable? Timing of repeat victimization Johnson, S.D., Bowers, K.J., and Hirschfield, A.F. (1997). New insights into the spatial and temporal distribution of repeat victimization. British Journal of Criminology, 37(2):
7 Explaining Repeat Victimisation Boost AccountRepeat victimisation is the work of a returning offenderOptimal foraging Theory (Johnson & Bowers, 2004) - maximising benefit, minimising risk and keeping search time to a minimum-repeat victimisation as an example of thisburglaries on the same street in short spaces of time would also be an example of thisConsider what happens in the wake of a burglaryTo what extent is risk to non-victimised homes shaped by an initial event?Black-browed albatrossJohnson, S.D., and Bowers, K.J. (2004).The Stability of Space-Time Clusters of Burglary. British Journal of Criminology, 44(1),
8 An analogy with disease Communicability Communicability - inferred from closeness in space and time of manifestations of the disease in different people.areaburglaries++++++++++++++++
9 Neighbour effects at the street level Bowers, K.J., and Johnson, S.D. (2005). Domestic burglary repeats and space-time clusters: the dimensions of risk. European Journal of Criminology, 2(1),Johnson, S.D. et al. (2007). Space-time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology, 23:
10 Patterns in detection data? For pairs of crimes:Those that occur within 100m and 14 days of each other, 76% are cleared to the same offenderThose that occur within 100m and 112 days or more of each other, only 2% are cleared to the same offenderJohnson, S.D., Summers, L., Pease, K. (2009). Offender as Forager? A Direct Test of the Boost Account of Victimization. Journal of Quantitative Criminology, 25,
11 “If this area I didn’t get caught in, I earned enough money to see me through the day then I’d go back the following day to the same place. If I was in, say, that place and it came on top, and by it came on top I mean I was seen, I was confronted, I didn’t feel right, I’d move areas straight away …” (P02)Summers, Johnson, & Rengert (2010) The Use of Maps in Offender Interviewing. In W. Bernasco (Ed.) Offenders on Offending. Willan.
12 “The police certainly see a pattern, don’t they, so even a week’s a bit too long. Basically two or three days is ideal, you just smash it and then move on … find somewhere else and then just repeat it, and then the next area …” (RC02)Summers, Johnson, & Rengert (2010) The Use of Maps in Offender Interviewing. In W. Bernasco (Ed.) Offenders on Offending. Willan.
13 Forecasting - ProMap Risk High Low Bowers, K.J., Johnson, S.D., and Pease, K. (2004). Prospective Hot-spotting: The Future of Crime Mapping? The British J. of Criminology, 44,
15 Event driven and Long-term factors (7- day forecast) Here I use only roads, but elsewhere we use sociodemographic factors of the environment and other variables.Johnson, S.D., Bowers, K.J., Birks, D. and Pease, K. (2009). Predictive Mapping of Crime by ProMap: Accuracy, Units of Analysis and the Environmental Backcloth, Weisburd, D. , W. Bernasco and G. Bruinsma (Eds) Putting Crime in its Place: Units of Analysis in Spatial Crime Research, New York: Springer.
16 Our most recent work looks at risk at the street segment level and we have shown that the risk of burglary is systematically higher on certain types of segment – types that can be identified through a pure mathematical analysis of the street network. We are developing the forecasting approach to generate street segment predictions.
17 ResourcesFielding & Jones (2012) – Disrupting the optimal forager…. Journal of Police Science and Management38% reduction in residential burglary!29% reduction in TFMV!JDi Briefs (http://www.ucl.ac.uk/jdibrief/analysis)POP guide (http://www.popcenter.org/tools/repeat_victimization/)Vigilance Modeller (https://www.vigilancemodeller.net/)Risk Terrain Modelling (http://www.rutgerscps.org/rtm/)