Target choiceOccupied properties are not burgled. Targets probabilistically picked weighted on ease, area attractiveness, and desperation (more desperate burglars worry less about being recognised close to home). Some weights calibrated. All burglaries are successful. Burgled properties and their neighbours increase in attractiveness for some period, however, security also rises for some (usually lesser) period (reflects recent findings on repeat victimisation).
Agent-based modelling of burglary
Andy Evans Nick Malleson Alison Heppenstall Linda See Mark Birkin Centre for Applied Spatial Analysis and Policy University of Leeds a.j.evans@leeds.ac.uk
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Background
Ongoing collaboration with SaferLeeds [local police/government crime prevention partnership]. Builds on work using microsimulation and gravity modelling to look at offender-to-target burglary flows.
You can find out more about the early work in: Kongmuang, C., Clarke, G.P., Evans, A.J. and Ballas, D. (2005) Modelling Crime Victimisation at Small-Area Level Using a Spatial Microsimulation Technique, Proceedings of the RSAIBIS 35th Annual Conference, 17th-19th August 2005 Kongmuang, C., Clarke, G.P., Evans, A.J. (2005) A Spatial Microsimulation Approach to Modelling Crime Proceedings of the British Society of Criminology Conference 2005, Leeds, UK, 12th-14th July 2005. Or, most easily: Kongmuang, C., Clarke, G.P., Evans, A.J. and Jin, J. (2006) SimCrime: A Spatial Microsimulation Model for the Analysing of Crime in Leeds. Working Paper. The School of Geography, University of Leeds http://eprints.whiterose.ac.uk/4982/ Malleson, N., A.J. Evans, and T. Jenkins (2009). An agent-based model of burglary. Environment and Planning B: Planning and Design 36, 1103–1123. http://www.envplan.com/abstract.cgi?id=b35071 ‹#›
Why burglary?
Spatially patterned therefore predictable(?) Spatio-temporally variations key to understanding system. System with history of qualitative theorisation that needs testing. Data good: high numbers of reported crimes and large numbers of convicted offenders. We also have some psychological surveys from local prisons. Largely individually initiated in UK therefore don’t need so much data-poor social interaction modelling. Should be possible to run “what if” tests (specifically, urban regeneration in Leeds). Significant component of fear of crime in UK.
Basic model
Real geographical environment (S.E. Leeds, UK) Offenders allocated homes and daily routines. Victims communities allocated from census. Offenders have drives including income generation. One way to raise income is burglary. They identify target locations, then search for appropriate and appealing houses.
Environment
Roads and public transport Ordnance Survey data House/garden geometry Ordnance Survey data Community strength and demographics Indices of deprivation / census data Building type National Land Use Database division into broad types, including commercial / social locations Drug dealer locations Real, but randomised within postcode area MasterMap Topographic Area Layer
Victims
Basic model: Houses take on demographic characteristics from their census Output Areas (~100 households in each area). Community demographics include economic variables like careers, levels of unemployment, retirement, etc. Also include probabilistic assessments of occupancy for houses in the area at different times of day (based on numbers of employed, unemployed, retired, and students and lifestyle of these groups over the course of a day). Current work: Victims individually microsimulated from census and British Household Panel Survey.
For more information on the microsimulation work, see: Malleson, N. and Birkin, M. (2011). Towards victim-oriented crime modelling in a social science e-infrastructure. Philosophical Transactions of the Royal Society A 369(1949) 3353-3371. http://rsta.royalsocietypublishing.org/content/369/1949/3353.full ‹#›
Offenders
Locations Real offender numbers allocated randomly to households in their real postcodes. Characteristics Offenders allocated employment based on their local characteristics. Work location (if any) chosen from appropriate properties randomly (area of interest reasonably compact – this could be improved with distance to work statistics). Drug supplier and socialisation space allocated randomly (socialisation biased on distance from home).
Offender drivers
Allows connection of drivers, decision-making/reaction, and behaviour. Drivers: Sleep: 8 hours a day, with desire varying on a diurnal cycle. Drugs: desire varying on a diurnal cycle. Socialisation: desire increasing in evening. Work: travel to and from depending on employment. Character introduced through variation in driver intensity changes, weights of drivers, and behavioural responses. Offenders modelled using the PECS framework (Schmidt,Urban) [Physical conditions; Emotional states; Cognitive capabilities; Social status].
Schmidt, B. (2000). The Modelling of Human Behaviour. Erlangen, Germany: SCS Publications. Schmidt, B. (2002). How to give agents a personality. In Proceedings of the 3rd Workshop on Agent- Based Simulation, April 7-9, Passau, Germany. Urban, C. (2000). PECS: A reference model for the simulation of multi-agent systems. In R. Suleiman, K. G. Troitzsch, and N. Gilbert (Eds.), Tools and Techniques for Social Science Simulation, Chapter 6, pp. 83–114. Physica-Verlag. ‹#›
Offender drivers
Cash for drug use a near ubiquitous driver in Leeds for burglary (not necessarily true elsewhere) (also socialising). In general, offenders don’t earn enough legitimately to maintain drug consumption. Wealth decays throughout the day (replicating food and housing needs). Income then topped up with burglary. Income from a burglary set to be sufficient for drug consumption and socialisation for a single day.
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Offender decision making
Offenders are reactive to their most significant driver (they don’t weigh drivers up). However, they are then deliberative in finding targets, based on partially-informed rational decisions. Based on Rational Choice Perspective (Clarke and Cornish).
Clarke, R. V. and D. B. Cornish (1985). Modeling offenders’ decisions: a framework for research and policy. Crime and Justice 6, 147–185. ‹#›
Offender behaviour
Offenders identify a community that will contain targets. They do this based on areas they know and area attractiveness. Their “awareness space” is built up during their daily routines visiting “anchor points” associated with work, socialisation and drug buying (based on Brantingham and Brantingham’s Geometric Theory of Crime). They then travel to this area by the shortest distance route, searching as they go for easy targets. They take larger risks on targets, the more desperate their drivers. If they don’t find anywhere in a given time, they pick a new area.
Brantingham, P. L. and P. Brantingham (1981). Notes of the geometry of crime. In P. Brantingham and P. Brantingham (Eds.), Environmental Criminology, pp. 27–54. Prospect Heights, IL: Waveland Press. Brantingham, P. L. and P. J. Brantingham (1981). Mobility, notoriety, and crime: A study in the crime patterns of urban nodal points. Journal of Environmental Systems 11(1), 89–99. Brantingham, P. L. and P. Brantingham (1993). Environment, routine, and situation: Toward a pattern theory of crime. In R. Clarke and M. Felson (Eds.), Routine Activity and Rational Choice, Volume 5 of Advances in Criminological Theory. New Brunswick, NJ: Transaction Publishers. Brantingham, P. L. and P. J. Brantingham (2008). Crime pattern theory. In R. Wortley and L. Mazerolle (Eds.), Environmental Criminology and Crime Analysis, Crime Science Series. UK: Willan Publishing. ‹#›
Source: Hamilton-Smith and Kent, 2005 Choosing victim areas and houses
Hamilton-Smith,N. and Kent, A. (2005). The Prevention of domestic burglary, in Tilley, N, (Ed) Handbook of Crime Prevention and Community Safety. Devon: Willan, pp 417-457 ‹#›
Area attractiveness
Act as “optimal foragers”. Pick a community areas to visit. Attraction: Wealth disparity Nearness to home Comfort (closeness in socio-economic variable space). Number of previously successful burglaries in area. Weights of these are calibrated.
Route to area
Shortest on weighted vector network constructed from road map. Different travel options assigned (walk, public, car). Search on way, then bullseye out from a house picked in the community area. Search shape tear-drop if away from home, bulls-eye around home.
Target ease
Collective efficacy: Calculated from deprivation and demographic variation. Traffic volume: Calculated using traffic estimates and space syntax. Accessibility: Calculated using property free walls (window/door proxy). Occupancy likelihood: Estimated from community demographics. Visibility: Estimated from garden dimensions and house arrangement. Security: Applied manually from stakeholder discussions. Based on Rational Choice Perspective (Clarke and Cornish) and Routine Activities Theory (Cohen and Felson).
Clarke, R. V. and D. B. Cornish (1985). Modeling offenders’ decisions: a framework for research and policy. Crime and Justice 6, 147–185. Cohen, L. and M. Felson (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review 44, 588–608. ‹#›
Target choice
Occupied properties are not burgled. Targets probabilistically picked weighted on ease, area attractiveness, and desperation (more desperate burglars worry less about being recognised close to home). Some weights calibrated. All burglaries are successful. Burgled properties and their neighbours increase in attractiveness for some period, however, security also rises for some (usually lesser) period (reflects recent findings on repeat victimisation).
Model runs
Model runs on minute time steps. 273 offenders, total population of 30000 households Model run until some set time (usually we check that the system has reached equilibrium and the pattern of new crimes is not varying spatially). NB: The model is not a socio-economic model; it does not predict absolute crime numbers, just spatial distribution. Model runs from starting data; no dynamic data. However, model environment and victim population can be altered during run, in which case run until no change in relative distributions of crime locations.
Model technology
RePast based model. Run multiple (50-100) times for probabilistic and/or parameter sweeps. Run in “lazy parallel” on grid facility (UK e-Science National Grid Service) i.e. whole model runs on a single processor, with multiple processors running a full model each. Run times: 30 simulated days ~20hrs on a standard desktop.
For more information on the system, see: Malleson, N. and Birkin, M. (2011). Towards victim-oriented crime modelling in a social science e-infrastructure. Philosophical Transactions of the Royal Society A 369(1949) 3353-3371. http://rsta.royalsocietypublishing.org/content/369/1949/3353.full ‹#›
Model verification
Behaviour tested alone where possible and in model within various environments: Aspatial environments Abstract environments Full environment
For more information, see: Malleson, N., A. Heppenstall and L. See (2010). Crime reduction through simulation: An agent-based model of burglary. Computers, Environment and Urban Systems 31(3) 236-250. http://dx.doi.org/10.1016/j.compenvurbsys.2009.10.005 ‹#›
Model calibration
We have an intelligent idea of many variables from literature and stakeholders. Calibration of rest by hand to 2001 data, checking against known 2001 crimes. Did try Genetic Algorithm calibration early on, but impractical for full model.
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