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Crime Research in Geography Resource allocation: we helped design - - PowerPoint PPT Presentation
Crime Research in Geography Resource allocation: we helped design - - PowerPoint PPT Presentation
Crime Research in Geography Resource allocation: we helped design the police Basic Command Unit families for the national resource allocation formula in the 1990s. More recently been advising West Yorkshire police. Predictive policing:
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Project
Modelling burglary in Leeds. Ongoing relationship with Safer Leeds Crime and Disorder Reduction Partnership Provide essential data. Expert knowledge to supplement criminology theory.
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Why Model?
Exploring theory (‘explanatory’ models) Simulation as a virtual laboratory: Linking theory with crime patterns to test it. Making predictions (‘predictive’ models) Forecasting social / environmental change. Exploring aspects of current data patterns through prediction.
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Why burglary?
Spatially patterned therefore predictable(?) Spatio-temporally variations key to understanding system. System with history of qualitative theorisation that needs testing. Data good (geocoding, reporting). 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.
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Why difficult?
Extremely complex system: Attributes of the individual houses. Personal characteristics of the potential offender. Features of the local community. Physical layout of the neighbourhood. Potential offender’s knowledge of the environment. Traditional approaches often work at large scales, struggle to predict local effects “Computationally convenient”. But cannot capture non-linear, complex systems.
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Individual-level Crime Modelling: Agent-Based Models (ABM)
Create an urban (or other) environment in a computer model. Stock it with buildings, roads, houses, etc. Create individuals to represent
- ffenders, victims, guardians.
Give them backgrounds and drivers. See what happens.
Much better understanding of relationship between:
Individuals (offenders, victims, and guardians). Their routines. Street-level environment. Perceptions of urban areas.
Inherently spatial and dynamic.
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Coding an Agent
Agent Class Has an update method called each iteration, eg. move(), trade(). Has a position. Has a list of all other agents and can get their position. Can communicate with other agents if necessary. Environment Class Has environmental conditions. Calls the agents to update. Agents might, for example, trade with their nearest neighbours.
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Agent-Based Modelling
Autonomous, interacting agents Represent individuals or groups Situated in a virtual environment
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Commonly Used Platforms
Netlogo: http://ccl.northwestern.edu/netlogo/ Repast: http://repast.sourceforge.net/ MASON: http://cs.gmu.edu/~eclab/projects/mason/ Ascape: http://ascape.sourceforge.net/ ABLE: http://www.research.ibm.com/able/ Agent Analyst: http://www.spatial.redlands.edu/agentanalyst/
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NetLogo basics
Two windows: Interface and Procedures Interface contains graphical elements Procedures are user-defined functions
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Better Representations of Theory
Environmental Criminology theories emphasise importance of
Individual behaviour (offenders, victims guardians) Individual geographical awareness Environmental backcloth
Routine Activity Theory
Crime G u a r d i a n Victim O f f e n d e r
Geometric Theory
- f Crime
Rational Choice Perspective
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Examples of Agent-Based Crime Models
Abstract Environment – Predictive
Birks et al. (2012)
Residential burglary Simple behaviour Switch on/off theoretical components Model dynamics reflect expected (theoretical)
- utcomes?
Spatially Realistic Environment – Explanatory
Groff (2007)
Street robbery Interactions of victims and
- ffenders
Simple behaviour Highlight high-crime intersections
Nick & Andy
Residential burglary GIS data Advanced (?) behavioural model
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Example 1 –Burglary (Explanatory)
Birks at al. (2012) Randomly generated environments Theoretical ‘switches’ Compare results to expected
- utcomes:
Spatial crime concentration Repeat victimisation Journey to crime curve
Results:
All hypotheses are supported Rational choice has lower influence
Theory Enabled Disabled Routine activities Agents assigned a ‘home’ and routine paths Random movements Rational choice Victim attractiveness (based
- n risk, reward, effort)
Homogenous target attractiveness Awareness space Dynamic awareness – alters
- ffender decision-making
Uniform environment awareness
Figure 1. Example Model Environment
Taken from Birks et al. (2012)
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Example 2 – Street Robbery (Predictive)
Groff (2007)
Street robbery in Seattle Interactions of victims and
- ffenders
Simple behaviour Highlight high-crime intersections
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Example 3 – Burglary (Predictive)
Virtual Environment
Physical objects: houses, roads, bars, busses etc. Social attributes: “communities” Virtual victims and guardians
Virtual Burglar Agents
Use criminology theories/findings to build realistic agent behaviour PECS
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Modelling Behaviour
Agents’ Burglary Decision Process
- 1. Attractiveness
- 2. Social difference
- 3. Previous successes
- 4. Distance
- 1. Collective Efficacy (community)
- 2. Occumpancy levels (community)
- 3. Accessibility
- 4. Visibility
- 5. Security
- 6. Traffic volume (road)
- 1. PECS Behaviour -> Decision to Burgle
- 2. Choose community to search
- 3. Travel to community and search
- 4. Choose property to burgle
Agent’s Thought Process Communities in the Agent’s Cognitive Map Objects in the Environment
PECS Framework Needs
“Lifestyle”, Sleep, Drugs
Cognitive map of environment Decision process leads to burglary
Drug level Sleep level Social level Intensity of drugs motive Intensity of social motive Intensity of sleep motive
m = p / s m = p f(t) / s m = p f(t) / s Time of day, t Personal preference, p Personal preference, p Personal preference, p Determine Strongest Motive Plan Actions Time of day, t SLIDE 22
Simulation Video
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Interesting Finding – Halton Moor
Result
Halton Moor area significantly under predicted by model
Explanation
Motivations of burglars in Halton Moor
Model failures can help to indicate where we misunderstand the real world
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Result – Forecasting Burglary after Urban Regeneration
Simulation
Test the effects of a large urban regeneration scheme A small number of individual houses were identified as having substantially raised risk
Why?
Location on main road In the awareness space of
- ffenders
Slightly more physically vulnerable
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How much realism?
Abstract Environment Tractable simulations, better able to understand fundamental rules Explore theory unencumbered by geographical complexity (e.g. Elffers & Baal, 2008) Not applicable to the real world? GIS Environment More accurate representation
- f the real world
Forecasts / predictions / scenarios Simulations are no easier to understand than the real system, and less accurate ?
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Who else is doing crime simulation?
Researchers: Elizabeth Groff: street robbery Daniel Birks: burglary Patricia Brantingham et al.: Mastermind (exploring theory) Lin Liu, John Eck, J Liang, Xuguang Wang: cellular automata Books / Journals: Artificial Crime Analysis Systems (Liu and Eck, 2008) Special issue of the Journal of Experimental Criminology (2008):``Simulated Experiments in Criminology and Criminal Justice'
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Future
Dynamic data We are currently looking at mining twitter feeds for population numbers around the city, and travel routes. More socio-economic data coming online all the time. Utilise this dynamically to dampen errors. Ethical issues Currently anonymize and randomise real offender data. Could we imagine a day when resources were directed to predictions of real people? Up to us to take a lead on what we do and don’t find acceptable.
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