Agent-Based Modelling, Ambient Populations and Models of Burglar Behaviour


Nick Malleson & Andy Evans

School of Geography University of Leeds

nickmalleson.co.uk

www.geog.leeds.ac.uk/people/a.evans

These slides: http://surf.leeds.ac.uk/presentations.html

Overview

Simulating (burglar) behaviour with agent-based modelling

Improved models of offending behaviour

Modelling everyone else...

Predictive Policing Project

Home Office / Innovation Fund supported.

Aims to implement predictive policing through apps.

Leeds running statistical tests on the various algorithms, looking at different crimes and urban (and rural) structure.

Also testing how crime theory plays out in predictability; for example, how do algorithms respond to crimes matching different theories.

To do this, probably use ABM to generate datasets in different localities and of different types, with different underlying theory.

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.

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.

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.

Agent-Based Modelling (ABM)

Create an urban (or other) environment in a computer model.

Stock it with buildings, roads, houses, etc.

Create individuals to represent offenders, victims, guardians.

Give them backgrounds and drivers.

See what happens.

ABM Example - Burglary

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Better Representations of Theory

Environmental Criminology theories emphasise importance of

Individual behaviour (offenders, victims guardians)

Individual geographical awareness

Environmental backcloth

Example of MasterMap Topographic data
Input layers into the virtual environment
PECS motives and actions

ABM Burglary Results

Malleson, N., A. Heppenstall, L. See, A. Evans (2013) Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environment and Planning B: Planning and Design 40 405-426. [DOI: 10.1068/b38057]

 

 

 

 

 

 

 

 

 

 

 

 

Did it work?

Results of model calibration

Did it work?

Results of model calibration

Behavioural Trends in Burglars

Analysis of Modus Operandi.

Interviews with prisoners.

Risk for areas of different MOs and target characteristics.

Next steps ...

Other Uses: Modeling Everyone

Quantifying the Ambient population

Daily flows of people have a significant impact on crime rates

But usually have to fall back on residential populatin for want of data

“The general patterns of movement towards and away from activity nodes such as work or school locations, major shopping areas, entertainment districts or bedroom suburbs provide a very general image of where crimes will concentrate” (Kinney et al., 2008)

We need better models of the ambient population!

Malleson, N. and M. Andresen (2015) The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science 42(2)
Hotspot disappearing from the city centre
Malleson, N. and M. Andresen (2015) The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science 42(2)
Hotspot disappearing from the city centre

Malleson, N., and Andresen, M.A. (2016) Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice 46 pp 52-63

surf

Big Data, ABM, Smart Cities, Ambient Populations

A city-wide dynamic ABM, constantly re-calibrated to streaming data

New insights into urban mobility patterns and footfall estimates.

surf.leeds.ac.uk/

Simulating Urban Flows (surf) prototype

github.com/nickmalleson/surf

Source: Improbable Spatial OS http://improbable.io/learn-more

Agent-Based Modelling, Ambient Populations and Models of Burglar Behaviour


Nick Malleson & Andy Evans

School of Geography University of Leeds

nickmalleson.co.uk

www.geog.leeds.ac.uk/people/a.evans

These slides: http://surf.leeds.ac.uk/presentations.html