Abstract

The 'data deluge', coupled with related 'smart cities' initiatives, have led to a proliferation of information about how people use urban areas. These data are potentially extremely valuable for a number of domains, including crime science. This talk will argue that, because it focuses explicitly on the actions and behaviours of individuals, agent-based simulation is the most appropriate tool for the study of many criminal phenomena. The talk will present two example applications of the methodology to the study of burglary, before outlining wider applications of agent-based modelling to the study of urban dynamics more broadly.

Overview

Dynamic Simulation - Agent Based Modelling

Explanatory Agent-Based Crime Models

Predictive Agent-Based Crime Models

Predictive Policing (?) & Ethical Implications

Modelling Broader Urban Dynamics

Quantifying Ambient Populations

Dynamic Data Assimilation

Why Model Crime?

Exploring theory ('explanatory' models)

Simulation as a virtual laboratory.

Linking theory with crime patterns to test it.

Making predictions ('predictive' models)

Forecasting the impacts of social / environmental change.

Exploring aspects of current data patterns.

Why is it Difficult?

Extremely complex system:

Attributes of the environment (e.g. individual houses, pubs, etc.).

Personal characteristics of the potential offender and/or victim.

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.

Introduction to ABM

Aggregate v.s. Individual

'Traditional' modelling methods work at an aggregate level, from the top-down

E.g. Regression, spatial interaction modelling, location-allocation, etc.

Aggregate models work very well in some situations

Homogeneous individuals

Interactions not important

Very large systems (e.g. pressure-volume gas relationship)

Introduction to ABM

Aggregate v.s. Individual

But they miss some important things:

Low-level dynamics, i.e. “smoothing out” (Batty, 2005)

Interactions and emergence

Unsuitable for modelling complex systems

Emergence

One of the main attractions for ABM

"The whole is greater than the sum of its parts." (Aristotle?)

Simple rules → complex outcomes

E.g. who plans the air-conditioning in termite mounds?

Possible to prove the with simple computer programs

Conways 'Game of Life'

Better Representations of Theory?

Environmental Criminology theories emphasise importance of

Individual behaviour (offenders, victims, guardians)

Individual geographical awareness

Environmental backcloth

Better Representations of Space?

Lots of research points to importance of micro-level environment

Brantinghams' environmental backcloth

Crime at places research (e.g. Eck and Weisburd, 1995; Weisburd and Amram, 2014; Andresen et al., 2016)

Agent-Based Modelling - Appeal

Modelling complexity, non-linearity, emergence

Natural description of a system

Bridge between verbal theories and mathematical models

Produces a history of the evolution of the system

Agent-Based Modelling - Difficulties

Tendency towards minimal behavioural complexity

Stochasticity

Computationally expensive (not amenable to optimisation)

Complicated agent decisions, lots of decisions, multiple model runs

Modelling "soft" human factors

Need detailed, high-resolution, individual-level data

Individual-level data

Creating an 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.

Who else is doing this?

Small, but growing, literature

(apologies to the many who are missing!)

Birks et al. 2012, 2013;

Groff 2007a,b; Hayslett-McCall, 2008

Liu et al. (2005)

Me! (Malleson et. al ...)

A special issue of the Journal of Experimental Criminology entitled "Simulated Experiments in Criminology and Criminal Justice" (Groff and Mazerolle, 2008b)

ABM Explanatory Example (Birks 2012)

Explanatory: exploring theory

Randomly generated abstract environments

Navigation nodes (proxy for transport network)

Potential targets (houses)

ABM Explanatory Example (Birks 2012)

One type of agent: potential offenders

Behaviour is controlled by theoretical 'switches'

Rational choice perspective (decision to offend)

Routine activity theory (how they move and encounter targets)

Geometric theory of crime (how they learn about their environment)

ABM Explanatory Example (Birks 2012)

Validation against stylized facts:

Spatial crime concentration (Nearest Neighbour Index)

Repeat victimisation (Gini coefficient)

Journey to crime curve (journey to crime curve)

Results:

All theories increase accuracy of the model

Rational choice had a lower influence than the others

Simple / abstract model:

Not directly applicable to practice

But simplicity allows authors to concentrate on theoretical mechanisms

Realistic backcloth might over-complicate model (Elffers and van Baal, 2008)

Did it work?

Aggregate results

Did it work?

Halton Moor

Did it work?

Journey to Crime

ABM Predictive Example

Scenario Results

Predictive Intelligent Policing

This is not predictive policing

It could be, in the future, maybe, but not yet

This is a useful tool for exploring the crime system.

It might lead to a better understanding of:

How different behavioural characteristics (offender, victim, or guardian) influence crime outcomes

How the physical infrastructure can be organised to discourage crime

How patrol routes might be most effective

Etc.

Ethics

This is not minority report!

"if an agent-based model is good enough, can it tell me where someone will commit their next crime?"

We can't (and wouldn't want to!) predict when/where/who will commit a crime.

Academics have a role to set the boundaries on what is ethically acceptable

Dynamic simulation models have great potential, we need to make the case that they can be used responsibly

Particularly relevant in the 'big data' / 'smart cities' era (e.g. informed consent)

Data protection / privacy legislation might not be sufficient (often there are clauses for research with crime data)

Agent-Based Models of the Ambient Population

Agent-based modelling could do better?

Could combine diverse, up-to-date data with a model to create the most accurate estimate of the real world

But:

Urban systems are complex

They will diverge rapidly from model forecasts

ABM development typically uses 'waterfall' approach

We lack tools to calibrate models dynamically

Early prototype:
Ensemble Kalman Filter (EnKF)

Maths

Broad literature, but generally tied to mathematical models (e.g. differential equations and linear functions)

Working with a mathematician to do the hard work!

In all its glory: Ward et al., (2016)

Advantages

Similar to Kalman Filter (best in class)

But better for nonlinear systems

Ensemble Kalman Filter - Basic Process

1. Forecast.

Run an ensemble of models (ABMs) forward in time.

Calculate ensemble mean and variance

2. Analysis.

New 'real' data are available

Integrate these data with the model forecasts to create estimate of model parameter(s)

Impact of new observations depends on their accuracy

3. Repeat

Ensemble Kalman Filter (EnKF)

Outline

References

Andresen, M. A., Linning, S. J., and Malleson, N. (2016). Crime at Places and Spatial Concentrations: Exploring the Spatial Stability of Property Crime in Vancouver BC, 2003–2013. Journal of Quantitative Criminology, pages 1–21.

Birks, D., Townsley, M., and Stewart, A. (2012). Generative Explanations of Crime: Using Simulation to Test Criminological Theory. Criminology, 50(1):221–254

Birks, D., Townsley, M., and Stewart, A. (2013). Emergent Regularities of Interpersonal Victimization: An Agent-Based Investigation. Journal of Research in Crime and Delinquency.

Bratman, M. E., Israel, D. J., and Pollack, M. E. (1988). Plans and resource-bounded practical reasoning. Computational Intelligence, 4(3):349–355.

Dray, A., Mazerolle, L., Perez, P., and Ritter, A. (2008). Policing Australiaâ’s heroin drought: using an agent-based model to simulate alternative outcomes. Journal of Experimental Criminology, 4(3):267–287.

Eck, J. and Weisburd, D. (1995). Crime places in crime theory. In Eck, J. and Weisburd, D., editors, Crime and Place, pages 1–33. Criminal Justice Press.

Elffers, H., and P. van Baal. 2008. “Realistic Spatial Backcloth Is Not That Important in Agent Based Simulation Research: An Illustration from Simulating Perceptual Deterrence.” In Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, 19–34. Hershey, PA: Information Science Reference.

Groff, E. (2007a). Situating Simulation to Model Human Spatio Temporal Interactions: An Example Using Crime Events. Transactions in GIS, 11(4):507–530.

Groff, E. R. (2007b). Simulation for Theory Testing and Experimentation: An Example Using Routine Activity Theory and Street Robbery. Journal of Quantitative Criminology, 23(2):75–103.

Hayslett-McCall, K., Qui, F., Curtin, K. M., Chastain, B., Schubert, J., and Carver, V. (2008). The Simulation of the journey to residential burglary. In Liu, L. and Eck, J., editors, Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. Information Science Reference, Hershey, PA.

Kennedy, W. G. (2012). Modelling Human Behaviour in Agent-Based Models. In Heppenstall, A. J., Crooks, A. T., See, L. M., and Batty, M., editors, Agent-Based Models of Geographical Systems, pages 167–179. Springer Netherlands.

Liu, L., Wang, X., Eck, J., and Liang, J. (2005). Simulating crime events and crime patterns in RA/CA model. In Wang, F., editor, Geographic Information Systems and Crime Analysis, pages 197–213. Idea Publishing, Reading, PA.

Malleson, N. and A. Evans (2013) Agent-Based Models to Predict Crime at Places. In G. Bruinsma and D. Weisburd (Eds) Encyclopedia of Criminology and Criminal Justice pp 41-48 . Springer.

Malleson, N. “Agent-Based Modelling of Burglary.” School of Geography, University of Leeds, UK, 2010.

Malleson, Nick, Alison Heppenstall, and Linda See. “Crime Reduction through Simulation: An Agent-Based Model of Burglary.” Computers, Environment and Urban Systems 34, no. 3 (2010): 236–50.

Malleson, Nick, Linda See, Andrew Evans, and Alison Heppenstall. “Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars.” In Theories and Simulations of Complex Social Systems, edited by Vahid Dabbaghian and Vijay Kumar Mago, 179–204. Intelligent Systems Reference Library 52. Springer Berlin Heidelberg, 2014.

Ward, J., A. Evans, N. Malleson (2016) Dynamic calibration of agent-based models using data assimilation. Royal Society Open Science. 3:150703. (open access). [DOI: 10.1098/rsos.150703]

Weisburd, D. and Amram, S. (2014). The law of concentrations of crime at place: the case of Tel Aviv-Jaffa. Police Practice and Research, 15(2):101–114.