A COMPUTATIONAL MODEL FOR PREDICTING THE LOCATION OF CRIME ATTRACTORS ON A ROAD NETWORK

Transcription

1 August, 2012 Natalia Iwanski Modeling of Complex Social Systems Richard Frank School of Criminology Andrew Reid School of Criminology Vahid Dabbaghian Modeling of Complex Social Systems Simon Fraser University A COMPUTATIONAL MODEL FOR PREDICTING THE LOCATION OF CRIME ATTRACTORS ON A ROAD NETWORK

2 Crime Pattern Theory INTRODUCTION

3 INTRODUCTION Location of nodes impacts Peoples daily travel patterns Target selection behaviour of offenders Concentrate criminal events Patterning of offences outside the immediate surrounds of attractors

4 INTRODUCTION In previous study Correlation between route from home to crime location Great proportion of crimes were found to be close to offenders paths with a distance decay pattern Iwanski, N., Frank, R., Dabbaghian, V., Reid, A., Brantingham, P.L., Analyzing an Offender s Journey to Crime: A Criminal Movement Model (CriMM), Proc. European Intelligence and Security Informatics Conference (EISIC). Athens, Greece, 2011

5 INTRODUCTION Question of this research: If we know the home and crime locations, can we NOT assume node location and instead model them?

6 GOAL: Find key node locations that offenders travel towards and commit crimes along the way THE MODEL APPROACH: For each offender Calculate offender s home-to-crime journey Extend the offender s path beyond their crime location Are there areas where a lot of offenders converge? If so, what are at those locations?

7 Problem! No easy way to extend a route. THE MODEL

8 METHODS How to extend an offender s path to the next node? Consider direction of travel C H

9 METHODS How to extend an offender s path to the next node? Consider road preferences C H

10 METHODS How to extend an offender s path to the next node? Combine probabilities into single probability C H

11 METHODS How to extend an offender s path to the next node? Sample

12 MODEL To find a potential path for a single offender Extend path from each crime location by n nodes To find reasonable pattern for a single offender Calculate m potential paths for each offender Aggregate all paths for all offenders to see areas where travel patterns converge

13 EXPERIMENTAL EVALUATION Crime Data-Warehouse A collection of databases At Institute of Canadian Urban Research Studies (ICURS) at Simon Fraser University (SFU) Five years of real-world crime data For the Province of British Columbia (BC), Canada From Canada s federal police, the Royal Canadian Mounted Police (RCMP) Calls-for-service data between August 1, 2001 and July 31, 2006 Information about crime-events (~4.4million) and people (~9million)

14 EXPERIMENTAL EVALUATION Model applied to offenders in three major suburban cities near Vancouver, Canada Burnaby Coquitlam Port Coquitlam + [New Westminster] + [Port Moody]

15 EXPERIMENTAL EVALUATION Resulted in 7,604 offenders Each offender s path was extended 200 nodes was calculated 100 times Run in MatLab 2009a on a Linux operating system

16 EXPERIMENTAL EVALUATION Road weights not available Simulated by picking randomly a pair of points Calculate path between them using Dijkstra s Repeat 25,000 times to see aggregate pattern

17 TRAVEL FREQUENCY OF ROAD SEGMENTS

18 FREQUENCY OF NODES AND ROAD SEGMENTS

19 NODES WEIGHTED BY THE NUMBER OF OFFENDERS TRAVELLING THROUGH THEM

20 NODES WEIGHTED BY THE NUMBER OF OFFENDERS TRAVELLING THROUGH THEM

21 DISCUSSION All shopping centers were detected by the model Metrotown Center Highgate Mall Brentwood Town Center Coquitlam Center Lougheed Mall Plus Highlighted particular routes leading to attractors that drew many offenders

22 DISCUSSION This model can shift the focus of crime prevention from isolated locations to routes Place attention on highly attractive routes may help law enforcement target their resources more successfully Model has uses in urban planning, especially on deciding where new commercial areas should be placed Placing new shops or high rises along highly travelled routes to attractors may make them more vulnerable to property crime, which will in turn raise security and policing costs

23 CONCLUSIONS Model presented here uses knowledge about an offender s home and crime locations to model their travel patterns Probabilities are used based on Direction of travel Road-weights Synthetic data was used to create road weights 100 paths were created each for 7604 offenders then analyzed to see areas which attracted them

24 LIMITATIONS Need to experiment on other cities The path from home to crime is based purely on directionality, and as such, the path is extended along that trajectory as well Path from home to crime could have deviated significantly from the direction from home to crime

25 August, 2012 Natalia Iwanski Modeling of Complex Social Systems Richard Frank School of Criminology Andrew Reid School of Criminology Vahid Dabbaghian Modeling of Complex Social Systems Simon Fraser University A COMPUTATIONAL MODEL FOR PREDICTING THE LOCATION OF CRIME ATTRACTORS ON A ROAD NETWORK