Analy zing an Offender’s Journey to Crime Using a Criminal Movement Model

1. Analyzing an Offender’s Journey to CrimeUsing a Criminal Movement Model Presenters Andre Norton Karen Lancaster-Ellis

2. What is the main goal? The main goal of this study is to model the movement of offenders journey to crime in order to investigate the relationship between their road network travel route and the actual locations of their crimes in the same geographic space.

3. Brief Historical Overview Trinidad– 1864 sq. m Tobago – 116 sq. m Est. Population-1.3m Ethnicity – Cosmopolitan Religions – Predominantly Muslim, Hindu & Christian Politics- Democracy Economy- Petro-Chemical sector Police Stations & Posts - 77 Strength – Approx. 6,500

4. Key Words

5. Background Crime Triangle Overlapping “Activity Space” Adapted from Clarke & Eck (2003)

6. Previous Research • Consensus across Academic community – Journey to crime research • Offender don’t tend to travel to far to commit crime • Distance decay pattern • Major weakness discovered • Sole focus on distance to crime • Distance in isolation (ambiguous results) • Shortcoming Addressed • Our research addresses all 3 dimensions • Starting point • Direction travelled

7. Introduction Research Title • Environmental Criminology (Brantingham & Brantingham, 1990) • Criminology & Computer Science • Crime Pattern Theory • Dijkstra’s Algorithm (Edgar Dijkstra, 1956)

8. Crime Pattern Theory • Criminals – preferred areas to commit crimes • Criminal events likely to occur • Activity space of offenders overlap with activity space of victims • Activity space of target is simply its location (Brantingham & Brantingham, 1990, Felson & Clarke,1998) • Theory- The social intervention level • Nature & Immediate situations in which crime occurs

9. Dijkstra’s Algorithm G (V,E)

10. Some uses of Dijkstra’sAlgorithm ? Urban traffic planning; Optimal pipelining of VLSI chip; Telemarketer operator scheduling; Routing of telecommunications messages; Network routing protocols (OSPF, BGP, RIP); Optimal truck routing through given traffic.

11. Offender & Victim Activity Space example Vertex / Node A – B – C – D = 28 A – F – E - D = 29 A – B – D = 22 A – C – F – E - D = 26 A – C – D = 20

12. Study Area • Four Attractor Locations – Southern Division • Gulf City Mall- (largest shopping mall in City) • Teddy’s Shopping Center • Space La Nouba (Social Night Club) • Pizza Hut Food Complex *** See depiction on next slide

13. Study Area Entertainment Activity Space Buffer Zone Home Awareness Space Recreation Recreation

14. Methodology Data Sets- 2006-2010 crime data (Serious Crimes) • Anonymised crime reference number (GO) • Offence type (robbery, vehicle theft) • Anonymised geographic co-ordinates (crime location & offender residence) • Offender’s name, date of birth, home address • Date, time and location (each offence committed) • Estimated monetary value of goods stolen

15. Methodology Data Sets- 2006-2010 crime data (Serious Crimes) Defaults to text file format (.txt) Converted to .csv & imported into Microsoft Excel Manual pre-processing to improve data quality Data Integrity- Significant number of offenders recorded multiple addresses (This caused some issues) Home node – Single address Distinct offender addresses – Table A

16. Methodology Table A • Weaknesses • Present in virtually all published studies that use data of a similar source or nature • Analyses – Directly comparable with previous work (extends)

17. Dijkstra’s Model Requirements INPUTS • Spatial data – road network connecting attractors • Distance Measures(length of road networks) • Temporal data- Estimated travel time (impactors) • Encoded into adjacency Matrices (plot network) • Crime address- Where committed • Offender address- Home address • Attractor location- towards which offenders travel

18. Assumptions

19. Dijkatra’s Pseudocode Function Dijkstra’s (Graph, source) for each vertex v in Graph //Initialization dist[v] := infinity //Initial distance from source to vertex v is set to infinity previous[v] := undefined //Previous node in optimal path from source dist[source] := 0 //Distance from source to source Q := the set of all node in the Graph // All nodes in the graph are unoptimized thus are in Q While Q is not empty: //Main loop u := node in Q with smallest dist[] remove u from Q for each neighbor v of u: //Where v has not been removed from Q alt := dist[u] + dist_between(u,v) if alt < dist[v] //Relax (u,v) dist[v] := alt previous[v] := u return previous

20. Experimentation • Octave program – High level programming language • Inputs imported into Octave (Matlab compatible) • Model run based on inputs (1hrs) • Dijkstra’s Algorithm – Time efficiency • worst-case running time O (n2) – Input size • Model took 2.5hrs with Dijkstra’s shortest path calculations

21. Results

22. Impact & Implications • Situational Crime Prevention • Policy Formulation • Evidence Based Approach (Sherman, 1998) • Predictive Policing – Data Mining & Big Data solutions

23. Conclusions & Future Research Priorities Test bed for geographic profiling of volume crime; Robbery offences (include victim’s JTC in model); Increase the number of offences being analyzed; Eliminate assumption of home node start point; Increase the geographical size of the analyzed study area.

24. Acknowledgement

25. THE END Andre.Norton@ttps.gov.tt (1-868-469-8523 / 489-4556) Karen.Lancaster-Ellis@ttps.gov.tt (1-868-2950 / 489-5382)