A NALYZING AN O FFENDER ’ S J OURNEY TO C RIME U SING A C RIMINAL M OVEMENT M ODEL Presenters Andre Norton Karen Lancaster-Ellis

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.

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 Brief Historical Overview Police Stations & Posts - 77 Strength – Approx. 6,500

Key Words KEY WORDMEANING NodeHome, Recreation, Entertainment, Work (locations). Activity Space An area familiar to an individual through his/her through everyday activities such as where they live, work, commutes or goes shopping; Crime Attractor A location that attracts offenders because of its known opportunity for crime; Crime Generator A location which attracts a large amount of people without and which presents opportunities to commit crimes (e.g. a shopping mall); Awareness Space Route individuals traverse to and from typical locations of activity in their daily lives; Self-Containment Index The percentage of crimes in an area that is committed by offenders who live in the area;

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

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

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

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

Dijkstra’s Algorithm G (V,E)

Some uses of Dijkstra’s Algorithm ? 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.

Offender & Victim Activity Space example Computer Science- Graph TheoryEnvironmental Criminology 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

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 Study Area

Home Activity Space Recreation Awareness Space Entertainment Study Area Buffer Zone

Data Sets 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 Methodology

Data Sets 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 Methodology

Crime TypeCrime Trips OffendersOffender Addresses Robbery Larceny Motor Vehicle 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)

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

Assumptions ScenarioAssumption Shorter distance from crime location to attractor than from home; Offender travelling in direction of attractor; Crime location in direction of specific attractor; Attractor location coded as offender destination; Crime location between attractors; Closest attractor assigned as potential crime location;

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

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 (n 2 ) – Input size  Model took 2.5hrs with Dijkstra’s shortest path calculations

Results Analysis (Crimes) Attractor Locations Crimes80% Activity Space Crimes20% Model generated paths55% simulated the offender crime location Self-Containment Index90% Euclidean Measure30% offenders used Dijkstra’s Measure70% Offenders used shortest Path Predictive Analytics25% (approximation)

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

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.

Acknowledgement

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