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Crime Pattern Theory Expanding our Understanding of Crime Using New Computational Strategies

Institute for Canadian Urban Research Studies


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Crime : Pathology or Normality? • Most criminology focuses primarily on the criminal offender. • Most criminology assumes that crime is produced by some pathology in or around the criminal. • Most criminology assumes criminals behave pathologically nearly all of the time. • Most criminology assumes that crime can only be reduced by curing a criminal’s pathology.

Institute for Canadian Urban Research Studies


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ELEMENTS OF THE CRIMINAL EVENT LAW OFFENDER

TARGETS

CRIMINAL EVENT

SITES

TECHNIQUE

SITUATION

Institute for Canadian Urban Research Studies


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Crime Pattern Theory Crime and Normal Behavior • Because the criminal offender is only one element of the criminal event, it is possible to reduce crime by understanding and changing any of the other elements necessary for commission of the crime. • Most crime is a by-product of normal, legal behavior. • Understanding the patterns in normal behavior can explain the patterns in most crime. Institute for Canadian Urban Research Studies


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Crime Pattern Theory • Looks at all of the elements of a crime, not just the offender. • Assumes that offenders and victims generally use time and space in a normal (not unique or pathological) way • Stresses the importance of: ▫ ▫ ▫ ▫ ▫

decision making by offenders and other people routine activities time imposed constraints on crime place imposed constraints on crime situation imposed constraints on crime Institute for Canadian Urban Research Studies


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How Crime Pattern Theory Helps Researchers and Police • Assists prediction of:

▫ Risk of crime at specific places and times ▫ Displacement of crime

• Can help in identification of: ▫ offenders ▫ targets

• Can help in evaluation of specific interventions: ▫ Crackdowns on hotspots or criminal gangs ▫ Problem Oriented Policing interventions in specific neighborhoods

• Can help police in providing information to other government agencies Institute for Canadian Urban Research Studies


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160

160

120

120

80

80

40 40 0 5.46E+006 5.456E+006 5.452E+006

Institute for Canadian Urban Research Studies

0 500,000 498,000 496,000 494,000 492,000 490,000 488,000 486,000 484,000


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Crime Pattern Theory: The Eight Rules

Institute for Canadian Urban Research Studies


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Rule 1: Crime Decision Templates • As individuals move through a series of activities they make decisions. • When activities are repeated frequently, the decision process becomes regularized. • This regularization creates an abstract mental template which guides decisions to act. • For decisions to commit a crime this is called a crime template. • The template specifies suitable targets, sites, situations and crime techniques. • The crime template structures later crime decisions. Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Rule 2: Social Networks Matter • Most people do not function as isolated individuals, but have a network of family, friends and acquaintances. • These network linkages have varying attributes and influence the decisions of the offender and others in the network. • These network linkages structure who might be involved as co-offenders in any given criminal event. • These linkages structure decisions about criminal target, site, situation and technique.

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Criminal Network

Institute for Canadian Urban Research Studies


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Rule 3: Crime Patterns • Individual Criminal Decisions: ▫ When individuals are making their decisions independently, individual decision processes and crime templates can be treated in a summative fashion, that is, average or typical patterns can be determined by combining the patterns of many individuals.

• Co-Offending patterns ▫ When co-offenders make decisions together the patterns are summative for the group or network of co-offenders Institute for Canadian Urban Research Studies


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Rule 4:

Criminals learn from criminal events

• Individuals or networks of individuals commit crimes when there is a triggering event and a process by which an individual can locate a target or a victim that fits within a crime template. • Criminal actions change the bank of accumulated experience and alter future actions.

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Rule 5: Nodes, Paths and Routines • People have a range of daily, weekly, monthly, and annual routines that structure their position and movements in space-time. • Activity occurs at routine nodes and along the normal pathways between these nodes. Such nodes may include: ▫ home and the homes of relatives and friends ▫ work and school sites, ▫ shopping and entertainment venues, or ▫ transportation junctures.

Institute for Canadian Urban Research Studies


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Rule 6: Awareness spaces and crime • People develop activity spaces composed of the nodes and paths they routinely utilize. • People develop an awareness space based on their activity spaces. • People who commit crimes have normal spatiotemporal movement patterns like everyone else. • The likely location for a crime is near this normal activity and awareness space.

Institute for Canadian Urban Research Studies


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Rule 7: Space-time intersections • Criminal opportunities present when the spacetime locations of potential targets and victims intersect the activity spaces of potential offenders. • The potential targets and victims become actual targets or victims when: ▫ the potential offender’s willingness to commit a crime has been triggered, and ▫ the potential target or victim fits the offender’s crime template. Institute for Canadian Urban Research Studies


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Rule 8: Urban form as a constraint • The prior seven rules operate within the built urban form which structures human movement into nodes and paths. • The built form sorts crime hot spots into: ▫ Crime generators which are created by high flows of people through and to nodal activity points. ▫ Crime attractors which are created when suitable targets are known by potential offenders to be concentrated at specific nodes. Potential offenders travel to a crime attractors for the specific purpose of committing a crime there. ▫ Crime neutral areas – which rarely experience crimes

Institute for Canadian Urban Research Studies


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Normal Movement Patterns • Structured by routine activities ▫ Individual ▫ Societal

• Structured by learned awareness spaces • Structured through Networks

▫ Family, Friends & Acquaintances ▫ Co-workers ▫ Criminal Associates

• Patterns can be found in both: ▫ Individual variety; and ▫ Aggregated data Institute for Canadian Urban Research Studies


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Amsterdam routines

Institute for Canadian Urban Research Studies


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Youth Awareness Spaces: California

Prof. Gisela Bichler Institute for Canadian Urban Research Studies


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Everyday Patterns and Crime Patterns

Institute for Canadian Urban Research Studies


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Space - Time • Least effort • Awareness and Activity Spaces ▫ Routine activities ▫ Exceptional activities ▫ Networks and Information interactions

• Physical Structuration ▫ Paths ▫ Edges ▫ Nodes

• Offender-Target Intersection Institute for Canadian Urban Research Studies


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The Traces of Least Effort • Least effort predicts crime close to an offender’s home and other routinely visited places such as work. • Least effort predicts a correspondence between clusters of offender residences and clusters of crime.

• Big Data sets make it possible to see the traces of least effort in criminal offending. • The next three slides show offender residence and crime concentrations for more than 200,ooo offender-crime data pairs in Metropolitan Vancouver

Institute for Canadian Urban Research Studies


Crime Event Location Hot Spot (based on 213,906 data, Point Density (100m,500m) ďƒ  Contour (>1,500))

Institute for Canadian Urban Research Studies


Crime Event / Offender Home Hot Spot (based on 213,906 data, Point Density (100m,500m) ďƒ  Contour )

Institute for Canadian Urban Research Studies


Offenders Home Hot Spot (based on 213,906 data, Point Density (100m,500m) ďƒ  Contour (>1000))

Institute for Canadian Urban Research Studies


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Structuring Activity and Awareness Space • Routine Movement ▫ Paths ▫ Edges ▫ Nodes

• Frequent activity locations ▫ Crime Generators ▫ Crime Attractors

• Networks ▫ Family, Friends & Acquaintances ▫ Co-workers ▫ Criminal Associates

Institute for Canadian Urban Research Studies


31

Work Home

Shopping & Entertainment

Shopping & Entertainment

Awareness Space/ Individual Offender Institute for Canadian Urban Research Studies


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Target Choice • Crime Opportunities (Yellow)

Institute for Canadian Urban Research Studies

• Crime Template (Red)


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Areas Fitting Crime Template

Work

Home

Shopping & Entertainment Institute for Canadian Urban Research Studies


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A Felson Convergence Zone

Institute for Canadian Urban Research Studies


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Paths and Barriers • Road networks and transit systems channel movement ▫ Destination points create crime generators

• Topographical and built features act as barriers and funnels • The permeability of a neighbourhood ▫ Physical access is important ▫ Social differences create barriers

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Paths predict: • Concentrations of crime between major nodes ▫ along major streets ▫ in areas near major streets ▫ along principle pedestrian paths

• Directional vectors in crime patterns ▫ crime pulled along paths between major nodes

Institute for Canadian Urban Research Studies


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Crimes and Suspect Activity Nodes

Institute for Canadian Urban Research Studies


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Criminal Activity Vector

Dr. Richard Frank

Institute for Canadian Urban Research Studies


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Spatially random crime commission

Spatially vectored crime commission

Institute for Canadian Urban Research Studies


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CAA capturing 50% of crimes

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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34% of offenders go towards Coquitlam Center, statistically we expect only ~6%

Institute for Canadian Urban Research Studies


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Maple Ridge There are 186 offenders 71% move towards Coquitlam Center Statistically expect ~6%

Institute for Canadian Urban Research Studies


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Crime Vectors of South Burnaby

See: Richard Frank, et al (2011) Power of Criminal Attractors. Journal of Artificial Societies and Social Simulation 14(1):6 Institute for Canadian Urban Research Studies


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Directionality on the Macro Level

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Edges predict: • Higher crime at: ▫ ▫ ▫ ▫ ▫

boundaries of neighborhoods changes in land use mixed land use entertainment strips socio-economic boundaries

• Recent important confirmatory empiricial work in: ▫ Netherlands ▫ Western Australia Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Prof. Gisela Bichler Institute for Canadian Urban Research Studies


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Burglary in Metro: Topology of Edges • Interiors and boundaries of small areas are predicted to have very different crime levels • Edges of “neighbourhoods” will have higher crime rates than interiors • Edges often feature a mix of land uses • Focus on single family dwelling areas and commercial areas • Commercial area edges with single family residential housing areas have 58% higher crime levels than interiors of residential or commercial areas

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Crime Generators • Channel large numbers of people past a set of criminal opportunities.

• Some potential offenders are mixed into groups of people passing the opportunities. • Crimes occur opportunistically.

Institute for Canadian Urban Research Studies


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Crime Attractors • Attract strongly motivated offenders intending to commit a crime. • Attraction is created by an ecological label. • Offenders may travel long distances to reach an attractor location. • Crimes often committed by area outsiders. • Offenders often follow a staged target search process once they reach the attractor neighbourhood.

Institute for Canadian Urban Research Studies


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BURNABY CRIMINAL CODE CALLS 1991

900 800 700 600 500 400 300 200 100 0

2

1

900 800 700 600 500 400 300 200 100 0

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40

30 35

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Institute for Canadian Urban Research Studies


3D Visualization of major mall – 2001-2006 (ArcScene, Kernel Density Map(12,200m))

Institute for Canadian Urban Research Studies


Cross section of Crimes Across Kingsway in MT mall ( Kernel Density Map)

135000

Kingsway

Metrotown Mall Across Kingsway

115000 95000 75000 55000 35000 15000

1528 1465 1403 1340 1278 1215 1152 1090 1027 964 902 839 777 714 651 589 526 463 401 338 276 213 150 88 25 38 100 163 225 288 351 413 476 539 601 664 726 789 852 914 977

-5000

Justin Song

Institute for Canadian Urban Research Studies

10/3/2011


Cross section of Crimes Along Kingsway in MT mall ( Kernel Density Map) 155000

Metrotown Mall Along Kingsway 135000 115000

95000 75000 55000 35000 15000

1501 1449 1397 1345 1294 1237 1180 1124 1067 1010 953 896 840 783 726 669 613 556 499 442 385 329 272 215 158 102 45 12 69 126 182 239 296 353 409 466 523 580 637 693 750 807 864 920 977 1034 1091 1148 1204 1261

-5000

Institute for Canadian Urban Research Studies


Offenders' Home Location (City) (based on Metrotown Mall Centre crimes) offenders count 1400 1200 1000 800 600 400 200 0

VANCOUVER

BURNABY

SURREY

Institute for Canadian Urban Research Studies

PORT MOODY

COQUITLAM


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Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Networks • Networks of people can structure crime patterns by: ▫ Changing awareness and activity spaces ▫ Providing multiple starting points for criminal target searches

• Networks of interest should include: ▫ ▫ ▫ ▫

Criminal Associates Girlfriends or significant others Family Friends

• Networks can be analyzed to: ▫ Identify a spatially likely suspect for a set of crimes ▫ Identify a set of crimes that relate to a set of offenders ▫ Understand larger crime patterns

Institute for Canadian Urban Research Studies


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W2

W1

H1

S&E2

H2 S&E1

W3

H3

S&E3 Institute for Canadian Urban Research Studies


W3 Low Occurrence

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W1 W2

H2 High Occurrence

H1

H3 S&E Institute for Canadian Urban Research Studies


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FLINTS NETWORK NODES WITH INTELLIGENCE

Co-Defendant Link

IMS Link

Co-Defendant & IMS Link

Institute for Canadian Urban Research Studies


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Focus on Prolific Offender Networks • Analyze police and court data • Explore networks of frequent offenders • Identify prolific offenders who are also key to keeping the network of offenders active • Target investigations on offenders who are most important to offending capacity of the entire network • Shred network

Institute for Canadian Urban Research Studies


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Graffiti Vandal Network

Institute for Canadian Urban Research Studies


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Key Players Jailed: Network Disintegrates Vandals become less active Secondary players can be targeted

Institute for Canadian Urban Research Studies


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A Co-Offending network meeting the legal definition of an Organized Crime Group

Source: Brantingham, Brantingham, Glaesser & Tyebi (2012) AN ANALYSIS OF RCMP ``E`` DIVISION DATA TO ESTIMATE POSSIBLE CRIMINAL ORGANIZATIONS: FINAL DESCRIPTIVE REPORT. OTTAWA: PUBLIC SAFETY CANADA.

Institute for Canadian Urban Research Studies


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Road Network Analysis • Ease of Movement ▫ Formal road network analysis  beta scores – ease of flow measurement  Tells us likelihood of offender or target from Point A to Point B on the network  Tells us most likely path

• Directionality ▫ Establishes resistance to movement ▫ Calibrates weights of competing nodes

Institute for Canadian Urban Research Studies


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The road network, land use and zoning as key determinants of the crime pattern Research by Rob Tillyer and Patricia Brantingham

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


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Sum m ed residential b reak and en ter.sh p 1 # 2-3 # 4-7 8 - 11 # # 12 - 16 High density.shp Co m mercialstreets .sh p Mainand skytrain.sh p #

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Land Use analysis • Criminality of land use mixtures

▫ Is a pub near a high school more problem than a pub near a hospital? ▫ Is a school near a parking lot more problem than a school near a bank? ▫ Is a convenience store more problem next to a school or a bar or a hospital?

• Crime fields of nodal uses

▫ How far will a thief travel to reach a convenience store, a super market, a shopping centre? ▫ How far will a customer travel to reach a drug market or a prostitution stroll?

• Housing mix

▫ What blend of different housing forms maximizes or minimizes criminal event volumes? Institute for Canadian Urban Research Studies


Land use of Crime Hot Spots (based on 213,906 data, GVRD*) Commercial Residential - Townhouse and Low-rise Apart Residential - Single Family Institutional Recreation and Protected Natural Areas

Open and Undeveloped Industrial Residential - High-rise Apartments Transportation, Communication and Utilitie Residential - Rural Commercial - Residential/Mixed Agricultural Lakes and Water Bodies 0

1000000

2000000

3000000

4000000

Area (m2) Institute for Canadian Urban Research Studies

5000000

6000000

7000000


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TOPO Using Topology to Group DA’s into ‘Neighbourhoods’

Institute for Canadian Urban Research Studies


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Topology

• Topology can be thought of as the ‘connectedness’ of two elements – specifically, which features are spatially adjacent to one another

Institute for Canadian Urban Research Studies


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TOPO • This project employs Topological connections (point-set topologies and graph theory) between areas in Greater Vancouver to create ‘similar neighbourhoods’ according to certain features ▫ Income ▫ Housing type ▫ Other forms of census data

Institute for Canadian Urban Research Studies


Topo:

A topological algorithm that clusters small areas on similarity,

identifies boundary zones, identifies internal barriers to vectored activity • Basis sets and the open sets of a neighborhood topology are built up by considering block-to-block similarities for several characteristics. ▫ ▫

Each variable of interest, is used to create a separate topology. The basis sets in each topology are constructed by clustering contiguous blocks. The blocks are clustered to maintain internal block-to-block similarity or homogeneity. That is, a basis set is the set of all contiguous block clusters such that the interblock variation of the variable of interest is less than some fixed percentage.

Every time the percentage of interblock variation is changed, new basis sets can be constructed. Many sets are, therefore, possible as the percentage variation is allowed to go up or down. • For example, let B, be a basis set and bi be a block. Let f(bj) be a functional value associated with block bj, say average cost of housing, average rent, percent black, or percent apartment houses. Then a basis set is: Bi = g bj| | f(bj) - f(b~ max ~af(bj), af(b~ where bi ~Bi; bi r~ bj ~L ~ (the blocks have a street in common); O < a < 1; b, ~ bj; i = 1, . . ., n; j = 1, . . ., m •

The contours of the neighborhood develop as the permitted interblock variation is changed and new basis sets are constructed. The basis sets form nests. ▫ ▫

Institute for Canadian Urban Research Studies

Blocks forming nieghbourhood edges are identified. Blocks wholly internal to the neighbourhood are identified. Blocks which are edges at some levels and interior at other levels of variation are identified.


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â&#x20AC;˘ It begins by selecting one DA (dissemination area) in Greater Vancouverâ&#x20AC;Ś

Institute for Canadian Urban Research Studies


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â&#x20AC;˘ From here, it uses TOPOLOGY to select contiguous DAâ&#x20AC;&#x2122;s, according to a certain level of similarity

Institute for Canadian Urban Research Studies


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Results â&#x20AC;˘ The final product has split Greater Vancouver into a number of different areas according to similar census variables â&#x20AC;˘ This is beneficial, because instead of relying on census tracts, which are often quite variable within their borders, smaller areas are combined solely according to one similar feature

Institute for Canadian Urban Research Studies


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Current Map

Institute for Canadian Urban Research Studies


Computational Criminology Dr. Uwe Gl채sser Software Technology Lab glaesser@cs.sfu.ca

Patricia Brantingham, Chair in Computational Criminology

Dr. Martin Ester

ester@cs.sfu.ca

Paul Brantingham. Chair in Crime Analysis SFU Darryl Plecas, Chair in Crime Reduction UCFV

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From Social Systems to ASM Models (1)

ASM Model Multi-Agent System Social System

Institute for Canadian Urban Research Studies


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Distributed ASM Model • Model the behavior of an offender commuting between home, work, recreation locations in an urban environment • An autonomous agent, called person agent

• Urban environment: ▫ Objective environment (geographic environment) ▫ Subjective environment

Institute for Canadian Urban Research Studies


The Environment • Objective Environment ▫ Physical reality

• Subjective Environment ▫ Subjective reality ▫ Agent’s perception

• Awareness Space

(1)

Geographic Environment

Perception

Awareness Space

▫ Part of perception of which agent is aware

• Activity Space

Objective Environment

Subjective Environment

Activity Space

▫ Part of awareness space ▫ Frequently visited

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The Environment(2) Abstract mathematical data structure Attributed Directed Graph

49 21’ 06” 123 15’ 04”

Max Speed 30 mi/h

 Construction Zone

Institute for Canadian Urban Research Studies

Traffic density


Agent Architecture: A BDI-based Model Communication FROM Environment

Beliefs Space Evolution Module

Profile

Target Selection Module

Cognition Rules

Agent Decision Module

Cognition Rules

Intentions

Intentions

Working Memory

Motivations

Intentions

Working Memory

Deliberation

Environment

Desires

Perception Action Rules

Means-End Reasoning

Awareness Space

Action Rules

Activity Space

Communication TO Environment

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Space Evolution Module (SEM) • SEM Operation Modes pathCompleted

▫ Basic

idle  pathPlanning  roadSelection  localRePlanning  running Suggested Path

Suggested Edge

Acceptable Edge

Traverse Edge

▫ Destination changed idle  pathPlanning  roadSelection  localRePlanning  running

▫ Unacceptable edge idle  pathPlanning  roadSelection  localRePlanning  running

▫ Random Choice idle  pathPlanning  roadSelection  localRePlanning  running

Institute for Canadian Urban Research Studies


SEM Path Planning Space Evolution Module Explorer

Case-Base Reasoner

• Path Planning ▫ Explore (using a map)  Explorer Algorithm: Combination of Dijkstra shortest path and A* algorithms

▫ Memory  Request a path from CBR  The exact match may not exist  If no path from CBR, then use the explorer

B C A

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Criminology

Multi Agent Systems System Dynamics

Environment Planning Experimental Validation

ASM Navigation AI/ALife

Knowledge Rep. Neural Net

Institute for Canadian Urban Research Studies

Decision Making Learning


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Crime Reduction Core • • • •

Reduce Opportunities and Temptations Prevent Repeat Victimization Reduce Anti-social Behavior Manage Offenders ▫ Catch and convict –with special attention to prolific offenders ▫ Treat underlying conditions ▫ Comprehensive aftercare to reduce recidivism

Institute for Canadian Urban Research Studies


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Institute for Canadian Urban Research Studies


Paul Brantingham