Paul Brantingham

Institute for Canadian Urban Research Studies

Crime Pattern Theory

Expanding our Understanding of Crime Using New Computational Strategies

1


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.

2


CRIMINAL EVENT

TARGETS

TECHNIQUE SITES

SITUATION

ELEMENTS OF THE CRIMINAL EVENT

LAW

OFFENDER

3



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.

4



• 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

5


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

6


484,000

486,000

488,000490,000

492,000494,000

496,000498,000

500,000

5.452E+006

5.456E+006

5.46E+006

0

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7


Crime Pattern Theory: The Eight Rules

8


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.

9


10


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.

11


12


Criminal Network

13


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

14


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.

15


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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.

17


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 spatio-temporal movement patterns like everyone else.

• The likely location for a crime is near this normal activity and awareness space.

18


Rule 7: Space-time intersections

• Criminal opportunities present when the space-time 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.

19


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

20


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

21


Amsterdam routines

22


Youth Awareness Spaces: California

Prof. Gisela Bichler

23


Everyday Patterns and Crime Patterns

24


Space - Time

• Least effort • Awareness and Activity Spaces

▫ Routine activities ▫ Exceptional activities ▫ Networks and Information interactions

• Physical Structuration ▫ Paths ▫ Edges ▫ Nodes

• Offender-Target Intersection

25


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

26


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


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


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


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

30


Shopping &

Entertainment Shopping &

Entertainment

Home

Awareness Space/ Individual Offender

Work

31


32

Target Choice

• Crime Opportunities (Yellow)

• Crime Template (Red)


Work

Home

Shopping & Entertainment

Areas Fitting Crime Template

33


A Felson Convergence Zone

34


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

35


36


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

37


Crimes and Suspect Activity Nodes

38


39

Criminal Activity Vector

Dr. Richard Frank


40

Spatially random crime commission Spatially vectored crime commission


41

CAA capturing 50% of crimes


42


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

43


Maple Ridge There are 186 offenders

71% move towards Coquitlam Center

Statistically expect ~6%

44


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

45


Directionality on the Macro Level

46


47


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

48


49


Prof. Gisela Bichler

50


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

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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.

56


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.

57


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BURNABY CRIMINAL CODE CALLS 1991

N

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3

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


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

Justin Song 10/3/2011

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Metrotown Mall Across Kingsway Kingsway


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

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VANCOUVER BURNABY SURREY PORT MOODY COQUITLAM

Offenders' Home Location (City) (based on Metrotown Mall Centre crimes)

offenders count

Offenders' Home Location (City) (based on Metrotown Mall Centre crimes)

offenders count


63


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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

65


W1 W2

W3

H1 H2

H3

S&E1

S&E2

S&E3

66


W3 W1

W2

H1

H3

H2 High

Occurrence

Low

Occurrence

S&E

67


FLINTS NETWORK NODES WITH INTELLIGENCE

Co-Defendant Link

IMS Link

Co-Defendant & IMS

Link

68


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

69


Graffiti Vandal Network

70


Key Players Jailed:

Network Disintegrates Vandals become less active

Secondary players can be targeted

71


A Co-Offending network meeting the legal

definition of an Organized Crime Group

72

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.


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

73


The road network, land use and zoning as key determinants of the crime pattern

Research by Rob Tillyer and Patricia Brantingham

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Summed residential break and enter.shp

# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

Residential B&E's in Burnaby, BC


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Main2.shp


# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

Add Street Network


77

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# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

Streets in Buffer along Main Roads


78

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Hastingsbuff.shp

Kingscombufmerge.shp

Mainandskytrain.shp


# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

Commercial Zoning


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# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

Commercial Streets


80

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Ea dwelling .shp

0 - 2472.1

2472.1 - 4387.1

4387.1 - 9275.8

9275.8 - 224305.6


# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

Housing Density for EA 's


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Highdensity.shp


# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

High Density


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Mainandskytrain.shp

Commercialstreets .shp

Highdensity.shp


# 1

# 2 - 3

# 4 - 7

# 8 - 11

# 12 - 16

2 0 2 4 Miles

N

EW

S

High Density and Commercial


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?

83


Land use of Crime Hot Spots (based on 213,906 data, GVRD*)

0 1000000 2000000 3000000 4000000 5000000 6000000 7000000

Lakes and Water Bodies

Agricultural

Commercial - Residential/Mixed

Residential - Rural

Transportation, Communication and Utilitie

Residential - High-rise Apartments

Industrial

Open and Undeveloped

Recreation and Protected Natural Areas

Institutional

Residential - Single Family

Residential - Townhouse and Low-rise Apart

Commercial

Area (m2)


85

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


86

Topology

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


87

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


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.

▫ 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.


89

• It begins by selecting one DA (dissemination area) in Greater Vancouver…


90

• From here, it uses TOPOLOGY to select contiguous DA’s, according to a certain level of similarity


91

Results

• The final product has split Greater Vancouver into a number of different areas according to similar census variables

• 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


92

Current Map

93

Computational Criminology

Dr. Uwe Glässer

Software Technology Lab

[email protected]

Dr. Martin Ester [email protected]

Paul Brantingham. Chair in Crime Analysis SFU

Darryl Plecas, Chair in Crime Reduction UCFV

Patricia Brantingham,

Chair in Computational Criminology

mailto:[email protected]


From Social Systems to

ASM Models (1)

94

ASM Model

Multi-Agent System

Social System


95

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

96

The Environment (1)

• Objective Environment ▫ Physical reality

• Subjective Environment ▫ Subjective reality

▫ Agent’s perception

• Awareness Space ▫ Part of perception of which

agent is aware

• Activity Space ▫ Part of awareness space

▫ Frequently visited

Geographic Environment

Perception

Awareness

Space

Activity

Space

Objective

Environment

Subjective

Environment


97

The Environment(2)

Abstract mathematical data structure

Max Speed

30 mi/h

Construction

Zone

Traffic

density

49 21’ 06”

123 15’ 04” Attributed Directed Graph

98

Agent Architecture:

A BDI-based Model

En

vir

on

men

t

Space Evolution Module

Perception

Awareness

Space

Activity

Space

Agent Decision

Module

Cognition Rules

Working

Memory

Action Rules

Communication FROM Environment

Communication TO Environment

Target Selection Module Profile

Motivations Intentions Intentions

Beliefs

Desires

Intentions

Deliberation

Means-End

Reasoning

Cognition Rules

Working

Memory

Action Rules


99

Space Evolution

Module (SEM)

• SEM Operation Modes ▫ Basic

▫ Destination changed

▫ Unacceptable edge

▫ Random Choice

idle running localRePlanning roadSelection pathPlanning

pathCompleted


Suggested

Path

Suggested

Edge Acceptable

Edge

Traverse

Edge



100

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 A

B

C


101

ASM

Knowledge

Rep.

Neural

Net

Decision

Making

Learning

Multi Agent

Systems

System

Dynamics

Navigation

Criminology

Environment

Planning

Experimental

Validation

AI/ALife


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

102


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106

Date post:	07-Mar-2016
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