C.A.M.P.A.I.G.N
Vijay Anam
Partnership Performance Manager,
Reading and Wokingham Local Police Areas
© Vijay Anam, All Rights Reserved
OUTLINE
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INTRODUCTION
NATIONAL INTELLIGENCE MODEL
CAMPAIGN MODEL
DATABASE MODULE
GIS MODULE
ADAPTIVE LAYER
SUMMARY
© Vijay Anam, All Rights Reserved
INTRODUCTION
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CRIME
ADAPTIVE
MAPPING and
PERFORMANCE
POLICE
REGISTERED
SOCIAL
LANDLORDS
using
• ARTIFICIAL
• INTELLIGENCE and
• GEOGRAPHICAL
• NEIGHBOURHOODS
© Vijay Anam, All Rights Reserved
LOCAL
AUTHORITY
CRIME AND
DISORDER
REDUCTION
PARTNERSHIP
PROBATION
SERVICE
HEALTH
AUTHORITY
FIRE
SERVICE
NATIONAL INTELLIGENCE
MODEL
© Lancashire Constabulary 2000
© Vijay Anam, All Rights Reserved
NATIONAL INTELLIGENCE MODEL
– PROBLEM SOLVING MODEL
• SCANNING
– IDENTIFY THE PROBLEM(S)
• ANALYSIS
– DETERMINE CONTRIBUTORY
FACTORS IN THE CONTEXT OF
LOCATION
LOCATION
• LOCATION
• VICTIM
• OFFENDER
• RESPONSE
– PLAN AND INTITIATE
INTERVENTIONS
• ASSESSMENT
– MONITOR PERFORMANCE OF
THE INTERVENTION(S)
– IDENTIFY SUCCESSES AND
FAILURES
– DEVELOP BEST VALUE
© Vijay Anam, All Rights Reserved
OFFENDER
OFFENDER
VICTIM
VICTIM
NATIONAL INTELLIGENCE MODEL
– PROBLEM SOLVING MODEL - GIS
ASSESS INTERVENTION(S)
(ASSESSMENT - LAYER 3)
DESIGN INTERVENTION(S)
(RESPONSE - LAYER 3)
IDENTIFY FACTORS
(ANALYSIS - LAYER 2)
IDENTIFY PROBLEMS
(SCANNING - LAYER 1)
Maps: © Crown Copyright, Account No.:100019672
© Vijay Anam, All Rights Reserved
N.I.M – PROBLEM SOLVING MODEL
– SCANNING
• SCANNING
– IDENTIFY HOTSPOTS
– CREATE BOUNDARIES
ENCIRCLING THE
HOTSPOTS.
– SELECT BOUNDRIES
WHICH NEEDS URGENT
ATTENTION
• IN OUR EXAMPLE WE
HAVE ENCIRCLED AN
AREA WHICH NEEDS
URGENT
INTERVENTION
– PROCEED TO THE NEXT
STAGE
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Maps: © Crown Copyright, Account No.:100019672
N.I.M – PROBLEM SOLVING MODEL
– ANALYSIS
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ANALYSIS (LIST ALL
CONTRIBUTORY FACTORS)
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IN CONTEXT OF LOCATION
SUCH AS:
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SIMILARLY FROM THE POINT
OF VIEW OF THE OFFENDER
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TYPES OF HOUSES
AGE OF HOUSES
DEPRIVATION
GEOGRAPHY
….ETC.
AGE OF OFFENDER
SEX OF OFFENDER
WHETHER L.P.O OR P.Y.O
DRUG DEPENDENCY
…..ETC.
AND FINALY FROM THE
VICTIM’S PERSPECTIVE
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AGE OF THE VICTIM
SEX OF THE VICTIM
WHETHER LIVING ALONE
…..ETC.
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Maps: © Crown Copyright, Account No.:100019672
AREA TO BE ANALYSED
N.I.M – PROBLEM SOLVING MODEL
– RESPONSE
• RESPONSE
– DESIGN
INTERVENTIONS TO
TACKLE PROBLEMS
– SET TARGETS
– SET MILESTONES
– IDENTIFY
LOCATIONS WHERE
INTERVENTIONS
WILL BE APPLIED
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REGION WHERE INTERVENTIONS
ARE PLANNED
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N.I.M – PROBLEM SOLVING
MODEL – ASSESSMENT
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BEFORE INTERVENTION
ASSESSMENT
– WHETHER MILESTONES WERE
ON TRACK?
– WHETHER TARGETS
ACHIEVED ?
– SUCCESSES
– FAILURES
– TYPES OF LESSONS LEARNT
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1
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AFTER INTERVENTION
Target
Actual
Milestone1
MileStone2
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Milestone3
Final Traget
CAMPAIGN MODEL
ADAPTIVE
GIS
MODULE
DATABASE
MODULE
MODULE
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CAMPAIGN - NETWORK
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CAMPAIGN MODEL –
DATABASE MODULE
SCANNING
ANALYSIS
RESPONSE
ASSESSMENT PERFORMANCE
PROJECT
LOCATION
INTERVENTIONS
TARGETS
PROBLEMS
OFFENDER
MILESTONES
VICTIM
TIME LINES
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ASSESSMENT
PERFORMANCE
CAMPAIGN MODEL – GIS
MODULE
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POINT MAPS
THEMATIC MAPS
TIMELINE ANALYSIS
TEMPORAL ANIMATION
DEFINING RESPONSE BOUNDARIES
AUTOMATIC PROJECT MONITORING
QUERYING OF DATA LAYER
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CAMPAIGN MODEL – ADAPTIVE
MODULE
PROBABILITY
DISTRIBUTION
REPORTING
BAYESIAN
NETWORK
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ARTIFICIAL
NEURAL
NETWORKS
STRUCTURE OF A BAYESIAN
NETWORK
A Bayesian Network is made
up of two parts:
1) qualitative part
• structure
• Directed Acyclic Graph (DAG)
• vertices represent random variables
• edges represent relations
between variables
2) quantitative part
• the strength of relationship
between variables
• conditional probability function
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P(N2|N1)
P(N1)
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P(N3|N1)
P(N4|N3,N2)
PURPOSE OF BAYESIAN
NETWORKS
• Models probabilities for any problem
• Provides posterior beliefs in presence of
some observations
• Provides classification
• Can explain why
• Can find the variables with the most impact
• Can model expert (subjective) knowledge
• Learned from raw data
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MODELLING A PROBLEM
© Henrik Bengtsson
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“The Year 2000 problem”
ARTIFICIAL NEURAL NETWORK
• AN ELECTRONIC MODEL OF THE
FUNCTIONING OF THE HUMAN BRAIN
– ON HOW TASKS OR FUNCTIONS ARE
PERFORMED
– BASED ON SIMPLE COMPUTATIONAL
BUILDING BLOCKS
• MAIN DESIGN CRITERIA:
– CONNECTIONS BETWEEN NEURONS
DETERMINE THE FUNCTION OF THE
NETWORK
– CAPACITY TO LEARN AND REMEMBER
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TYPICAL NEURAL NETWORK
Input
Layer
Weights
Hidden
Layer
Weights
x1
Input
Signals
x2
Output
Target
Output
y1
t1
Error
y2
xm
Layer of
Input
Neurons
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Layer of
Hidden
Neurons
t2
Layer of
Output
Neurons
© Matthew Casey
SUMMARY
• CAMPAIGN CAN EFFECTIVELY MONITOR
PERFORMANCE OF ALL WORK UNDERTAKEN BY
CRIME AND DISORDER REDUCTION
PARTNERSHIPS
• THE GIS MODULE IS USED AS AN INTERFACE TO
REGISTER PROJECT BOUNDARIES AND PERFORM
SPATIAL ANALYSIS.
• DIFFERENT BAYESIAN NETWORKS (ADAPTIVE
MODULE) TO ANALYSE SITUATIONS WHERE
CAUSALITY PLAYS A ROLE AND UNDERSTANDING
WHAT IS ACTUALLY GOING ON IS INCOMPLETE.
• NEURAL NETWORKS (ADAPTIVE MODULE) USED
FOR DETECTING PATTERNS AND FOR OPTIMISING
DESIGN OF PROJECTS (PLANNING AND DELIVERY)
© Vijay Anam, All Rights Reserved