Crime, Policing and Citizenship (CPC) Space-Time Interactions of Dynamic Network
Tao Cheng + CPC Team {tao.cheng@ucl.ac.uk} Department of Civil, Environmental & Geoma@c Engineering (CEGE), UCL Crime Po icinggCitizenshipp
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Outline
•  Why CPC?
–  background
–  opportunities
–  Aims & objectives
•  Programme and Methods
•  The team
•  Your involvement & participantion
Background
•  ‘We all want to feel confident and safe in our
neighbourhoods and our shared public spaces, safe at
home, at work, when we're out and about – no matter
where we are or what time it is in this wonderful city
of ours.’
www.london.gov.uk/priorities/crime-community-safety
• BUT
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Existence of two ‘perception gaps’ (Home office)
(1) between official statistics and recorded crime
According to the BCS (British Crime Survey),
2010: 66% of adults believe crime has risen nationally in the past year
2011: 60% of adults believe crime has risen nationally in the past year
BUT Police recorded crime
2010: fell by 8% in the year ending in March 2010
2011: fell by 12% in the year ending in March 2011
(2) between crime nationally and locally
2010: - only 31% think it has risen in their local area
20011: only 28% think it has risen in their local area
- 10% said that crime in their local area was ‘higher than average’
- 51% said that crime was ‘lower than average’
- 39% said that crime in their local area was ‘about average’
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•  It is widely understood that policing, crime and public trust each
have strong spatial and temporal dimensions.
•  Our understanding of offenders’ use of time and space in ways
that may be both localised and coordinated remains
underdeveloped, and in need of alignment with community
policing initiatives
•  At the other extreme, organised crime and terrorism are structured
over wider spatial extents and longer timescales, and require
collaboration between police forces.
These polar examples illustrate that an integrated
approach to space-time analysis is needed
-  in order to analyse crime patterns, police activities
and community support
-  in order to understand and predict when and where
different criminal activities are likely to emerge.
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Opportunities – Crime & Policing
•  Every day, about 10,000 geo-referenced incidents are
recorded in the London Metropolitan Police CAD database.
–  allows crime patterns to be explored at particularly fine temporal
granularity and at multiple spatial resolutions.
•  33,000 foot patrols and community support officers have
been equipped with GPS radios
–  20-metre precision at 15-minute intervals throughout the
working day.
•  GPS logs of police vehicle movements are recorded at 15second intervals.
Together, these sources make it
possible to represent criminality and
police activity as interpenetrating
networks, set in a mosaic of
different neighbourhood conditions.
Opportunities – Public Perception
•  Valuable neighbourhood geodemographic context can be
derived from
–
–
–
–
2011 Census of Population data,
the British Crime Survey (BCS),
ESRC’s Understanding Society (USoc) survey,
MPS’s Public Attitude Survey & Victim Survey.
•  Together these detailed sources represent the
potentially huge number of factors that shape criminal
activity patterns and public perceptions, as well as the
trajectories in space and time along which they coevolve.
The aim of the project will be to utilise integrated spatiotemporal data mining and network complexity theory to
model the interaction of networks of police activities, crime
occurrences and alerts from the public, in order that
policing can be improved at scales from the local to the city
wide.
The specific objectives will be
1. to identify emergent crime patterns;
2. to analyse factors that accelerate or curtail crime ‘waves’;
3. to develop different policy scenarios so that criminal
activity can be migrated if not prevented.
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Programme & Methods
1 Month
Phase 1: Data Acquisition and Conflation
UK Data Archives
MPS
CAD
Database
(F1)
7 Months
Policemen
GPS Data
(F2)
Transport,
Weather, ..,
(F2)
Community
Mapping
(F3)
N3: Citizen
N2: Police
Police Movement
(F2)
Citizen Profiles
(F3)
Q2: Police & Citizen
(F2/F3)
Q2: Citizen & Crime
(F3/F1)
Q3: Police Resources Allocation
(F2)
Q3: Citizen Engagement
(F3)
Phase 3: Interaction of Networks
Q1:Crime & Police
(F1/F2)
Phase 4: Policy Evaluation
Q3: Crime Intervention
(F1)
37 Months
Census
(F3)
Exploratory Space-Time Analysis &
Visualisation
(STC, SVM; STWR; STK; STV)
Crime Patterns
(F1)
28 Months
Understanding
Society
(F3)
Phase 2: Space-Time Patterns of Individual Networks
N1: Crime
16Months
BCS
(F1)
MetP
Surveys (F1/F3)
MfC
Phase 5: A Web-base Platform for Dynamic Visualization and Simulation
Data & Model Updating
(whole team)
Knowledge Transfer
(whole team)
User Evaluation
(whole team)
42 Months
Figure 1: Workflow of CPC (F1, F2 and F3 are PDRAs)
Online Courses/Software
(whole team)
CPC Team (April 2012-September 2015)
Investigators: Kate Bowers; Tao Cheng; Paul Longley; John Shawe-Taylor
Industrial partner: Trevor Adams, Director of GIS, Met Police Service (MPS)
PDRAs: Suzy Moat; Leto Peel; Ryan Davenport
Advisory Committee
•  Prof. Mike Goodchild, GISc
–  Univ. of California, Santa Barbara
•  Prof. Mike Batty, Network Complexity
–  CASA,UCL
•  Prof Muki Haklay, Public Engagement
–  CEGE & Mapping for Change, UCL
•  Prof. Gloria Laycock, Crime Science
–  UCL’s Jill Dando Institute
Associated projects
STANDARD Spatio-Temporal Analysis of Network Data and Route Dynamics
http://standard.cege.ucl.ac.uk
The Uncertainty of Identity Linking Spatiotemporal Information between Virtual and Real Worlds
http://www.UncertaintyOfIdentity.com
Your involovement & participantion
more details at: http://www.ucl.ac.uk/cpc
- news
- future workshops
- publications
- sample data, visualisations
Keep up to date:
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Crime Po icinggCitizenshipp
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Acknowledgements
Crime Po icinggCitizenshipp
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01100010111space-time interactions of dynamic networks
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