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 ¡ ¡ ¡ C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
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 C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
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’ C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
• 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. C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
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 15- second 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 co- evolve.
The aim of the project will be to utilise integrated spatio- temporal 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. C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
Programme & Methods Phase 1: Data Acquisition and Conflation 1 Month UK Data Archives MPS MfC CAD Policemen Understanding Census Community Transport, MetP BCS Database GPS Data Society (F3) Mapping Weather, .., Surveys (F1/F3) (F1) (F1) (F2) (F3) (F3) (F2) Phase 2: Space-Time Patterns of Individual Networks 7 Months (STC, SVM; STWR; STK; STV) N3: Citizen N1: Crime N2: Police Exploratory Space-Time Analysis & Crime Patterns Citizen Profiles Police Movement (F1) (F3) (F2) Phase 3: Interaction of Networks Visualisation 16Months Q2: Citizen & Crime Q1:Crime & Police Q2: Police & Citizen (F3/F1) (F1/F2) (F2/F3) Phase 4: Policy Evaluation 28 Months Q3: Police Resources Allocation Q3: Crime Intervention Q3: Citizen Engagement (F2) (F1) (F3) Phase 5: A Web-base Platform for Dynamic Visualization and Simulation 37 Months Data & Model Updating Knowledge Transfer User Evaluation Online Courses/Software (whole team) (whole team) (whole team) (whole team) 42 Months Figure 1: Workflow of CPC (F1, F2 and F3 are PDRAs)
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: mailing list, Twitter, LinkedIn, blogs C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
Acknowledgements C rime P o icing C itizenship g p p 010001110010110111010101011100011101 1110000101011010111000011 00 01100010111 space-time interactions of dynamic networks 1100001010101011010101011110001010111000101100001100
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