Crime, Policing and Citizenship (CPC) - Space-Time Interactions of - - PowerPoint PPT Presentation

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 ¡ ¡ ¡

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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

• f 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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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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• 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
• ver wider spatial extents and longer timescales, and require

collaboration between police forces.

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.

• 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.

Opportunities – Public Perception

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

• ccurrences 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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Phase 1: Data Acquisition and Conflation

Policemen GPS Data (F2) CAD Database (F1) Understanding Society (F3) Census (F3)

Phase 2: Space-Time Patterns of Individual Networks Police Movement (F2) Q2: Police & Citizen (F2/F3) Q3: Police Resources Allocation (F2) N2: Police Crime Patterns (F1) Q1:Crime & Police (F1/F2) N1: Crime User Evaluation (whole team) Data & Model Updating (whole team) Phase 5: A Web-base Platform for Dynamic Visualization and Simulation

1 Month 7 Months 37 Months 42 Months

Knowledge Transfer (whole team)

BCS (F1)

Phase 3: Interaction of Networks

16Months 28 Months

Phase 4: Policy Evaluation

Exploratory Space-Time Analysis & Visualisation (STC, SVM; STWR; STK; STV)

Citizen Profiles (F3) Q2: Citizen & Crime (F3/F1) N3: Citizen Q3: Citizen Engagement (F3)

Figure 1: Workflow of CPC (F1, F2 and F3 are PDRAs)

Transport, Weather, .., (F2)

Q3: Crime Intervention (F1)

MetP Surveys (F1/F3) Community Mapping (F3)

Online Courses/Software (whole team)

MPS UK Data Archives MfC

Programme & Methods

Investigators: Kate Bowers; Tao Cheng; Paul Longley; John Shawe-Taylor

CPC Team (April 2012-September 2015)

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

more details at: http://www.ucl.ac.uk/cpc

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• sample data, visualisations

Keep up to date: mailing list, Twitter, LinkedIn, blogs

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Your involovement & participantion

Acknowledgements

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