Profiling Burglary in London using Geodemographics Chris Gale 1 , - - PowerPoint PPT Presentation

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Nov 26, 2023 221 likes •521 views

Profiling Burglary in London using Geodemographics Chris Gale 1 , Alex Singleton 2 , Paul Longley 1 1 2 Geodemographic Classifications A Geodemographic Classification: Simplifies a large and complex body of information about a

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Chris Gale1, Alex Singleton2, Paul Longley1

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Profiling Burglary in London using Geodemographics

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Geodemographic Classifications

A Geodemographic Classification:

– Simplifies a large and complex body of information about a population, where and how they live and work – Based on premise that similar people live in similar locations, undertake similar activities and have similar lifestyles and that such area types will be distributed in different locations across a geographical space

Clustering algorithms partition demographic data into groups sharing similar

characteristics

Commercial (such as Mosaic created by Experian and Acorn created by CACI)

and free (2011 OAC) classifications available

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Geodemographic Classifications of London

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2011 LOAC and coding crime data

Coding crime data to

the 2011 LOAC

Police.UK data from

December 2010 to July 2014

Burglary the only crime

category used

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2011 LOAC and coding crime data

Assignment of different

2011 LOAC Supergroups to Output Areas in London

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2011 LOAC and coding crime data

Road network overlaid
n the 2011 LOAC

Supergroups

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2011 LOAC and coding crime data

Police.UK reported

crime centroids added (for 4 million crime events)

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2011 LOAC and coding crime data

94,667 Voronoi

polygons created based on reported crime centroids

Provides an estimation
f the geography used

to report crime on Police.UK

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Assigning crime events to the 2011 LOAC

Two methods of

attributing reported crime events to 2011 LOAC Supergroups tested:

– Centroid location – Proportional assignment

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Example of centroid location method

100 burglary events assigned to centroid = 100 burglary events assigned to ‘High Density and High Rise Flats’

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Example of proportional assignment method

100 burglary events assigned to centroid = 80 burglary events assigned to ‘High Density and High Rise Flats’

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Example of proportional assignment method

100 burglary events assigned to centroid = 20 burglary events assigned to ‘City Vibe’

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Assigning crime events to the 2011 LOAC

Only small differences

between the two methods

However, if fewer crime

event records and/or a smaller geographic area of study then greater discrepancies between the two methods are likely

32 BOROUGHS OF LONDON

Recorded crimes based

n centroid

locations Recorded crimes based

proportional assignment Difference A: Intermediate Lifestyles 10.25% 10.26% 0.02% B: High Density and High Rise Flats 13.88% 13.56%

0.31%

C: Settled Asians 11.38% 11.09%

0.28%

D: Urban Elites 16.36% 16.51% 0.16% E: City Vibe 15.28% 15.48% 0.20% F: London Life-Cycle 8.69% 8.52%

0.18%

G: Multi-Ethnic Suburbs 18.34% 19.10% 0.76% H: Ageing City Fringe 5.84% 5.47%

0.36%

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Index Scores of burglary rates by 2011 LOAC Supergroup

Score of 100 equates

to London’s 2010 to 2014 average of 98 burglaries being committed per 1,000 dwellings

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MPS Neighbourhood Classification

Built by the GLA
Based on the 2011 OAC

methodology

Uses a different

geography and 99 variables (from the 2011 Census and the London Datastore Ward Atlas)

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Public Attitude Survey (PAS)

Used to elicit the public’s

perceptions of policing needs, priorities and experiences with the Metropolitan Police Service (MPS)

21 questions used to measure

public confidence in the MPS

33 to 34 interviews carried out per

month per Borough

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Neighbourhood Confidence & Crime Comparator

Interactive web-tool built

visualising the 12 MPS Neighbourhood Classification clusters, Public Attitude Survey data and crime data

Overall confidence in the

MPS across London

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Neighbourhood Confidence & Crime Comparator

MPS Neighbourhoods

assigned to the ‘Single- Living Centre’ cluster

Overall confidence in

the MPS

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Neighbourhood Confidence & Crime Comparator

MPS Neighbourhoods

assigned to the ‘Settled Multi-Ethnic’ cluster

Overall confidence in

the MPS

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Neighbourhood Confidence & Crime Comparator

MPS Neighbourhoods

assigned to the ‘Crowded Outer Suburbia’ cluster

How well do the MPS

communicate?

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Neighbourhood Confidence & Crime Comparator

MPS Neighbourhoods

assigned to the ‘Stressed Urban’ cluster

How well do the MPS

understand issues that affect the community?

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Neighbourhood Confidence & Crime Comparator

MPS Neighbourhoods

assigned to the ‘Single- Living centre’ cluster

Overall crime rate

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Summary

Profiling Police.UK data with the 2011

LOAC is an example of using geodemographics to derive insight from

pen data sources
MPS Neighbourhood Classification and

Neighbourhood Confidence & Crime Comparator are examples of creating bespoke geodemographic applications to contextualise public confidence in the police and crime levels in London

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www.ons.gov.uk/ons/guide-method/geography/products/area-classifications/ns-area-classifications/ns-2011-area-classifications/index.html

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www.opengeodemographics.com

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geogale.github.io/2011OAC

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ac.datashine.org.uk

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plus.google.com/u/0/communities/111157299976084744069

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Thank You

This work was supported by EPSRC grants EP/J004197/1 (Crime, policing and citizenship (CPC) - space-time interactions of dynamic networks) and EP/J005266/1 (The uncertainty of identity: linking spatiotemporal information between virtual and real worlds) and ESRC grants ES/K004719/1 (Using secondary data to measure, monitor and visualise spatio-temporal uncertainties in geodemographics) and ES/L011840/1 (Retail Business Datasafe).