chain ey

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Article

Identifying Priority Neighbourhoods Usingthe Vulnerable Localities IndexSpencer Chainey∗

Abstract The growth of the intelligence-led paradigm in policing and crime reduction partnerships has also called forthe need to develop analytical techniques that can aid the development of neighbourhood-level intelligence. A techniquethat has generated increasing interest in England and Wales to help identify neighbourhoods that require prioritisedattention is the Vulnerable Localities Index (VLI). This is a composite measure that is calculated using six variables.The VLI aids the systematic identification of priority neighbourhoods, using a methodology that can be applied inany part of England and Wales (regardless of differences in crime levels), and at any level of geographic scale. It hasbeen pilot tested across eight sites and is gaining particular interest in aiding neighbourhood policing and partnershipintelligence requirements. This paper describes the background to the VLI, the criteria that were considered to helpidentify suitable variables, and the methodology for combining the variables to form a single composite index. This isillustrated with data from Middlesbrough which is then used to demonstrate the VLI’s use in practice, illustrating howthe Safer Middlesbrough Partnership has used the VLI to support partnership intelligence.

Introduction

The growth of the intelligence-led paradigm inpolicing and crime reduction partnerships has calledfor a better understanding of crime, disorder andanti-social behaviour problems. This has also calledfor the need to develop analytical techniques that canaid the development of neighbourhood-level intel-ligence. One of these techniques is hotspot analysis,the process of identifying places that display highconcentrations of crime (Eck et al., 2005; Chaineyand Ratcliffe, 2005). This type of analysis now com-monly feeds into intelligence-led policing processesby helping to identify areas that require some formof targeted resourcing. However, hotspot analysis

∗Spencer Chainey, Director of Geographical Information Science at the Jill Dando Institute of Crime Science, UniversityCollege London, UK. E-mail: [email protected]

does tend to focus attention towards town centres,shopping malls and entertainment complexes. Thisoften means that neighbourhood areas where peo-ple live are given less attention and can even be over-looked. This can also limit the ability to recognizeother characteristics about these residential neigh-bourhoods, such as their socio-economic conditionsand how these norms influence an area’s commu-nity safety. Recognition of these characteristics mayalso provide a clearer opportunity for police agen-cies to work closer with local partners by connect-ing to their particular stream of service delivery (e.g.housing, education, youth services and neighbour-hood renewal), and in doing so, identifying clearer

Policing, Volume 0, Number 0, pp. 1–14doi: 10.1093/police/pan023C© The Authors 2008. Published by Oxford University Press on behalf of CSF Associates: Publius, Inc. All rights reserved.For permissions please e-mail: [email protected]

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2 Policing Article S. Chainey

opportunities to develop crime prevention and of-fender diversionary schemes.

A technique that has generated increasing inter-est in England and Wales to help identify neigh-bourhoods that require prioritized attention is theVulnerable Localities Index (VLI). The VLI is a com-posite measure that is calculated using six vari-ables. It was designed by the Jill Dando Insti-tute of Crime Science (JDI) at University CollegeLondon in collaboration with the National Centrefor Policing Excellence (NCPE—now part of the Na-tional Policing Improvement Agency). The VLI aidsthe systematic identification of priority neighbour-hoods using a methodology that can be applied inany part of England and Wales (regardless of dif-ferences in crime levels), and at any level of geo-graphic scale most suitably the Basic Command Unit(BCU) or local authority district. It has been pilotedacross eight sites1 and is gaining particular interestin aiding neighbourhood policing and partnershipintelligence requirements (see Ottiwell, 2008;Bullen, 2008; GMAC, 2007).

This paper describes the background to the VLI,the criteria that were considered to help identify suit-able variables, and the methodology for combiningthe variables to form a single composite index. This isillustrated with data from Middlesbrough. The ‘Ap-plying the VLI in Middlesbrough’ section then takesthis Middlesbrough example and demonstrates theVLI’s use in practice, illustrating how the Safer Mid-dlesbrough Partnership has used the VLI to supportpartnership intelligence.

The Vulnerable Localities IndexThe VLI is a measure that can be used to supportpolice and Crime and Disorder Reduction Partner-ships (CDRPs) identify neighbourhoods that requireprioritized attention. In problem-solving method-

1 Chainey (2004) and Dallison (2005) provide examples from pilot sites in the West Midlands and Lancashire.2 The UK Neighbourhood Policing Programme recommends the use of the VLI for supporting strategic analysis that supportsneighbourhood policing tasks. See NCPE (2006).3 See Home Office (2001a,b; 2003) and LGA (2002) for the UK Government review.

ological terms, it can act as a ‘scanning’ techniquethat helps to identify and assess particular localitiesthat require further analytical attention. This in-cludes drawing out reasons why the area may be con-sidered as particularly vulnerable and testing thesehypotheses.

The origins of the VLI derive from research thatdeveloped new national policing guidance on com-munity cohesion. With an initial focus that drewfrom the reviews of the riots in Bradford, Burnley,Oldham and Wrexham in 2001, the applicationof the VLI is now applied more widely for sup-porting Neighbourhood Policing2 and partnershipintelligence development. In this section, we beginby exploring its methodological foundation forsupporting national policing community cohesionguidance that then helps explain why its applicationis gaining interest in supporting wider agendas.

Defining and Measuring CommunityCohesionThe riots in Bradford, Burnley, Wrexham andOldham in 2001 resulted in a number of govern-ment reviews that explored how the civil disordersemerged, if there were common themes between theincidents, and recommendations that would helpprevent them from happening again, not just in thefour towns that were affected, but to pre-empt andprevent similar disorders in other areas.3 The re-views’ findings included the recognition of an un-dercurrent of poor socio-economic conditions ineach of the communities that were affected, charac-terized by high levels of deprivation, disenfranchise-ment of young people, high unemployment, lack ofa strong cultural identity, active far right groups andhigh levels of crime. The reviews also suggested that‘community cohesion must be a central aim of gov-ernment, reflected in all policy making including

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Identifying Priority Neighbourhoods Using the VLI Article Policing 3

regeneration’, and that ‘further violence is likely ifgovernment, police and community leaders fail tobreak this polarisation’.4

Soon after the reviews, national guidance on com-munity cohesion was published,5 with the policeservice seen to have a specific role in protectingcommunities by identifying and addressing issuesof disproportionate criminality, victimization andtension, whilst also appreciating the factors that in-fluence the undercurrent of disproportionality.6 Toperform this role, it therefore required the police ser-vice to identify those communities that were mostaffected and respond to their needs in coordinationwith their local partners.

In policing, the practicality of systematically iden-tifying areas displaying disproportionate levels ofcriminality, victimization and tension, all of whichmay rise and fall across a spectrum of time lines, wasthough problematic. In those police forces that firstattempted to do this, the starting point was to lookfor ‘tension indicators’, expecting them to be easilyfound so that a precise prediction could be made ofwhen and where disorder would most likely occur(Knowles, 2003). In practice, these ‘tension indi-cators’ do not readily exist, or police informationsystems that were in place failed to routinely col-lect and analyse the assessment of these risks. Therewas therefore a need to consider other systematicprocesses, drawing upon data that were available toidentify neighbourhoods that required some priori-tized attention. From this, a deeper understanding ofthe problems experienced in these neighbourhoodscould be established from further analysis and com-munity engagement.

By 2003 the National Neighbourhood PolicingProgramme and Reassurance Programme were alsogathering pace, and due to the synergies betweenthese programmes and the development of policecommunity cohesion guidance, it was felt that each

4 Home Office (2001a).5 Home Office (2003) and LGA (2002).6 NCPE (2003).

was addressing complementary themes and so couldcome together under a single Neighbourhood Polic-ing Programme. This also meant that the creationof any systematic analytical technique for identifyingpriority neighbourhoods should also ideally supportthese wider agendas.

There is an extensive literature on communitycohesion and the related fields of social exclusion,social efficacy and social capital that has identifiedmany of the conditions that help explain why certaincommunities suffer. These include identifying theinfluence of educational attainment (Putnam, 2000;Coleman and Hoffer, 1987; Marjoribanks and Kwok,1998), family structure and parenting (Teachmanet al., 1996), social isolation (Portes, 1998), socialnetworks (Putnam, 2000) and crime (Young, 2002).

There is however no widely held consensus onhow to measure community cohesion. Many ofthose working locally with communities can oftenintuitively sense the level of community cohesionthat is present, but quantifying this as a measure hasproven to be problematic. However, any approachesthat have been developed recognize that commu-nity cohesion has many facets, therefore requiringdifferent metrics to be applied to different func-tions. This was recognized when a measure to iden-tify communities in breakdown for policing pur-poses was being considered, with the intention todraw from metrics that identified disproportionatelevels of criminality and victimization set in con-text against an undercurrent tone of socio-economicconditions.

In methodological terms, it was considered prac-tical if the technique that was adopted fitted intoa problem-oriented approach for identifying, un-derstanding and responding to problematic neigh-bourhoods. This would complement the reform andmodernizing programmes in policing and commu-nity safety partnerships and also help to introduce

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further scientific rigour to their strategic assessmentprocesses. The intention was therefore to develop atechnique that supported the scanning of vulnerablelocalities and identify those areas that required pri-oritized attention. This prioritized attention wouldinitially include analysis that helped better under-stand the problems in those areas identified.

In addition to considering the findings from re-search on factors that influence community cohe-sion, the determination of a suitable technique(based on discussions held between NCPE, the JDIand the pilot sites) also had to meet the followingcriteria:

� It needed to be consistently applicable to all po-lice forces and CDRPs in England and Wales.This meant that all areas could apply the tech-nique, regardless of their metropolitan or ruralstatus.

� It had to identify communities and neighbour-hoods, and therefore required data to be of ahigh geographic precision.

� The methodology needed to be consistent, inthat the data that were used were available forany area in England and Wales.

� The data had to be easy to source and apply.� The technique needed to be simple to calculate

and practical.� The calculation of the measure and its appli-

cation should assume the need for very littletraining or no training.

� The technique had to fit into existing NationalIntelligence Model processes.7 Ideally the mea-sure should be developed so that it helps in-form strategic priorities on a routine basis (i.e.within a strategic assessment) and that addi-tional analysis of vulnerable localities wouldbe performed within a problem profile.

� The output would need to be concise, ide-ally generating a single indexed measure rather

7 National Criminal Intelligence Service (2000).

than a number of variables that had to be con-sidered separately.

� While the use of geographical information sys-tems (GIS) is now standard in most policeforces in England and Wales, different types ofGIS are used. Any calculations would thereforebe best performed in software that was moregeneric (e.g. Microsoft Excel) to help ensuresome standardization in the calculation pro-cess, but allowing for results to be importedand visualized in a GIS.

� The technique had to be accurate in the areas itidentified. This also meant that any data usedhad to be of good quality.

The measure that was developed as a result ofthese considerations was the VLI.

The Methodology for Calculating the VLIThe VLI combines crime data with other variablesabout neighbourhoods to identify localities thatare considered to require prioritized attention. Sixdatasets are required to calculate the VLI. Two ofthese are based on recorded crime data: burglarydwelling and criminal damage to dwelling; two de-scribe deprivation conditions: income and employ-ment deprivation; and two are derived from the Cen-sus of Population: educational attainment and theproportion of young people who make up the lo-cal population. These six variables were chosen aftercareful consideration of both the criteria set for theVLI’s use and from research that identified thesevariables as being relevant for identifying neigh-bourhoods that require priority attention.

To identify areas at the local level, the VLI usespoint-level crime data, Census data for Output Areas(OAs) and Lower Super Output Area (LSOA) dataon deprivation. To add these data into a single com-posite index requires each variable to be normalizedto the same statistical form. For example, it makeslittle sense in aggregating a count of burglaries for

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Table 1: Neighbourhood Statistics Service sources for OA household data, OA population data, LSOA income andemployment deprivation data, OA educational attainment data and OA young population dataa

OA households • File name: Accommodation type household spaces (UV56)• Data field: All household spaces

OA population • File name: Usual resident population (KS01)• Data field: 2001 population, all people

LSOA income and employment deprivation • File name: Indices of deprivation for super output areas, 2007• Worksheets: Income and employment• Data field: Income score and unemployment score

OA educational attainment • File name: Qualifications (UV24)• Data fields to use: ‘No qualifications’ and ‘Level 1’ achievement

OA young population • File name: Age (UV04)• Data fields: Population in each OA aged between 15 and 24 (inclusive)

aSource: http://neighbourhood.statistics.gov.uk/.

an area with a number describing the population ofyoung people in that same area. Both these statisticsneed to be converted to a form that allows them to beaggregated. This process is explained in the sectionsbelow. We begin though by directing the reader tothe data sources and how they can be collated into asingle application for aggregation.

Calculating the VLI is best performed in spread-sheet software such as Microsoft Excel. An Exceltemplate for the VLI has been created to help calcu-late VLI values.8

Output Areas, Household and PopulationValuesOAs are the definitive polygon geography for theEngland and Wales Census and now form the build-ing blocks for many other forms of local-level geo-graphical analysis. OAs are available in each policeforce and local council through the Mapping Ser-vices Agreement licence arrangements. These dataexist as GIS-formatted polygons, attributed with aunique reference code for each OA.

The VLI normalizing approach uses householdand population data for each OA, therefore requir-ing this data to also be sourced. These data areavailable for free, and can be downloaded as Mi-crosoft Excel files from the Neighbourhood Statis-

8 This is available at http://www.jdi.ucl.ac.uk/crime mapping/vulnerable localities/index.php.

tics Service. Table 1 lists the sources for these data.Once downloaded, OA data that are relevant to thestudy area can be identified and inserted into theVLI Microsoft Excel template file. This first step alsoincludes inserting the OA codes for the study areainto the VLI template. This is illustrated in Figure 1,showing example data for the BCU and CDRP dis-trict of Middlesbrough.

Crime VariablesThe VLI uses two crime variables: burglary dwellingand criminal damage to a dwelling. Crime datashould be for the period of the previous 12 monthsand include metre precise geocoded coordinates.These crime data can be sourced from the localpolice force’s crime-recording information system.These data need to be converted into counts foreach OA. This can be performed in a GIS using aroutine that generates a count of the number of bur-glary dwellings and criminal damage to dwellingsin each OA in the study area. These data can thenbe exported from the GIS and inserted into the VLItemplate, matching the crime counts to the relevantOA records. The result of this process is illustrated inFigure 2 that shows burglary dwelling and criminaldamage to dwelling counts for a sample of OAs inMiddlesbrough.

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Figure 1: OA, household and population data for Middlesbrough, extracted from data sourced from theNeighbourhood Statistics Service and inserted into the VLI template

Deprivation VariablesData on deprivation conditions in England andWales are available from the Index of Deprivation.This Index is updated approximately every fouryears, with the most recent version being published

in December 2007. The Index presents deprivationin different domains, including domains on incomeand employment. Income and employment depriva-tion are considered to be useful variables that helpto describe some of the underlying conditions in

Figure 2: OA burglary dwelling and criminal damage to dwelling data for Middlesbrough inserted into the VLItemplate

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Figure 3: OA income and employment deprivation data for Middlesbrough inserted into the VLI template.OAs 00ECNC0001 and 00ECNC0002 fall in the same LSOA, hence they have the same income and employmentdeprivation values

neighbourhoods that influence the area’s vulnera-bility to crime and disorder.9

The Index of Deprivation can be sourced fromthe Neighbourhood Statistics Service. Table 1 liststhe sources for these data. Data that are relevantto the study area can be extracted from the Indexof Deprivation, imported into a GIS and joined totheir relevant OAs. This linking process thereforerequires income and employment deprivation datato be linked to OAs via the LSOA within which eachOA is part. This means that those OAs that fall withinthe same LSOA will be assigned the same incomeand the same employment deprivation values. Thisis illustrated in Figure 3, showing a sample of OAsin Middlesbrough listed with their income and em-ployment deprivation values. Deprivation data atthe LSOA level help to complement the OA-baseddata used in the VLI by profiling the neighbour-hood environment that may underpin communityproblems.

9 This is illustrated in Chainey (2004) and the government reviews of the riots; see Home Office (2001a,b).10 This is illustrated in Chainey (2004) and the government reviews of the riots; see Home Office (2001a,b; 2003) and LGA(2002).

Demographic VariablesOA data on educational attainment and populationage bands are available from the NeighbourhoodStatistics Service and derived from the 2001 Census.These two variables describe other important fea-tures about neighbourhoods that could be indica-tive of community safety problems.10 That is, an areathat has low levels of educational attainment, a highrelative concentration of young people, is deprivedand experiences high levels of burglary and criminaldamage to dwellings is likely to be a neighbourhoodthat requires some prioritized attention.

Education and qualification data in the 2001Census are based on the highest qualification at-tained by each member of the population. Personswho have low levels of educational attainment areconsidered to have no qualifications or have onlyachieved Level 1 status (1 + ‘O’ Level passes, 1 +CSE/GCSE any grades, NVQ Level 1, FoundationGNVQ). Table 1 lists the source for these data. After

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Figure 4: OA educational attainment data (population that only achieved a status that was less than Level 2)and young persons’ population data for Middlesbrough inserted into the VLI template

the file has been downloaded and data relevant to thestudy area have been extracted, these data can be im-ported into the VLI template by firstly summing the‘no qualifications’ and ‘Level 1’ qualifications, andmatching these values to the relevant OA records.The result of this process is illustrated in Figure 4that shows the number of people that only achievedan educational attainment status that was less thanLevel 2 for a sample of OAs in Middlesbrough.

Table 1 lists the source of the young populationdata. After the file has been downloaded and datarelevant to the study area have been extracted, thesedata can be imported into the VLI template by firstlysumming each of the individual age categories be-tween 15 and 24 (to create a population count ofthose in each OA between 15–24), and matchingthese values to the relevant OA records. The resultof this process is illustrated in Figure 4 that showsthe number of young people for a sample of OAs inMiddlesbrough.

Normalizing the Six Variables andGenerating the VLITo aggregate these six variables to form the VLI re-quires each data variable to be converted to some

normalized form. In the first instance, this requirescrime data to be converted into crime rates, and ed-ucational attainment and young population countsto be converted into percentage representations. Theaverage of each statistic across the OAs in its studyarea is then calculated and acts as the benchmarkvalue against which each OA value is converted intoindex form. The following example helps to demon-strate this method.

� An OA recorded four burglary dwellings over a12-month period. The number of householdsin this OA is 110. The burglary dwelling ratefor this OA is 36.4 per 1000 households perannum.

� The average burglary rate across all OAs in thestudy area is calculated as 20.3 burglaries per1000 households per annum.

� The average (20.3) acts as the benchmark. In-dex values for each OA are calculated usingthe formula: (Rate in OA/Average rate acrossall OAs in the study area)∗100.

� The OA that experienced four burglaries there-fore has a Burglary Dwelling Index score of 179[i.e. (36.4/20.3)∗100].

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Table 2: VLI indexed variables and the total VLI for an OA in Middlesbrough

OA

Hou

seho

lds

Popu

lati

on

Burg

lary

dwel

ling

BDra

te

BDin

dex

Cri

min

alda

mag

e

CD

rate

CD

inde

x

Inco

me

depr

ivat

ion

IDin

dex

Empl

oym

ent

depr

ivat

ion

EDin

dex

<Le

vel2

Perc

enta

ge<

Leve

l2

Edu.

inde

x

Youn

gpe

ople

aged

15--2

4

Perc

enta

geof

peop

leag

ed15

--24

YPin

dex

VLI

00ECND0019 128 303 5 39.1 177 13 101.6 250 0.52 191 0.32 146 134 44.2 115 30 9.9 70 158

Normalizing burglary data and the other variablesto a common form makes it possible to aggregatethem in a meaningful manner. This is illustratedin Table 2. Income and employment deprivationdo not require conversion to a rate, percentage orother similar value because each of these variablesare already in a normalized form. All that is requiredfor each of the deprivation measures is that they beconverted into an index form similar to the crimeand Census variables. For example, if an OA had anincome deprivation value of 0.32 and the averageincome deprivation value across all OAs in the studyarea was 0.27, the indexed value for this OA wouldbe 119: (0.32/0.27)∗100. The VLI is calculated asthe sum of each of the six variables’ indices dividedby 6.

Once VLI values have been calculated for each OAin the study area, these data can be imported into aGIS and thematically mapped. Practice suggests thatfive thematic classes should be used and set with thefollowing thematic thresholds:

� Greater than 200� 160–200� 120–160� 80–120� 0–80.

OAs with a value of 100 are considered to be aver-age for the area. The higher the VLI score, the morevulnerable the locality.

Applying the VLI in MiddlesbroughTo help the Safer Middlesbrough Partnership iden-tify priority neighbourhoods for its partnershipstrategic assessment, the VLI was calculated for eachOA in the district and thematically mapped in a GIS.Figure 5 shows the VLI for Middlesbrough and fromthis, eight areas were identified as priority neigh-bourhoods by the Partnership:

� Gresham� Pallister� Grove Hill� Beckfield� University� Brambles Farm� Berwick Hills� Hemlington.

Identifyingthese priority areas then enabled thePartnership to explore additional qualities abouteach area. Examples are provided in Figure 6 show-ing those areas where offenders who are consideredto be at a high risk of being re-convicted reside(based on Probation Service offender assessments),hotspots of nuisance caused by young people (basedon Middlesbrough Council recorded anti-social be-haviour complaints), fear of crime levels (derivedfrom the Middlesbrough Fear of Crime Survey) andareas where people using or dealing with drugsare considered to be a problem (based on British

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Figure 5: Priority neighbourhoods in Middlesbrough calculated using the VLI (based on crime data from1 August 2006 to 31 July 2007). An area with an index value of 100 is similar to the district average. The higherthe index value, the more vulnerable the locality

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Figure 6: Offender residence, anti-social behaviour, fear of crime and perceptions of neighbourhood problemsin Middlesbrough: (a) the distribution of offenders with a high risk of re-offending, (b) hotspots of nuisancecaused by young people, (c) fear of crime levels (by wards) and (d) locations where people using or dealing withdrugs are considered to be a problem

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Table 3: Priority neighbourhoods in Middlesbrough in relation to crime, disorder, anti-social behaviour anddeliberate fire hotspots, fear of crime and presence of large relative numbers of offenders at risk of re-offendinga

Criminal damage Disorder, teenager Fear of crime (percentagePriority and theft from ASB, flytipping and of very and a bit Offendersneighbourhood motor vehicles deliberate fires unsafe--wards) (re-offending risk)

Gresham CD and TFMV Dis:Fly:Fire 7% High:Med:LowUniversity CD and TFMV Dis:Fly 10.8% Med:LowPallister CD Dis:ASB:Fly:Fire 1.5% LowBrambles Farm CD Dis:ASB:Fly:Fire 3.5% LowGrove Hill CD and TFMV Dis:ASB:Fire Not available High:Med:LowBerwick Hills CD and TFMV Dis:ASB Not availableBeckfield CD Dis:ASB:Fly 4.9% LowHemlington CD Dis:ASB:Fly:Fire 14% Med:Low

CD: criminal damage; TFMV: theft from motor vehicles.aSource: Safer Middlesbrough Partnership, 2007.

Crime Survey data linked to ACORN postcode-levellifestyle classifications). An assessment of these datathen allowed the Safer Middlesbrough Partnershipto succinctly capture in its strategic assessment cer-tain characteristics about each of the eight priorityneighbourhoods. This is shown in Table 3 whereeach priority neighbourhood is listed with a descrip-tion of the presence of certain crime, disorder andanti-social behaviour hotspots, fear of crime levelsand the presence of offenders who are at a high,medium or low risk of being re-convicted. As a re-sult, the neighbourhoods of Gresham and GroveHill were seen to stand out as two areas requiringparticular attention.

The assessment of these priority neighbourhoodshas in turn helped Middlesbrough focus strate-gic community safety plans to the eight priorityneighbourhoods.11

Discussion and PracticalConsiderationsA testament to the suitability of the VLI for support-ing the identification of priority neighbourhoods forpolicing and community safety purposes in Eng-land and Wales has been in its increasing use inpractice. Its application has been seen to support

11 Safer Middlesbrough Partnership (2007).

policing intelligence requirements in Cumbria, De-von and Cornwall, Lancashire, Cheshire, Norfolk,Warwickshire, Cambridgeshire, Sussex, North Walesand London, and in community safety partnershipapplications across Greater Manchester, Merseyside,Nottinghamshire, Middlesbrough, Hartlepool, Red-ditch and Northumberland County. Whilst the VLIdoes not explain why certain places are experiencingparticular problems, it does meet its ‘scanning’ ob-jective by identifying neighbourhoods that requiresome prioritized attention from which hypothesesover their cause can be tested.

Some practitioners have queried the use of thesix variables, suggesting that data on anti-social be-haviour, housing voids, racial incidents and ethnic-ity mix (amongst others) may be more suitable;however, in each case to date no suitable alterna-tives have been found that also meet the criteria thatwas set in creating the VLI. For example, anti-socialbehaviour is not consistently recorded nationallytherefore making it difficult for data of this type tobe implemented as a variable into the VLI in a man-ner that could be applied in all BCUs and CDRPs inEngland and Wales. Indeed in Wigan, the CDRP cre-ated an ‘alternative VLI’ using several other variablesfor identifying priority neighbourhoods, and aftermuch effort in sourcing the alternative datasets, the

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areas identified were the same as the priority neigh-bourhoods the VLI had originally identified (Bullen,2008).

The VLI uses Census data, updated every 10 years,and deprivation data that are updated every 4 years.This has led some people to question if the VLIprovides an up-to-date measure. Practice tends tosuggest that patterns of educational attainment lev-els and demographic characteristics do not changeat a high rate, and although the population thatmade up the numbers in the 2001 Census couldhave changed (e.g. 18 year olds who lived in the areain 2001 may no longer live there in 2008), the char-acteristics of the population that are resident theretoday are broadly similar. The same considerationsapply to deprivation. The VLI is though a scanningtool, and in some instances may incorrectly identifyplaces that require prioritized attention because theyare already receiving targeted activity. For example,in Burnley, one area identified by the VLI had since2001 been targeted for a large-scale neighbourhoodrenewal scheme, and although before the scheme,the area had all the ingredients for prioritized at-tention, action in this area was already underway(Dallison, 2005).

Those areas that have been using the VLI for sev-eral years also see its applicability in helping to mon-itor service improvements, by updating the VLI an-nually with a refresh of crime data (and every fouryears with deprivation data) for their strategic as-sessment and checking if resources that have beentargeted to priority neighbourhoods are having im-pact. The VLI has also importantly exposed policeforces to non-crime data, and introduced them toanalysis techniques that they have not previouslyused.

ConclusionThe VLI was initially developed to support guid-ance on community cohesion for the police service,but has since then also supported wider police andpartnership intelligence requirements. Its increas-ing popularity in practice is considered to be due

to its accuracy and ease of application, stemmingfrom the tight criteria that the JDI and NCPE es-tablished when commencing the design of a suitablesystematic technique. The VLI has also helped po-lice forces and CDRPs identify areas that requirefocus beyond those that exist purely as high-crimeneighbourhoods, and has helped encourage part-nership activity by using information that not onlyconsiders data recorded by the police, but also socio-economic data that helps recognize and connect toother local-service delivery agendas, such as neigh-bourhood renewal.

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