Fire Safety Journal 62 (2013) 37–48

Contents lists available at ScienceDirect

Fire Safety Journal

0379-71http://d

n CorrE-m

ghiggs@

journal homepage: www.elsevier.com/locate/firesaf

Examining the use of a geodemographic classification in an exploratoryanalysis of variations in fire incidence in South Wales, UK

Jonathan Corcoran a,n, Gary Higgs b, Tessa Anderson c

a School of Geography, Planning and Environmental Management, The University of Queensland, 4072, Australiab GIS Research Centre, Wales Institute of Social and Economic Research, Data and Methods, Division of Computing and Mathematics,Faculty of Advanced Technology,University of Glamorgan, Pontypridd, CF37 1DL, United Kingdomc The Kadoorie Institute, 8/F T.T.Tsui Building, The University of Hong Kong, Pokfulam Road, Hong Kong

a r t i c l e i n f o

Available online 6 April 2013

Keywords:Fire incidentsGISCensus measuresGeodemographic classificationExploratory analysis

12/$ - see front matter & 2013 Elsevier Ltd. Ax.doi.org/10.1016/j.firesaf.2013.03.004

esponding author.ail addresses: jj.corcoran@uq.edu.au (J. Corcorglam.ac.uk (G. Higgs), tessa81@hku.hk (T. An

a b s t r a c t

Geodemographic classifications are increasingly being used to examine spatial patterns in for examplecrime incidence, higher education opportunities and inequalities in health outcomes. At the same timefire and rescue services are increasingly employing geodemographic classifications in range of opera-tional and strategic tasks. Geodemographic classifications have been used in a number of applications tocharacterise areas based on their social circumstances and are multi-dimensional by design; in contrastcensus derived measures tend to be uni-dimensional, measuring social or material deprivation on a scaleof high to low albeit derived as a composite of contributing factors. This study uses a database of fireincidents to examine the extent to which applying such classifications enables a discrimination of suchareas when compared to the use of more commonly used deprivation measures. Specifically trends in fireincidence are compared with both census-derived data and small area geodemographic classifications inorder to assess the value of such classifications as exploratory tools in investigating potential associationswith socio-economic patterns. These findings are couched in terms of wider debates regarding the use ofneighbourhood classifications in adequately capturing what are often complex patterns in fire incidentpatterns in relation to such factors as community cohesion and social capital. This in turn highlights theneed for more research to explore how geodemographic classifications can be used to provide acontextual basis for detailed analysis of local patterns of fire incidents.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

“Geodemographics is the analysis of people based on a statisticalclassification of the area in which they live” which “aims tocapture the important socio-economic dimensions of and differ-ences between, neighbourhoods” [1, p. 26]. Such classifications arecreated from data primarily collected from the census (e.g. [2]) or bycombining census, survey and commercially motivated lifestyleinformation using statistical clustering [1]. Traditionally theseclassifications have been used in the retail sector for a number ofcommercially driven aims [3–5]. More recently, such classificationshave been used to investigate variations in access to informationtechnology as part of wider ‘e-society’ debates at regional andsub-regional levels [6,7]. Geodemographics have also been used toexamine participation rates and attainment in secondary schools [8]and modelling participation in higher education [9]. These research

ll rights reserved.

an),derson).

efforts build on a growing literature on the use of such classifica-tions in analysing various aspects of public service delivery [10].Longley et al. [11] draw attention to the reasons why the applicationof geodemographics in such policy applications may differ to theiruse in private-sector organisations. First, the variables that areassociated with the consumption of private goods are likely todiffer from those that influence the collective consumption of forexample policing, health and fire services. Second, measures ofneighbourhood size and scales are not explicitly incorporated intosuch measures which tend to be based on the “clustering of socialsimilarities”. Such factors, they postulate, “are likely to be importantwhen considering local preferences for school services or attitudesto local policing” [11, p. 60]. Third, the cocktail of variables that areused to construct such measures are often not available to certaintypes of users who may remain oblivious to how they have beencreated. Knowledge regarding the derivation of such classificationsmay be vitally important to certain types of public sector organisa-tions who may be required to justify funding allocation mechan-isms to taxpayers and policy-makers alike. This in turn has led to anumber of research initiatives that have explored the development

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–4838

of ‘open source’ geodemographics in applications such as participa-tion in higher education [12].

Geodemographic classifications are increasingly being exploredby a number of fire and rescue services in the UK in a range ofoperational and strategic tasks [13]. Nevertheless the theoreticaljustification for the use of such classifications as indicators ofneighbourhood characteristics potentially associated with variationsin for example fire incidence has remained a relatively neglected areaof research. Public agencies involved in analysing trends in crimepatterns, health inequalities and fire incidence have in the past usedmore traditional segmentation approaches by identifying sub groupswithin a population that might have similar individual or area-levelattributes such as age, gender and socio economic status. Geodemo-graphic segmentation has extended this approach to include infor-mation on behaviours, beliefs, habits and preferences to potentiallyoffer a more robust understanding of sub groups (or ‘at risk’ groups)within the population. It focuses on the distinctiveness of areas andpotentially offers the capacity to offer new insights into socialprocesses using a range of demographic and contextual variables toclassify small geographical areas by the predominant characteristicsof the locality and its residents. This in turn coincides with a growingrecognition of the importance of local context and policy respon-siveness to the needs of different communities [10]. The aim of thispaper is to draw on the previous research that has applied geodemo-graphic indicators in thematically similar areas such as crime patternanalysis in order to explore the potential application of one widelyavailable measure in the analysis of fire incidents in a fire and rescueservice area in South Wales, UK.

Williamson et al. [14, p. 194] suggest, in the context of the useof such measures in crime research, that such analyses “provide aricher perspective of neighbourhoods than can be provided bydeprivation indices alone”. An important element of our metho-dology therefore is to compare such findings with those from theapplication of a more ‘traditional’ measure of social deprivation inorder to explore potential associations with trends in differenttypes of fire incidents across the study area.

Previous research has used regression analysis to identify potentialassociations between spatial patterns of fire incidence and deprivationas measured by the Townsend Index [15] as well as a range of ‘free-standing’ socio-demographic variables such as the age profile, house-hold size and levels of car ownership [16,17]. To date, however, therehave been few published studies that have used geodemographicclassifications as exploratory tools in order to investigate detailedneighbourhood variations in the patterns of fire incidents, a researchgap addressed in this paper. The rest of the paper is structured asfollows; in Section 2 a brief overview of the use of geodemographicclassifications in the emergency services is presented which draws onrecent research that has demonstrated the potential use of suchclassifications in predicting dwelling fire risk. In the third section ofthe paper, the methodology used in the case study area is outlined inmore detail including a description of the database of fire incidentsand the geodemographic and deprivation measures used to studyspatial trends. The overall trends as revealed by an analysis of the twosets of measures are presented in Section 4 in order to gain a moredetailed understanding of the distribution and patterns of fireincidence before the final section of the paper which summarisesthe advantages and current limitations of such measures in explainingpatterns of fire incidence in relation to the socio-economic variationsacross the study area.

2. Use of geodemographic classifications in the emergencyservices

With the active marketing of geodemographic classificationsto public sector agencies, there is a growing literature on the

potential use of geodemographic classifications outside theirtraditional applications in the private and commercial sector[18]. These include for example those studies that demonstratetheir potential in addressing health inequalities [19–21] in profil-ing urban areas for regeneration policy initiatives [22,23], intargeting specific health campaigns [24–26], in contextualisingschool examination results [27], in investigating differential accessto green spaces [28], in examining demand for services such aschoice of school [29] or service provision planning [30], inexamining participation in higher education [31,32], in profilingroad accident drivers and casualties [33] and in investigatingpatterns of population migration [34]. Although many of thesestudies have their source in academic research initiatives, andseveral have remained prototype or illustrative studies, there is anincreasing use of these classifications in operational and strategicplanning within the emergency services. For example, in policeforces, geodemographic classifications are increasingly being usedin a number of application areas, including for example in;

�

deploying and targeting police resources [35] � analysing geographic variations in crime and policing perfor-

mance [36]

� identifying high crime-risk neighbourhoods [37] � targeting behavioural support and crime prevention pro-

grammes [38]

� predicting levels of social disorganisation [14] � analysing variations in youth offending [39]

Recent research has also demonstrated the potential of suchtechniques in profiling at-risk neighbourhoods in other contexts(for example, South Africa – [40]). Such studies have invariablyinvolved assigning a neighbourhood typology derived from ageodemographic classification to individual events or incidencesuch as the residential postcode of offenders, pupils attendingschools or patient episode data. In such cases the geodemographicclassification is often being used as a surrogate measure of socio-economic conditions in the absence of direct indicators at theindividual level [41]. Subsequent statistical analysis may involvethe use of attributes of the offenders or the nature of the crime forexample [39], the examination performance of pupils [42,43] orthe diagnosis for patients [26].

A number of fire and rescue services in the UK are usinggeodemographic classifications in combination with historicalrecords of fire incidence within fire safety teams to identifyrelationships between fire incident types and neighbourhoodgroups to help inform community safety initiatives [12]. This hasinvolved, for example, targeting areas for fire reduction initiativesthrough an investigation of fire dwelling rates for groups based onthe classification. For example, with regard to the ExperianMOSAIC™ classification, research has shown that fire incidenceis higher for people who live in areas described as havingsignificant numbers of:

�

people living in social housing with uncertain employment indeprived areas

�

low income families living in estate based social housing � older people living in social housing with high care needs

Geodemographic analysis is increasingly being used alongsideother techniques, with supporting information including localknowledge of fire incidence, to identify where safety and educa-tion initiatives should be targeted. The study by Smith et al. [13]used dwelling fire data at postcode level, but in order to compareto other datasets, such as census data and the Index of MultipleDeprivation, aggregated data to Lower Super Output Area (LSOA)and Local/Unitary Authority area to examine possible relationships

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–48 39

with rates of dwelling fire per million population using regressionanalysis. Specifically, they used a geodemographic classification toallocate risk categories to groups based on the individual factorsfound to be associated with increased risk. Thus a qualitativeapproach was used to provide a subjective fire risk ranking to eachgeodemographic group. The availability of geodemographic classi-fications at unit postcode and output area level, however, suggeststhat a more discriminatory analysis is possible. Whilst the use ofspatial models to investigate patterns of fire incidence at detailedscales continues to form the basis of research projects (see [44] fora recent example) to date at least few studies have examined thelevels of associations with geodemographic classifications bydifferent incident types. Methods by which this can be investi-gated are described in the next section.

3. Methodology

The South Wales fire and rescue service area covers a geogra-phical area of approximately 2811 km2, is made up of ten con-stituent unitary authorities with an estimated total population of1.44 million living in just over 600,000 households (Fig. 1).

The area includes some of the most deprived communities ofWales (in areas such as the former South Wales coalfield area andsome of the peripheral areas of the major urban areas) as well assome of the most affluent (for example the northern suburbs ofCardiff and Newport) and has large rural tracts in the Vale of

Fig. 1. The South W

Glamorgan and Gwent. A database comprising individually geo-coded fire brigade call-outs for incidents of primary fires of fourincident types was procured from the service. This included thoseinvolving property, vehicle and secondary fires and false alarmswith malicious intent (hoax calls). The database covers a four yeartime period spanning the 2001 Census of Population (1st January2000 to 31st December 2004) and describes 98,268 incidents(13,157 property fires, 16,723 vehicle fires, 62,444 secondary firesand 5944 hoax calls) for the South Wales region and has beendescribed in more detail by [15]. A summary description of the fireincident types is provided in Table 1 and a panel of maps depictingtheir spatial distribution across the study area in Fig. 2.

In order to discriminate between areas of high and low fire risk,two principal measures of socio-economic conditions have beenused at the output area level (an output area is the smallest spatialunit for which 2001 census data is provided with an averagepopulation of 297 people and 124 households for those in Englandand Wales with a confidentiality threshold of 40 households).These are the Townsend deprivation index [45] and Office forNational Statistics Output Area Classification (OAC) [46]. TheTownsend measure is a census-based index of material deprivationthat has been extensively used in social research studies in the UKas an indicator of socio-economic circumstances. It is calculatedusing four census variables based on relative levels of unemploy-ment, non-car ownership, non-home ownership and householdovercrowding [47]. The pattern of deprivation as revealed byTownsend is presented in Fig. 3 and such measures have been

ales study area.

Table 1Definitions of fire incident types (adapted from [15]).

Fire incident type Definition Number of incidents Percentage of total incidents

Property fire All fires involving property for example, dwellings,public buildings, workplaces

13,157 13.39%

Vehicle fire Vehicles 16,723 17.02%Secondary fire Derelict buildings and vehicles 62,444 63.54%

Refuse and refuse containersOutdoor structures, for example, fences, gates,and road signsGrass

Hoax call False Alarm deemed malicious or deliberate 5944 6.05%

Fig. 2. Spatial distribution of fire incident rates across South Wales (Output Areas). (Top left: property fires; Top right: hoax calls; Bottom left: vehicle fires; Bottom right:secondary fires).

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–4840

previously used, together with ‘free-standing’ census variablesfrom the 2001 Census of Population, to show the associationsbetween fire risk and deprivation [15]. This demonstrated that forall fire incidence types, rates were highest in the more deprivedcommunities of the service area but also that the use of suchcompound measures have the potential to “‘straightjacket’ theanalysis since high and low values of the separate input variablesmay cancel out in any one ward and thereby affect our ability tofully capture the associations” [15, p. 639].

In this study a descriptive analysis is provided of variations inrates of fire incidence after classifying output areas into quintilesof deprivation based on their Townsend score. This is thencompared to an analysis of such rates through the use of theOAC to characterise output areas in the study area. This geodemo-graphic classification of the UKs 223,060 output areas has theadvantage that it is freely available from the National Statisticswebsite and uses a methodology to group output areas that isfully transparent and is capable of being replicated by interested

Fig. 3. Townsend Deprivation Index for South Wales.

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–48 41

researchers [2,48] The classification, first released in July 2005,uses data from the 2001 Census to group output areas into groupsof similarity based on 41 census variables which measure socio-economic attributes of residents within each area. This includesinformation on demography, household composition, ethnic iden-tity, health, employment, occupation, commuting practices, hous-ing tenure and accommodation type. At the highest level ofaggregation seven ‘super-group’ clusters representing output areaswith similar socioeconomic profiles are derived using the k-meansclustering algorithm (see [46] for a more detailed account of themethodology). This enables potential recognition of those outputareas that exhibit similarities and contrasts across a broad range ofsocioeconomic indicators rather than the rather narrow focus ofthe Townsend Index quintiles. The distribution of the super-groupclassification for the South Wales study area is presented in Fig. 4.The analysis presented here is largely based on these super-groupsalthough we have also explored the use of the second tier in theclustering process—so called group level classifications (this con-sists of 21 clusters at the national level).

Traditionally measures such as the OAC have been used byprivate sector companies to for example append a geodemo-graphic code to each individual responding customer in theirdatabase based on their residential postcode to create an areatypology subsequent to further analysis. In the case of event data,this permits an analysis of the types of neighbourhood in whichsuch events are most likely to be located and to identify variationsin for example patterns in fire, crime, health data for example thatcharacterise each of the different geodemographic neighbourhoodtypes. In this study an OAC super-group and group were allocated

to each fire event (using a point in polygon operation spatiallyjoining the x,y location of the fire with the super-group and groupclassification of the census output area polygon in which it waslocated) that permitted a total count of fires for each to begenerated. Using the household population count of South Walesper OAC super-group and group and the count of fire events anindex score was then computed. This was achieved by dividing thetotal fire count by the total household population count andmultiplying this by the total household population for eachsuper-group and group. This produces what is known as theexpected value for each super-group and group (Eq. 1). To deter-mine the index score, the total number of fires event for eachsuper-group and group is divided by the expected value andmultiplied by 100 (Eq. 2).

EVg ¼∑fires∑pop � ∑popg ð1Þ

IVg ¼∑firesgEVg

� 100 ð2Þ

where

EVg ¼ expected value for a particular super-group and group

IVg ¼ index value for a particular super-group and group

∑fires¼ total number of fires

∑firesg ¼ total number of fires for a particularsuper-group and group

Fig. 4. Distribution of OAC super-groups across South Wales.

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–4842

∑pop¼ total household population

∑popg ¼ total household population for a particular

super-group and group

An index value (IVg) above 100 indicates higher than averagenumber of fires for that super-group (or group) and a value ofbelow 100 suggests that the area experiences fewer fires of thattype of fire incident than expected given the population in thatpostcode. The computed index values are calculated for aggregatetotals of fire incidents and by incident type. A key problem withrelying on index scores which has been recognised in recentresearch is that there is no indication of confidence regardingthese statistics. Such research has drawn attention to the need tocalculate confidence intervals for these rates in order to give abetter indication of the robustness of the index scores and theirpotential sensitivity to small counts. In the present study upperand lower 95% confidence intervals for index values were com-puted using Byar’s approximation following the method outlinedin Singleton’s [41] geodemographic analysis of educational pro-gression which in turn has been widely used in epidemiologicalstudies [50]. Here the 95% upper and lower confidence intervalsare calculated as depicted in the following:

IVLower ¼ IVg ¼ 1−19O

−Z∝=2

3ffiffiffiffiO

p� �3

ð3Þ

IVUpper ¼ IVg ¼ Oþ1O

� �1−

19ðOþ1Þ−

Z∝=2

3ffiffiffiffiffiffiffiffiffiffiffiOþ1

p� �3

ð4Þ

where O is the observed number of fire incidents for a particularsuper-group or group and Z∝=2 pertains to the value of thestandard normal distribution for a given significance level. In our

case we are interested in determining the 95% confidence limitswhere an alpha of 0.05 and Z∝=2 of 1.96 delineates this interval.

The Output Area Classification has been used in a number ofpolicy application areas such as accessibility to public transportopportunities [50] and household consumption modelling [51].There have also been attempts to tailor the OAC to particular localcircumstances such as the targeting of public health campaigns inLondon [25] and to build on the OAC methodology to create abespoke educational geodemographic system [12]. However, toour knowledge, there are no published academic studies that haveapplied the OAC in an exploratory analysis of small area variationsin fire incidents. Preliminary findings in this regard are describedin the next section.

4. Results

Fig. 5 shows the variations in incident rates for the four types offire incidents by plotting the range of incident output area rates byTownsend quintile (from greatest levels of deprivation on the lefthand side to more affluent quintiles in the right hand side of thegraph). As postulated these show a clear trend of highest incidencerates for the most deprived groups of output areas and an almostconsistent trend across quintile groups corroborating with theprevious work on fire using these data (see for example, [15]). The2001 Output Area Classification presented in Fig. 6 provides analternative approach to the analysis of fire incidence across the fireand rescue service area. The analysis groups output areas accord-ing to the seven (super-group) categories using the broader rangeof socio-economic indicators used in the clustering algorithms of[46] than used in Townsend Index calculations. In Fig. 6, to enablecomparison of trends in incidence rates, super-group categories

Fig. 5. Plots of incident rates against Townsend quintiles.

Fig. 6. Rates of fire by incident type and OAC super-group.

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–48 43

are arranged in the order of their average Townsend score left toright in the box-graphs enabling a comparison for the fourcategories of fire incidents to be made with the analysis byTownsend scores. This reveals a number of common trends acrossfire types which demonstrate the effectiveness of the classificationin consistently identifying the types of areas with relativelyhigh or low rates of different types of fire incidents. Ratesappear for example to be consistently higher for the ‘Multicultural’

super-group output areas which tend to be the most spatiallyrestricted clusters in and around the centres of the major urbanareas (for example Butetown, Grangetown and Riverside inCardiff). However, rates are also high for ‘Blue Collar Communities’which include those output areas made of peripheral housingestates to the north of Cardiff city centre for example as well asareas such as Ely, Llanrumney and Caerau on the eastern andwestern suburbs of the city. Importantly many of the output areas

Table 3OAC groups ordered by population and associated index score with 95% confidenceintervals.

OAC groups South Walespopulation

IndexScore

Lowerbound(95%)

Upperbound(95%)

1c: Older Blue Collar 156,896 104 102 1051b: Younger Blue Collar 144,202 145 143 1486a: Settled Households 135,356 89 87 911a: Terraced Blue Collar 125,324 125 123 1285b: Older Workers 121,346 132 129 1344c: Prospering Semis 101,070 45 43 466c: Young Families inTerraced Homes

100,131 93 91 96

4a: Prospering YoungerFamilies

81,126 37 36 39

4b: Prospering OlderFamilies

75,211 30 28 31

6b: Least Divergent 64,195 127 123 1304d: Thriving Suburbs 47,281 42 40 447a: Asian Communities 39,171 163 158 1672b: Settled in the City 37,104 67 64 706d: AspiringHouseholds

35,009 35 33 38

5c: Public Housing 34,508 191 186 1973c: AccessibleCountryside

31,144 144 139 149

3a: Village Life 21,305 127 121 1333b: Agricultural 18,653 78 74 835a: SeniorCommunities

11,414 96 89 103

2a: TransientCommunities

8399 195 184 207

7b: Afro-CaribbeanCommunities

5318 216 202 232

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–4844

in the valley communities that have been hit hardest by periods ofeconomic decline since the 1980s are also categorised in thissuper-group and these coincide with areas where rates of propertyfire and secondary fire incidences in particular are highest.

In contrast, rates are lowest for each category of fire incidenttype in the ‘Prospering Suburbs’ which are areas with aboveaverage percentages of detached houses and two or more carowning households and are associated with most affluent quintilesof output areas based on Townsend scores. These output areastend to be located in northern parts of Cardiff and Newport and inmore affluent areas of the Vale of Glamorgan and rural towns ofMonmouthshire (such as Chepstow, Abergavenny and Mon-mouth). Output areas categorised as ‘Countryside’ have similarlow rates except importantly for the rates of vehicle fires whichcould be a result of vehicles being burnt out in immediatelyadjacent rural areas after being stolen in other (urban) parts ofthe study area. Again further research is needed to explain suchpatterns but the use of geodemographic patterns has tended toconfirm the findings from the analysis of rates by deprivation(Townsend) scores. Areas of high incidence relating to vehicle fireare also characterised by a transient more deprived environment,consisting mainly of public sector housing. Our earlier research hasfound that crime levels are higher than average in these areas andthere are typically fewer children with the average age of peopleliving in these areas between 18 and 40 [15]. Usually these arehigh density neighbourhoods which are typically low income andtransient in nature. High incidence areas of hoax calls are locatedthroughout the central parts of the study region, with particularemphasis on the centre of the city, associated with the ‘City Living’and ‘Multicultural’ super-groups. Further research is now neededto investigate the nature of locations of the hoax caller given thatour data identifies the locales where fire units respond to ratherthan the locations from which the hoax call is made.

Table 2 shows the Output Area Classification super-group andassociated index score for all fire types aggregated where an indexscore of 100 represents the average expected number of fireincidents. This confirms the general patterns highlighted abovein that index scores tend to be highest for ‘Blue Collar Commu-nities’, ‘Constrained by Circumstances’ and in particular the ‘Multi-cultural’ super-groups and lowest for the ‘Prospering Suburbs’.Furthermore, the index values for the ‘Countryside’ super-group isalso higher than average, swayed by the anomaly of high rates ofvehicle fires as described above. If we look at the index scores forthe Output Area Classification groups (Table 3) this is confirmed bythe high index score for the ‘Accessible Countryside’ group of theclassification. High index scores for these aggregate fire totalsare also evident for the ‘Asian Communities’, ‘Public Housing’,‘Transient Communities’ and ‘Afro-Caribbean Communities’

Table 2OAC super-groups and associated index score with 95% confidence intervals.

OAC super-group South Walespopulation

IndexScore

Lowerbound (95%)

Upperbound(95%)

(1) Blue CollarCommunities

426,422 124 123 125

(2) City Living 45,503 91 88 94.2(3) Countryside 71,102 122 119 125(4) ProsperingSuburbs

304,688 39 38 39

(5) Constrained byCircumstances

167,268 142 139 144

(6) Typical Traits 334,691 92 91 93(7) Multicultural 44,489 169 165 174Total population 1,394,163

groups which again re-affirms some of the trends of the analysisbased on Townsend Index scores.

Fig. 7 provides a disaggregation of the index scores for each fireincident type as well as for the aggregate total number of fires.As well as re-affirming some of the patterns identified above thisalso draws attention to a number of trends which may be worthyof further research building on this exploratory analysis. The highindex score for Hoax calls associated with the ‘City Living’ super-group output areas which are a mixture of inner city areas whichhave above average proportions of single person households inyounger age groups is one such interesting finding here. Given thatthe incident data represents the location to which fire units arecalled it is not possible to identify whether these hoax calls arelikely to be made by residents of such areas. These areas consist ofwell educated young people often without children, tend to betransient in nature, and with a high level of mobility there is littleroom for social ties in the community which may exert an impacton the nature of the fire incidence. The index scores for all fireincident types are notably consistently greater than 100 for somesuper-groups. For example, index values for the ‘Constrained byCircumstances’ output areas (which have broadly a similar geo-graphical distribution as the ‘Blue Collar Communities’ outputareas and tend to have higher than average proportions of flatsand properties that are social rented) and ‘Multicultural’ outputareas consistently exceed 100 for all four incident types. These areareas with higher than average levels of social deprivation whichcould go part of the way to explaining the higher prevalence of fireincidents. In contrast, ‘Prospering Suburbs’ output areas (and lessso ‘Typical Traits’ output areas) have index values for all incidenttype below 100. In other super-groups, index values vary byincident types so that for example the ‘Countryside’ super-groupoutput areas have an index value that exceeds 100 for vehicle fires

Fig. 7. OAC super-groups and index scores across South Wales (100¼average expected number of fire incidents). Error bar indicate the 95% upper and lower confidenceintervals.

Fig. 8. Spatial distribution of OAC index scores across South Wales (100¼average expected number of fire incidents) (Top left: property fires; Top right: hoax calls; Bottomleft: vehicle fires; Bottom right: secondary fires).

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–48 45

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–4846

(for the potential reasons suggested previously) and is less than100 for hoax calls.

Fig. 8 provides a spatial depiction of the index scores for eachfire type and focussing on scores of 100 and greater (i.e. where avalue of 100 equates to the average expected number of fireincidents). This corroborates with earlier evidence provided inFig. 4 illustrating the degree to which fire is spatially skewedacross the study region, however, Fig. 8 builds upon this story tohighlight the degree to which these locales experience elevatedlevels of fire than would be expected by chance. For property fires,clustering of high index scores is predominantly within the Cardiffregion, Newport and Barry, with a number of isolated clusters inmore rural locations between Merthyr Tydfil and Monmouth. Forhoax calls and vehicle fires the degree of spatial clustering acrossthe study region is the highest across all of the fire types with ahigh propensity of both incident types within central and westernparts of Cardiff and to the north east of Cardiff in the case ofvehicle fires. Newport, Merthyr Tydfil and Barry are also high-lighted as areas with high index scores for these fire types. Highindex scores for secondary fires show the greatest degree ofspread across the study area with clusters typically flanking themajor towns and cities of the region.

5. Discussion

Previous studies have drawn attention to on-going debatessurrounding the theoretical justification for geodemographic clas-sifications in representing neighbourhood characteristics andlinkages [3,52,53]. The use of such techniques in policing applica-tions for example has shown the potential to highlight areas ofhigher than expected levels of victimization or offender rates or toassess the effectiveness of targeting [36,35]. The primary aim ofthis study has been to demonstrate the effectiveness of a geode-mographic classification to enhance our capacity to interpret thespatial patterns of four types of fire incidents in South Wales,United Kingdom. The use of the free and transparent Output AreaClassification has enabled a succinct representation of socio-economic, demographic and lifestyle characteristics of localitiesto be linked with fire data at a relatively final spatial scale. Thismulti-dimensional characterisation of areas can then be applied tocompare incidence rates between area types (super-groups) anduni-dimensional measures such as the Townsend Index whichhave been used in previous studies. In a broad sense the findingspresented here accord with those of recent studies in the UK thatrelate the incidence fire types, including property, vehicle, sec-ondary fires or a hoax calls, to the particular facets of the socialgeography of urban areas [17,54]. This study has also highlightedthe types of areas that are relatively more vulnerable to fireincidence than others given their underlying social ecology. Moregenerally the study findings demonstrate that geodemographicclassifications have the potential, when used in conjunction withother exploratory approaches, to investigate trends in fire inci-dence and examine spatial variations in incidence by neighbour-hood type.

At the same time the conceptual difficulties in using suchclassifications in examining potential risks and understandingbehaviour as well as the implications of the inherent uncertaintyin the derivation of classifiers such as the OAC need to beacknowledged and communicated to potential users and haveimplications for their use in different application areas [55].In particular questions still remain over the accuracy of generalpurpose national geodemographic classification systems and howrepresentative they are of the local neighbourhoods they aremeant to portray as well as the potential implications of hetero-geneity within neighbourhood types [53]. Despite this there is

evidence of the increasing use of geodemographics in the emer-gency services, particularly police forces, in tasks such as deploy-ing resources. The respective advantages and limitations of suchclassifications in crime application areas have been alluded to inprevious studies (see for example [56,18]). Williamson et al. [39, p.203 ] draw on their analysis of variations in youth offenders byneighbourhood type to suggest that “geodemographic analysis canleverage extra value and substantial insight from operationaldatabases collected on a routine basis”. Ashby [36] demonstratesthe advantages of geodemographic profiling of operational crimedata in terms of the calculation of propensities of different types ofcrime for different neighbourhood types within a study area. Hisbasic contention here is that “these neighbourhoods differ pre-dictably in their crime profile and policing environment” and that“the variation in policing environments between neighbourhoodtypes is greater than within each type [36, p. 423]. These maps canin turn be compared to hot spot maps of crime for the area inorder to highlight those areas experiencing higher than predictedlevels of different crime types. Such tools therefore have thepotential to complement existing crime investigation techniquesin for example examining patterns of relative levels of risk [36].To date, however, there has been less focus in the publishedacademic literature on the use of such classifications in the fire andemergency services and this paper represents an attempt tohighlight their potential as an exploratory tool in investigatingspatial variations in fire incidents.

A more recent study by Andrews and Brewer [57] highlightedlinkages between levels of social capital, as measured by factorssuch as political engagement and social trust, and fire serviceoutcomes such as fatalities for US states between 1980 and 2003.However, this analysis was conducted at a very aggregate (i.e.state) level and more research is needed to establish if such trendsare replicated at the intra-urban scale. Our findings suggest that, atthe intra-urban scale, geodemographic classifications can be usedto explore variations in incidence in different types of fire.Although such classifications have the potential to establishassociations between neighbourhood characteristics and fireevents (total and incident types), more research is needed toexamine exactly what is being captured here. [14, p. 209] suggestfor example that “geodemographics as a strategic diagnostic toolcan provide the intelligence base to identify differential levels ofsocial capital and community cohesion in local neighbourhoods.”In the context of research using such classifications to examinevariations in fire incidents, however, more studies are needed toestablish whether there are linkages with neighbourhood mea-sures of social capital which may help to explain such trends. Datacovering a longer time period and that related to any injuries/fatalities arising from fire events would enable greater confidenceto be placed in our findings. We have used just one suchgeodemographic classification here – another interesting exten-sion would be to see if other (commercially-available or bespoke)classifications are “useful” in this regard [52].

Finally, Longley and Goodchild [58] suggest a number ofpotential barriers to wider implementation of geodemographicsin academic or public service applications. They highlight concernsregarding the general lack of transparency and sources (forexample lifestyles data) used to create such classifications largelystemming from their use in the commercial sector. Although theuse of such classification has a long track record in commercial andretail applications [3], the theoretical rationale for using suchmeasures in public sector applications is perhaps more controver-sial and has led researchers such as [59] to develop bespokeclassifications targeted for particular application areas. As Longley[60, p. 619] suggests “there is no evident rationale why any general‘one size fits all’ classification based upon a particular cocktail ofvariables should be appropriate to all applications”. Although

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–48 47

there has been some progress in recent years particularly, forexample, in criminology and policing applications more research isrequired to explore how geodemographic classifications can beused with more ‘traditional’ approaches to provide context fordetailed analysis of local patterns of fire incidents.

6. Conclusion

Geodemographic classifications have been advocated as meth-ods which go beyond the use of census indicators to help usunderstand the underlying risk population “to represent socio-economic neighbourhood effects in the study of human behaviourand to provide context for in-depth social investigations” [61, p. 6].Petersen et al. [25, p. 191] in their study of the use of suchclassifications in targeting health campaigns conclude thatalthough they “can be used to differentiate neighbourhoods onan array of demographic and socio-economic variables….thestrength of geodemographics is to explore or describe, but notnecessarily to explain particular health outcomes.” Nevertheless itis crucial that researchers continue to explore the validity andusefulness of geodemographic classifications as their use in thepublic sector widens. In terms of operational tasks within fire andrescue services such classifications continue to show promise inareas such as promoting education campaigns and targetingprevention activity (such as Home Fire Safety Checks) by profilingand mapping such areas against fire incident data. At the sametime researchers have to be realistic about what can be revealed interms of neighbourhood effects in the modelling of fire risk usingsuch classifications. It is possible for example that the analysis offire events for multiple causes may represent a complicatedpicture not amenable to such detailed prediction and forecastingand that, in such situations, a neighbourhood classification used inisolation may not adequately capture such complexity.

Harris et al. [29] re-iterate that the focus of previous studies on theuse of geodemographic classifications has been on finding patterns insocial and economic data rather than on explaining them whichmakes the use of such classifications in exploratory data analysis apromising strand of research for social scientists. As they suggest “atits simplest, geodemographics is only a structured method of makingsense of the spatial and socio-economic patterns encoded withincomplex datasets” [29, p. 555]. These types of classifications canprovide “valuable demographic context” [25, p. 191] and “a widerview of social circumstances than simply deprivation” [62, p. 192]but need to be considered as but one such technique in a fullerexamination of spatial patterns in for example fire incidence.Nevertheless with refinement of indicators the use of such neigh-bourhood classifications may also provide a route through whichfactors such as community cohesion and social capital (which havebeen associated with fire incidence) are incorporated into suchanalysis to provide a more holistic picture of the types of factorsassociated with spatial variations in fire incidence. Further research isneeded, however, to try to understand those behaviours whichinfluence the occurrence of fires and how these may map ontogeodemographic clusters.

Acknowledgements

This paper is based on research conducted on a projectfunded by the Australian Research Council Linkage program GrantLP120100172 and supported by the Wales Institute of Social andEconomic Research, Data and Methods (WISERD), funded by theEconomic and Social Research Council (ESRC) (Grant Reference:RES-576-25-0021) and the Higher Education Funding Council forWales (HEFCW). We wish to acknowledge South Wales Fire and

Rescue Service (SWFRS) for their co-operation and the supply ofthe data on which this paper is based. We also acknowledge thefollowing data sources: ESRC/JISC Census Programme, CensusGeography Data Unit (UKBORDERS), DIGIMAP, EDINA (Universityof Edinburgh); 2001 Census: Standard Area Statistics (England andWales) Source: Office for National Statistics, ESRC/JISC CensusProgramme and the Census Dissemination Unit, MIMAS (Univer-sity of Manchester). However, any views or analysis conductedwithin the paper are those of the authors alone.

References

[1] R. Harris, P Sleight, R. Webber, Geodemographics, GIS and NeighbourhoodTargeting, Wiley, Chichester, 2005.

[2] D.W. Vickers, P.H. Rees, Introducing the Area Classification of Output Areas,Popul. Trends 125 (2006) 15–29.

[3] M. Birkin, G.P Clarke, M. Clarke, Retail Geography and Intelligent NetworkPlanning, Wiley, Chichester, 2002.

[4] M. Birkin, G.P Clarke, M. Clarke, A. Wilson, Intelligent GIS: Location Decisionsand Strategic Planning, Wiley, Chichester, 1996.

[5] P.A. Longley, G.P. Clarke, GIS for Business and Service Planning, Wiley,Chichester, 1995.

[6] P.A. Longley, D Ashby, R. Webber, C. Li, Geodemographic classifications, thedigital divide and understanding customer take-up of new technologies, BTTechnol. J. 24 (3) (2006) 67–74.

[7] P.A. Longley, A.D. Singleton, Linking social deprivation and digital exclusion inEngland, Urban Stud. 46 (7) (2009) 1275–1298.

[8] A.D. Singleton, The geodemographics of access and participation in Geogra-phy, Geogr. J. 178 (3) (2012) 216–229.

[9] A.D. Singleton, A.G. Wilson, O. O’Brien, Geodemographics and spatial interac-tion: an integrated model for higher education, J. Geogr. Syst. 14 (2) (2012)223–241.

[10] P.A. Longley, Geographical Information Systems: a renaissance of geodemo-graphics for public service delivery, Prog. Hum. Geogr. 29 (2005) 57–63.

[11] P.A. Longley, M.F. Goodchild, D.J. Maguire, D.W. Rhind, Geographic InformationSystems and Science, 3rd ed., Wiley, Chichester, 2010.

[12] A.D. Singleton, P.A. Longley, Creating open source geodemographics: refining anational classification of census output areas for applications in highereducation, Pap. Reg. Sci. 88 (3) (2009) 643–666.

[13] Smith, R., Wright, M. and Solanki, A. Analysis of fire and rescue serviceperformance and outcomes with reference to population socio-demographics,Fire Research Series 9/2008, Department for Communities and Local Govern-ment, London 2008.

[14] T Williamson, D. Ashby, R. Webber, Classifying neighbourhoods for reassur-ance policing, Policing Soc. 16 (2006) 189–218.

[15] J. Corcoran, G. Higgs, C. Brunsdon, A. Ware, P. Norman, The use of spatialanalytical techniques to explore patterns of fire incidence: a Cardiff case study,Comput. Environ. Urban Syst. 31 (6) (2007) 623–647.

[16] J. Corcoran, P. Chhetri, R. Stimson, Exploring the spatio-temporal dynamics offire incidence and the influence of socio-economic variables: a case study fromSouth East Queensland, J. Spatial Sci. 54 (1) (2009) 79–91.

[17] J. Corcoran, G. Higgs, A. Higginson, The importance of socio-economic factorson fire incidence: a comparison of trends for Brisbane (Australia) and Cardiff(United Kingdom), Appl. Geogr. 29 (1) (2011) 63–76.

[18] P.J.B. Brown, A.F.G. Hirschfield, P.W.J. Batey, Adding value to census data:public sector applications of the Super Profiles geodemographic typology, J.Cities Reg. 10 (2000) 19–32.

[19] P. Aveyard, S. Manaseki, J. Chamber, The relationship between mean birthweight and poverty using the Townsend deprivation score and the SuperProfile classification system, Public Health 116 (6) (2002) 308–314.

[20] M. Farr, J. Wardlaw, C. Jones, Tackling health inequalities using geodemo-graphics: a social marketing approach, Int. J. Market Res. 50 (4) (2008)449–467.

[21] M. Tickle, P. Brown, A. Blinkhorn, T. Jenner, Comparing the ability of differentarea measures of socio-economic status to segment a population according tocaries prevalence, Commun. Dent. Health 17 (3) (2000) 138–144.

[22] P. Batey, P. Brown, The spatial targeting of urban policy initiatives: ageodemographic assessment tool, Environ. Plann. A 39 (2007) 2774–2793.

[23] P. Batey, P. Brown, S. Pemberton, Methods for the spatial targeting of urbanpolicy in the UK: a comparative analysis, J. Appl. Spatial Anal. 1 (2008)117–132.

[24] J. Petersen, P. Atkinson, S. Petrie, M. Gibin, D. Ashby, P. Longley, Teenagepregnancy – new tools to support local health campaigns, Health Place 15 (1)(2009) 300–307.

[25] J. Petersen, M. Gibin, P. Longley, P. Mateos, P. Atkinson, D. Ashby, Geodemo-graphics as a tool for targeting neighbourhoods in public health campaigns,J. Geogr. Syst. 13 (2011) 173–192.

[26] R., Webber Neighbourhood inequalities in the patterns of hospital admissionsand their application to the targeting of health promotion campaigns, Centrefor Advanced Spatial Analysis working Paper 90, CASA, UCL, (2004) 26 pp.

[27] R. Webber, T. Butler, Classifying pupils by where they live: HOWwell does thispredict variations in their GCSE results? Urban Stud. 44 (7) (2007) 1229–1253.

J. Corcoran et al. / Fire Safety Journal 62 (2013) 37–4848

[28] O. Barbosa, J.A. Tratalos, P.R. Armsworth, R.G. Davies, R.A. Fuller, P. Johnson, K.J. Gaston, Who benefits from access to green space? A case study fromSheffield, UK, Landsc. Urban Plan. 83 (2/3) (2007) 187–195.

[29] R. Harris, R. Johnston, S. Burgess, Neighbourhoods, ethnicity and schoolchoice: developing a statistical framework for geodemographic analysis,Popul. Res. Policy Rev. 26 (5/6) (2007) 553–579.

[30] R. Webber, P.A. Longley, Geodemographic analysis of similarity and proximity:their roles in the understanding of the geography of need, in: P.A. Longley,M. Batty (Eds.), Advanced Spatial Analysis, ESRI Press, 2003, pp. 233–266.

[31] C. Brunsdon, P. Longley, A. Singleton, D. Ashby, Predicting participation inhigher education: a comparative evaluation of the performance of geodemo-graphic classifications, J. Roy. Stat. Soc. A 174 (1) (2011) 17–30.

[32] D. Tonks, M Farr, Widening access and participation in UK higher education,Int. J. Educ. Manage. 17 (1) (2003) 26–36.

[33] T. Anderson, Using geodemographics to measure and explain social andenvironment differences in road traffic accident risk, Environ. Plann. A 42(2010) 2186–2200.

[34] N. Walford, Geographical and geodemographic connections between differenttypes of small areas as the origins and destinations of migrants to mid-Wales,J. Rural Stud. 23 (3) (2007) 318–331.

[35] D.I. Ashby, P.A. Longley, Geocomputation, geodemographics and resourceallocation for local policing, Trans. GIS 9 (2005) 53–72.

[36] D.I. Ashby, Policing Neighbourhoods: exploring the Geographies of crime,policing and performance assessment, Policing Soc. 15 (4) (2005) 413–447.

[37] P. Mayhew, N.A. Maung, C. Mirlees-Black, The British Crime Survey HomeOffice Research Studies, London (1993).

[38] C. Hayden, T. Williamson, R. Webber, Schools, pupil behaviour and youngoffenders – using postcode classification to target behaviour support andcrime prevention programmes, Br. J. Criminol. 47 (2) (2007) 293–310.

[39] T Williamson, D. Ashby, R. Webber, Young offenders, schools and theneighbourhood: a new approach to data-analysis for community policing, J.Commun. Appl. Soc. Psychol. 15 (2005) 203–228.

[40] G.D. Breetzke, A.C. Horn, A geodemographic profiler for high offenderpropensity areas in the city of Tshwane, South Africa, Environ. Plann. A 41(1) (2009) 112–127.

[41] A.D. Singleton, The geodemographics of educational progression and theirimplications for widening participation in higher education, Environ. Plann. A42 (2010) 2560–2580.

[42] T. Butler, C. Hamnett, M. Ramsden, R. Webber, The best, the worst and theaverage: secondary school choice and education performance in East London,J. Educ. Policy 22 (1) (2007) 7–29.

[43] C. Hamnett, M. Ramsden, T. Butler, Social background, ethnicity, schoolcomposition and educational attainment in East London, Urban Stud. 44 (7)(2007) 1255–1280.

[44] O. Spatenkova, A. Stein, Identifying factors of influence in the spatialdistribution of domestic fires, Int. J. Geogr. Inform. Sci. 2496 (2010) 841–858.

[45] P.. Townsend, Deprivation, J. Soc. Policy 16 (1987) 125–146.[46] D.W. Vickers, P.H. Rees, Creating the National Statistics 2001 Output Area

Classification, J. Roy. Stat. Soc., Series A 170 (2) (2007) 379–403.[47] M. Senior, in: P. Rees, D. Martin, P. Williamson (Eds.), Deprivation indicators,

The Census Data System Wiley, Chichester, 2002, pp. 123–137.[48] D., Vickers, P., Rees, M., Birkin Creating the national classification of census

output areas: data, methods and results (2005). Available online at:⟨http://eprints.whiterose.ac.uk/5003/1/05-2.pdf⟩ [accessed 31.08.12].

[50] M. Langford, G. Higgs, R. Fry, Using Floating Catchment Analysis (FCA)techniques to examine intra-urban variations in accessibility to public trans-port opportunities: the example of Cardiff, Wales, J. Transp. Geogr. 25 (1)(2012) 1–14.

[51] A. Druckman, P. Sinclair, T. Jackson, A geographically and socio-economicallydisaggregated local household consumption model for the UK, J. Clean. Prod.16 (2008) 870–880.

[52] E. Uprichard, R. Burrows, S. Parker, Geodemographic code and the productionof space, Environ. Plann. A 41 (12) (2009) 2823–2835.

[53] D. Voas, P. Williamson, The diversity of diversity: a critique of geodemographicclassification, Area 3391 (2001) 63–76.

[54] M.J. Taylor, E. Higgins, P.J.G. Lisboa, V. Kwasnica, An exploration of causalfactors in unintentional dwelling fires, Risk Manage. 14 (2) (2012) 109–125.

[55] A. Slingsby, J. Dykes, J. Wood, Exploring uncertainty in geodemographics withinteractive graphics, IEEE Trans. Vis. Comput. Graph. 17 (12) (2011)2545–2554.

[56] K. Bowers, A. Hirschfield, Exploring links between crime and disadvantage innorth-west England: an analysis using geographical information systems, Int.J. Geogr. Inform. Sci. 13 (1999) 159–184.

[57] R. Andrews, G.A. Brewer, Social capital and fire service performance: evidencefrom the US states, Soc. Sci. Q. 91 (2) (2010) 576–591.

[58] P.A. Longley, M.F. Goodchild, The use of geodemographics to improve publicservice delivery, in: J. Hartley, C. Donaldson, C. Skelcher, M. Wallace. (Eds.),Managing to Improve Public Services, Cambridge University Press, Cambridge,2008, pp. 176–194.

[59] A.D. Singleton, P.A. Longley, Geodemographics, visualisation, and social net-works in applied geography, Appl. Geogr. 29 (2009) 280–298.

[60] P.A. Longley, Some challenges to geodemographic analysis and their widerimplications for the practice of GIScience, Comp. Environ. Urban Syst. 31(2007) 617–622.

[61] D.W. Vickers, P.H. Rees, Ground-truthing geodemographics, Appl. Spatial Anal.4 (1) (2011) 3–21.

[62] R. Reading, S. Openshaw, S. Jarvis, Are multi-dimensional social classificationsof areas useful in UK health service research? J. Epidemiol. Commun. Health48 (1994) 192–200.