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DOI: 10.1177/0042098012458003

2013 50: 923 originally published online 29 August 2012Urban Stud Elizabeth Delmelle, Jean-Claude Thill, Owen Furuseth and Thomas Ludden

Trajectories of Multidimensional Neighbourhood Quality of Life Change

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Trajectories of MultidimensionalNeighbourhood Quality of Life ChangeElizabeth Delmelle, Jean-Claude Thill, Owen Furuseth and Thomas Ludden

[Paper first received, December 2011; in final form, July 2012]

Abstract

This paper provides an empirical analysis of the multidimensional, spatio-temporalquality of life (QoL) trends followed by neighbourhoods in Charlotte, NC, between2000 and 2010. Employing a combined geocomputational and visual techniquebased on the self-organising map, the study addresses which types of neighbourhoodexperienced the most change or stability, where (in attribute and geographicalspaces) did neighbourhoods that began the decade with a particular set of character-istics evolve to, and where did neighbourhoods that concluded the decade transitionfrom? Results indicate that the highest QoL neighbourhoods were most stable, whilethose with lower homeownership, closer to the city centre, exhibited the sharpestlongitudinal trajectories. Lower-income neighbourhoods are found to be heteroge-neous in terms of their social problems, dividing between high crime concentrationsand youth-related social problems. An exchange of these social issues over time isobserved as well as a geographical spread of crime to middle-ring suburbs.

1. IntroductionUnderstanding the ways in which urbanneighbourhoods evolve over time is of great importance to city planners, policy-makers and community leaders who sharea common interest in devising policies andaction plans aimed at community develop-ment. Healthy neighbourhood initiativesare often multifaceted and pursue a two-prong strategy of maintaining or improving

the vibrancy of living experiences of neigh-bourhood residents and establishingplaced-based communities as economically competitive environments for businesses.Quality of life (QoL) in either of these con-texts refers to the multidimensional notionof livability, desirability or competitivenessof a locale. From a community planning orpolicy-making perspective, quality of life

Elizabeth Delmelle, Jean-Claude Thill, Owen Furuseth and Thomas Ludden are in the Departmentof Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University CityBlvd, Charlotte, North Carolina, 28223, USA. Email: [email protected], [email protected], [email protected] and [email protected].

Urban Studies at 50Article

50(5) 923941, April 2013

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indicators present alluring metrics formonitoring neighbourhood conditions overtime in general and for developing targetedaction plans in particular (Galster et al .,

2005; Myers, 1998; Sawicki and Flynn,1996). The concept of QoL is not new tothe debate on urban geographies as it offersa framework for understanding processes of placed-based social change across urbanlandscapes (Helburn, 1982; Pacione, 2003).

A significant body of literature coversvarious aspects of the integrative and mul-tifaceted social construct of neighbourhoodquality of life. Much of this research per-tains to measurement issues, while few studies have utilised the multidimensional,longitudinal datasets resulting from theseQoL efforts to investigate neighbourhoodtrends. Exploring these data may provideconfirmation of traditionally held concep-tions about how neighbourhoods transitionover time, while more detailed inspectionsof the trajectories followed by neighbour-

hoods may illuminate unanticipated pathsof change across the array of attributes.Common methods of exploratory spatialdata analysis (ESDA) are ill-equipped toextract emergent trends or patterns fromcomplex, spatial-temporal and multidimen-sional data such as the aforementionedQoL indicators. This paper draws fromrecent advances in geovisual analyticswhich seek to blend computational or data

mining techniques with highly visual out-puts to explore such datasets. The outputsproduced from this analytical techniqueportray non-linear relationships betweenQoL indicators, providing general state-ments on the overall spatial-temporaldynamics of neighbourhoods, while thevisualisation of longitudinal change trajec-tories enables one to identify meaningfultrends in the results. This methodology holds particular promise to urban leaderswishing to gain new insights into thedynamics of neighbourhood QoL and to

communicate trajectories of change to thepublic.

Specifically, this paper utilises neighbour-hoods within the city of Charlotte, North

Carolina, as a case study to explore trendsacross a series of 17 biennially collected QoLindicators from 2000 to 2010. A combinedcomputational and visual methodologicalapproach based on the self-organising mapis adopted to address four major questions:what is the typical QoL profile of neighbour-hoods that experienced the most change orstability; how did neighbourhoods thatbegan the decade with a given QoL profilechange 10 years later; conversely, where inattribute space did neighbourhoods thatconcluded the decade transition from; and,did geographically adjacent neighbourhoodsundergo similar transformations in terms of QoL? The visual analytical approach furtherallows for the identification of neighbour-hood trajectories that have clearly differedfrom other neighbourhoods with similar

characteristics, or outlier trajectories.The remainder of the paper is structured

as follows. In section 2, the theoretical andempirical background on neighbourhoodchange is presented, followed by an overview of the study area, data and methodology insection 3. Results are presented in section 4and the discussion and conclusions insection 5.

2. Neighbourhood ChangeTraditional models of neighbourhoodchange largely focus on shifts in a neigh-bourhoods socioeconomic or racial compo-sition, through a process of invasion andsuccession as proposed by sociologists inthe Chicago School in the 1920s (Burgess,1925), as a function of housing age and dete-rioration according to filtering theories of decline (Grigsby et al ., 1987; Hoyt, 1933;Leven et al ., 1976), or as a product of

924 ELIZABETH DELMELLE ET AL .



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residential location choices driven by atrade-off between commuting costs andland values as suggested by urban economicbid rent models (Muth, 1969). In the United

States, the urban landscapes generatedaccording to each of these models featurethe highest concentrations of the poorestresidents and minorities living close to thecity centre in deteriorating homes, while thewealthiest reside in newer, suburban struc-tures on the outskirts of urban areas. Recentmodifications to these theories have pro-posed that middle-aged homes are most sus-ceptible to decline as older buildings areprime for revitalisation (Brueckner andRosenthal, 2009; Rosenthal, 2008). Indeed,empirical studies on neighbourhood incomeand poverty dynamics have recorded a gen-eral concentration of poverty in neighbour-hoods situated in the urban core of citiesthroughout the country, as well as a spatialspread to older, inner-ring suburbs, espe-cially in rapidly suburbanising metropolitan

areas where housing supply continues toexpand (Cooke and Marchant, 2006; Lee,2011; Madden, 2003).

Recent work documenting the decline of older, first-ring suburban neighbourhoodsaddresses a debate on the evolution of sub-urban neighbourhoods initiated in the1960s. Proponents of the idea of suburbanpersistence have argued that, even as peoplein suburban neighbourhoods change, theirsocioeconomic characteristics persist asthese neighbourhoods consistently attractthe same types of people. Conversely, othershave held a more ecological view accordingto which these neighbourhoods do changeover time, and the trend followed is one of gradual decline (Vicino, 2008). A numberof empirical studies have since illustrated achanging diversity and decline of suburban

neighbourhoods (Hanlon et al ., 2006; Leeand Leigh, 2007; Lucy and Phillips, 2000;Vicino, 2008).

While neighbourhood economic condi-tions are important predictors or descrip-tors of a neighbourhoods overall quality of life, they are only one aspect of this multidi-

mensional concept, and focusing solely onthis dimension ignores the greater complex-ity of social processes in which neighbour-hoods evolve (Chow and Coulton, 1998).This is especially true in light of a numberof studies which have suggested that neigh-bourhoods with high levels of poverty areheterogeneous in natureso, while somemay experience severe social problems,others may not fare as poorly across alldimensions (Longley and Tobon, 2004;Morenoff and Tienda, 1997). The link between concentrated poverty and socialproblemsincluding teenage pregnancy,high school dropout or education levels, aswell as crime rates, is primarily attributedto Wilson (1987) and Massey and Denton(1993) who both argue, through differentdemographic processes, that concentrated

poverty exacerbates social problems as youths growing up in these neighbourhoodsare not exposed to role models, are isolatedfrom employment both physically and viasocial job networks, and are detached fromsocial norms and behaviours. According tothese views, social problems are hypothesisedto increase in concentration in the poorestneighbourhoods through time. Chow andCoulton (1998) address this hypothesis in astudy of three categories of neighbourhooddistress between 1980 and 1990 in Cleveland.Using a factor analysis, the authors reveal agreater interdependence among these dis-tress factors over time and conclude thatsocial conditions did in fact worsen duringthe decade. No mention is made of the spa-tial dynamics of change, however.

Morenoff and Tienda (1997) explore the

spatial and temporal evolution of socialchange in Chicago neighbourhoods between1970, 1980 and 1990 by applying a cluster

NEIGHBOURHOOD QUALITY OF LIFE CHANGE 925



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analysis on socioeconomic variables todevelop four neighbourhood typologies(stable middle class, gentrifying yuppie,transitional working class and ghetto under-

class). Transitional working-class neigh-bourhoods with lower socioeconomiccharacteristics, educational attainment andhomeownership were identified as the mostlikely to change, while neighbourhoods thatbecame transitional working-class neigh-bourhoods by 1990 were associated with arapid Hispanic population increase.Underclass and gentrifying/yuppie neigh-bourhoods exhibited the most stability across the time-period. Spatially, an increas-ing concentration of affluence and a spreadof ghetto underclass neighbourhoods werealso recorded.

Other studies on neighbourhood quality of life change have aggregated multipleindicators into a composite score, reportedon the number of neighbourhoods thattransitioned between groups and have cre-

ated maps of neighbourhoods that haveimproved, declined, or remained the same(Kitchen and Williams, 2009; Randall andMorton, 2003). These studies, however,provide little insight into the ways in whichneighbourhoods change across the multidi-mensional attribute space. The purpose of this paper is to provide a more comprehen-sive view of multidimensional neighbour-hood change by incorporating a widerrange of QoL variables capturing the social,economic, physical and crime dimensions,while simultaneously enabling the spatialdynamics of these changes to be examined.

3. Study Area, Data andMethodology

3.1 Study AreaCharlotte, North Carolina (Figure 1) is thelargest city in the state and one that has

experienced rapid population growth andurban expansion in recent years, particu-larly over the course of the previous decade.According to US census estimates, between

2000 and 2010, the city of Charlottes popu-lation rose from 540 828 to 731 424 resi-dents, an increase of 35.2 per cent.Mirroring other New South cities,Charlottes neighbourhood geography hasbeen dynamic and transformational duringthis period. In particular, four significantland use, demographic and public policy shifts have shaped neighbourhood develop-ment. First, expansive suburbanisation haspushed new greenfield urbanisation to theedges of the county. Broadly speaking, thesenew neighbourhoods are upper-middle toupper income and largely White. A secondcharacteristic is strong gentrification trendsin the city centre and selected pre-World-War-II suburbs surrounding the urbancore. Affluent, White residents dominatethe gentrifiers profile. In the city centre,

older public housing projects have beenreplaced by high-density condominiumprojects, while poor and minority rentershave relocated to lower-priced neighbour-hoods west and north of downtown.Thirdly, Charlotte has become a Hispanichypergrowth metropolitan area (Suro andSinger, 2002), absorbing a 128.2 per centincrease in Latino residents since 2000.Middle-ring suburban neighbourhoods

(post-World-War-II) are the overwhelmingdestination for new Latino settlers, wherethey have succeeded the original White andsubsequent African American middle-classdominance (Smith and Furuseth, 2008).Finally, overlaying Charlottes robust physi-cal growth and demographic restructuringwas a unique shift from the traditionalbusing of public school students sincethe 1970s to a neighbourhood schoolassignment plan implemented in 2002 thataffected the desirability of housing marketsmidway through the research period




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(Smith, 2010). Recognising the role thatperceived school quality performanceplays in neighbourhood desirability, theneighbourhood-based school assignment pro-cess has undoubtedly affected neighbourhoodattractiveness. Yet how much effect this policy shift had in the second half of the study

period in realigning the trajectory of gentrify-ing inner-city neighbourhoods is unclear andcertainly warrants further investigation.

3.2 Data

Data for this study come from the CharlotteNeighborhood Quality of Life Study Group(Metropolitan Studies, 2010), a biennialcompilation of primary and secondary indi-cators representing the economic, social,

physical and crime dimensions for 173neighbourhood statistical areas (NSA),units of analysis similar to US census block

Figure 1. The study area, Charlotte, North Carolina.




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groups, but customised for the Charlotteregion based on community feedback.Beginning in 2000, and continuing every other year, 17 attributes were collected for

each neighbourhood describing its social,physical, crime and economic conditions.Most of the variables were compiled fromexhaustive data collected over time, in con-trast to the biennial American Community Survey from the US census which reliesupon sampling. Table 1 describes each of the QoL attributes collected between 2000and 2010, resulting in a 6-year panel of data.

3.2 Analytical Methodology

In order to investigate trends in this multi-dimensional, multitemporal and spatialdataset, this research draws on recent work in the field of geovisual analytics, whichseeks to combine computational and visualmethodologies to facilitate exploratory spacetime analysis (for example,Andrienko et al ., 2010a; Andrienko et al .,2010b; Guo et al ., 2006; Yan and Thill,2009). Geocomputational methods typically impose fewer distributional assumptionsregarding data structures and relationshipsas compared with traditional statistics,while visualisation techniques exploit theability of human vision and intelligence inrecognising patterns, relationships, trends

and anomalies (Yan and Thill, 2009). As anintermediary between purely computa-tional and visual analytical methods, theself-organising map (SOM) offers advan-tages of both. It is a neural network-basedcomputational method for reducing thedimensionality of large datasets and reveal-ing embedded structures; it generates aninherently visual output for exploringresults (Andrienko et al ., 2010a; Guo et al .,2005). The SOM further enables the identi-fication of non-linear relationships amongQoL variables, thus giving it an advantage

over traditionally utilised dimensionality reduction methods such as principal com-ponents or factor analysis.

Recently, in the context of the analysis of

intraurban neighbourhoods, SOM has beenused to examine geographical positions of neighbourhoods with similar demographicattributes (Spielman and Thill, 2008), todefine or examine changes within housingsub-markets (Kauko, 2004, 2009a, 2009b)or of indicators related to neighbourhooddeprivation (Pisati et al ., 2010). A host of applications of self-organising maps withinthe geographical discipline can be found inthe edited volume by Agarwal and Skupin(2008). Skupin and Hagelman (2005) pro-pose a methodology based on the self-organising map to visualise attribute changeof census tracts by creating trajectoriesacross the SOM attribute space, thus elimi-nating the need for multiple maps depictingthe pattern of each attribute at each periodof time, which becomes increasingly ineffec-

tive as the number of time stamps increases.Although geocomputational methods of

data reduction such as SOM offer a numberof advantages over traditional statisticalanalyses, they do suffer from some cri-tiques. One major objection to the SOMalgorithm is in being a-theoretical, lettingthe data drive results, rather than testing astated theory as is the case in confirmatory statistical analyses. However, as with otherexploratory data and spatial data analyses,the purpose is to generate, rather than test,hypotheses.

Self-organising map. A self-organisingmap is an artificial neural network devel-oped by Kohonen (1990) that projectsmulti-dimensional input data onto anoutput attribute space of lower dimension-ality (normally 2 dimensions) so that simi-lar observations across the multiple inputattributes are placed in proximity to one




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Table 1. QoL indicators

Description Source SOM label

Social dimension

Percentage age 64+ Percentage of population age65 years and older

Claritas Age64

Kindergarten score Average math and verbalscore for each kindergartenstudent at the end of each year

Charlotte-Mecklenburgschool system

Kinder

Dropout rate Percentage of high schoolstudents who dropped out of the school system


HsDo

Percentage passingcompetency exam

Percentage of studentspassing 9th grade competency

exams


Comp

Percentage births toadolescents

Percentage of children bornto women 18 years and younger

Mecklenburg County Health Department

TeenBirths

Youth opportunity index Measure of potentialopportunities for youths tobe involved in

Charlotte Area YMCAs YouthOpCharlotte-MecklenburgLibrary system, Parks andRecreation Department,school system

Physical dimensionAppearance index Index of code violations for

each NSANeighbourhooddevelopment

Appear

Home ownership Percentage of owner-occupied residential units

Mecklenburg county property records and landmanagement

Homeown

Infrastructureimprovement costs

Estimated publicconstruction costs forsidewalk, curb, minordrainage

Charlotte Engineering andBuilding Maintenance

Infrastr

Percentage access topublic transportation

Percentage of housing unitswithin one-quarter of a mileof bus stop, one-half of a mileof light rail station.

Charlotte AreaTransportation System

Transit

Percentage access tobasic retail

Percentage of housing unitswithin one-quarter of a mileof grocery store or pharmacy

Mecklenburg county property records and landmanagement, Bell SouthYellow Pages, Charlotte

Retail

Economic dimensionMedian income Median household income Claritas, US Census IncomePercentage food stamps Percentage of population

receiving food stampsMecklenburg County Department of SocialService, Office of Planningand Evaluation

FoodStamp

(continued)




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another on the output space. It is a non-linear generalisation of PCA. A SOM con-sists of a set of input and output nodes,also referred to as neurons. Each neuron, k ,is represented by a series of unstandardisedweights, or an n-dimensional vector suchthat

mk mk 1 . . .mkn

where, n is the dimensionality of the inputspace.

Output nodes are connected to sur-rounding nodes via a neighbourhood rela-

tion which establishes a topologicalstructure to the output grid. Training theSOM is an iterative process: at each step, arandom input vector, x , is selected and pre-sented to the output neuron grid, where thenodes compete for x based on the similarity of the inputs vector of attributes and eachneurons weight values. Similarity is com-puted as the Euclidean distance between x and all of the weight vectors. When the best-matching unit or neuron is identified, itsweights and the weights of its neighbours areupdated resulting in an ordered output grid

so that neighbouring neurons have similarweight vectors (Skupin and Agarwal, 2008;Vesanto et al ., 1999). This ordering is a dis-tinguishing feature between SOM and thek -means algorithm, which also utilises aEuclidean similarity measure, but no rela-tion is formed between clusters, or observa-tions within clusters. The size of the outputgrid is determined a priori, with a smallnumber of output nodes forcing the SOM tobehave solely as a clustering technique, and avery large number of nodes (exceeding thenumber of input observations) enabling theemergence of structures. For the purpose of

this paper, an intermediate number isselected (20 x 8 grid), smaller than thenumber of input nodes to allow clusteringwhere observations are very similar, but cre-ating enough output space to visualise longi-tudinal change. The SOM toolbox for Matlab(Vesanto et al ., 2000) is used for the SOMtraining portion of the study; details of thealgorithm can be found in the software doc-umentation. All variables are standardised

before entering the SOM procedure toensure an equal weighting of the dimen-sional attributes.

Table 1. (Continued)

Description Source SOM label

Crime dimensionViolent crime rate Location quotient of

homicides, rapes, robberies,aggravated assaults for eachNSA

Charlotte-MecklenburgPolice Department

Violent

Juvenile arrest rate Location Quotient of arrestsof individuals under the ageof 16 for each NSA


Juvenile

Property crime rate Location quotient of burglaries, larcenies, vehiclethefts, arsons, vandalism


Property

Crime hot-spots Proportion of NSA that has adurable concentration of violent crime


HotSpot




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In order to utilise the SOM procedure tovisualise change, each neighbourhood isincluded in the initial input dataset six times;once for each time-period. The input data arethen trained according to the aforementionedprocedure so that each neighbourhood isassigned to a node on the output space six times. Finally, trajectories of change are cre-ated for each neighbourhood by tracing itsposition across the output nodes; its location

at each time stamp serves as the vertices of the directed line. The procedure is illustratedin Figure 2 and the result is a decennial trajec-tory for all 173 neighbourhoods.

4. Results of Analysis4.1 Cross-sectional View

One primary output of the SOM procedureare so-called component planes, which arevisual depictions of the relative contributionof each QoL attribute to the overall sorting

of neighbourhoods in the final layout of theSOM output space. These planes revealnon-linear and partial correlations betweenvariables and thus provide an interestingcross-sectional view of the 17 input vari-ables. Figure 3 illustrates the resulting com-ponent planes and shows a distinct orderingof observations across the SOM outputspace. For example, the first four compo-nents in Figure 3income, homeowner-

ship, kindergarten and competency examscoresall exhibit a similar pattern of highscores towards the top of the planes, des-cending towards low scores in the bottomportion of the planes. Conversely, attributescommonly associated with lower QoL have alargely opposite pattern of low levels of foodstamps, appearance violations, high schooldropout rates, teen births and crime rates atthe top of the space, and increasing in valuetowards the bottom.

Partial correlations can also be identifiedfrom the plots, including a high concentration

Figure 2. SOM training and development of trajectories.




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of youth social problems (HsDo, TeenBirths)and physical deterioration indicators (Appear,Infrastr) in the lower right-hand corner, whilehigh crime is largely concentrated on theopposite, lower left-hand corner. The plotsalso reveal that, while neighbourhoods locatedalong the top of the output space generally

fair well across all QoL dimensions, thosetowards the bottom are not the mirror image,scoring poorly across all QoL dimensions; aheterogeneity in social, physical and crimeconditions exists.

In order to examine longitudinal changeacross this composite output space, a trajectory for each neighbourhood is createdas described earlier. Displaying the resultsof each of these lines simultaneously uponthe output space creates an uninterpretablesituation. Accordingly, to aid in the inter-pretation and to address the research

questions, a clustering procedure is appliedto the multidimensional weights assignedto each node in the output space to deline-ate homogeneous regions of nodes withsimilar characteristics. This procedure isperformed in two steps: first, a hierarchicalWards clustering method is used to deter-

mine an appropriate number of clusters,followed by a k-means approach to assignnodes to clusters. In this case, a k = 6 solu-tion exhibits the best discriminating powerand is illustrated in Figure 4. The compact-ness and contiguity of the clusters are adirect result of the ordering of thenodes on the SOM output map; like obser-vations are arranged near one another.Characteristics of the clusters are obtainedby interpreting the component planes andexamining their values within each cluster;they are briefly summarised next.

Figure 3. Component planes.




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(1) Cluster 1highest qol neighbourhoods:high income, homeownership, educa-tion scores; low food stamp depen-dency, crime rates, high school dropout

and teen birth rates.(2) Cluster 2middle-QoL neighbourhoods,

type I: middle-class suburban charac-teristics: median incomes, high home-ownership rates, median educationscores; low crime, low social problems,few appearance violations and low accessibility.

(3) Cluster 3middle-QoL neighbourhoods,type II: median incomes and homeowner-ship rates. Slightly higher education scoresthan cluster 2 (especially for nodes at thetop of the cluster; towards the bottom, the

education scores become similar to cluster4); demographically older, with greateraccess to transit and retail.

(4) Cluster 4lower-QoL neighbourhoods

type I: lower income and education,median homeownership, appearanceviolations and high school dropoutrates; above-average teen birth rates.Low juvenile and violent crime rates,but median property crime. Older pop-ulation, high transit access, but low retail access.

(5) Cluster 5lower-QoL neighbourhoodstype II: highest concentration of highschool dropout rate and teen births, aswell as physical deterioration. Medianproperty and violent crime rates, low juvenile crime, but high crime hot-spots. Low income and homeowner-ship, high food stamp dependency.

(6) Cluster 6lower-QoL neighbourhoods,type III: highest concentration of vio-lent, juvenile, and property crime rates,

median teen births and high schooldropout. Low income and homeow-nership, high food stamp dependency.

4.2 Trajectories of Change

To analyse the longitudinal trajectories of neighbourhoods across attribute and geo-graphical spaces, we now consider each of the clusters in turn. First, the decennial tra-

jectories of all neighbourhoods whose start-ing position in 2000 is in a node belongingto the first cluster are displayed on theoutput space, while the corresponding, geo-graphical location of these neighbourhoodsis highlighted on a second plot. Figure 5(a)illustrates this for the first cluster; accord-ing to the figure, neighbourhoods thatbegan the decade in this highest QoL clus-ter have a geographical concentration inthe southern wedge of the city, expandingfrom close to the city centre outward to thecity limit boundary. The majority of these

Figure 4. Clusters of output nodes identifiedby the k -means technique.




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neighbourhoods remained within the samegroup, with the exception of five neigh-bourhoods whose trajectories indicate adownward trend towards the more moder-ate income characteristics of the secondgroup, and one other that moved intothe third cluster, marginally declining inthe concentration of homeowners; thesedeclines are all slight, as evidenced by theirending position (in 2010) in nodes in thefirst three rows below their starting posi-tion in 2000.

In Figure 5(b), the trajectories of neigh-

bourhoods that concluded the decade incluster 1 are displayed, illustrating thatneighbourhoods that transitioned into this

highest QoL group came from nearby inattribute space and exhibit an apparentgeographical pattern: they are adjacent toexisting neighbourhoods in the southernwedge and along the outermost periphery of the city. Overall, neighbourhoods in thisgroup have a large degree of decennial sta-bility in quality of life.

The geographies of neighbourhood inthe second cluster (Figure 6) reveal a very suburban pattern along the outermost per-iphery of the city, corroborating the middle-class suburban attribute descriptions. The

trajectories of neighbourhoods that beganthe decade in this group and evolved away from it follow two distinct paths. One is a

Figure 6. Visualisation of cluster 2 and longitudinal trajectories: (a) 2000; (b) 2010.

Figure 5. Visualisation of cluster 1 and longitudinal trajectories: (a) 2000; (b) 2010.




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path of improvement in QoL indicators, joining the first cluster, while the other ismarked by decline, clearly depicted by thedownward facing arrows. While neighbour-hoods that declined began the decade in thesame group as those that improved, theirstarting positions within the cluster weretowards the bottom. Neighbourhoods thattransitioned into this group were few; thedeclines from the first group and one neigh-bourhood with a geographical location farfrom the others, towards the city centre. Itstrajectory is also distinct, moving from astarting position in cluster 3, but increasingin homeownership, while otherwise main-taining its moderate education scores, andgenerally low social problems.

Neighbourhoods in cluster 3, charac-terised by lower levels of homeownershipand greater accessibility to transit and retail

opportunities as compared with the secondgroup, are located within the city beltway,with a larger presence in the southernwedge (Figure (7a)). Close inspection of the component planes for nodes within thecluster reveals that neighbourhoods towardsthe top of the group have higher learningachievement than those towards the bottom(the horizontal line in Figure 3 separatingthe 2nd and 3rd cluster serves as a cut-off).This is an important distinction whenexamining the neighbourhoods that left thegroup by 2010; those that experiencedlower QoL all began with lower learningachievement scores and all increased in thenumber of youth social problems, whereasthose that improved began the decade

towards the top of the group. The trajec-tories of neighbourhoods that ended thedecade in the 3rd cluster (Figure 7(b))

Figure 7. Visualisation of clusters 3 and 4 and longitudinal trajectories: (a) cluster 3, 2000;(b) cluster 3, 2010; (c) cluster 4, 2000; (d) cluster 4, 2010.




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come from much greater distances acrossthe output space as compared with the pre-vious two cases, suggesting that many of theneighbourhoods with these characteristics

in 2010 are very much in transition (pri-marily on an improvement trajectory).Given that the right side of the outputspace contains similar social and crimescores, but is distinguished by higher home-ownership rates, the resulting trajectoriessuggest that improvements to these socialand crime dimensions preclude increases inhomeownership. In addition, a higher con-centration of renters may also facilitatelarger QoL changes as populations are pre-sumably more fluid.

The fourth group of neighbourhoods hassimilar, median levels of homeownership ascompared with the previous cluster, but haslower income levelsand a higher concentrationof social problems. These communitiesabsorbed large numbers of inner-city residentsdisplaced by gentrification. They were not,

however, destinations for Latino settlement.Geographically, these neighbourhoods are inthe middle-ring suburbs around the city. Theirtrajectories reveal considerable variability,showing some movement towards a higherconcentration of renters with arrows pointingtowards nodes on the left-hand side and a largeamount of longitudinal fluctuation in QoLconditions (Figure 7(c)). Neighbourhoods thatmoved into this group were largely in decline,

with the exception of two located south of thecity centre, which follow an ascending trajec-tory, transitioning to the lowest nodes in thegroup (Figure 7(d)).

Finally, neighbourhoods in the twolowest-scoring clusters present disparate set-tlement histories. Specifically, cluster 5broadly represents communities which hadlarge resettlement streams resulting fromgentrification. They display significant youth-related social problems. Cluster 6neighbourhoods are communities with thehighest crime rates and are strong

destinations for Latino immigrant settle-ment. As is seen in Figures 8(a), 8(b), 8(c),and 8(d), over the decade, group 5 neigh-bourhoods that transitioned away generally

moved towards the high crime group, withtwo exceptions; the first is a small neigh-bourhood north of the city, whose trajectory follows a path more akin to group 3, aneighbourhood well known for its revitalisa-tion and gentrification during the decade; asecond neighbourhood already highlightedin the previous group, moving to a bottomnode in cluster 4. On the other hand, nearly all the neighbourhoods that increased in youth-related social problems began thedecade with high crime concentrations. Thisis apparent in both the plots of neighbour-hoods that transitioned into group 5 (Figure8(b)), as well as in Figure 8(c), showing thetrajectories of neighbourhoods that began ingroup 6. All neighbourhoods that exitedgroup 6 moved to group 5; none followedpaths of revitalisation. Geographically, all

but one of the neighbourhoods that transi-tioned into cluster 6 were adjacent to aneighbourhood already in the group or onethat also transitioned in, possibly indicatinga spatial spillover of high crime (Figure8(d)). Neighbourhoods in cluster 5 have amuch more obviously geographical concen-tration than the high-crime neighbour-hoods and have a greater presence closer tothe urban core. Conversely, the high-crimeneighbourhoods, especially by 2010, aremuch more dispersed in older suburbanneighbourhoods, expanding eastward,whereas cluster 5 neighbourhoods arelargely confined to neighbourhoods justnorth and west of the city centre.

5. Discussion and Conclusions

During the first decade of the century,Charlotte has experienced dynamic growthmarked by explosive housing expansion in




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inner-city and suburban areas, with stronggentrification trends and vibrant immigrantnewcomer streams. This research uses aquality of life template to present a casestudy for assessing existing urban theoriesand conventions regarding longitudinalneighbourhood change, while also enabling

the emergence of new relationships andtrends to be observed. Results of the analy-sis provide some affirmation of traditionaltheories of change as neighbourhoods thattransitioned to the highest quality of lifehad a large spatial presence on the outer-most ring of the city, and the highest QoLneighbourhoods proved to be the moststable across the decade.

In line with recent demographic researchand house filtering theories, older, suburbanneighbourhoods located in the citys middlering underwent significant transformations

during the decade. The resulting neighbour-hood trajectories, however, indicate a largedegree of variability in the changes acrossthe multidimensional attribute space. Anumber of neighbourhoods within thismiddle ring followed a path towards thehighest crime concentrations in the city in

conjunction with low incomes and highfood stamp dependency (cluster 6), whileothers saw declines in educational attain-ment and increases in youth-related prob-lems coupled with housing deterioration(cluster 4). These two categories encompassneighbourhoods experiencing large streamsof disparate in-migrants: low-income resi-dents displaced by gentrification (cluster 4)and Hispanic newcomers to Charlotte (clus-ter 6).

Another major finding that emergedfrom this work is a divergence in social

Figure 8. Visualisation of clusters 5 and 6 and longitudinal trajectories: (a) cluster 5, 2000;(b) cluster 5, 2010; (c) Cluster 6, 2000; (d) cluster 6, 2010.




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problems between economically disadvan-taged neighbourhoods. Two distinct groupswere formed by the clustering analysis:those with the highest crime concentrations

and those with more youth-related socialproblems (high school dropout rates andteenage pregnancy rates). Attribute changein these neighbourhoods generally occurredbetween the two groups, exchanging highcrime for more social problems, or viceversa, lending some evidence to the notionthat low-income, high-food-stamp-depen-dent neighbourhoods experience anincrease in social problems over time.However, a contrasting geographical pat-tern was observed between the two groupsover time. While the neighbourhoods withthe greatest concentration of youth-relatedproblems generally remained confined to aninner-city neighbourhood cluster, the high-est crime rates dispersed outward towardsolder, suburban neighbourhoods by the endof the decade. Evidence of a spatial spillover

of crime was also uncovered as many of theneighbourhoods that transitioned into the6th group by the close of the decade wereadjacent to neighbourhoods that werealready in the group at the start of thedecade, or to other neighbourhoods thatalso transitioned into it. Notably, none of the high crime neighbourhoods followedpaths towards revitalisation, while sharpchange trajectories depicting revitalisationcould be identified from the visual plots of several neighbourhoods that entered the3rd cluster (characterised by median home-ownership rates and access to transit andbasic retail opportunities). These neigh-bourhoods tended to improve along many social and economic QoL dimensions whilemaintaining median to low levels of homeownership, suggesting that increases

in aggregate homeownership levels may follow other QoL improvements to a

neighbourhood. Educational attainmentwas identified as a distinguishing character-istic between middle-income neighbour-hoods that followed improvement or

decline trends.The analytical approach implemented in

this study, which blends a computational,data projection and reduction procedure,the self-organising map, with visualisationtechniques, provides a framework forexploring the multidimensional, multitem-poral, quality of life data collected by muni-cipalities. Clustering the SOM output aswas done for this analysis enables generalpatterns of change for neighbourhoods withsimilar QoL profiles to be identified, whilevisualising longitudinal trajectories allowsfor a disaggregated view of the trends fol-lowed by each neighbourhood across thearray of QoL attributes. Both visualisationscales equip policy-makers with communi-cation tools for illustrating longitudinalQoL trends with constituents that would

otherwise be impossible to depict fromstatic change maps of the six time stamps.Furthermore, geographical maps portrayingneighbourhoods with coinciding socialproblems also provide a means for pin-pointing local, targeted initiatives.

The methodology used in this research isintended to be exploratory in nature, visua-lising patterns and trends within this com-plex spatial, temporal and multidimensionaldataset. Future research will utilise confir-matory statistical approaches to test thehypothesised relationships and to tease outcausal relationship. In particular, the impactof recent population influxes to the city andits neighbourhoods in shaping QoL changesshould be evaluated, as should the influenceof local policies including fundamentalstructural changes to the public school

system, targeted policing and neighbour-hood initiatives aimed at enhancing QoL.




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FundingThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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