Diversity in Knoxville: An applied perspective

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Applied Geography 42 (2013) 140e154

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Applied Geography

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Diversity in Knoxville: An applied perspectiveq,qq

Madhuri Sharma*

University of Tennessee, 416 Burchfiel Geography Building, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA

Keywords:Diversity scoreMetropolitan statistical areaIsarithmic surface density mapsRegressionPrincipal components analysesInvasion-successionFilteringEthnic-enclavesSustainable

q Earlier versions of this paper were presented atAssociation of American Geographers at Seattle, WA aand suggestions from participants are appreciated anthe paper. Special thanks to Dr. Hyowan Ban ofUniversity-Long Beach, Dr. April Luginbuhl, freelanceOhio State University, and Dudley Bonsal (ABD) at Gnesota at Minneapolis, for providing valuable inputs oSpecial thanks to Will Fontanez, our department’s Cgraduate student assistant Chaney Paul Swiney withmaps for this project.qq This is an open-access article distributed undeCommons Attribution License, which permits unresreproduction in any medium, provided the original au* Tel.: þ1 876 974 6077; fax: þ1 865 974 6025.

E-mail addresses: [email protected], madhursha

0143-6228/$ e see front matter � 2013 The Author.http://dx.doi.org/10.1016/j.apgeog.2013.05.002

a b s t r a c t

This study contributes to the literature on applied urban geography by examining spatial patterns andprocesses of changing racial/ethnic diversity within the intra-urban context of Knoxville metropolitanstatistical area. Knoxville embraces a diverse economic set up with opportunities in high-tech researchand development, manufacturing, tertiary/service-sectors, construction, as well as entertainment in-dustry. This serves well for its continued population growth, including minorities during 1990e2009.This paper explores how the neighborhood-level socioeconomic, demographic, and built-environmentcharacteristics relate to tract-level racial/ethnic diversity, measured by multi-group diversity score andits components. Tools such as isarithmic surface density maps, correlations, principal components andregression analyses are used to examine processes of change. Results indicate that diversity in 1990associates with negative change whereas diversity in 2000 associates with positive change. Thoughoverall diversity sprawls and increases during 1990e2009, diversity among non-White declines during2000e2009 and shows spatial confinement. Regressions suggest complicated mosaics of changingneighborhoods, providing evidence of invasion-succession, filtering and resurgence of ethnic-enclaves inspecific neighborhoods. Concerning the six counties of the MSA, Knox is the most diverse whereas Unionthe least, though the share of Hispanics tops in Loudon and Asians in Knox. In terms of strategic planning,findings from this research can be used in creating equitable and sustainable urban communities that canimprove the overall well-being of people by reducing racial/ethnic and socio-economic disparities thatmight occur as undesirable consequences of fast increasing diversity.

� 2013 The Author. Published by Elsevier Ltd. All rights reserved.

Introduction

Recent demographic and economic changes in the AmericanSouth has earned it the name of The New South (e.g., McDaniel &Drever, 2009; Smith & Furuseth, 2004; Winders, 2006, 2011a,2011b). This region, like the rest of the country, has been gaining inits racial/ethnic diversity over past two decades, particularly

the annual meetings of thend New York, NY. Commentsd have been incorporated inGeography, California Stateconsultant and a friend fromeography, University of Min-n earlier versions of this draft.artographer, and our under-their assistance on making

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concerning the share of Hispanics (Smith & Furuseth, 2004;Winders, 2006, 2011a, 2011b) and Asians (Sharma, 2011a, 2011b).Much of it is driven by the growth of manufacturing and assemblyplants and other service-sector opportunities moving to thesouthern states such as Tennessee, Alabama, South Carolina, NorthCarolina and Georgia, especially after the initiation of North Amer-ican Free Trade Association (NAFTA) (Cobb, 2005; Perreira, 2011).

Knoxville, a southernmid-sizedmetropolitanstatistical area (MSA)in East Tennessee had thrived as a major manufacturing and whole-saling center up until 1950s when its textile industry such as the LeviJean, Knoxville Knitting Works, and other manufacturing industriescollapsed. During past two decades though, Knoxville’s economy hasgrown and diversified again, with several new manufacturing plantssettinguptheirheadquarters inKnoxvilleand inEast Tennessee (Flory,2011). These growing economic opportunities in Knoxville havecontributed to its gain in racial/ethnic diversity, particularly during1990e2009; this paper explains these changing residentialmosaics atthe scale of census tracts (CTs) by contextualizing themwith its socio-economic and built-environment characteristics.

Concerning its economic vibrancy, Lisega Inc. a Germanmanufacturing company producing pipe supports and hangers

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M. Sharma / Applied Geography 42 (2013) 140e154 141

for the energy industry, opened up its only North American head-quarter at Kodak City in Sevier County that was originally a part ofKnoxville MSA (Flory, 2011).1 Knoxville has also thrived through thetough economic times of past two decades when thewhole countryencountered two recessions. In January 2012, while the nationalunemployment rate was 8.3% and the State of Tennessee’s unem-ployment rate was 8.2%, Knox County’s was at 6.2% while KnoxvilleMSA was at 6.7% (Labor Force Estimates, March 2012). Knoxville’stenacity to tread through the economically tough times have hel-ped it gain and retain its diversity in terms of races, cultures, na-tionalities, classes and life cycles including the retired community(http://www.retirementplacesreport.com/tennessee_cities.html)as it also ranks high in the list of “most affordable places”. Con-cerning industries and employability, Knoxville is headquarter tomajor employers such as the Aluminum Company of America(ALCOA), the Oak Ridge National Laboratory (ORNL), University ofTennessee (UT), UT-Medical Center, Blount Memorial Hospital,Covenant Health, Summit Medical Group, The Baptist Health Sys-tem of East Tennessee, St. Mary’s Medical Center, Home and GardenTelevision Network (HGTV), the Great Smoky Mountains NationalPark, Dollywood, Mid America Corp., Denso Manufacturing Ten-nessee, DeRoyal Industries, Sea Ray Boats. Inc., Philips Electronics,North America Corp., City/County of Knoxville, Boeing Defense &Space, Science Applications International Corp. (SAIC), BellSouth,Pilot Corporation, Matsushita Electronic Components Co. ofAmerica, and many more multinational corporations (http://web.knoxnews.com/jobs/knoxville/employ.shtml).

Knoxville, a southern MSA, has 11.1% of its total population in2009 as non-Caucasian (Table 1-A, www.census.gov). Knoxvilleconsists of six counties e Anderson, Blount, Knox, Loudon, Sevierand Union, (2000 Census definition), with the city of Knoxvilleseated in Knox County, and the six counties consisting of 139 censustracts (CTs).2 Knoxville is interesting due to its mid-size college-town characteristics with the benefit of a moderate climate andbeautiful landscape tucked in the Appalachian Valley (Fig. 1). It alsohas a diverse economic base in comparison to other MSAs of samesize. Its growth in the share of Asians and Hispanics at 131.68% and408.01% during 1990e2009 was far above the growth rates forWhite or Black (Table 1-A).3 Changing characteristics of

1 Concerning Knoxville and east Tennessee, as of 2011, about 67 German com-panies are doing business in Tennessee; prominent automaker Volkswagen recentlyopened an assembly plant in Chattanooga, and solar company Wacker Chemical isbuilding a $1.5 billion polysilicon plant in Bradley County (Flory, 2011).

2 In 2010, Sevier County was removed from being considered as a part of KnoxvilleMSA. In this analysis, however, I use the six-county definition for Knoxville. I do thisas I had already computed diversity score indices for 1990 and 2000 for a previousanalysis, and I wanted to measure two decades of change in diversity. Using 2010census data would have served better, but normalized data are available from geo-lytics only upon purchase. In 2010 Census, several tracts changed their boundarydefinitionmaking them incomparablewith1990 and 2000 statistics, and hence I used2005e09 ACS five year estimates data that still follows 2000 tract boundary defini-tions. Thus, using the ACS 5 year estimates for 2005e09 enabled this comparison andanalyses of computed indices across 1990e2000e2009. Also, ACS 5 year estimatedata for extracting socio-economic and built-environment variables for the censustracts that enabled further analyses with correlations and regressions. For moredetailed information on geolytics products, see www.geolytics.com/USCensus,Neighborhood-Change-Database-1970e2000 Products.asp

3 In this paper, I use the terms African-American and Black interchangeably,likewise White and Caucasian are used interchangeably. Asian refers to the com-bined group of non-Hispanic Asians along with Hawaiian and Pacific islanders;American Indian refers to the combined group of non-Hispanic American Indiansalong with Alaskans/Aleuts/Eskimos, etc.; All-Other has been used for non-HispanicAll-Other along with Some Other Races and Two or More Races. Also, the termsWhite, Black, Asian, American-Indian and All-Other refer to the non-Hispanicgroups whereas Hispanics are a separate group. While the term Black doesn’tsound very pleasant, most segregation literature use this term very often, and I havetried to use African-American instead, but at some places I have also used Black alsobecause of its acceptance by the larger academic community.

demographic diversity in a metropolitan area that mostly had Blackdiversity until 1990s may create demand of multilingual schools,multi-cultural centers, health care professionals and other civicfacilities/social/welfare institutions to support and retain them.

This analysis is part of an ongoing larger project on Knoxvillethat examines how racial/ethnic diversity and intermixing ismanifested within intra-urban contexts, and how do they getimpacted by the housing market elements such as bankers/lender,realtors, builders/developers and local communities. An earlier partof this analysis discussed broad spatial patterns of diversity, inter-mixing and clustering during 1990e2000 that has been published.This paper moves ahead by exploring the relationship betweentract-scale racial/ethnic diversity at a point in time, i.e., in 2009 andits change during 1990e2009 with their socio-economic and built-environment characteristics to explain the evolutionary process ofchanging diversity. In this paper, I explain these processes usingmultiple statistical and cartographic tools such as isarithmic surfacedensity maps for computed diversity scores, followed up withanalytical tools such as bivariate correlations, principal compo-nents and regression analyses. This paper adds to the genre ofresearch on diversity/urban geography within the context of anemerging mid-sized southern metropolitan area, particularly inlight of Knoxville’s significance as the third largest new attractioncenter for Latinos, following Nashville in Tennessee and Birming-ham in Alabama. The remainder of this paper proceeds in foursections: literature review, research design, analyses and findings,and finally discussions and conclusions.

Literature review

Racial/ethnic diversity and transitioning neighborhoods

Upuntil late 1900s, a large bodyof diversity/segregation literaturehas situated their empirical analysis using existing frameworks onurban ecology. These include the Classical Assimilation, Place Stratifi-cation and the Resurgent Ethnicity frameworks.4 Concerning empir-icalwork, so far several researchersworkingon diversity/segregationhave (i) focused on the largest MSAs/gateway cities(e.g., Charles,2000, 2003; Clark & Blue, 2004; Ellis, Wright, & Parks, 2004; Farley& Frey, 1994; Frey & Farley, 1996; Singer, 2003, 2004; Timberlake &Iceland, 2007), and/or (ii) treated cities/metropolitan areas as theobject of analysis (Brown& Sharma, 2010; Charles, 2000, 2003; Clark& Blue, 2004; Ellis et al., 2004; Farley 1996; Farley & Frey,1994; Frey &Farley, 1996; Logan, Alba, Dill, & Zhou, 2000; Singer, 2003, 2004).Those focusing on diversity/immigrants’ dispersal to non-traditionalurban/rural locations include Lichter, Parisi, Grice, and Taquino(2006), Singer (2003, 2004), Smith and Furuseth (2004). Intra-urban analyses has been relatively rare, except for some recentones like Acevedo-Garcia and Osypuk (2008), Grady and Darden(2012), Lobo, Flores, and Salvo (2002), Sharma (2011a), Sharma andBrown (2012), Smith and Furuseth (2004), Winders (2006, 2011a,2011b).

4 Assimilation embraces the melting pot ideal (Alba & Nee, 2003) where immi-grants, having adapted to US society and gained in socio-economic status, moveinto established neighborhoods that are spatially more distant from the CBD with ahigher representation of Caucasians (Brown and Chung 2006, 2008). Stratificationfocuses on the persistence of segregation from housing discrimination, racial-stereotypes, and prejudicial preferences (Bobo & Zubrinsky, 1996; Brown andChung 2006; Charles, 2000, 2003; Logan et al., 2000). Resurgent Ethnicity empha-sizes the persistence of racial/ethnic clusters in residential choices even after theimprovement of socio-economic status that could eventually lead to choosing anintermixed neighborhood, and yet, some people choose to reside in neighborhoodswhere a majority of their neighbors belong to their racial/ethnic background(Brown & Chung 2006; Logan, Zhang, & Alba, 2002, Logan et al., 2004).

http://web.knoxnews.com/jobs/knoxville/employ.shtml


http://www.census.gov

http://www.geolytics.com/USCensus

M. Sharma / Applied Geography 42 (2013) 140e154142

Concerning intra-urban changing mosaics in major gateways,Lobo et al. (2002) discuss the effect of growing shares of Latina,African-Americans and Asians in the CTs of New York City during1970e90, eventually transforming 38% of its White-dominated CTsmulti-ethnic. Sandoval and Li’s (2004) analysis of increasing di-versity in Chicago finds their spatial dispersal beyond city centers.Concerning intra-urban context, Denton and Massey (1991)examine the 50 largest Standard Metropolitan Statistical Areas(SMSAs) plus 10 others that had substantial Hispanic population,using census data for 1970 and 1980, and find that only 31.5% of All-WhiteCTs remained so by 1980 whereas others become multi-ethnic. Chung’s (2005) analysis of Columbus, Ohio suggests sub-stantial dispersal of minorities beyond Interstate-270 during 1990e2000. Dingemans and Datel’s (1995) work on Sacramento, Califor-nia findsinvasion-succession occurring from growing presence ofBlacks and Latinos in central city locations.

Racial/ethnic diversity, changing socio-economic contexts and theNew South

Concerning changing economic contexts, Florida (2004) notesthat the economic diversity of a place contributes by transformingit into a “creative place” as it gains in diversity of sorts e racial,ethnic, lifestyles, etc. since such places garnish the three Ts e

technology, talent and tolerance. The relationship between eco-nomic contexts and diversity can be best understood from historywherein the American Manufacturing Belt (AMB) thrived throughthe 1950s under Fordism, serving as magnets for African-Americans during the Great Migration of 1910e1930, as well asfor other immigrants from European and the Middle East region(Geib, 1998). While racial/ethnic enclaves were important land-scapes of major gateways such as Chicago, New York, Miami, LosAngeles, etc. (Charles, 2003; Clark & Blue, 2004), the growth ofAfrican-Americans during early-to-mid 1900s further facilitatedthe formation of Black ghettos in large-to-mid-sized MSAs, initiallydeveloping into monocentric city patterns and later through for-mation of edge cities/suburban enclaves, etc. The improvements inthe conditions of Black in theMidwest started only afterWorldWarII when they achieved better education, enlisted in the military, gotbetter job opportunities, eventually creating a Black-middleclass (Geib, 1998). They competed for manufacturing jobs, partic-ularly after unionization, and these collectively led to their upwardmobility, and their fast growth in major Midwestern cities (Geib,1998). Concerning the economic status of minorities, particularlyas a result of the economic restructuring of the 1970s and 1980s,Levine (2000) and Sassen (1991) note that Black and Latino in thelarger cities of the Midwestern region were the most adverselyaffected (Levine, 2000) and the minorities in the global cities wereonce again the worst sufferers of socio-economic and racial polar-ization (Sassen, 1991). Concerning residential segregation andeconomic contexts in other large-to-mid-sized MSAs, Brown andSharma (2010) in their analysis of 49 MSAs (larger than 1 millionpopulation in 2000) find that when the geographic region serves asa surrogate for analyzing AMB/Rust Belt versus Sun Belt di-chotomies, the AMB-MSAs suffer a heavy burden of sunk costsinitially, but are soon absorbed (or written off) by 2000 and thatthey subsequently observe substantial shift and larger degrees ofgain in intermixing over the duration 1990e2000 compared toothers.

Concerning the New South, several southern MSAs have gainedin their shares of immigrants and racial/ethnic diversity, triggeredfrom economic opportunities in formal and informal sectors(Cornelius, Fitzgerald, Fischer, & Muse-orlinoff, 2010; Perreira,2011; Sharma 2011a; Shultz, 2008; Smith & Furuseth, 2004;Winders, 2011a, 2011b). The southeastern USA has the second

largest concentration of Hispanics, and their share has increased bymore than 100% during 1990e2000 across all southeastern states,with North Carolina experiencing the highest at 386%. Charlotte’sHispanic, non-Hispanic African-American and non-Hispanic Whiteshare constituted 2.6%, 35.1% and 56.8% of its total population in1990; by 2000, these changed to 23.85%, 34.5%, and 28.4% respec-tively, indicating significant gain for Hispanics and huge decline forCaucasians (Smith & Furuseth, 2004: 221).

Among other mid-sized southern MSAs, Nashville, Tennessee,has drawn attention because of its significance as a “new destina-tion” for Hispanic immigrants and refugees, driven from its vibrantmusic and health-care/insurance industry (Chaney, 2010; Winders,2006). Going further south, McDaniel and Drever’s (2009) analysisof Birmingham, Alabama finds that immigrant entrepreneurs arenot only building ethnic enclaves, but they are also taking advan-tage of automobiles and advanced communication technologies,and are residentially dispersing with no more confinement to innercities. Bobby Wilson’s America’s Johannesburg (2000) and laterConnerly’s ‘The Most Segregated City in America’ (2005) note thatBirmingham has long since followed segregationist city planningpolicies through racial zoning, urban renewal, and placement ofinterstate highways that collectively ensured that the city remained‘the most segregated city in America’. However, recent economicgrowth in northeastern Alabama have employed thousands ofHispanics in the poultry/vegetable/fruits farming in Russellville,Gadsden, Attalla, Raul and Collinsville, and in the fishing/tourismindustries in southern Alabama (Cobb, 2005).

Racial/ethnic diversity, health/social inequality and planning andpolicy

A new line of research has addressed the effects of growingdiversity on rising inequalities/health risks of minorities/popula-tion residing in highly segregated spaces. Grady and Darden (2012),for example, in their analysis of Detroit MSA find that institutionalracism, unfair policies and practices in the housing industry andother institutions have produced racially inequitable outcomes forblack mothers while benefitting white mothers. Grady and Darden(2012: 928) find that when they control for high black segregationand very low socio-economic position indicator (SEP), there wassignificant reduction in the racial disparities and preterm birth.Thus, by ignoring racial segregation, one might be misestimatingthe effect of individual-level risk factors on health of minorities andchildren, particularly for Latinos and Blacks, that affect them notonly in their childhood years, but lingers on throughout their lifecourses (Acevedo-Garcia & Osypuk, 2008; Acevedo-Garcia, Osypuk,McArdle, &Williams, 2008; Grady & Darden, 2012; Osypuk, Bates, &Acevedo-Garcia, 2010).

Very often planning and zoning policies, particularly in US urbanareas, create spaces of distinction, with low quality housing struc-tures, inaccessibility to healthy food options, lack of adequatehealth-care facilities, environmentally degraded locations,congestion and likewise (Acevedo-Garcia & Osypuk 2008; Acevedo-Garcia et al., 2008; Connerly, 2005; Jones, 1995; Wilson, 1992,2000; Wilson, Hutson, & Mujahid, 2008). Unfortunately, thesezoning policies more adversely affect the minorities, which chal-lenge the communities and cities across the country plagued withracial/ethnic fragmentation, environmental injustice, and healthdisparities (Jones, 1995; Wilson et al., 2008).

Concerning diversity and policy making, Letki (2008) in heranalyses of communities in the Great Britain, notes that diversitycreates tension, mistrust, doubts, concerns, unacceptability andintolerance among people. However, she also finds that thenegative consequences of diversity actually reduce, wheneconomic variables are controlled. In short, she concludes that

S ¼Xn

r¼1

ðPrÞlnð1=PrÞ (1)


diversity is good and in the British society, it has become a basisfor drawing out important plans and policies to create sustain-able and equitable communities. Likewise, Papillon’s (2002)analysis of Canadian cities finds that diversity is an importantingredient for sustainability, and that the capacity of institutionsat the national, provincial and local levels should be improved tocounter the patterns of socio-economic and spatial exclusion. Intheir analyses of participatory planning and inclusion of localcommunities in a Jewish-Arab community, Shmueli and Kipnis(1998) indicate that involving the community ensures thattheir needs and concerns are addressed adequately, that a senseof ownership helps its implementation by creating a willingnessamong the community to accept alternatives and compromises.These demand integrating the new immigrants and diversity intothe labor market (Papillon, 2002) and guarantee them access tovarious types of social/civic services, language training, educa-tional (Jones, 1995) and housing opportunities (Vliet, 1996), andhelp create urban space(s) where they can build social networksand participate in various cultural and political life without giv-ing up their own cultural and communal ties. These aspects havealso been discussed by Winders (2011a, 2011b) concerning thesocial, cultural, and political integration of Latinos in the musiccity of Nashville.

Methodological approaches in measuring diversity

Among numerous measures of diversity/segregation, mostcommonly used are the Dissimilarity Index (Duncan & Duncan,1955; Wong, 2008), Location Quotient (Brown & Chung, 2006;Chung, 2005; Ellis, Holloway, & Wright, 2007; Isserman, 1977;Leigh, 1970; Moineddin, Beyene, & Boyle, 2003; Sharma, 2011a,2011b), and Exposure Index (Logan, Stults, & Farley, 2004; Wright,Holloway, & Ellis, 2011; Wong, 2003). Séguin, Apparicio, and Riva(2012) use location quotient to analyze poverty in the neigh-borhoods of Montreal, Canada during 1986e2006, and find thatchanges were minor over time, except for CTs in the gentrifica-tion trajectory where changes in poverty levels were more sig-nificant. Among those using Local Moran’s I include Brown andChung’s (2006) analysis of segregation in Columbus, Ohio,Monkkonen’s (2010) analyses of segregation by ethnicity andsocioeconomic status in over 100 cities of Mexico, and Robertsand Wilson’s (2009) analyses of new urban growth, socialdisparity and segregation in the largest MSAs of Latin American.Recently, a more accurate measure of clusters and geodemo-graphic segmentation was created by Grekousis and Thomas(2012), known as the Fuzzy C-Means algorithm and the Gus-tafson-Kesselalgorithm. They use this measure to examine seg-mentation and clusters in the prefecture of Attica, Greece. Theyfind that the residents of eastern and northern suburbs haveaverage to high incomes, belong mostly to working class as wellas high-flying executives and/or employed in science andacademia. On the other hand, residents of western suburbs havelower income and education, with several children, are unable tospeak foreign languages, and work as technicians, machine op-erators and unskilled laborers.

Concerning conceptual notions of measuring diversity/segre-gation, Ellis et al. (2007) raise questions about the inaccuraciesassociated with these measures as most indices ignore the mixed-race households that are rising in contemporary America,particularly in the largest gateways. They find that racial mixingwithin households has meaningful effects on these measures, andthat there is a need to re-conceptualize these measures whenanalyzing diversity and mixing in an increasingly multi-ethnicsociety. To address this issue, Wright et al. (2011) use Scaled En-tropy to measure racial/ethnic diversity at the census tract scale,

by categorizing diversity into three levels e low, medium andhigh, using numerically dominant racial/groups. They demon-strate the benefits of using this measure that forces one to thinkbeyond single-group numerical dominance, or even pairs ofgroups. Holloway, Wright, and Ellis’s (2012) analysis of sixteenMSAs also use Scaled Entropy, and find that residential neigh-borhoods are becoming racially more diverse despite significantshares of urban landscapes exhibiting high levels of segregation,and that the relationship between segregation and diversity arenot simply the mirror images of one another, instead diversity isbecoming enfolded within segregation and vice-versa in verycomplicated ways.

Given the increasingly multi-racial/ethnic nature of urbanAmerica, a small group of scholars have used the Multi-group TheilEntropy Score/Index (Brown & Sharma, 2010; Ellis et al., 2007;Fischer, Stockmayer, Stiles, & Hout, 2004; Iceland, 2004; Reardon &Firebaugh, 2002; Reardon & Sullivan, 2004; Sharma, 2011a; Sharma& Brown, 2012; Timberlake & Iceland, 2007; Wright et al., 2011),which follows the conceptualization created long ago by Theil andFinezza (1972). Given the focus of this paper is changing multi-group diversity, I use the Theil Entropy/Diversity Score as a mea-sure of analysis.

Research design

Study area and data sources

The city of Knoxville is seated in Knox County, and the MSAincludes other incorporated cities/urban areas such as Alcoa, Clin-ton, Farragut, Lenoir City, Loudon, Maryville, Maynardville, OakRidge, Sevierville, Seymour, and Pigeon Forge (Fig. 1). Importantfreeways/state routes cutting through the six counties of the MSAinclude I-75, I-40, I-640, 11, 441, 411, 129, 321, 66, and 25W. Most ofthe new residential and commercial development in Knoxvillefollows the East-West corridor, along US-11, more commonlyknown as the Kingston Pike, because of availability of flatter landand the construction difficulties along the north-south orientationof the Appalachian Mountains.

In this analysis, CTs of the six-county MSA form the unit ofanalysis. Block group (BG) data for six population groups e All-Others (including Other-Races/Two or More Races), AmericanIndian, Asian (including Hawaiian and Pacific Islanders), Black,Hispanic and White are used for computing diversity score (DS)and its components for each CT. The NCDB product of Geolyticsfor 1990 and 2000 and ACS 5-year estimates for 2005-09constitute the data source. For correlations, principal compo-nents and regression analyses, tract-level data are used. Care istaken to assemble and create similar types of variables acrossdemographic, socio-economic and built-environment categoriessince the definition of variables have changed during 1990e2000, and for the ACS-five year estimates. Where comparativedata/variables are not available, they are excluded fromanalyses.

Measurement statistics and methodological steps

Measurement statistics for this analysis include Diversity Score(DS) and its components. DS is computed using BG data using thespecifications below (Timberlake & Iceland, 2007: 341; Sharma,2011a: 313e314; Sharma & Brown, 2012: 326e327). Thus, theequations for computing diversity score(s) are:

D

DSi ¼Xn

r¼1

ðPriÞlnð1=PriÞ (2)


Where DS is the Diversity Score for census tract, DSi is the DiversityScore for each block group i(BGi) within that census tract, Pr is theproportion of a particular racial/ethnic group r in the census tract,Pri is the proportion of a particular racial/ethnic group r in blockgroup i within that census tract, where there are n racial/ethnicgroups.5 DS varies based on the number of groups and proportionsof each group. Its lower bound is zero when only one racial/ethnicgroup is found, and has an upper boundwhen all racial/ethnic (R/E)groups are equally represented. In a simulation exercise, DS¼ 1.799for 6 R/E groups (Pr ¼ 0.167 for each), 1.609 for 5 R/E groups (Pr ¼0.200 for each), 1.386 for 4 R/E groups (Pr ¼ 0.250 for each) (seeBrown & Sharma, 2010).

In this analysis, I use BG data to first compute DSi for each blockgroup, which enables me to compute DS for the CTs, using SPSSsyntax commands. I use BG data instead of tract level data as theSPSS commands enable me to compute two components of DS(Overall diversity score) for each census tract: (i) DSWNW referringto the diversity because of the two major groups e White versusnon-White, and (ii) DSNW referring to the diversity among the non-White groups (i.e., the diversity occurring from the presence ofother five segments e Black, Asian, American-Indian, All-Othersand Hispanic). These two components provide valuable informa-tion concerning which component of diversity contributes towardlarger or smaller share of overall diversity for each CT.6 Besidescomputing the DS for tracts, I also compute DS for the six countiesof theMSA for 1990, 2000 and 2009, using Eq. (1) where Pr refers tothe proportion of each racial/ethnic group in each County (see re-sults in Table 1-B & C, Fig. 2).

Once the diversity scores and its components are computed forall 139 census tracts, these are mapped using isarithmic densitysurface mapping techniques, using natural breaks as the class-categories (Figs. 3 and 4), and analyzed for spatial variationacross time. Next, a bivariate correlation analysis is conducted usinga select list of tract-level demographic, socio-economic, and built-environment variables to identify multicollinearity and eliminatethem from further use in principal components analyses (PCA).7

PCA helps extract four components that distinguish in terms ofplace-based characteristics. These components along with diversityscore in 1990 and 2000 are used as independent variables inregression analyses whereas dependent variable consists of di-versity score in 2009 and its change (2009e1990). I also conduct

5 DS and DSi values are used to compute Theil Entropy Index (measure ofintermixing) for the census tracts. However, in this paper, I focus only on the di-versity score and its components, honoring the length of the manuscript.

6 The SPSS syntax for computing the diversity score and its components can bewritten for any group-combination one is interested in analyzing (for example,Hispanic versus non-Hispanic and among non-Hispanic or Black versus non Blackand among non-Black, and so on); in this paper, I focus on White vs. non-White andamong non-White.

7 The variables in demographic, socio-economic, and built-environment charac-teristics were identified based on earlier academic work done by several scholars. Inthis paper, my focus is to distinguish the neighborhoods based on their race-basedand socio-economic and geographic contexts, and hence I select variables thatreflect these characteristics well. For example, manufacturing/blue-collar versuswhite-collar/professional indicate class effect, median household values, age ofhousing, share of newer housing etc. reflect the spatial characteristics of a place,educational achievements and income levels of population subgroups explain theoverall well-being and class aspects associated with people and place. Thus, theseformed my basis for identifying the variables for this analyses. Also, before con-ducting PCA, stepwise-backward and OLS regressions were also explored to find afit model, but they did not yield good results and hence I conducted the PCA.

regression analyses for change in diversity score for White versusnon-White group during 1990 to 2009.8

Analysis and findings

Racial/ethnic diversity at intra-urban contexts: spatial patterns fortracts and counties

During 1990e2009, Knoxville’s share of Hispanics, Asians andBlacksincreased by 408%, 131.68% and 28.41% respectively, and itsdiversity score changed from 0.325 (1990) to 0.486 (2009) (Table 1-A). While Nashville and Memphis, Tennessee’s two largest MSAshave contributed toward a larger part of Tennessee’s overall racial/ethnic diversity (Sharma, 2011b), Knoxville’s diversity score con-tinues to increase (Table 1-A, C) whereas Nashville’s andMemphis’shave declined during 1990e2000 (Sharma, 2011b: 312, Table 19.2).Concerning the six counties of Knoxville, Table 1-B indicates thatevery County has gained in its share of non-White population,particularly Knox, Loudon and Sevier (www.census.gov). For KnoxCounty, Hispanic share has changed from 0.6% (1990) to 1.2% (2000)and 2.3% (2009); for Loudon these are 0.3%, 2.2% and 4.3%; forSevier these are 0.7%, 1.0% and 2.4%. Concerning Asians, KnoxCounty’s share increased from 0.9 (1990) to 1.7 (2009). Andersonand Union counties also have good presence of Asians, and both arewithin close proximity to ORNL. Overall, Knox County has thehighest share of minorities among all six counties, with 14.4% in2009 as against 10.5% in 1990 (www.census.gov). Fig. 2 and Table 1-C indicate that diversity scores have increased for all countiesduring 1990e2009, with Knox at the highest (DS2009 ¼ 0.576),Anderson’s the second highest (DS2009 ¼ 0.419) while Union at thelowest (DS2009¼0.163) with a small decline in its white share from99.2% (1990) to 97.1% (2009) (Table 1-B-III).

Concerning diversity at the scale of census tracts, Fig. 3 indicatesspatial dispersal during 1990e2000e2009, from inside of Interstate-640 inKnoxCounty in1990 towardoutwards intoAnderson, Loudon,Blount andSevier by2009; highest levels of diversity (dark color) hasalso sprawled during 1990e2009. By 2000, diversity spread towardEast Knox County and Oak Ridge; by 2009, it expanded further intoMaryville, Sevierville, Loudon, Union and Sevier counties.

Diversity forWhite versus non-White (Fig. 4, left) shows a spatialspread-out during 1990e2000e2009, particularly in the vicinity ofKnoxville, Oak Ridge, Loudon, Maryville, Pigeon Forge and Sevier-ville. However, diversity for non-White (Fig. 4, right) indicates itsspread during 1990e2000 and a spatial confinement during 2000e2009, possibly forming into ethnic enclaves(note Resurgent Ethnicityframework). Figs. 3 and 4 also suggest that the hot spots of diversityare around the downtown areas of most cities/urban areas of theMSA. In particular, Anderson, Blount, Knox, and parts of Seviercounties show hot spots for non-White diversity in 2009 whereasthis was more contiguous throughout the whole MSA in 2000.9 This

8 The regression models for change in diversity score for the “among non-White”group did not generate any significant models, and hence those results are notpresented here.

9 To substantiate this finding about the spatial pattern of diversity scores, theauthor’s neighborhood reconnaissance while conducting field work with home-owners and foreclosures in Knoxville MSAsuggests that there is visible presence ofLatino labor engaged in various types of service-sector jobs such as hotel cleaningpersonnel and restaurant services in Sevier County, in new construction projectsalong Alcoa Highway in South Knox and Blount counties and toward west inLoudon County. Field work also suggests that many souvenir shops, hotel busi-nesses, and other such business catering to the tourists in Sevier County wereowned by Indian and Pakistani businessmen (Asians). The author has not investi-gated further concerning data on race/ethnic businesses in the location, butthought it was worthwhile to mention about these observations and discussionswith respondents from the field notes.



Table 1Demographic composition and divesity score of Knoxville MSA and its six counties, 1990e2000e2009

A. Demographic composition of Knoxville MSA and its Change, 1990e2009MSA statistics 1990 Share 2000 Share 2009 Share Pct. Change,

1990e2009

Total population 586,025 1.000 687,249 1.000 764,077 1.000 30.38White 541,525 0.924 622,899 0.906 679,542 0.889 25.49Black 35,126 0.060 39,628 0.058 45,104 0.059 28.41American Indian 1536 0.003 2153 0.003 1791 0.002 16.6Asian 4224 0.007 6256 0.009 9786 0.013 131.68All others 180 0.000 8883 0.013 10,409 0.014 5682.78Hispanic 3434 0.006 7430 0.011 17,445 0.023 408.01Diversity score 0.325 0.420 0.486

B. Demographic composition of the six counties of Knoxville MSA, 1990e2009B-I: Racial/ethnic proportions in counties in 1990 PopulationCounties White Black Amer-Indian Asian All-Others Hispanic County

Anderson 0.942 0.039 0.002 0.008 0.000 0.009 68,251Blount 0.957 0.031 0.003 0.005 0.001 0.004 85,921Knox 0.895 0.087 0.003 0.009 0.000 0.006 33,5748Loudon 0.982 0.012 0.001 0.002 0.000 0.003 31,321Sevier 0.985 0.004 0.003 0.002 0.000 0.007 51,090Union 0.992 0.000 0.001 0.002 0.000 0.006 13,694MSA population 586,025

B-II: Racial/ethnic proportions in counties in 2000 PopulationCounties White Black Amer-Indian Asian All-Others Hispanic County


B-III: Racial/ethnic proportions in counties in 2009 PopulationCounties White Black Amer-Indian Asian All-Others Hispanic County


C. Population and diversity score for counties of Knoxville MSA, 1990e2000e2009Counties 1990 2000 2009

Diversity score County population Diversity score County population Diversity score Countypopulation

Anderson 0.278 68,251 0.356 71,330 0.419 73,382Blount 0.220 85,921 0.299 10,5823 0.363 119,489Knox 0.404 335,748 0.509 382,032 0.576 423,655Loudon 0.112 31,321 0.257 39,086 0.336 45,176Sevier 0.099 51,090 0.219 71,170 0.284 83,448Union 0.055 13,694 0.101 17,808 0.163 18,927


pattern is further supported by the computed scores for all 3 years,where the maximum value of non-White diversity increased from1.513 (1990) to 1.576 (2000) which declined to 1.509 (2009), eventhough overall diversity score increased from 0.938 (1990) to 1.09(2009).

10 Table 2 presents correlations for a selected list of variables due to space con-straints, and detailed table may be provided upon request. Variables were identifiedbased on preliminary analyses with a larger set of variables pertaining to race-based socio-economic, demographic/change in racial groups, and built-environment characteristics. Race-based variables were selected to extract char-acteristics associated with the racialized aspect of neighborhoods/places reflectedin the PCA components. In the PCA, a larger set of variables have been used anddisplayed as most of them have significant loadings that talk about place-basedcharacteristics.

Neighborhood correlates

Bivariate correlation analysis (Table 2) indicates an interestingrelationship between diversity score in 2009 (DS2009) and itschange during 1990e2009 (DDS(2009e1990)) with tract-level attri-butes.10 Population size (2000 and 2009), change in populationduring 1990e2009 and 2000e2009, change in White (1990e2009) and share of non-movers (1985, 1995) are all significantlybut negatively associated with DS2009 whereas share of foreign-born (F-B) in 1990, 2009 and share of F-B entered during 1990e2000 are positively associated with DS2009. Concerning socio-economic characteristics, those positively associated with DS2009include Bachelors/above levels of education (overall population in1990, 2009), and for White, Black and Asian in 1990, medianhousehold incomes (1990, 2009) and per capita income for Black

Fig. 1. Knoxville MSA with counties, census-tracts, cities and incorporated urban areas and important roads/highways


(1990, 2009). Most education variables relate positively withDDS(2009e1990)whereas only Black per capita income (1990) relatespositively with DDS(2009e1990). Concerning built-environment/spatial attributes, it is no surprise that those employed inmanufacturing occupations (1990, 2000) relate with low DS2009and have no association with DDS(2009e1990). Interesting to note,however, is that race-based occupation in either manufacturing ormanagerial in 2009 are all positively associated with DS2009whereas they have no association with change in diversity. Otherinteresting finding is that homeownership by White, Black and

Asian in 1990 and White, Black and Hispanic in 2009 are posi-tively associated with diversity in 2009 and insignificantly withchange whereas Hispanic homeownership (2009) and Hispanicprofessional occupations (2009) are both positively associatedwith change in diversity. Median year of housing structure built(2009) and the share of newer homes built, i.e., share of homesbuilt during 1990e2000 and 2000e2009 out of 2009 totalhousing stock are all associated with lower diversity in 2009.Concerning diversity score and its components, the surprisingnegative relationship between diversity score among non-White

Fig. 2. Diversity scores and population size in six counties of Knoxville metropolitanarea over 1990e2009

a is the intercept;b01 is the coefficient on DS1990 when used in the model,

otherwise 0, (Table 4, panel I);b02 is the coefficient on DS2000 when used in the model,

otherwise 0, (Table 4, panel II); andb1, b2, b3, b4 are the coefficients associated with PC-I, PC-II, PC-III

and PC-IV in the model (Table 4, panel III).


(1990, 2000) with diversity score in 2009 is indicative ofminority-clusters within intra-urban context of Knoxville.

Principal components analyses: place-based perspective

The four components cumulatively explain 66.50 percent of totalvariation (Table 3). These components along with DS1990 andDS2000are used as independent variables in regression analyseswhereas dependent variable(s) consist of: (i) Diversity Score in 2009(DS2009), (ii) Change inDiversity Score during 1990e2009 (DDS(2009e1990)), and (iii) Change in Diversity Score forWhite versus non-Whiteduring 1990e2009 (DDSWNW(2009e1990)) (Table 4, panels A, B and Crespectively). ThoughusingDS1990 andDS2000 as predictorsmay/willincrease the R-square values substantially, I use these to measuretheir independent effects on the predictability of place-based char-acteristics. The following briefly explains the characteristics of pla-ces/neighborhoods captured in the four components.

Component I (PC-I) gets positive loadings on median house-hold values (3 years), median year of housing structure built,population engaged in managerial/professional occupations in2000 and 2009, White and Black population in managerial/pro-fessional occupation (2009), median household income (threeyears), per capita incomes for White, Black, Asian, and Hispanic in1990, per capita incomes for Black, Asian and Hispanic in 2009, andeducational achievements of Bachelors/above for overall popula-tion in all three years. Negative loadings occur on high school/lessereducated in all 3 years, White with high school/lower educated in1990, and change in Hispanic share during 1990e2009. Thiscomponent, thus, characterizes with creative-class/high-income/newness of a place.

Component II (PC-II) gets positive loadings on median house-hold values(2000, 2009), share of housing structure built (1990e2000, 2000e2009), median years of housing structure built (1990,2000, 2009), White homeownership (2009), manufacturing/blue

collar (2009), per capita income (Black, 2009), median householdincome (1990, 2000, 2009), population (1990, 2000, 2009) andchange in total population and for White during 1990e2009.Negative loadings occur on share of foreign-born (F-B) in 2000 and2009, F-B entered (1990e2000, 2000e2009) out of total 2009 F-Bpopulation, Hispanics in managerial (2000), manufacturing/bluecollar occupation in 2009 (overall and Black, Asian and Hispanic),and Hispanic homeownership (1990). This component, thus,identifies with newer/medium-to-high income White-Black neigh-borhood versus others with newly arriving F-B (Black, Latino andAsian) engaged in blue or white collar jobs.

Component III (PC-III) gets positive loadings on non-movers(1985, 1995), high school/lesser educated (1990, 2009), medianhousehold income (2000, 2009), blue collar/manufacturing (2000,2009) and White in manufacturing (2009), White homeownership(1990, 2009), and share of housing structure built during 1990e2000. Negative loadings occur on Bachelors and/or above educated(all three years), forWhite and Asian (1990) and for Black and Asian(2000), F-B share (1990, 2000) and F-B entered during 1990e2000.This component, thus, characterizes with stable/blue-collar/whiteversus educated/Black-Asian-diverse neighborhoods.

Component IV (PC-IV) gets positive loadings on Blackwith highschool/lesser educated (1990), per capita income for Hispanic(2009), managerial occupations (2000), Hispanics in managerialoccupations (2009), and Black homeownership (1990, 2009);negative loadings occur on White homeownership (1990,2009).This component characterizes with racially segregateddistinctness of Knoxville–Black/Latino diversity versus White onlyneighborhoods.

Regression analyses

The generic regression equation for dependent variable Ybecomes:

Y ½DS� 2009; DSð2009� 1990Þ; DSWNWð2009� 1990Þ�¼ aþ b01*DS1990 þ b02*DS2000 þ b1*PC� Iþ b2*PC� II

þb3*PC� IIIþ b4*PC� IV (3)

Where Y is the dependent variable of Diversity Score in 2009(Y[DS-2009]) or Change in Diversity score during 1990e2009(YDDS(2009e1990)) or Change in Diversity Score for White vs. Non-white during 1990e2009 (YDDSWNW(2009e1990)).

On the right hand side of the equation,Using the above specifications to analyze intra-urban variation

of diversity in 2009 and its change during 1990e2009, highervalues of the dependent variable at a point in time (YDS-2009)represent higher diversity, and positivevalues for change in di-versity during 1990 to 2009 [i.e., YDS(1990e2009) and YDSWNW(1990e2009)] represent gain in diversity.

Diversity at a point in time: 2009Table 4-A suggests that DS2000(Beta ¼ 1.257) serves as a better

predictor of DS2009 compared to DS1990(Beta ¼ 0.491), but the roleof the four components get overshadowed in the presence of either

Fig. 3. Diversity score-overall in Knoxville MSA, 1990, 2000 and 2009.


of these two variables and the direction of predictability for thecomponents also changes. In panel-III, Table 4-A (four components-models), PC-II, PC-III and PC-IV are significant predictors, with Betasof �0.574, �0.314 and 0.462 respectively. This indicates thatneighborhoods with newer/medium-to-high income White-Blackneighborhood have lower diversity (Beta ¼ �0.574) as againstothers with newly arriving F-B engaged in blue/white collar jobs thatare more diverse. PC-III (Beta¼�0.314) associated with stable/blue-collar/white neighborhoods are less diverse compared to others thathave educated, diverse population. Finally, PC-IV (Beta ¼ 0.462)suggests that the diverse (Black/Latino) neighborhoods are morediverse in 2009 whereas white-only/sluggish-low-income are lessdiverse. Even among lower-income groups, there are pockets of

diverse versus homogeneous clusters such as White-poor clustersin South Knox County and Black/Latino poor clusters toward East ofdowntown in Knox County.

This analysis also suggests that while diverse neighborhoods of2000 become more diverse in 2009, they are also transitioning intominority enclaves, as diversity among non-White (1990, 2000)relate with lower diversity in 2009.This is also evident fromregression analyses as minority clusters are forming, particularly inblue-collar neighborhoods (PC-III Beta¼�0.314). At the same time,there are other neighborhoods with Hispanics in managerialoccupation (2009), higher Hispanic per capita income (2009)alongwith Black homeownership (1990, 2009) that is contributing to-ward greater diversity (PC-IV Beta ¼ 0.462). These processes

Fig. 4. Diversity score-white versus non-white (left) and among non-white (right) in Knoxville MSA, 1990, 2000 and 2009.


reflect transitioning neighborhoods where filtering and/or invasion-succession (Buzar et al., 2007; Rerat, 2011) occurs with newlyarriving/diverse population when they buy/rent in relativelydiverse neighborhoods, increasing temporary diversity, and latermove elsewhere after gain in socio-economic status (assimilationframework of Alba, Logan, Stults, Marzan, & Zhang, 1999). Thisanalysis also suggests that Hispanics and Blacks are likely co-residing adding to diversity whereas White areas are difficult tochange, irrespective of their class e such as affluent White westernsuburbs (except well off Asians) and poorer white south Knoxneighborhoods. At the same time, neighborhoods in east Knoxville(e.g., Fourth & Gill, the Holstein Hills) have gone through gentrifi-cation and now attract newer/younger/relatively more diverse,yuppie and academic community.

Changing diversity as a process: 1990e2009Models (Table 4-B and C) suggest that PC-II is the only signifi-

cant predictor (Beta ¼ �0.544) with DS1990 in the model, whereasthe model with DS2000 suggests all four components and DS2000 assignificant predictors. In the components-only model (panel III,Table 4-B), PC-III is the only significant predictor (Beta ¼ 0.413).These models suggest that neighborhoods with higher diversity in1990 have negative change (Beta ¼ �0.544, Table 4-B, panel I)whereas those with higher diversity in 2000 have positive change.PC-III, the strongest of all four (Table 4-B, panel II) relates withstable/blue-collar versus newer/dynamic/diverse neighborhoods,and a Beta of 1.124 indicates that stable/blue collar neighborhoodsare also becoming more diverse in the recent times, probably frominvasion and succession. While this sounds contradictory to whatwas noted about stable/sluggish neighborhoods pertaining to di-versity in 2009 (Beta on PC-III ¼ 0.359, Table 4-A-panel IIand �0.314 for panel III), when it comes to change, the diversity

levels in 2000 has a stronger effect on change during the two de-cades. Also interesting to note is that Beta on PC-III ¼ 0.413(Table 4-B, panel III) is far lower than Beta ¼ 1.124 (panel II) whichindicates that the degree of change during 1990e2009 is muchhigher in neighborhoods that were diverse in 2000 compared tothose that were not. This process can be explained from theCommunity Norm perspective that offers an alternative way ofexplaining this change, illustrated as an overall catching-up phe-nomenon at the scale of MSA (Brown & Sharma, 2010) and withinintra-urban context (Sharma & Brown, 2012) such that the neigh-borhoods that are diverse try to catch up and gain more to keep upwith the norm, whereas neighborhoods that are not diversemaintain their inertia, per Community Inertia perspective (Brown &Sharma, 2010; Sharma & Brown, 2012). Concerning the compo-nents of diversity, regression tests concluded that models forchange in diversity for “non-White” were not significant. Con-cerning change in diversity for White vs. non-White (Table 4-C),the best model (panel II) has three components as significantpredictors and that DS2000 has the most effect on the direction anddegree of change during 1990e2009.

Discussions and conclusions

During the last decade (2000s), the whole country has gonethrough recessions, job losses, housing foreclosures, increase inpoverty and unemployment, and Knoxville is no exception. At atime when jobs are very difficult to find, the engagement of mi-norities (e.g., Blacks, Asians, Hispanics) in blue-collar and/or white-collar jobs (as indicated by PC-II and IV) contributes to overall di-versity at neighborhoods irrespective of their homeownership orrenter-ship status. At the same time, this analysis also indicates thatminority clusters have formed during 1990e2009 in specific

Table 2Correlations of diversity score and change with neighborhood characteristics and computed indices.

A: Correlations with demographic characteristics DS-2009 DDS (2009e1990)Total population, 2009 �0.238** 0.117Change, population, 1990e2009, share L0.344** 0.014Change, White, 1990e2009, share �0.227** 0.019Change, Black, 1990e2009, share 0.008 0.384**Change, Asian, 1990e2009, share 0.005 0.218*Change, Hispanic, 1990e2009, share 0.053 0.263**Proportion foreign born, 1990 0.314** 0.01Proportion foreign born, 2009 0.521** 0.498**Foreign-born entered during 1990e2000, as share of total F-B in 2000 0.300** 0.061Foreign-born entered during 2000e2009, as share of total F-B in 2009 0.124 0.262**Population lived in same house, 1985 (share 1990 population) L0.402** �0.078Population lived in same house, 1995 (share 2000 population) L0.427** �0.102

B: Correlations with socio-economic characteristics DS-2009 DDS (2009e1990)Bachelors and/or graduate degree 1990, proportion) 0.263** 0.159Bachelors and/or graduate degree 2009, proportion) 0.194* 0.175*Bachelors and/or graduate degree, White, 1990, proportion) 0.187* 0.178*Bachelors and/or graduate degree, Black, 1990, proportion) 0.187* 0.178*Bachelors and/or graduate degree, Asian, 1990, proportion) 0.192* �0.089Median household income, 1999 �0.235** 0.085Median household income, 2009 L0.254** 0.052Per capita income, African American (1990) 0.260** 0.192*Per capita income, African American (2009) L0.317** �0.007

C: Correlations with built-environment characteristics DS-2009 DDS (2009e1990)Manuf./warehouse/trans.-empl. 1990, as share of total empl. L0.386** �0.018Manuf./warehouse/trans.-empl. 2009, as share of total empl. L0.313** �0.047Manuf./warehouse/trans.-black, empl. 2009, as share of total empl. 0.328** 0.112Managl./prof.empl. Black, 2009, as share of total empl. 0.565** 0.058Manuf./warehouse/trans.-Asian, empl. 2009, as share of total empl. 0.282** 0.170*Managl./prof.empl. Asian, 2009, as share of total empl. 0.095 0.084Manuf./warehouse/trans.-Hispanic, empl. 2009, as share of total empl. 0.213* 0.178*Managl./prof.empl.-Hispanic, empl. 2009, as share of total empl. 0.251** 0.322**Homeownership, White, 1990 L0.504** 0.036Homeownership, African-American, 1990 0.476** �0.03Homeownership, Asian, 1990 0.251** �0.064Homeownership, White, 2009 L0.659** �0.134Homeownership, African-American, 2009 0.502** 0.023Homeownership, Hispanic, 2009 0.394** 0.464**Median year housing, 2009 L0.425** �0.063Share, housing built 1990e2000, of 2000 HH-Stock L0.489** �0.056Share, housing built, 2000e2009, of 2009 HH-Stock L0.262** 0.006

D: Correlations with computed indices DS-2009 DDS (2009e1990)Diversity score, 1990 0.773** �0.103Diversity score, among non-White, 1990 �0.239** 0.003Diversity score, White vs. non-White, 1990 0.762** �0.104Diversity score, 2000 0.821** 0.123Diversity score, among non-White, 2000 L0.410** 0.02Diversity score, White vs. non-White, 2000 0.818** 0.102Diversity score, 2009 1 0.552**Diversity score, among non-White, 2009 �0.039 0.310**Diversity score, White vs. non-White, 2009 0.973** 0.463**Change in diversity score, 2009e1990 0.552** 1Change in diversity score, 2009e2000 0.354** 0.738**Change in diversity score-among non-White, 2009e1990 0.174* 0.277**Change in diversity score-among non-White, 2009e2000 0.379** 0.299**

Note: Bold for correlations ��0.250.DS-2009 is Diversity Score in 2009; DDS (2009e1990) is Change in Diversity Score from 1990 to 2000.** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level (2-tailed).


pockets of the metropolitan area, in accordance with the ResurgentEthnicity framework (Brown & Chung, 2006; Charles, 2003). Di-versity that was confined inside Knox County in 1990 has spread toKnox, Loudon, Anderson, and Blount counties by 2000, and furtherinto Union and Sevier counties by 2009 with the hot-spots of di-versity visible all across the metropolitan area. In particular, di-versity in the adjoining counties are most evident in Alcoa,Sevierville, Pigeon Forge, Gatlinburg, Loudon and Oak Ridge e

though the reasons for gain in diversity in these cities range fromhistorical presence of Black in Alcoa to availability of service sector

economy in Sevierville, Gatlinburg, and Pigeon Forge, and newerurban development and proximity to ORNL for Loudon, and high-tech, research and development jobs in Knoxville. Also, gain indiversity occurs for neighborhoods that weremore diverse in 2000,whereas those with higher diversity in 1990 become less diverse by2009(r ¼ �0.103 with DDS(2009e1990). These processes provide ev-idence of Community Norm and Community Inertia perspectives atwork. This study also provides evidence of invasion/succession andfiltering (Buzar et al., 2007) as inner-city locations that were pre-viously occupied by white segments get emptied over time with

Table 3Principal components analyses.

Rotated components matrix Components

PC-I PC-II PC-III PC-IV

Demographic characteristicsTotal population, 1990 0.058 0.523 0.067 �0.224Total population, 2000 0.176 0.696 0.185 �0.234Total population, 2009 0.186 0.747 0.164 �0.176Change, population, 1990e2009, share 0.240 0.737 0.306 �0.048Change, White, 1990e2009, share 0.183 0.787 0.246 0.003Change, Black, 1990e2009, share 0.273 0.337 0.279 �0.077Change, Asian, 1990e2009, share 0.259 0.077 0.197 �0.065Change, Hispanic, 1990e2009, share �0.376 0.260 0.162 �0.105Proportion foreign born, 1990 0.040 �0.161 �0.927 0.102Proportion foreign born, 2000 0.043 �0.393 �0.835 0.046Proportion foreign born, 2009 0.293 �0.610 �0.230 �0.282Foreign-born entered during 1990e2000, as share of total F-B in 2000 �0.150 �0.675 �0.388 0.046Foreign-born entered during 2000e2009, as share of total F-B in 2009 �0.343 �0.530 �0.223 �0.106Population lived in same house, 1985 (share 1990 population) �0.215 0.089 0.880 0.155Population lived in same house, 1995 (share 2000 population) 0.026 0.317 0.801 0.121Socio-economic characteristicsHigh school or less education (1990, proportion) �0.780 �0.108 0.560 �0.053Bachelors and/or graduate degree (1990, proportion) 0.779 0.031 �0.582 �0.030High school or less education (2000, proportion) �0.759 �0.224 �0.071 0.023Bachelors and/or graduate degree (2000, proportion) 0.874 0.056 �0.427 �0.023High school or less education (2009, proportion) �0.823 �0.174 0.455 0.226Bachelors and/or graduate degree (2009, proportion) 0.899 0.093 �0.360 �0.094High school or less education, White, (1990, proportion) �0.717 �0.085 0.595 �0.259High school or less education, Black, (1990, proportion) �0.231 �0.070 �0.082 0.942High school or less education, Asian, (1990, proportion) �0.122 �0.221 �0.816 0.037Bachelors and/or graduate degree, White (2000, proportion) 0.919 �0.056 �0.219 0.086Bachelors and/or graduate degree, Black (2000, proportion) 0.405 0.024 �0.445 0.123Bachelors and/or graduate degree, Asian (2000, proportion) �0.017 �0.134 �0.873 �0.031Bachelors and/or graduate degree, Hispanic (2000, proportion) 0.380 �0.592 �0.276 �0.255Median household income, 1989 0.784 0.461 0.311 0.027Median household income, 1999 0.701 0.536 0.375 �0.024Median household income, 2009 0.640 0.580 0.386 �0.101Per capita income, White, 1990 0.900 0.238 0.170 0.149Per capita income, African American, 1990 0.712 0.277 0.134 0.117Per capita income, Asian, 1990 0.747 0.152 0.059 0.055Per capita income, Hispanic, 1990 0.609 0.021 �0.076 0.007Per capita income, African American, 2009 0.537 0.549 0.220 �0.117Per capita income, Asian, 2009 0.534 0.289 0.090 0.149Per capita income, Hispanic, 2009 0.263 0.075 0.019 0.764Built-environment characteristicsManuf./warehouse/trans.-empl. 2000, as share of total empl. �0.628 0.413 0.496 �0.050Managl./prof.empl. 2000, as share of total empl. 0.506 �0.158 0.094 0.589Manuf./warehouse/trans.-empl. 2009, as share of total empl. �0.373 �0.476 0.371 �0.122Managl./prof.empl. 2009 as share of total empl. 0.909 0.182 0.033 �0.067Managl./prof.empl. White, 2009, as share of total empl. 0.857 0.316 0.138 �0.131Manuf./warehouse/trans.-white, empl. 2009, as share of total empl. �0.673 0.159 0.443 �0.027Managl./prof.empl. Black, 2009, as share of total empl. 0.400 0.128 �0.344 �0.006Manuf./warehouse/trans.-Black, empl. 2009, as share of total empl. �0.124 �0.668 0.248 �0.106Managl./prof.empl. Asian, 2009, as share of total empl. 0.328 �0.005 �0.060 �0.114Manuf./warehouse/trans.-Asian, empl. 2009, as share of total empl. 0.326 �0.439 �0.208 �0.125Managl./prof.empl. Hispanic, 2009, as share of total empl. �0.116 �0.572 0.206 �0.126Manuf./warehouse/trans.-Hispanic, empl. 2009, as share of total empl. 0.060 0.007 0.097 0.813Homeownership, White, 1990 0.147 0.239 0.473 �0.809Homeownership, African-American, 1990 �0.186 �0.198 �0.156 0.921Homeownership, Asian, 1990 �0.070 �0.067 �0.894 0.110Homeownership, Hispanic, 1990 0.429 �0.417 0.142 0.092Homeownership, White, 2009 �0.109 0.443 0.386 �0.656Homeownership, African-American, 2009 �0.046 0.155 0.000 0.874Homeownership, Asian, 2009 �0.080 0.129 0.205 �0.063Homeownership, Hispanic, 2009 0.013 �0.120 0.061 0.154Median year housing, 1990 0.464 0.690 0.174 �0.060Median year housing, 2000 0.385 0.699 0.196 �0.248Median year housing, 2009 0.278 0.767 0.117 �0.229Share, housing built 1990e2000, of 2000 HH-Stock 0.220 0.716 0.390 �0.156Share, housing built, 2000e2009, of 2009 HH-Stock 0.249 0.615 �0.033 �0.105Median value of owner occupied housing, 1990 0.877 0.335 0.113 �0.120Median value of owner occupied housing, 2000 0.811 0.412 �0.103 �0.038Median value of owner occupied housing, 2009 0.833 0.380 0.125 0.018Percent variance accounted for, by component 25.830 16.584 14.848 9.240Percent variance accounted for, cumulatively 25.830 42.414 57.262 66.502

Note: A cutoff value of �0.35 was considered significant when gauging Component Loadings for Interpretation.Cells � þ0.35 indicated by bold.Cells � �0.35 indicated by italics.

Table 4Regression models for diversity score in 2009, change in diversity during 1990e2009, and change in diversity score for white vs non-white during 1990e2009.

A. Dependent variable: diversity score in 2009 (DS2009)Panel I: with DS-1990 Panel II: with DS-2000 Panel III: only four components

Independent Y [ DS-2009 Independent Y [ DS-2009 Independent Y [ DS-2009Variables b Beta t-Value p-Value Variables b Beta t-Value p-value Variables b Beta t-Value p-Value

PC-I 0.028 0.152 1.140 0.269 PC-I 0.046 0.250 2.946 0.009 PC-I 0.024 0.129 0.962 0.348PC-II �0.074 �0.401 �2.108 0.049 PC-II 0.030 0.160 1.053 0.306 PC-II �0.107 �0.574 �4.278 0.000PC-III �0.002 �0.011 �0.039 0.970 PC-III 0.067 0.359 2.505 0.022 PC-III �0.058 �0.314 �2.336 0.031PC-IV 0.029 0.155 0.564 0.579 PC-IV �0.025 �0.133 �1.006 0.328 PC-IV 0.086 0.462 3.441 0.003DS-1990 0.491 0.494 1.269 0.221 DS-2000 1.127 1.257 5.725 0.000 None x x x xConstant 0.425 x 3.162 0.005 Constant 0.036 x 0.370 0.715 Constant 0.592 x 24.307 0.000r-Value 0.828 r-Value 0.937 r-Value 0.811r-Squared 0.686 r-Squared 0.879 0.658

B. Dependent variable: change in diversity score from 1990 to 2009, DS (2009e1990)Panel I: with DS-1990 Panel II: with DS-2000 Panel III: only four components

Independent Y [ DS (2009e1990) Independent Y [ DS (2009e1990) Independent Y [ DS (2009e1990)Variables b Beta t-Value p-Value Variables b Beta t-Value p-Value Variables b Beta t-Value p-Value

PC-I 0.028 0.206 1.140 0.269 PC-I 0.050 0.366 2.417 0.026 PC-I 0.033 0.238 1.305 0.207PC-II �0.074 �0.544 �2.108 0.049 PC-II 0.065 0.477 1.754 0.096 PC-II �0.041 �0.299 �1.638 0.118PC-III �0.002 �0.014 �0.039 0.970 PC-III 0.154 1.124 4.389 0.000 PC-III 0.056 0.413 2.259 0.036PC-IV 0.029 0.211 0.564 0.579 PC-IV �0.116 �0.850 �3.593 0.002 PC-IV �0.030 �0.221 �1.210 0.241DS-1990 �0.509 �0.697 �1.318 0.204 DS-2000 0.878 1.330 3.388 0.003 None x x x xConstant 0.425 x 3.162 0.005 Constant �0.183 x �1.412 0.175 Constant 0.251 x 10.248 0.000r-Value 0.649 r-Value 0.783 r-Value 0.605r-Squared 0.422 r-Squared 0.613 r-Squared 0.367

C. Dependent variable: change in diversity score for white vs. non-White from 1990 to 2009, DS-WNW (2009e1990)Panel I: with DS-1990 Panel II: with DS-2000 Panel III: only four components

Independent Y ¼ DS-WNW (2009e1990) Independent Y ¼ DS-WNW (2009e1990) Independent Y ¼ DS-WNW (2009e1990)Variables b Beta t-Value p-Value Variables b Beta t-Value p-Value Variables b Beta t-Value p-Value

PC-I 0.020 0.198 1.163 0.260 PC-I 0.034 0.341 2.181 0.043 PC-I 0.023 0.235 1.339 0.196PC-II �0.052 �0.522 �2.152 0.045 PC-II 0.040 0.404 1.439 0.167 PC-II �0.024 �0.243 �1.385 0.182PC-III �0.002 �0.015 �0.044 0.966 PC-III 0.106 1.064 4.027 0.001 PC-III 0.047 0.472 2.693 0.014PC-IV 0.021 0.210 0.597 0.558 PC-IV �0.081 �0.807 �3.305 0.004 PC-IV �0.028 �0.283 �1.616 0.123DS-1990 �0.425 �0.795 �1.600 0.127 DS-2000 0.534 1.107 2.734 0.014 None x x x xConstant 0.294 x 3.191 0.005 Constant �0.114 x �1.173 0.256 Constant 0.149 x 8.703 0.000r-Value 0.700 r-Value 0.767 r-Value 0.646r-Squared 0.489 r-Squared 0.588 r-Squared 0.417

Note: None of the regression models were significant when explaining change in diversity among the non-white groups during 1990e2009, hence I don’t show those modelshere.


gain in SES, and are filled in by newly-arriving foreigners and mi-norities, per Assimilation framework of Alba et al. (1999).

Earlier research has suggested that systemic factors, such as lackof recognition of foreign credentials (Papillon, 2002), in this casethe educational degrees of immigrant populations and Hispanicsbeing treated as “the outsiders”, racial discrimination and prejudicein the work environment, as well as lack of access to affordablehousing and suitable language training may contribute to socialexclusion of more vulnerable newcomers (Jones, 1995; Papillon,2002; Shmueli & Kipnis, 1998). This, I believe, needs to beaddressed in context of Knoxville and other small-to-mid-sizedmetropolises in USA that dream of creating sustainable urban en-tities. As indicated earlier (Séguin et al., 2012; Shmueli & Kipnis,1998; Sharma, 2011a; Watson, 2006; Wilson et al., 2008), suchresearch on diversity and sustainability must be used to concep-tualize and implement comprehensive strategies focused atmobilizing residents at the grassroots level to address public policy.

This study provides useful information about changing contextsof Knoxville’s neighborhoods that can be useful for participativeplanning and strategic community development. In a society whereracial/ethnic diversity continues to increase, it has its own impli-cations on changing attitudes, trust and relationships toward eachother, as indicated by Letki (2008). In particular, Knoxville is still88.9% White, but is fast changing, and field reconnaissance by theauthor indicates that Hispanics, in particular, are being lookeddown upon as “the outsiders”. This requires creating a safe andwelcoming environment for all, including Hispanics, Asians and

other groups. Knoxville is home to a diverse set of economy, andwith holistic planning, it can become a creative class city, inaccordancewith Florida’s (2004) recipe aboutmaking a communityprogressive and sustainable in the long run.

From the perspective of planning and zoning, earlier researchindicates the discomfort of staying in a heterogeneous communitycreates segregated spaces, eventually forming spaces of difference,disinvestment, urban blight and poverty concentration (Jones,1995; Séguin et al., 2012; Smith, 1986; Weaver & Baghchi-Sen,2013). Drawing parallels with Knoxville, investors have continuedto pour in big dollars in commercial and residential developmentin the west, whereas south Knox County remains the mostdilapidated and poverty stricken part of the MSA (field reconnais-sance by the author). The city continues to invest in its renovatedMarket Square and high-rise expensive condominiums in thedowntown area whereas just few blocks away to the east remainsthe ghetto, struck with the metro’s highest rates of crime (http://www.spotcrime.com/tn/knoxville). Such issues demotivate pri-vate and public investors that eventually create spaces of differ-ence, exacerbating social and economic inequality. Planners andpolicy makers in Knoxville must invest in equitable and sustainabledevelopment to accommodate the demands of growing diversity interms of public infrastructure, social, cultural, health, economic andhuman capital needs, as has also been suggested by other scholars(see Acevedo-Garcia et al., 2008; Jones, 1995; Letki, 2008; Papillon,2002; Shmueli & Kipnis, 1998; Vliet, 1996; Watson, 2006;Weaver & Baghchi-Sen, 2013). To improve the overall well-being of


communities, and to reduce racial, ethnic and socio-economicdisparities, one must consider comprehensive strategies that inte-grate the best urban planning approaches such as the smartgrowth, sustainability, new urbanism, and socially just practices.

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Diversity in Knoxville: An applied perspective

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