Journal of Diversity in Higher EducationCollege Desert and Oasis: A Critical Geographic Analysisof Local College AccessAmalia Dache-GerbinoOnline First Publication, December 15, 2016. http://dx.doi.org/10.1037/dhe0000050

CITATIONDache-Gerbino, A. (2016, December 15). College Desert and Oasis: A Critical GeographicAnalysis of Local College Access. Journal of Diversity in Higher Education. Advance onlinepublication. http://dx.doi.org/10.1037/dhe0000050

College Desert and Oasis: A Critical Geographic Analysis of LocalCollege Access

Amalia Dache-GerbinoUniversity of Missouri

In an effort to challenge the dominant discourses of college access and highlightnondominant discourses of college access such as geographic racism and segregation,I employ a Critical Geographic College Access (CGCA) framework. This frameworkconsists of critical geographic theories such as power-geometry and spatial mismatch.Using Geographic Information Systems (GIS), I conducted spatial and proximityanalysis of urban and suburban areas of a county in Western New York. The resultsrevealed a college desert in the urban core and a college oasis in the suburbanperiphery. Using a critical geographic approach, this article asserts that a depopulatingcity consisting of high concentrations of people of color has less college accessibilityalthough more need than suburban areas.

Keywords: college access, college proximity, critical geography, urban decline, sub-urban sprawl

Many benefits flow from where one lives. Indeed, asurprisingly large number of life’s advantages and op-portunities are parceled out by residence, duration,domicile, and location.

(Olivas, 2005, p. 181)

The impact of urban decline on Black andLatina/o residents’ college access to local areacolleges are understudied areas within the col-lege access research. Urban areas are shaped byspatial mismatches between low-income resi-dential areas and educational and economic re-sources located outside city limits (Sanchez,Stolz, & Ma, 2003). Traditional factors ofhigher education admission (Carnevale & Rose,2003), college access and choice privilege mid-dle - and higher-income populations (Kezar,2011) who are more likely to be White. Fur-thermore, “policymakers and colleges choosecommunities/students, not just vice versa”(Rhoades, 2014, p. 919). Researchers havelargely overlooked the impact of urban depop-ulation and increased suburbanization on Blackand Latina/o local college access and choice.

Pointedly, education scholars find that, “theUnited States has increasingly become a nationof suburbs” (Siegel-Hawley, 2013, p. 580); yet,how suburbanization impacts geographies forlocal urban communities residing in depopulat-ing cities is seldom understood as a collegeaccess and choice factor. Although reducing theskyrocketing costs of U.S. higher education ison the policy agenda for presidential candidates,proposals like Hillary Rodham Clinton’s, whichaims to provide states with funding for studentsto attend public institutions without taking outfederal loans (Healy, 2015), need further nuanc-ing when applied to states with cities sufferingfrom urban decline.

My local college access experience, as a for-mer Afro-Cuban refugee who migrated with herfamily to Rochester, New York in 1983, is thebackdrop of this study. As a city resident for 20years, my college accessibility was structuredby the geography of the city and county. Myentire higher education journey was set withinmy city and county of residence. Growing up ina depopulating city—a phenomenon occurringin many midsize northeastern U.S. cities(Hevesi, 2004)—I remember vividly the viewfrom my childhood front porch: Carlito’s Gro-cery to the left, Rivera’s Grocery and Liquor tothe right, and the place where we would getsecond-hand clothes, directly across the streetfrom our little blue house. What I could see

Correspondence concerning this article should be ad-dressed to Amalia Dache-Gerbino, Department of Educa-tional Leadership and Policy Analysis, University of Mis-souri, 202 Hill Hall, Columbia, MO 65211-2190. E-mail:dachegerbinoa@missouri.edu

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Journal of Diversity in Higher Education © 2016 National Association of Diversity Officers in Higher Education2016, Vol. 9, No. 4, 000 1938-8926/16/$12.00 http://dx.doi.org/10.1037/dhe0000050

1

visually from my front porch was my world formost of my precollege life. This view is what Iwish to explore in this paper, but more sothrough a birds-eye view of the county in whichthe city rests at the center.

Like many cities across the United States,Rochester, New York, can be described as ex-periencing urban decline and depopulation. InMapping Decline: St. Louis and the Fate of theAmerican City, Gordon (2008) reveals how ur-ban decline in St. Louis Missouri negativelyimpacts Black residents. This context assists inunderstanding how similar urban declines inother Rust Belt cities (Hartley, 2013) impactlocal higher education opportunities for resi-dents of color. “Cities such as Buffalo, Cleve-land, Detroit, and Pittsburgh have each lostmore than 40 percent of their populations overthe last four decades” (Hartley, 2013, p. 1). Thispaper asks: (a) How can proximity analysis ofRochester’s local colleges and universities beused in understanding college accessibility forurban and suburban residents? and (b) What arethe residential, economic and educational char-acteristics of Rochester’s urban and suburbanareas?

The relationships between urban and subur-ban residential demographics and local collegeproximity need to become part of current andfuture research on student success and collegechoice models. Previous research on “educationdeserts” (Hillman, 2014) finds that publichigher education institutions across the countryare in areas with high concentrations of resi-dents of color and that roughly 10% of the U.S.population lives in said deserts. In this study, Iprovide a comparative county and city analysisof residential segregation and college locationsacross varying institutional types, which includefor-profit and private institutions.

Major findings relate to urban core and sub-urban periphery relationships across education,demographic and employment variables. More-over, urban and suburban areas are what I de-scribe as a college desert and a college oasis.The terms college desert and oasis are derivedfrom studies of “food deserts”: urban areas withminimal grocery store and quality food options(Walker, Keane, & Burke, 2010, p. 876). Thecollege desert comprises the City of Rochester’slimited number of colleges and high concentra-tions of need. The college oasis comprises thecounty suburbs, specifically the southeastern

suburbs, with higher numbers of colleges andhigh concentrations of education and economicattainment.

The significance of this study lies in showinghow proximity as a local college access andchoice factor (Hossler, Braxton, & Cooper-smith, 1989; Perna & Thomas, 2008) is shapedby deindustrialization, suburbanization and de-population. Therefore, place-bound college stu-dent opportunities overlap local college choiceoptions. Considering geography as a factor con-tributing to college opportunities of marginal-ized local groups allows for a more nuanced andgeographic understanding of access and choiceenvironments for Black and Latina/o communi-ties in depopulating urban areas.

This centering is important because proxim-ity matters; not only is suburbanization deplet-ing resources from residents and city gover-nance (Gordon, 2008), suburbanization isshaping educational opportunities for studentsof color who are more likely to stay home forcollege (Turley, 2006), which is evidenced bystudent of color overrepresentation at commu-nity colleges (Rhoads & Valadez, 1996), andproprietary institutions moving into areas thatare closer to the urban fringe (Chung, 2008).This extends inquiry into the local county land-scape and college opportunity for urban resi-dents of color.

Theoretical and Conceptual Framework

In response to the gaps in the college prox-imity literature, I employ a Critical GeographicCollege Access (CGCA) framework. TheCGCA combines critical geographic theoriessuch as power geometry and spatial mismatchtogether to understand local college proximityfor urban residents. Turley’s (2009) finding thata “predisposition mechanism” and “conve-nience mechanism” shape students’ collegechoices centers discussions of college accessand choice within understandings that highereducation institutions in close proximity towhere students live are essential in their collegeaccess opportunities.

Critical geography is a framework that high-lights how power and domination are embeddedin the creation of geographic boundaries andcharacteristics (Soja, 1989). Historically, thecreation of colonial cities, the landscape of cit-ies and how cities were organized and main-

2 DACHE-GERBINO

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tained are a result of industrialization and mo-dernity (Sharp, 2009). How land was owned andparceled went against indigenous knowledgeand beliefs, and became mathematical and sci-entific with an aim at increasing capital. Colo-nialism as an imperial process created class-based hierarchies that were marked by race, andcontinue to be a modern form of classification.

Critical geography challenges the classical,normative methods of collecting and analyzinggeographic data as silos not embedded in socio-cultural history (Nash, 2003). For example,Lowe’s (2004) work on gated communities re-veals that modern housing communities in sub-urban areas are enclaves of White fears ofgroups of color. Some critical geographic con-cepts also challenge the notion that technologyhas made the world smaller or more accessible.For example, the concept of power geometry(Massey, 1994) describes the world as morecomplex and difficult to navigate if you are in amarginalized societal position. In essence theworld is growing larger for particular groupssuch as undocumented immigrants and Blackand Latina/o populations, while it may be grow-ing smaller for wealthier and White popula-tions.

The power geometry concept can be under-stood in terms of spatial mismatch as well.Spatial mismatch theory, was originally definedas “the perceived spatial mismatch between theresidential location of low-income, urban (andoften minority) households and the location oflow-skill jobs” (Sanchez et al., 2003, p. 17). Inthis study, however, the unit of analysis is notjobs but rather college locations, and the mis-match is between these and the residential lo-cations of residents of color, specifically Blackand Latina/os. Within a Critical GeographicCollege Access (CGCA) framework these dis-crepancies of local spatial access across raceand space are interrogated. Understanding po-tential college students as more than high schoolgraduates is consistent with critical geographicapproaches: these populations are seen as partof geographically marginalized residential ur-ban groups (Hargrove, 2009). Thus the CGCAframework is used to examine how a county andcity are divided across racialized spaces and thespatial relationships these areas have to wherelocal colleges and universities are located.

Literature

It is well known that college choice is influ-enced by student predisposition (Hossler et al.,1989; Perna & Thomas, 2008; Turley, 2009).The college proximity literature shows that pre-disposition is in turn shaped by geography:whether or not students choose to apply to andattend a given college depends in part on howclose they live to it. The most recent studies onproximity examine how distance from home—and spatiality more generally—structures edu-cational opportunities for students (Turley,2009). Specifically, one of Turley’s (2009) mostsignificant contributions was to show that prox-imity functions through two means of increas-ing students’ likelihood of attending a postsec-ondary institution. First, the “predispositionmechanism” (p. 138) relates to the visibility oflocal colleges; mere visibility impacts students’aspiring for a college degree overall. Second,the “convenience mechanism” (p. 138) relatesto living closer to a college as a factor in ap-plying to and attending it. These two contribu-tions call attention to space, location and localstudent predisposition to college.

In college access and choice studies, geo-graphic inquiry falls under the term proximityresearch (Rouse, 1995; Turley, 2006, 2009).Proximity research examines the influence oflocation on where students enroll in college.The research reveals that whether collegechoice is understood from a student or an insti-tutional perspective (i.e., enrollment manage-ment), geography plays a major role in whetherand where students attend postsecondary insti-tutions (Turley, 2009). The proximity literatureconfirms that low-income students are morelikely to live in residential areas of color. Turley(2009) notes that higher education institutionstend to be closer to White and wealthier popu-lations, which aligns with geographic and crit-ical theories suggesting that segregation im-pacts educational opportunities (Frankenberg,2013).

Most proximity studies use proximity as vari-able in statistical models testing if distanceplayed a factor in applying to (Griffith & Roth-stein, 2009; Turley, 2009), enrolling in (Goble,2010; Turley, 2009), and completing college(Goble, 2010). Findings suggest that at allstages of the college choice process, proximitymatters. International research also reveals that

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proximity is a major factor in accessing collegesand is a barrier for low-income students’ accessto more selective higher education institutions(Frenette, 2004). These findings suggest thatcollege choice and access inclusive of proxim-ity factors have implications for increasingchoice and access for low-income students. Be-ginning with the application process, Griffithand Rothstein (2009) revealed that distanceplayed a role in whether students applied to a2-year or 4-year selective institution. Lookingdeeper, Turley (2009) found that urban studentswithin 12 miles of a 2- or 4-year college oruniversity were more likely to apply to theseinstitutions. Living closer to a 4-year selectiveinstitution made it more likely that a studentwould apply to that institution (Griffith & Roth-stein, 2009). These applicant-related findingsare instrumental in contextualizing how incomeand geography play a role in advantaging high-er-income students while disadvantaging lower-income students in their college application pro-cess.

The acceptance rates of students of color atselective institutions suggest that several poli-cies like affirmative action, have increased thesechances; affirmative action, however, which isone of the strongest policies in increasing stu-dent of color access in higher education, hasbeen and continues to be challenged in thecourts. Several researchers (Griffith & Roth-stein, 2009; Turley, 2009) have found that stu-dents from low-income households were morelikely to attend colleges in close proximity totheir homes and argued the need for payingparticular attention to what types of colleges arelocated in what types of areas. The interconnec-tions of college admissions policies focusing onmerit and the selective institutions recruitmentand marketing to particular populations revealsthat colleges are choosing higher income stu-dents (Rhoades, 2014), which is contradictoryto the proportion of students graduating fromunderfunded schools in predominantly low-income areas with high concentrations of stu-dents of color (Frankenberg, 2013). Earlierstudies support the current finding that proxim-ity increases applicant odds of enrolling at in-stitutions closer to home. Students living near2-year colleges attended 2-year colleges andstudents living near 4-year colleges attended4-year colleges at higher rates (Kane & Rouse,1995). Turley (2009) investigated the odds of

applying to and enrolling in college and foundthat “the number of colleges within commutingdistance is associated with higher odds of ap-plying to college . . .” (p. 138). The literature oncommunity college reveals that there is a highpercentage of students of color attending com-munity colleges (Rhoads & Valadez, 1996).Community colleges, because of their democra-tizing and open-enrollment status, enroll highproportions of students from “the community”which is a spatial factor that is rarely interro-gated especially when looking at how deindus-trialization has shaped the urban core and sup-ported a relocation of industry to the suburbs(Dache-Gerbino & White, 2016; Gordon,2008). In relation to local areas and using dataspecific to city and county college proximityfactors, Jones and Kauffman (1994) presentedfindings related to institutional type and studentchoice that Turley’s (2009) research does notaddress. For example, Texas border region stu-dents traveled distances to 4-year comprehen-sive universities that were approximately 5times more than students who lived outside theborder region (Jones & Kauffman, 1994). Sim-ilarly, compared to Anglo students, Latina/ostudents were more severely impacted by dis-tance because they were more likely to be low-income, have less access to transportation, andhave family obligations tying them to their res-idence (Jones & Kauffman, 1994). Comparingcities and counties with and without 2-year col-leges, Kane and Rouse (1995) found more stu-dents attended college in cities and counties thathad 2-year colleges. Although the college prox-imity literature has advanced the study of col-lege access and choice, it remains methodolog-ically and theoretically limited with respect tothe relationship between residential segregationand college location. The literature frames stu-dent college decisions as silos not influenced bythe context of racially isolated geographies. Forexample, Turley (2009) discusses issues ofWhite concentrations of wealth in areas with agreater number of colleges, thereby confirmingthat proximity has complex sociohistorical ram-ifications. Using a similar framing, Goldsmith(2010) looked at racial segregation at the neigh-borhood level and its influences on racial seg-regation at the high school and college level.Findings show that students’ precollege racialenvironments influence their racial environ-ments as adults, suggesting that there are con-

4 DACHE-GERBINO

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tinual segregating patterns that follow students,creating new segregated environments.

Within the college proximity literature, onlya few studies have examined race and ethnicvariables in the nuanced context of residentialsegregation and urban resident disfranchise-ment (Briscoe & De Oliver, 2006; De Oliver,1998). Geography has been shown to accentuateracial disadvantage in urban areas for Latina/ocollege students in San Antonio, Texas (DeOliver, 1998). Although more recent models ofcollege choice take account of factors of policyand institutional context (Perna & Thomas,2008), and although sociological understand-ings of how students choose college have beenput forward (Tierney, 2006), work to date ismethodologically limited in that it tends not tobe informed by a critical geographic under-standing of the experiences of minoritized pop-ulations in racially segregated residential areas.

State Depopulation, Residential Segregationand College Relocations

In the cities of New York State there have beensignificant population declines for decades(Hevesi, 2004). From 1970–2000, there was a20% decrease in cities, from 2.8 million to 2.3million. At the same time, the suburban popula-tion increased by 16% (with New York City as anexception; Hevesi, 2004). Although several fac-tors led to population decreases in cities, subur-banization and the movement of manufacturingjobs out of cities and into towns were major ones(Hevesi, 2004). The depopulation of cities and theincreased population in suburbs and towns sur-rounding cities paints a broader state picture of thefactors related to depopulation. However, whatHevesi (2004) neglects to address is the racialmake-up of cities and the shifts in racial/ethnicgroups over the last 200 years. Segregation andSouth-to-North migration were factors in the de-population of cities (Gordon, 2008).

To understand the City of Rochester one mustunderstand its history of raced groups, immigra-tion, and migration. Table 1 shows U.S. censusestimations of Rochester’s raced groups from1830 through 1990. It is evident that as theBlack and Latina/o populations began to in-crease from the 1940s through the 1970s, theWhite population simultaneously began to de-crease. Rochester also had an early history ofEastern European immigration followed by La-

tino immigrants, mostly of Puerto Rican decent(McKelvey, 1967).

However, the immigrants were not equallyspread out across the city. Ever since 1834 theywere concentrated in the northeast quadrant. Ger-man and Jewish immigrants started arriving in theearly 1900s (McKelvey, 1967). The northeast ar-eas of Rochester especially were considered urbanslums populated mostly by African Americans.Housing conditions were very poor. “While manyresidents were paying high rents for unsatisfactoryquarters, those available to Negroes were invari-ably the most wretched and most over-priced”(McKelvey, 1967, p. 13). It is evident that thenortheast area was a low-income city quadrantthat had high populations of immigrants and Af-rican Americans who were living very poorly.

On the other hand, the southeast area of the cityhas historically been the place where many pres-tigious and wealthy families reside. East Avenuewas described by the city historian as having“Successive waves of town lot promoters, of af-fluent families displaying old and new wealth, ofarchitects with varied skills and tastes, [and] ofinstitutions seeking to share the avenue’s prestige”(McKelvey, 1966, p. 1). It is no surprise that themost prestigious university in the city, Universityof Rochester, was founded in the southeast quad-rant a few streets from East Avenue.

It is evident that in the areas of the city withthe highest immigrant and African Americanand Latina/o populations during the early tomid-20th century, poverty was rampant. At thesame time the southeast was continuing to pros-per and grow. Table 2 shows that there are moreBlack and Latina/o residents living in the city,which is a much smaller geographic area com-pared to the county. Given this historical con-text questions of college founding’s and reloca-tions within county and city provide a morenuanced understanding of how geographies areracialized (see Table 3). As such, most residen-tial colleges and universities were founded orrelocated in the southeast and southwest quad-rants.

Data and Method

Residential Data

The residential data were collected from U.S.Census American Community Survey (ACS)2013 5-year estimates. The data were derived

5COLLEGE DESERT AND OASIS

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Tab

le1

Cit

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Roc

hest

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lack

,W

hite

and

Lat

ino

Pop

ulat

ion

Fro

m18

30–1

990

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year

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Tot

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tion

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nic

orig

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ce)

Whi

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His

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n)

Num

ber

Per

cent

Num

ber

Per

cent

Num

ber

Per

cent

Num

ber

Per

cent

Num

ber

Per

cent

Num

ber

Per

cent

1990

(66,

3)23

1,63

610

0.0

141,

503

61.1

73,0

2431

.511

,925

5.1

20,0

558.

713

5,09

758

.3Sa

mpl

e23

1,63

610

0.0

141,

952

61.3

73,1

0231

.611

,797

5.1

18,9

368.

213

5,76

658

.619

80(5

7,3)

241,

741

100.

016

8,10

269

.562

,332

25.8

8,75

73.

613

,153

5.4

163,

587

67.7

Sam

ple

241,

741

100.

016

9,51

070

.162

,256

25.8

7,13

23.

012

,961

5.4

164,

553

68.1

1970

(49,

3)29

6,23

310

0.0

244,

118

82.4

49,6

4716

.81,

012

.3(N

A)

(NA

)(N

A)

(NA

)15

%sa

mpl

e29

6,23

310

0.0

244,

759

82.6

49,6

3516

.88,

255

2.8

237,

514

80.2

5%sa

mpl

e29

6,31

010

0.0

244,

349

82.5

49,6

4716

.89,

394

3.2

236,

233

79.7

1960

(38,

3)31

8,61

110

0.0

294,

383

92.4

23,5

867.

413

9—

(NA

)(N

A)

(NA

)(N

A)

1950

(32,

3)33

2,48

810

0.0

324,

643

97.6

7,59

02.

326

—(N

A)

(NA

)(N

A)

(NA

)19

40(2

3,3)

324,

975

100.

032

1,55

498

.93,

262

1.0

(X)

(X)

(NA

)(N

A)

(NA

)(N

A)

1930

(22,

3)32

8,13

210

0.0

325,

294

99.1

2,67

9.8

(X)

(X)

(NA

)(N

A)

(NA

)(N

A)

“Mex

ican

”in

Oth

erra

ce32

8,13

210

0.0

325,

294

99.1

2,67

9.8

——

(NA

)(N

A)

(NA

)(N

A)

1920

(23,

3)29

5,75

010

0.0

294,

089

99.4

1,57

9.5

(X)

(X)

(NA

)(N

A)

(NA

)(N

A)

1910

(25,

3)21

8,14

910

0.0

217,

205

99.6

879

.4(X

)(X

)(N

A)

(NA

)(N

A)

(NA

)19

00(2

4,3)

162,

608

100.

016

1,99

499

.660

1.4

(X)

(X)

(NA

)(N

A)

(NA

)(N

A)

1890

(22,

�)

133,

896

100.

013

3,31

899

.655

9.4

(X)

(X)

(NA

)(N

A)

(NA

)(N

A)

1880

(22,

�)

89,3

6610

0.0

88,8

5999

.449

7.6

Bla

ck

1870

(22,

�)

62,3

8610

0.0

61,9

5999

.342

7.7

Tot

alFr

eeSl

ave

1860

(18,

�)

48,2

0410

0.0

47,7

9499

.141

0.9

410

100.

041

010

0.0

——

1850

(21,

�)

36,4

0310

0.0

35,8

5498

.554

91.

554

910

0.0

549

100.

0—

—18

40(1

9,�

)20

,191

100.

019

,741

97.8

450

2.2

450

100.

045

010

0.0

——

1830

(25,

�)

9,20

710

0.0

8,90

996

.829

83.

229

810

0.0

298

100.

0—

—

�N

atio

nal

rank

thro

ugh

100;

stat

era

nkth

roug

h3.

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ce:

U.S

.C

ensu

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from the following data sets: ACS 2013 5-yearestimates: (a) Demographic and housing esti-mates, (b) Educational attainment, and (c) Eco-nomic characteristics. Census tracts were thesmallest units of analysis in this study and al-lowed for the most recent and up-to-date esti-mates of social characteristics of the ACS to beincorporated.

College proximity and choice studies typi-cally rely on national educational or collegesurveys (Nuñez & Kim, 2012; Perna, Steele,Woda, & Hibbert, 2005). However, these sur-veys do not take into account as possible collegestudents adults who have not earned high schooldiplomas, residents on public assistance or foodstamps, or city residents above traditional col-lege age. The ACS data affords a more accurateestimate of college choice for Black and Lati-no/a urban populations. A second reason forusing the ACS is that it is based on censustract-level data, which allows for more under-

standings of neighborhoods as physical and spa-tial but also social geographies (Soja, 1989).Whereas previous studies have usually used zipcode data to measure proximity (Goble, 2010;Turley, 2009), census tract data is a finer-grained measure. Indeed, census tract-level dataprovides the smallest unit of analysis availablefor ACS population data. Three race variables,Black, Hispanic/Latino, and White were used todescribe how the county and city are racialized.Other variables used were based on educationattainment data: Residents without a highschool diploma, residents over 25 with less thana ninth grade education, and residents withsome college or an associate’s degree. Eco-nomic variables such as resident public trans-portation use, resident public assistance andfood stamp use, and other income and employ-ment variables were used to contextualize resi-dential areas. These measures assist in under-standing literature on local college accessibility.

College Point Data

I used Integrated Postsecondary EducationData Systems (IPEDS) 2014 dataset of all post-secondary institutions in the United States. Thedataset was not geocoded, but did provide lon-gitude and latitude coordinates. Using ArcGIS,I imported the data and added geocodes to da-taset. I then selected Monroe County postsec-ondary institutional points and created a newdataset with only these points. Since the focus

Table 2County and City Demographics

Social characteristics County City

Total Population 744,344 210,565White 78% 43.7%Black 16% 41.7%American Indian .4% .5%Asian 3.4% 3.1%Hispanic/Latino 7.5% 16.4%Persons below poverty line 14.40% 31.10%

Table 3County College Founding and Relocations

Name FoundingFoundinglocation Relocation Current geography

SUNY Brockport College 1835 Northwest None NorthwestUniversity of Rochester 1850 Downtown 1853–Downtown;

1930–SoutheastSoutheast�

Everest Institute 1863 Northeast 1896 NortheastRoberts Wesleyan College 1869 Southwest None SouthwestRochester Institute of Technology 1885 Downtown 1968 SouthwestNazareth College 1924 Northwest 1928–Northwest;

1942–SoutheastSoutheast�

St. John Fisher College 1948 Southeast� None Southeast�

Monroe Community College 1962 Northeast 1968–Southeast Two Campuses:1991–Downtown;1968–Southeast�

Bryant & Stratton 1973 Downtown No date avail Two Campuses:Northwest; Southeast�

� Colleges founded in the southeast or currently in the southeast of the county.

7COLLEGE DESERT AND OASIS

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of this paper is on colleges and universities, Iselected the Monroe County institutional datathat were under Carnegie Classifications as as-sociate’s, bachelor’s, master’s and doctoral-granting institutions. Special institutions such asseminary graduate centers or nursing schoolswithin hospitals were selected out, along withbeauty schools and barber schools. For the pur-poses of this study, we focused on residential2-year and 4-year public, private, not-for-profit,and for-profit institutions (see Table 4).

Data Analysis

Geographic Information Systems (GIS) anal-yses were used to analyze both residential dataand college point data. Two types of spatialanalysis were used: Spatial statistics and prox-imity analysis. Two types of spatial statisticswere used to analyze spatial distributions andrelationships. Spatial statistics show strong orweak clustered geocoded data. The spatial sta-tistics used in this study were spatial autocorre-lations and hot-spot analysis. Spatial autocorre-lations “measures the degree to which a set ofspatial features and their associated data valuestend to be clustered together in space (positivespatial autocorrelation) or dispersed (negativespatial autocorrelation)” (Esri, 2015, p. 7). Spa-tial autocorrelations in this study display visu-ally the relationships between where residentslive and their social characteristics. This is crit-ical to this study since it provides evidence ofresidential segregation across racial and classlines, factors related to the history of depopula-tion in the City of Rochester. Furthermore, theseresults assist in understanding college proximity

for local residents. Hot-spot analysis identifieshot spots as “a feature with high value sur-rounded by other features with high values anda cold spot is low value features surrounded byother low value features” (Esri, 2015, p. 6). Inother words, hot-spot analyses are spatial teststhat show, for example, how race variableswhen mapped across Monroe County are sur-rounded by census tracts with either high num-bers of Black, White, or Latina/o residents (hotspots) or with low numbers of Black, White orLatina/o residents (cold spots). The number ofresidents in each census tract is compared toeach other across all variables and is the basis ofboth the spatial autocorrelation and the hot-spotanalysis.

For proximity analysis, I employed an areaexpanding spatial method, a multiple-ring buf-fer. The multiple-ring buffer was used to createcircular rings around the geographic centerpoint of the City of Rochester (Euclidian meth-od). Each ring represented 1 to 4 miles from thecenter of the city. I used a mean center spatialtool to find the mean center of all census tractsin the city.

Turley (2009) used proximity measure-ments—the distance between colleges and stu-dent residential locations—of 24 miles in ruralareas and 12 miles in urban areas. De Oliver(1998), who studied college access in San An-tonio, found substantial differences in commut-ing distances between White populations andLatina/o populations: on average, the Latina/opopulations had to travel 6 miles more thanWhite populations to attend the University ofTexas San Antonio. However, proximity needs

Table 4County Colleges and Universities

Institution Institutional type Carnegie classification

University of Rochester Private nonprofit Research universityVery high research activity

Roberts Wesleyan College Private nonprofit Master’s levelSUNY Brockport College Public nonprofit Master’s levelRochester Institute of Technology Private nonprofit Master’s levelMonroe Community College–Main Campus Public nonprofit Associate’sMonroe Community College–Branch Campus Public nonprofit Associate’sNazareth College Private nonprofit Master’s levelSt. John Fisher College Private nonprofit Doctoral research universityEverest Institute Private for-profit Associate’sBryant & Stratton–Greece Private for-profit Associate’sBryant & Stratton–Henrietta Private for-profit Associate’s

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to be understood contextually; that is, based onsociohistorical and geographic variables. Thedemographics of the county in the present studyshow that there are 11 colleges and universitiesacross a county that measures 658 square miles,and 33.8 square miles across the city—a muchsmaller area than San Antonio. The more local-ized analysis of a particular portion of the cityand county required proximity areas that weresmaller than those used by Turley and by DeOliver. GIS mapping and spatial statistics paintan accurate picture of residential areas and therelationship these areas have to where countycolleges are located.

Geographic Information Systems Resultsand Findings

I describe two major findings in this area: (a)Proximity analysis of colleges and universitiesfrom Rochester’s city center resulting in what Iterm are a college desert and college oasis, (b)and the residential demographics, economiccharacteristics and educational attainment ofCollege Desert and Oasis residents, which arefactors in college accessibility.

College Desert and College Oasis

The presence of the college desert and col-lege oasis in Monroe County borrows from thefood desert literature, which defines food des-erts as areas barren of grocery stores and nutri-tional food items (Walker et al., 2010). Simi-larly, education deserts (Hillman, 2014) as aterm aligns with my concept of a college desert,in that the location of colleges and universitiesnationally are spatialized along race and classlines. However, my concept of a college desertand oasis is theorized through a critical spatialanalysis situated within the history of countycolleges, universities and residential demo-graphic shifts. Although, having concepts likeeducation deserts (Hillman, 2014) are necessaryin understanding spatial (in)justice nationally,the binary concept of the college oasis if notconcurrently explored makes invisible how ra-cialized spaces are privileging White popula-tions through suburban sprawl.

Visually, Figure 1 illustrates the concepts ofthe college desert and oasis through a multiplering buffer of Rochester’s city center. The cir-cles show 1–4 mile markers from the city cen-

ter, capturing the 4-mile radius of the cityboundary. Through this tool, I visually displaythe number and type of colleges in and outsideof city limits. The first mile buffer reveals thatthere is one college at the city center: the branchcampus of Monroe Community College. It isevident that the branch campus of MonroeCommunity College is the most central collegeoption for city residents. The second mile bufferanalysis reveals no colleges. The third mile buf-fer reveals two colleges, one in the north, Ever-est Institute, a for-profit 2-year college, and onein the south, the University of Rochester, aprivate not-for-profit research-intensive univer-sity. Everest Institute has an open-admissionmodel like a community college system model.At the fourth mile, there are no colleges. Yet,outside of the 4-mile center city buffer is an-other world of higher education opportunities,in the southeastern suburbs of Monroe Countythere are numerous and diverse types of collegeoptions.

In the southeast suburbs of Monroe Countythere is a clustering of colleges and universities.This finding aligns with the history of collegesand universities being founded in the south andsoutheast of the city. The clustering of collegesand universities in the southern and eastern por-tions of the county calls into question collegeproximity for city residents with high needs fora variety of college opportunities.

These findings suggest that residential areasare racialized geographies that contribute to theproximity, number, and type of colleges acrossa county. I argue that residential areas in thiscounty are critical geographies that may be met-aphorically captured by the terms college desertand college oasis.

The naming of college desert and oasis isconsistent with the CGCA analysis, revealingthe power-geometry of higher education re-sources lying mostly outside of city limits, awayfrom residents with the most need. This alsoaligns with spatial mismatches of where Blackand Latina/o residents live and where highereducation resources are concentrated. In thisstudy, the college desert is the City of Roches-ter, where few colleges are located and whereinstitutional types are less diverse. The collegeoasis is the Monroe County suburb, specificallythe southeast suburbs, with relatively more col-leges of diverse types.

9COLLEGE DESERT AND OASIS

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Apart from the sheer number of colleges anduniversities, the types of institutions differacross desert and oasis, and reflect variables ofrace and income. Table 5 is a taxonomy of thecollege desert and college oasis in MonroeCounty. In the oasis, 4-year private colleges anduniversities are clustered closer to higher in-come White populations, whereas in the desertonly a community college and a proprietaryinstitution are nearby for lower-income Blackand Latina/o residents. Racializations of popu-lations of color and the social construction ofinstitutions founded on racist and classist prin-ciples (Harper, Patton, & Wooden, 2009; Kezar,2011; Olivas, 2005) may be culprits that con-tinue to impact the mobility of Black and Browngroups across local higher education geogra-phies.

Residential Characteristics in a CollegeDesert and Oasis

Mapping residential demographics, economicand educational attainment variables reveal that

there are sociospatial relationships betweenWhite, Black and Latina/o residents living withina college desert or oasis. These results supportearlier census and historical information on racedgroups in Monroe County and the City of Roch-ester. Through ArcGIS spatial autocorrelationanalysis p values were statistically significantacross all variables used in this study (See Table6). Spatial statistical significance adds rigor to thespatial autocorrelations, which provide evidenceof residential and racial segregation and supportthe taxonomy of a college desert and oasis. “Whenthe Z score or p-value indicates statistical signifi-cance, a positive Moran’s I index value indicatestendency toward clustering while a negative Mo-ran’s I index value indicates tendency toward dis-persion” (Esri, 2015, p. 7). Moran’s index indicatesthat across all variables, there is a positive clusteringof high values with high values and low values withlow values due to Moran’s indexes having positivevalues rather than negative. Therefore, each variableresult within census tracts shows that the clusteringof residents with particular characteristics is not due

Figure 1. College desert and college oasis.

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to chance. Using an ArcGIS hot spot analysis tool tofurther investigate spatial patterns, results suggest acore/periphery dimension; that is, distinctions be-tween the college desert area and the college oasis

area. All variables under study were found to havestatistically significant spatial clustering. Maps areshown with raw census tract numbers and hot-spotanalysis.

Table 5Taxonomy of College Desert and College Oasis

Characteristics College desert College oasis

Racial demographicsBlack Hot spot Cold spotLatina/o Hot spot Cold spotWhite Cold spot Hot spot

Economic characteristicsEmployed population � age 16 Cold spot Hot spotMean household income Cold spot Hot spotPublic assistance Hot spot Not significant�

Food stamps Hot spot Cold spot��

Commute to work via public transit Hot spot Cold spotOccupation: Management Cold spot Hot spotOccupation: Sales and Office Cold spot Hot spot

Educational attainmentNon high school graduate Hot spot Cold spot��

25� years old and �9th grade education Hot spot Cold spotSome college through Associate’s degree Cold spot Not significant�

Bachelor’s degree or higher Cold spot Hot spotInstitutional types 1 Community College

(branch campus)1 Community College

(main campus)1 Proprietary 1 Proprietary1 Research intensive 3 four-year private

institutions

� Most census tracts were not significant due to size of sample in this area. �� Some census tract had cold spots and somewere not significant due to the size of sample.

Table 6Spatial Autocorrelation Results

Variable Min Max Sum SD z-score Moran’s P-value

Total Pop 0 9,066 746,548 1763.73 29.155364 .397099 .000Black 0 2,649 113,723 632.154 38.6772 .527479 .000Latino 0 1,549 55,990 281.664 31.7245 .429648 .000White 0 8,092 569,984 1,971.75 46.661 .639323 .000� HS Grada 0 100 3,373 16.839 36.1567 .494688 .00025� �9th

Gradeb0 21.7 826 4.4161 39.18914 .53592 .000

Some college 3.3 41 3,518 6.198 21.32334 .291017 .000Bachelors or � 0 62.7 2,459 12.379 18.07024 .244662 .00016� Employed 0 4,938 353,757 998.023 35.54092 .485332 .000Mgmt./Bus.c 0 2581 149,230 773.212 33.87125 .462341 .00016� Comm. PTd 0 449 8,540 58.803 22.55192 .299541 .000Mean HIe 0 203,705 12,086,716 32,899.5 34.162053 .463654 .000Cash Pub. Asst.f 0 449 12,767 69.2105 28.38189 .381332 .000Food/SNAPg 0 1,132 40,322 190.673 32.667486 .443284 .000

Note. SD � standard deviation.a Less than High School Graduate. b 25 years and older with less than 9th grade education. c Employed in Management/Business. d Sixteen years and older that commute to work using public transportation. e Mean Household In-come. f Cash Public Assistance. g Receiving Food Stamps/SNAP benefits.

11COLLEGE DESERT AND OASIS

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Race/Ethnicity Variables

When analyzing these results through a CGCAframework, it is evident that where predominantlyof color communities reside and where Whitepopulations reside have spatial relationships withwhere colleges and universities are located, whichsupports critical geographic theories of spatialmismatch and power geometry. The results ofmapping race/ethnic variables reveal that racial-ized spaces are related to living within either acollege desert or an oasis. In Figure 2, it is evidentthat Blacks are concentrated in the college desert.Similarly, Figure 3 illustrates where Latina/o res-idents are concentrated in the college desert oroasis areas. As was the case for Black residents,the hot spot analysis revels that they tend to beconcentrated in the desert, especially in the north.Few Latina/os live outside the city in the county.

Figure 4 illustrates where White residents in thecity and county reside, resulting in a pattern op-posite to that of the Black and Latina/o residents.

Indeed, Figure 4 is a stark picture of the desert/oasis relationship of White residential popula-tions. Here it is evident that Whites overwhelm-ingly reside in the oasis area. In the college desertarea, high concentrations are located only in thesoutheast; in the north and in the southwest, theyare scarce.

Economic Characteristics

For economic and education variables, I willrefer to the taxonomy of a college desert and oasis(see Table 5), which indicates which variables hadhot or cold spots in the college desert area or thecollege oasis area. The total population of em-ployed workers ages 16 and more reveals thatmost employed residents live outside of the col-lege desert area, and the least amount of clusteringof employed residents is within the college desert.Mapping of mean household income reveals spa-tial relationships of a low clustering cold spot oflower household incomes within the college desert

Figure 2. Black population.

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and hot spots of the highest incomes clustering ineast and south suburbs of the college oasis.

The variables associated with residents living inpoverty are: Residents whose household incomescome from public assistance, receive food stamps/SNAP benefits, and use public transportation totravel to work. The hot-spot analysis revealed highlevels of clustering for all three variables presentwithin the college desert area. There are some coldspots outside of the college oasis area. These threevariables reveal that there are higher economicneeds within the college desert and lower eco-nomic need in the college oasis area.

However, economic variables related to profes-sional employment such as sales, office work,management and business careers reveals oppo-site patterns to the poverty variables above. Mostresidents employed in these jobs reside in thecollege oasis area. It is evident that college oasisarea residents in the south and east suburbs ofMonroe County have the highest incomes of thecounty and hold professional jobs and careers.

Furthermore, geography and economic variablesare closely related and provide evidence of thewidening gap between wealthy and poor (Lipsitz,2011); they clearly show geographic disparities ofincome and employment distribution in collegedesert and college oasis areas.

Educational Attainment

Since the focus of this study is on local col-lege access and choice, variables that are typi-cally understudied such as residential demo-graphics within a college desert and a collegeoasis provide support for these concepts. In thearea of educational attainment, the college ac-cess literature rarely focuses on residents whodid not earn high school degrees and residentswho may have had some college experience orearned an associate’s degree as their highestdegree. The literature on students of coloracross the educational pipeline has shown thatstudents of color are overrepresented at the

Figure 3. Latino population.

13COLLEGE DESERT AND OASIS

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community college level (Rhoads & Valadez,1996). The hot-spot analysis of residents withonly some college or an associate’s degree re-veals low levels of clustering within the collegedesert area. Furthermore, residents with lessthan a high school diploma and residents 25 andolder with less than a ninth grade educationhave high clustering within the college desertarea with low levels of clustering in the collegeoasis area. These educational attainment vari-ables reveal the high needs of college desertarea residents in the area of higher educationattainment. Consistent with the CGCA frame-work, the results show that there are educationand economic disparities across suburban andurban geographies, which mirror other depopu-lating cities (Gordon, 2008) across the country.

Through both proximity and spatial analysisof the county and city residential demographicsand local colleges, I found that the lack ofcolleges and universities within city limits mayexplain the low levels of higher education at-

tainment for city residents based on predisposi-tion and convenience mechanisms (Turley,2009) of local college visibility. Moreover, thecollege desert has the highest concentrations ofBlack and Latina/os residents and highest con-centrations in need of higher education attain-ment. Having more equitable access for resi-dents in the college desert area could increasethe city workforce in areas such as sales andmanagement. The college oasis area has lowconcentrations of Black and Latina/o popula-tions, high concentrations of higher educationattainment and residents with higher householdincomes, which align with the history of thesoutheast area. The data show that not only arethere racial, economic and educational attain-ment divisions across the city and county, theserelationships are eerily dichotomous. WhereBlack and Latina/o populations are highest,White populations are lowest. Where highestincomes are concentrated, we find high numbersof Whites and low numbers of Blacks and

Figure 4. White population.

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Latina/os. It is clear, that the taxonomy of thecollege desert and oasis fit well with spatialresults of the city and county.

Discussion

Using the CGCA framework allows for thedetection of patterns of geographic sociospatialhierarchies in the city and county under study.This framework allows for hegemony to beunderstood visually though geographies that arepart of deindustrialization and failures of mod-ernization. Depopulation is grounded in racialand residential segregation (Gordon, 2008). He-gemony prevails in urban and suburban areasthrough inequitable housing, economic and ed-ucational opportunity. A CGCA framework ex-plores the “complex patterning of racism”(Hickling-Hudson, 2007, p. 205) within localcollege access for city and county residents.This analysis brings to the forefront how geog-raphies are constructed and how space and placeshape local college access and proximity.

It is clear that race is a factor in collegechoice and proximity studies (Nuñez & Kim,2012; Perna et al., 2005). However, some schol-ars argue that using race as a unit of analysiswithout a critical understanding of race as aconstruction of power and hegemony leads tothinly theorized understandings of racializationas a social construct (Darder & Torres, 2011).Describing possible taxonomies of a collegedesert and oasis in depopulating cities, such asRust Belt cities, could provide geographic andvisual data of local education and economicneeds and help to identify factors contributingto segregation of local higher education institu-tions and policies to address these spatial phe-nomena. Education policy can be informed bysuch taxonomies that can provide a clearer pic-ture of the geospatial barriers contextualizinglocal college access opportunities for residentsin the most need.

This study finds that college locations are notcoincidentally placed within certain geographicboundaries (Soja, 1989). Rather, locations aresocially constructed factors shaped by the localhistory, residential segregation, and hegemonicnational trends of urban resident disenfranchise-ment (Bullard, Johnson, & Torres, 2007).Hence, in the City of Rochester, residents arepredominantly low-income and Black or

Latina/o. These facts complicate higher educa-tional opportunities for city residents.

The results of this study are grounded intheoretical assumptions of critical geographythat challenge the normative ways we under-stand local higher educational opportunities.College access studies should pay particularattention to geographic social factors of race,class, and proximity if we are to truly createequitable higher education systems for residentsin the most need of higher education attainment.

As noted earlier, Turley (2009) identified twoways that proximity assists or hinders access forstudents. The predisposition mechanism relatesto “seeing” colleges in one’s area and thereforeinfluences aspirations for college. The conve-nience mechanism relates to colleges in one’sarea being easier to physically access. Themechanisms of predisposition and conveniencemay help explain why college desert residents,who are predominantly Black, Latina/o, andlow-income, have higher representations na-tionally at community colleges and proprietaryinstitutions, whereas college oasis residents,predominantly White and higher-income, havehigher representations at private and public4-year institutions (Aud, 2010). The proximityliterature shows that living closer to a 4-yearselective institution makes it more likely thatstudents apply there (Griffith & Rothstein,2009). Therefore, I argue that although cityresidents may have low levels of educationalattainment such as high school diplomas, itshould be the prerogative of private and publiclocal higher education institutions to serve theneeds of the populations in the greatest need,not just the objective of community collegesand proprietary institutions. The CGCA frame-work allows for the visualization of the power-geometry and spatial mismatch to emerge asfactors that may be contributing to resident loweconomic and educational attainment.

A conclusion from the present study is thatliving in an urban area that is depopulatinginhibits the type and amount of local highereducation opportunities within proximity toBlack and Latina/o residential areas. How-ever, taxonomies of a college oasis and desertcan assist researchers, policymakers, andpractitioners in identifying such areas anddeveloping appropriate responses. In otherdisciplines, research on food deserts led tosignificant improvements in decreasing food

15COLLEGE DESERT AND OASIS

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scarcity and increasing quality food items ingrocery stores within desert neighborhoods(Walker et al., 2010). Although the task maybe more difficult, there seems to be no reasonin principle that city and county government,researchers and policymakers in higher edu-cation couldn’t similarly reverse the presentlack of college in proximity (i.e., creatingextension campuses) to college desert areas,making them more like college oases.

Through a CGCA framework, it is also evi-dent how Whiteness equals property (Lipsitz,2011) in the sense that colleges and universitiesconfer degrees similar to how currency func-tions in a local and national economy.

County and city college location and relo-cation history and the clustered natured ofthese colleges reveal geographic patterns ofoppressive structuring related to where peopleof color have historically and contemporarilylive and work. For instance, power geometryargues the world is getting smaller for thosewith societal advantage—White and wealthiergroups—while it’s getting bigger for thosewithout societal advantages. My study is sup-ported by this theory since geographically theexistence of the college desert and oasis asdefined in this paper is an example of howspatially the location of colleges and univer-sities in proximity to the lowest income andmost highly concentrated areas of Black andLatina/o residents are inequitable. The spatialmismatch theory also supports my findings,since 4-year higher education institutions arein closest proximity to residents with the low-est higher education needs. Lastly, using Tur-ley’s (2009) argument that if mechanisms thatcreate a likelihood of attending college inyour community are not functioning well,which is the case for the college desert area,then these mechanism are simultaneouslyfunctioning well in sustaining residential seg-regation and lack of college access for cityresidents of color.

Policy Implications

Policy implications for this study are cen-tered on the local community and higher edu-cation institutions. In the northeast part of thecity, where the college desert area has the high-est concentrations of Black and Latina/o resi-dents, there are not-for -profit agencies that fo-

cus on addressing the socioeconomic andeducational inequities facing Black andLatina/o residents. Two in particular have overa 20-year history of serving these residents: TheUrban League of Rochester and Ibero AmericanAction League. Both organizations face contin-ual funding cuts and provide programming thatserves the needs of residents of color. Hence,providing more funding for these agencies isneeded. The college desert and oasis conceptcan assist these organizations in addressing geo-graphic-specific outreach and programming tothose living in areas most in need of highereducation access. At the organizational level,applying for grant funding to support agencyprogramming may be more successful with ev-idence-based data results pertaining to residentsin college deserts.

Local Higher Education Policy Implications

The institutional types of colleges and uni-versities in varying parts of the city aregrounded in a history of residential segregationand college admission selectivity. Since propri-etary institutions were the second type of insti-tution in proximity within the college desertarea it supports how their student markets arelikely to be disproportionately Black, Latina/oand low-income Dache-Gerbino, Kiyama, andSapp (in press). The racial/ethnic local collegeenrollment gap is evidence of how college ad-mission selections work as checkpoints weed-ing out potential college students. MonroeCounty 4-year college admission policies andprocesses value factors associated with domi-nant cultural factors (i.e., GPAs and SATscores). Unfortunately, by valuing overwhelm-ingly characteristics associated with historicallyadvantaged groups, admission policies under-value factors associated with low-income pop-ulations and students of color. However, thisprocess of choosing or unchoosing studentsprior to their predisposition stage or first stageof the college choice process due to sociospatialfactors evidenced in this study can be circum-vented by policies and programs aimed at in-cluding local low-income residents of color aspotential college students.

Through a city and county college consor-tium, local admission policies can become moreinclusive to area residents with the highest ed-ucational needs. College enrollment practices

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can be designed to increase the diversity of anentering class with city residents who live inlow-income areas. A local college consortiumcan assist in grooming future students by tar-geting families and neighborhoods in high-needareas (i.e., college deserts). Since proximity tocounty colleges are mostly outside city limits,this consortium can work together to createpolicies and programs that provide diverse post-secondary options that are within the collegedesert area. For example, 4-year colleges shouldhave visible extension or satellite campuses inthe college desert area. Since these would beextension campuses the postsecondary institu-tions and the communities surrounding the ex-tension campuses would need to form neighbor-hood collaborations and relationships. Theextension campuses cannot exist in a vacuum.Furthermore, extension campus enrollmentshould be open and or have alternative selectionwhere SAT scores and standardized tests aregiven less importance than the lived experi-ences of college desert residents. Designing ad-mission policies within a funds of knowledge(Kiyama, 2010; Moll, Amanti, Neff, & Gonza-lez, 1992) framework for example, would assistin having a more racially and ethnically inclu-sive and transformative enrollment process forurban residents at local postsecondary institu-tions. Partnerships in higher education areneeded to build infrastructure—“irrigation”—allowing college deserts to be transformed intooases.

Conclusion

Through community support and campus–community partnerships, college desert areascan become more like oases. Although the col-lege oasis enjoys more diverse local choicesthan college deserts, they are not diverse inracial/ethnic mix: Black and Latina/o residentsin these areas are noticeably absent. This find-ing speaks to overt and covert policies of racialexclusion, such as divisive public transportationand bus-riding structures constraining Blackand Latina/o residents solely to city limits(Dache-Gerbino, 2014). Constituents and stake-holders must work together. Governments,foundations, and policy organizations at all lev-els must assist in creating higher education in-stitutions that are more receptive to the needs oflocal communities in the most need (Maurrasse,

2001). This will take time, leadership, trust, andthe investment of financial and organizationalresources.

Looking inward at the role and responsibilityhigher education institutions have to their sur-rounding communities, access of marginalizedresidents to local college campuses is necessaryfor economic and educational progress (Kezar,2005). In higher education, the training of citi-zens for a global economy is central to collec-tive progress (Kezar, 2005; Slaughter &Rhoades, 2004). However, “marketing to stu-dents in a way that serves the economic interestsof the college or university” (Slaughter &Rhoades, 2004, p. 279) continues to benefitWhite and higher-income students. From a not-for-profit college/university perspective, “insti-tutions are moving to serve more privilegedstudent markets” (Slaughter & Rhoades, 2004,p. 279), which is evident in the level of selec-tivity in private versus public, 2- versus 4-yearinstitutions. At the same time, the for-profitsectors are enrolling Black and Latina/o popu-lations at much higher rates than their not-for-profit counterparts to the detriment of commu-nities of color (Dache-Gerbino, Kiyama, &Sapp, in press). The role of higher education isnot solely to provide a workforce, however.More importantly, it is to enhance social andcivic participation in a democratic society (Ke-zar, Chambers, & Burkhardt, 2005), inclusiveof Black and Latina/o urban populations livingin college desert areas.

The geographic layout of higher educationopportunities available to low-income city res-idents of color raises issues of urban demarca-tion and residential segregation. The power-geometry and spatial mismatch between wherepopulations of color reside and where 4-yearcolleges and universities are located seems to beclear evidence that the founding locations ofthese colleges were part of racialized historicaldiscourses (Olivas, 2005). Future research canlook at other depopulating cities, such as St.Louis and Detroit, to see whether and how thedesert/oasis metaphor deepens our understand-ing of their geographic higher education land-scapes.

The present results support the claim thatcollege locations are shaped by de facto colonialmodes of control, understood through an “im-perial logic of domination” (Shahjahan, 2013, p.2). Modes of control, such as the proximity of

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4-year public and private colleges to urban res-idents of color are supported by this study’s dataresults. As Olivas (2005) argues in the epigraphto this paper, place and space determine manyof life’s advantages and opportunities. Theseadvantages and opportunities intersect with res-idential segregation, revealing a national col-lege access issue at the local level. This studyprovides evidence of how conceptualizations ofcollege proximity are in part conceptualizationsof racialized local space, exposing some of theinequities that bear on Black and Latina/o resi-dents. It is evident that, “for low-income stu-dents, college choice is likely shaped by highereducation’s local ecology” (Rhoades, 2014, p.919). Systems of racism, segregation, and cul-tural supremacy from the past continue to func-tion unabated in the present. The CGCA frame-work together with concepts such as deserts andoases are recommended to future researchers asthe kinds of productive tools needed if we are toever understand, let alone alter, complex pat-terns of local college access and proximityacross geopolitical landscapes.

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Received January 6, 2016Revision received September 16, 2016

Accepted October 16, 2016 �

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