![[PDF]](/pb-assets/Icons/adobe-pdf-icon-1498649896243.png){kind=icon}
Abstract
This study examines the impact of school closures on the sociospatial distribution of equitable access to schooling following the school closure policy pursued by the Chicago Public Schools in 2013. By examining access in terms of proximity between students and schools, the study estimates the changes in accessibility before and after school closings. The change in accessibility is compared with density maps constructed around a number of variables, including population aged 5 through 14 by race and ethnicity, proportion of families with children younger than 18 years old below the poverty level, and crime incidence during the previous 12 months. The overall results suggest that school closing may cause sociogeographic inequality in access to education.
Every child in every neighborhood in Chicago deserves access to a high quality education that prepares them to succeed in life.
—Barbara Byrd-Bennett, Chief Executive Officer, Chicago Public Schools, “Media Briefing,” March 21, 2013.Closing or consolidating schools is a policy often pursued to increase efficiency in education by limiting financial losses, which in turn can be used to improve educational quality for students grouped into remaining school buildings. For this reason, some school districts in the early 20th century implemented plans to close many small rural schools (Post & Stambach, 1999). More recently, some larger cities are experiencing demographic pressures that could justify closing and consolidating schools. As a case in point, the Chicago Public Schools (CPS), which is the third largest school district in the United States, announced in the spring of 2013 that 54 primary schools would close in the upcoming school year. Through closing and relocating about 8% of schools, the CPS expected to save US$43 million annually (CPS, 2013a).
One concern is the potential for inequitable access to desirable school options after closing neighborhood schools. In fact, some parents and teacher organizations in the CPS voiced strong opposition against the closings because the schools in question have served mostly minority students in disadvantaged communities with “hyper-segregation, unemployment, youth violence, and disinvestment” (Uetricht, 2013). While research has focused on the impact of closing schools on financial efficiency, we have little information about the impact of school closings on equal educational opportunity in large urban cores. By comparing access before and after school closures in the recent CPS case, this study aims to examine two questions:
Research Question 1: How does an urban school district’s decision on school closure result in changes in a student’s access to schools?
Research Question 2: Is there any relation between the changes in access and patterns in neighborhood characteristics?
School Closure and Equal Education Opportunity
Distinct from district reorganization, school consolidation means “closing schools and sending students from the closed schools to other schools (or building a new and larger school)” (Howley, Johnson, & Petrie, 2011, p. 1). In general, the policy initiative of school consolidation started from the preference for larger schools due to a desire to improve efficiency through centralized control (Bard, Gardener, & Wieland, 2006). In view of economies of scale and a low school utilization rate (estimated as the ratio of enrollment in a school to a school’s capacity), underenrolled schools are not cost-effective (Lytton, 2011). Hence, school closings are thought to reduce the cost of maintenance at school buildings and result in lower expenditures per pupil (Cohn, 1968). In this logic, closing schools with a high vacancy rate is a relatively efficient way to cut expenditures, particularly by reducing the number of school employees—an expense that commands a large proportion of school budgets. Also, school district managers regard this as a chance to offer more intensive and broader courses to students and better professional programs to teachers in new environments (Bjork & Blase, 2009; Nitta, Holley, & Wrobel, 2010). Under these arguments, some U.S. large urban school districts, such as Detroit, Milwaukee, Pittsburgh, and the District of Columbia, implemented large-scale school closings. As the number of students at traditional public schools decreased by 17% in Chicago, 23% in Philadelphia, and 54% in Detroit over one decade due to out-migration, the growth of charter schools, and depopulation in urban cores (Dowdall, 2011), empty seats and underutilized school buildings became focal points in debates over budget crises. Even though the recent school reform policies at the federal and state governments compel chronically underperforming schools to close, or take other such corrective actions, school closings still offer savings in school budgets.
However, despite the hope of channeling savings into improved education for students, school closings do not always have positive effects on students, families, and communities. Through a 1-year investigation on the impact of school closing on rural students in West Virginia, Eyre and Finn (2002) found that longer bus rides due to school closings took away students’ time to study, participate in extracurricular activities, and play outside. Furthermore, school closures increased the financial burden of bus service and concerns about safety and lower parental involvement. In light of the notion that community schools represent more than a physical place to educate children (Kearns, Lewis, McCreanor, & Witten, 2009; Peshkin, 1982; Sell & Leistritz, 1997; Witten, McCreanor, Kearns, & Ramasubramanian, 2001), parents were likely to see school closures as a loss of community-based services. Thus, local residents in urban areas, which were often heterogeneous and segregated, tended to express a strong resistance to school closures driven by the state and local governments, compared with those from more homogeneous suburban or rural school districts (Berger, 1983b; Post & Stambach, 1999).
Furthermore, a number of studies on school closures challenge the premise that the resources obtained by school closures result in academic benefits. In a study examining the expenditures and revenues since school consolidation, Streifel, Foldesy, and Holman (1991) demonstrated that school closings yielded significant efficiency only in the administration category, whereas other categories, including instruction, transportation, operations, maintenance, total costs, and capital projects, failed to produce significant savings. In particular, rising transportation costs for students who must travel farther than before led to an increase of school budgets (Cohn, 1968; Kenny, 1983; Uetricht, 2013). Some studies revealed that closing a school and merging schools could lower the costs of only small districts and would have little impact on large districts (Duncombe, Miner, & Ruggiero, 1995; Duncombe & Yinger, 2007; Howley et al., 2011). With this in mind, Andrews, Duncombe, and Yinger (2002) suggested that potential savings in administration and instruction might be apparent in a district with 2,000 to 4,000 students, and elementary schools with 300 to 400 students and high schools with 600 to 900 students could maximize benefits from school consolidation by controlling for the transportation cost increases. Zimmer, DeBoer, and Hirth (2009) also presented similar evidence that school districts with a small number of students had benefited financially from school consolidation. Namely, the research on financial impact as a result of school consolidation consistently concludes that, as the costs saved by school closings in large urban school districts were relatively small (Cohn, 1968; Dowdall, 2011), the broad implementation of school closings in large urban school districts could hardly address financial deficiencies in those school districts.
Spatial Equality
While the extant studies have investigated the effects of school closures on school budgets, relatively little is known about changes in access due to school closing in urban school districts. In view of spatial considerations such as density, proximity, and so forth (Bjork & Blase, 2009; Morrill & Symons, 1977; Nicholls, 2001; Nitta et al., 2010; Talen & Anselin, 1998; Truelove, 1993), it is not surprising that depopulation or other causes of declining enrollments in urban areas leads to school closures. Yet, school closures in urban areas, especially using enrollments as the main criterion for identifying efficiency in school management, require a special attention to equality of access, in contrast to school closings in homogeneous rural areas with low population density.
In the United States, where urban populations are not evenly distributed by demographic characteristics and socioeconomic status, closing schools has the potential to deprive at least some students remaining in those urban districts of the opportunity to equitably access education opportunities, as well as to impose transportation (and time) costs on them and lead to crowded classrooms at their new schools (Dowdall, 2011; Zars, 1998). Beginning with Taeuber and Taeuber’s (1965) finding that African American and White families in metropolitan areas did not share the same neighborhoods, a number of studies have shown uneven racial distribution between these groups in urban areas (Denton & Massey, 1988; Farley, Allen, & National Committee for Research on the 1980 Census, 1989; Kearns et al., 2009; Lubienski, 2005; Lubienski, Gulosino, & Weitzel, 2009; Massey & Denton, 1989, 1993; Massey & Fischer, 1999; Pattillo-McCoy, 1999; Peshkin, 1982; Sell & Leistritz, 1997; Witten et al., 2001). Despite advancements in social and economic status of minorities (Clark & Ware, 1997), segregated housing patterns informed by poverty, education, employment, and occupation are apparent across U.S. metropolitan areas (Abramson, Tobin, & VanderGoot, 1995; Massey & Denton, 1993; Mayer, 1996; Rothwell & Massey, 2010; Van Valey, Roof, & Wilcox, 1977). Therefore, African Americans more concentrated in neighborhoods with poorer amenities were more segregated in comparison with Whites of comparable socioeconomic status (Bayer & McMillan, 2005; Darden & Kamel, 2000; Pais, South, & Crowder, 2012). In particular, urban “ghettos” created by the geographic isolation of at-risk populations reinforce widening disparities within and between neighborhoods, and the disparities bring increases in vacancy rate and decreases in population to these sections of large cities (Card & Rothstein, 2007; Cutler & Glaeser, 1997; Dufur, Parcel, & Troutman, 2013; Massey & Denton, 1993; Swanstrom, Dreier, & Mollenkopf, 2002; Turley, 2009; W. J. Wilson, 1987, 2012). Also, high crime and drug usage rates can inhibit residential mobility in selected urban areas (Krivo, Peterson, & Kuhl, 2009; South & Crowder, 1997).
Segregated geography divides areas into ones than can vary considerably in terms of access to job markets and public services, and can restrict social interaction within and between neighborhoods (Cutler & Glaeser, 1997; Massey & Denton, 1993; W. J. Wilson, 1987). In light of the spatial mismatch hypothesis, which posits that employment opportunities tend to be less available where minorities reside (Kain, 1968, 1992, 2004), disadvantaged populations in inner cities are unlikely to have wide access to schooling irrespective of their needs. In this sense, the recent school closing policies in urban school districts may lower ease of access to schools for students who already reside in less advantaged neighborhoods. Indeed, early school closing cases, especially those linked with court-ordered desegregation plans, revealed that less advantaged Black and Hispanic students were more likely to see their schools close in racially segregated urban school districts (Berger, 1983a; Dean, 1983; Scott, 1983; Shavers, 2005; Talen, 2001; Valencia, 2013). By drawing on the general definition of equality as a condition where socioeconomic status and demographic characteristics do not inform one’s opportunities, this study of school closings helps us construct an understanding of equal education opportunity in light of area deprivation in urban areas. As the results indicate, accessibility can be a substantial factor to shed light on the landscape of social and economic opportunities (Müller, 2011).
Research Design
To examine the possibility that school closings shape inequitable access to schools for children in less advantaged neighborhoods, this study tests two hypotheses informed by the extant literature:
Hypothesis 1: School closures bring about changes to a student’s access to schools.
Hypothesis 2: Changes in access due to school closings are related to community characteristics.
It describes the relation between equal education opportunities and school closures through the measure of accessibility based on geographic proximity from neighboring school enrollment schemes. As the list of 54 closing schools approved by the Chicago Board of Education largely targets elementary schools, this study focused on the schools serving kindergarten through Grade 8 students. The school data, including all public school locations and enrollments, were extracted from the school directory lists provided by CPS. As CPS issued a request for proposals to expand charter schools, the changes of accessibility subsequent to school closures were investigated by taking both traditional public schools and charter schools into account.
Research Context
The third largest school district in the United States, the CPS District is located in a highly segregated metropolitan area. According to the average segregation rankings provided by the U.S. Census Bureau, the Chicago metropolitan area ranked ninth in Black segregation and fifth in Hispanic segregation among 43 metropolitan areas (Iceland, Weinberg, & Steinmetz, 2002). Besides housing segregation by race and ethnicity, the city of Chicago has been spatially separated around the central business district around the northwest areas by the Chicago River, especially since the 1990s. As “the Loop” has grown with the increases in housing prices and number of residents, Chicago has invested extensively in schools and libraries in the area, whereas neighborhoods populated by lower income and minority residents have been isolated from the wealthier areas (Lipman, 2002; Podmolik, 1998). Furthermore, due to the most recent economic recession, including the decline in median household income and the increase of the foreclosure and vacancy rates, underinvestment in urban core areas led to many local residents leaving their home communities (Chicago Area Fair Housing Alliance, 2013; Lawyers’ Committee for Better Housing, 2013; Pendall, 2012).
Similar to other major cities, Chicago witnessed a dramatic decline in the number of school-age children. A decade ago, total population in the city of Chicago decreased by 6.3%, whereas primary school-age children declined by 23.7%. In response to empty seats comprising approximately 20% of the CPS district capacity, in the fall of 2013 the district closed 46 of the 478 schools serving kindergarten through eighth grade, excluding charter schools and schools for students with disabilities. CPS was expecting to save US$560 million for 10 years through this move (CPS, 2013c). The resources saved from closing schools, about US$233 million, would be redirected to investing in air conditioning systems and libraries at receiving schools, providing technical supports such as iPads for students, and expanding security for the routes between school and home (CPS, 2013c, 2013d). With regard to the concern about traveling longer to new schools, CPS noted that the average change of distance after school closings would be fewer than two blocks from home (CPS, 2013a). In addition, CPS announced the Safe Passage Plan to offer safe routes to students, in cooperation with the Chicago Police Department. As vacancy rates tended to be highly correlated with the changes of population and household size in urban areas (Glaeser, Gyourko, & Saks, 2005), the school closures in 2013 were mostly clustered in two areas with high vacancy rates, as illustrated in Figure 1.
Even though CPS initiated the latest school closing policy on a relatively large scale, this is not the first time that CPS has closed schools. CPS has previously closed schools for two reasons: academic underperformance and space underutilization (CPS, 2012a, 2013b; de la Torre & Gwynne, 2009). If a school fails to make adequate progress after being placed on probation as determined by performance on standardized tests, attendance, and drop-out rates, CPS may close the school under the CPS Performance, Remediation, and Probation Policy. Furthermore, CPS can close a school if the student enrollment is less than 80% of ideal enrollment, which is estimated under the assumption that each homeroom, equaling 76% of total classrooms within its main facility, holds 30 students (CPS, 2011; Commission on School Utilization, 2013). Therefore, the CPS closed 13 primary schools for underutilization and nine for poor academic performance between 2001 and 2006 (de la Torre & Gwynne, 2009). CPS plans to continue to close underutilized schools and proposes public hearings with regard to school consolidation and re-location.
Even while closing schools, CPS has launched several new school efforts. For instance, the Renaissance 2010 program, introduced in 2004, closed around 70 underperforming public schools, yet created or converted 100 schools into performance, charter, or contract schools. In the 2010-2011 school year, 82 charter school campuses and nine contract schools were opened in Chicago (CPS, 2012b). Following Renaissance 2010, CPS initiated a new fund, the New Schools for Chicago, in 2011 in an attempt to create new schools such as contract or charter schools to serve about 30% of total students in the CPS by 2020. In a similar manner, CPS announced a proposal to create new charter schools in August 2013 after the Chicago Board of Education decided on the large number of school closings in March.
Measure of Accessibility to Schools
There has been much research on unequal spatial distribution of public services based on accessibility, such as medical care, playgrounds, parks, and preschools (Knox, 1978; Kwan, 1998; McLafferty, 1982; Mladenka, 1989; Nicholls, 2001; Oh & Jeong, 2007; Pinch, 1987; Shen, 1998; Talen & Anselin, 1998). In the education sector, Pacione (1989) and Talen (2001) measured access to schools, and Müller (2011) evaluated access to determine where schools would open and close. Still, little attention has been paid to the change of access in connection with school consolidation in highly segregated U.S cities. To measure the degree of access from school to home, existing work has relied upon straight-line distance from schools. Such an “as the crow flies” approach, like a Euclidean distance, tends to underestimate how individuals have different opportunities and constraints on access, such as distance, transportation, and availability (Larsen & Gilliland, 2008; Smith et al., 2010; Witten, Exeter, & Field, 2003; Zenk et al., 2005). Therefore, more sophisticated studies measure accessibility with driving time and school capacity (Dillon, 2008; Müller, 2011). This study also employed a road-based network distance to obtain more realistic findings by minimizing a false representation of potential accessibility. Based on the two-step floating catchment area model developed by Radke and Mu (2000), this study estimated accessibility as the ratio of schools to student density within an area centered at a school location (Luo & Qi, 2009; Luo & Wang, 2003; McGrail & Humphreys, 2009; Radke & Mu, 2000; Wan, Zhan, Zou, & Chow, 2012; Wang & Luo, 2005). The accessibility at census track i is drawn from Equation 1:
where is the accessibility at census tract i, Rj is the school enrollment-to-student number ratio of school j, j falls within the catchment area of travel time centered at location i, k is all census tracts within a threshold travel time from location j, dkj is the travel time between k and j, d0 is a threshold travel time from location j, Sj is the number of school enrollments at location j, and Pk is the number of students of census tract k. A large value of indicates that location i has better accessibility.
Data
As shown in Equation 1, a threshold travel time is critical in calculating accessibility. Numerous studies investigated the trends of students’ travel distance, time, and transportation modes. In general, students aged 5 to 11 years were less likely to travel farther than older students (Fyhri, Hjorthol, Mackett, Fotel, & Kyttä, 2011; Martin & Carlson, 2005; McDonald, 2008; McMillan, Day, Boarnet, Alfonzo, & Anderson, 2006). While only 13% of all students commuted from home to school by walking or biking, 45% and 40% of them depended on automobiles and school buses, respectively (McDonald, 2007; McDonald, Brown, Marchetti, & Pedroso, 2011). As recent studies found that only 10% of students used public transportation and most students chose walking and biking only if the distance is less than 0.5 miles (McDonald & Aalborg, 2009; McDonald et al., 2011), about 90% of the students chose busing or auto use as the main transportation modes (E. J. Wilson, Marshall, Wilson, & Krizek, 2010). Given that automobiles play an important role in accessing public services in U.S. metropolitan areas (Gautier & Zenou, 2010; Shen, 1998), the patterns and trends of students’ travel to school are more influenced by time than by distance. With this regard, Ulfarsson and Shankar (2008) indicated the average observed travel time by automobile for 77% of students is 12.3 min. This study set a threshold driving time of Chicago students as 10 min based on the previous research. Accessibility was calculated using the Network Analyst extension tool in ArcGIS Desktop 10.0, which allows the identification of accessible zones by working with many spatial layers including streets, bus stops, bus routes, rail stops, and rail lines (Langford, Fry, & Higgs, 2012; Peipins et al., 2011).
Linking Accessibility to Socioeconomic Characteristics
Along with estimated accessibilities at every census tract using StreetMap North America provided with ArcGIS, this study used cartograms to investigate the relationship between accessibility changes and sociogeographical attributes. Unlike a choropleth map that has been conventionally used, a cartogram is a thematic, value-added map that presents area or distance by distorting the representation of space through a certain variable (Dorling, 1996; Gastner & Newman, 2004; Hennig, 2013; Henriques, Bação, & Lobo, 2009; Tobler, 2004). As a cartogram technique, well-known through the Worldmapper Project,1 is effective for representing variations over space and population, it is often used to illustrate equitable and even—or inequitable and uneven—distributions in the incidence of disease, the mortality rate, or the proportion of wealth (Dorling, 1996). Hence, a density-unequalized map like a cartogram is an appropriate way to illustrate the relation between geographic distributions of socioeconomic features and changes of accessibility in terms of spatial equality, as well as to discern how changes of accessibility are over- or underrepresented. With the ArcSript Cartogram Geoprocessing Tool developed by Tom Gross,2 based especially on the diffusion algorithm by Gastner and Newman (2004), this study re-sized each census tract according to demographic and socioeconomic characteristics within communities.
Data
Grounded in prior studies about segregation and stratification in urban areas, this study paid special attention to the density of the African American population aged 5 through 14 years without Hispanic or Latino origin and the population aged 5 through 14 years of Hispanic or Latino origin. A proportion of families below the poverty level with children younger than 18 years old were selected. The poverty level designed by the U.S. Census Bureau is estimated by the ratio of a family’s total income to the family’s threshold. In 2010, the Office of Management and Budget’s Statistical Policy Directive 14 set the threshold as US$22,113 for a family of four and US$26,023 for a family of five. These selected features were derived from the American Community Service 2011 five-year estimate. Finally, this study focused on two crime indicators at the community level as CPS parents and students were concerned about the rising likelihood of exposure to crime such as violence and drugs if students were required to pass through dangerous neighborhoods due to longer travel time and distance between school and home (Ahern, 2013; Davey, 2013). Two indicators extracted from Chicago Police Department reports included the indexed crime frequency and the community concerns during the preceding 12 months from June 2012 through May 2013.3 The indexed crime frequency includes homicide, criminal sexual assault, robbery, aggravated assault, aggravated battery, burglary, larceny, motor vehicle theft, and arson. The community concerns include gangs, narcotics, prostitution, conditions such as vacant buildings, poor lighting, overgrown foliage, street flooding, graffiti, and abandoned vehicles, troubled buildings, disturbances, vandalism, and traffic violations.
Illustrations
With regard to the question of whether school closings in the CPS are equitable or discriminatory, Figure 2 represents the respective distributions of accessibility to CPS schools before and after the CPS school closing policy. Overall, students in the city core in darker colors are likely to have more available seats to attend within the designated commute time of 10 min in this study, than are students away from the city center. However, the areas with high accessibility in the fall semester after the closing policy considerably decrease compared with the spring distribution. These patterns are similarly represented in the accessibility distributions of the non-charter, traditional schools. Namely, the city core still has a high accessibility to the non-chartered schools in both spring and fall, but the advantage of living in the city core tends to diminish with the school closing policy in the same manner.
What is interesting in Figure 2 is that the effect of charter schools on accessibility is found in the central areas of Chicago. In estimating potential accessibility to school for a given area, the number of available seats can either increase or decrease accessibility by affecting the school enrollment-to-student ratio within a commutable area. Yet, though the operation of charter schools in CPS should have theoretically led to changes in accessibility across areas, there is little change of accessibility in the city fringes. This suggests that charter schools for which all students in CPS are able to apply are clustered in the city core. Such geographic concentration of charter school locations tends to overstate the gap of accessibility to CPS schools between the city core and periphery. When only considering the market for non-chartered schools, children in the city core do not necessarily enjoy greater access to schools than do school children in other areas.
The value of accessibility change at a single census tract is obtained by the difference between the value of accessibility after and before school closings. Accessibility changes across the CPS are discriminated by quintile with a geometric interval scheme, instead of the traditional methods by the natural breaks or standard deviation classification. The geometric interval scheme provides more reasonable breaks for continuous but highly skewed data with a number of duplicated values. In Figure 3, most of the census tracts, but only some areas in the city fringes, experience the declines of accessibility with the implementation of the closing policy. The notable differences between the accessibility distributions are largely concentrated into particular areas, especially in the north of downtown Chicago. It shows that the school closings in 2013 bring accessibility changes not to the entire Chicago areas, but mostly to the city core. Furthermore, as the areas with a large change of accessibility overlap areas with high accessibility before the school closings, children in these areas experience a sudden change due to closing neighboring schools. While the absolute differences in the overall accessibility from before to after school closures are greater for all CPS schools than for non-chartered schools, the two distributions of accessibility change confirm that students in the city core are most harmed by the school closing policy, albeit with some small variation.
The next cartogram in Figure 4 reflects the density of total children who are mainly affected by the closings, and each census tract is shaded according to its accessibility change. The eastern city core including the Chicago Loop areas and most city fringes slightly shrink due to fewer children, whereas the areas surrounding the city core, especially in western Chicago, are relatively inflated with a high density of school-age children. Yet, the density map of school-age children in CPS is not dramatically distorted in comparison with Figure 3. Overall, most CPS students witness a mild decline in accessibility as a number of children live outside of the city core where there is a high accessibility change. Rather, the low density of school-age children shrinks the city core in this cartogram with a large change of accessibility to CPS schools. In view of this pattern that closing underutilized schools as identified by the number of empty seats brings about considerable changes of accessibility in areas with fewer children, the CPS school closures’ pursuit of efficiency seems to do no harm to equal education opportunities.
However, the density maps by race and ethnicity in Figure 5 suggest interesting findings in conjunction with the distribution of accessibility disparity after school closings. The situations depicted in Figure 5 differ remarkably from Figure 4 representing relatively mild decreases in accessibility across Chicago after the closings. Unlike Figure 4 illustrating that the areas with a high accessibility change become smaller than their actual sizes, and that most children experience moderate changes, the maps highly distorted by race and ethnicity in Figure 5 indicate that the CPS school closures cause unintended changes in access for students with certain demographic backgrounds. As the geographic clustering of minority children in Chicago inflates certain areas and shrinks others, the enlarged areas present substantial accessibility changes for neighborhoods where schools close, as gradated in darker colors. Namely, the school closing policy is likely to result in a negative change of relative access in the areas with a high density of both African American and Latino American children aged 5 to 14 years. Looking at the patterns by race and ethnicity in depth, areas where African American children live show a small but substantial difference of accessibility after school closings. African American children who are significantly clustered in the southern areas experience a mild change in the level of access, whereas African American students in northern Chicago experience a relatively large increase in travel time and distance after the CPS school closings. This pattern implies that even individual students in the same race group can be exposed to different levels of accessibility change depending on their residential choice. However, the rescaled distribution by the density of Hispanic children illustrates that the vast majority of Hispanic students experience substantial decreases in ease of access to schools. These findings suggest that population subgroups would be differently treated by neutral school closures grounded on the ratio of enrollment to capacity.
In Figure 6, the cartogram by proportion of families with children younger than 18 years old below the poverty level is notably distorted in all Chicago areas except the city periphery. As the distribution of population from lower income families then becomes more disproportionate relative to the original sizes, the density-unequalized map in Figure 6 indicates that the city core had been understated, and the city fringes had been overstated. Above all, this cartogram demonstrates that the areas inflated by a higher proportion of families below the poverty level also undergo large changes in access. Given the significance of social class as the determinant of public service distributions such as recreational programs (Mladenka, 1989), the pattern of accessibility change concentrated in disadvantaged areas supports the claim that education policies designed to improve efficiency in school management could undermine equality of educational opportunities. In other words, the CPS school closings are likely to yield unequal opportunities of access to schools within a commutable time for children from less advantaged communities.
In general, extant research indicates that exposure to a local homicide reduces African Americans’ academic performance on vocabulary and reading skills (Sharkey, 2010), and students with criminal records underperform more on achievement tests, are underenrolled in university, and are more likely to drop out of high schools (Burdick-Will, 2013; Kirk & Sampson, 2012). Thus, parents put an emphasis on safety in accessing schools along with travel distance and time (Timperio et al., 2006). In this context, the two cartograms in Figure 7 highlight the potential for school closure policies advanced on the basis of the decline in school enrollments to push children in communities with high incidence of crime to travel further through the neighboring crime-prone communities. Though the city center areas are commonly distorted by a high frequency of both indexed crime and community concerns, the inflation by the incidence of community concerns, including gangs, vacant buildings, and graffiti, is more prominently inflated in the areas with a high accessibility change than the inflation by the indexed crime frequency, including homicide and robbery. As the inflated areas become gradated in darker colors, children residing in dangerous and unhealthy communities are placed under threat of the large change in access after the school closings. Also, children in those areas are more likely to travel to farther schools with a high exposure to crime.
Discussion
Although research on equal opportunities in education has proliferated over several decades, few studies have focused on the spatial distribution of schools with regard to access. By examining the relation between the change of school accessibility and community characteristics using a cartogram method in a Geographic Information System (GIS), this study investigated the increase of spatial inequality as a result of school closings. In view of the efficiency concern, the closed CPS schools in the fall of 2013 were inevitably concentrated in areas with a high vacancy rate, as illustrated in Figure 1. However, if we define spatial equality as geographically fair quantity and quality of resources and services on the basis of community needs, and particularly the needs of specific groups (Talen, 1997), we need to think carefully about potential impacts of school closing on segregated urban areas.
As prior research has consistently pointed out, African American communities receive smaller shares of service benefits than do White neighborhoods (Cingranelli, 1981). Also, housing patterns defined by demographic and socioeconomic factors play a primary role in inhibiting social interaction within and between neighborhoods, especially in schooling, academic performance, college admission, employment, and public services (Bradford, 1990; Card & Rothstein, 2007; Charles, Dinwiddie, & Massey, 2004; Cutler & Glaeser, 1997; Davidoff, 2005; Dufur et al., 2013; Lindbom, 2010; Massey & Denton, 1993; Swanstrom et al., 2002; Teske, 2012; Turley, 2009; W. J. Wilson, 1987). In light of spatial inequality, disadvantaged neighborhoods shaped by segregated housing markets have traditionally been associated with poor job markets, lower housing values, high vacancy rates, overcrowdings, high rates of crime incidence, and not-equivalent police and fire protection systems (Coulton & Pandey, 1992; Galster & Mikelsons, 1995; Kain, 1969; Krivo & Peterson, 1996; South & Crowder, 1997; Wallace, 1991; Williams & Collins, 2001). In this sense, the recent school closures in CPS, in an initial attempt to improve financial efficiency, may also produce the undesirable effect of increased sociogeographic inequality in access to education. The results of this study provide evidence supporting the claim that the CPS school closing policy—based on the capacity and the number of empty seats at schools—raised the likelihood that students in segregated, geographically discontinuous communities had less access to neighboring schools within the commutable travel time. In other words, the school closings bring educational inequality to predominately disadvantaged neighborhoods, which in turn exacerbates segregation and inequality in metropolitan areas (Chicago Area Fair Housing Alliance, 2013; New York Appleseed, 2013).
In response to the arguable results for children with certain backgrounds, CPS plans on creating alternatives that could alleviate the detrimental impacts of changes in accessibility. As several court decisions ruled that closing existing public schools and instead opening charter schools did not violate the equal protection clause of the Constitution, opening charter schools, which are public entities under state statues, may be an option for students in districts closing neighborhood schools (Council of Organizations and Others for Education About Parochiaid, Inc. v. John Engler, Governor of the State of Michigan, 1997; Villanueva v. Carere, 1994; Wall, 1998). Still, it does not necessarily mean CPS has the right to essentially force students into charter schools. In accordance with the Illinois School Code 105 ILCS5/27A-4 (d), a local education agency shall not demand that students within its attendance boundary attend a charter school as the decision of attending a charter school appropriately relies upon individual behaviors in the school choice market. Furthermore, it is not at all clear that nearby charter schools automatically provide access to students seeking other educational options after the closing of their nearest community school as charter schools do not require any resident proof for enrollment, but have often been associated with exclusionary enrollment policies (Lubienski et al., 2009; Rotberg, 2014). Indeed, several studies demonstrate that the project to create new schools, such as the Renaissance 2010 Plan, failed to reduce spatial exclusion (Burdick-Will, Keels, & Schuble, 2013; Davis & Oakley, 2013; Lipman, 2008, 2009). Thus, it is doubtful whether opening charter schools has a positive impact on accessibility of CPS students at schools that are to be closed.
Along with the concern that inequitably distributed sociogeographies lower the level of access following school closures, the question of which schools students move to after their neighborhood schools close is an important consideration in terms of quality of spatially equitable access. In general, previous studies find a negative relation between distance to school and student achievement provided by school openings, closings, and mergers (Kuziemko, 2006; Talen, 2001), but such negative effects on attendance and achievement frequently disappeared later. Especially, they tend to be minimized when students move to high-performing schools (Engberg, Gill, Zamarro, & Zimmer, 2012). However, in CPS where a number of repeatedly underperforming schools were located in at-risk communities (Lee, & Joo, 2012), most students in the schools that closed between 2001 and 2006 were displaced into schools with academically weak records (de la Torre & Gwynne, 2009). Even though this study focuses simply on changes in accessibility brought about by school closings, the findings of this study suggest directions for future research in examining issues such as the purported savings in school budgets and questionable impacts of school closings on test scores.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Notes
1.
See related examples at http://www.worldmapper.org
2.
See detailed information at http://arcscripts.esri.com/details.asp?dbid=15638
3.
See detailed information at http://gis.chicagopolice.org/CLEARMap_crime_sums/startPage.htm#
References
|
Abramson, A. J., Tobin, M. S., VanderGoot, M. R. (1995). The changing geography of metropolitan opportunity: The segregation of the poor in U.S. metropolitan areas, 1970 to 1990. Housing Policy Debate, 6, 45-72. Google Scholar | Crossref | ISI | |
|
Ahern, M. A. (2013, October 25). Chicago pulls back on Safe Passage staffing. NBC Chicago. Retrieved from http://www.nbcchicago.com/blogs/ward-room/Chicago-Pulls-Back-on-Safe-Passage-Staffing-229290951.html Google Scholar | |
|
Andrews, M., Duncombe, W., Yinger, J. (2002). Revisiting economies of size in American education: Are we any closer to a consensus? Economics of Education Review, 21, 245-262. Google Scholar | Crossref | ISI | |
|
Bayer, P., McMillan, R. (2005). Racial sorting and neighborhood quality (No. 11813). Cambridge, MA: National Bureau of Economic Research. Google Scholar | Crossref | |
|
Berger, M. A. (1983a). Neighborhood schools: The new(?) legal response to enrollment decline and desegregation. Urban Education, 18, 7-28. Google Scholar | SAGE Journals | ISI | |
|
Berger, M. A. (1983b). Why communities protest school closings. Education and Urban Society, 15, 149-163. Google Scholar | SAGE Journals | ISI | |
|
Bjork, L. G., Blase, J. (2009). The micropolitics of school district decentralization. Educational Assessment, Evaluation and Accountability, 21, 195-208. Google Scholar | Crossref | ISI | |
|
Bradford, M. (1990). Education, attainment and the geography of choice. Geography, 75, 3-16. Google Scholar | ISI | |
|
Burdick-Will, J. (2013). School violent crime and academic achievement in Chicago. Sociology of Education, 86, 343-361. Google Scholar | SAGE Journals | ISI | |
|
Burdick-Will, J., Keels, M., Schuble, T. (2013). Closing and opening schools: The association between neighborhood characteristics and the location of new educational opportunities in a large urban district. Journal of Urban Affairs, 35, 59-80. Google Scholar | Crossref | ISI | |
|
Card, D., Rothstein, J. (2007). Racial segregation and the black–white test score gap. Journal of Public Economics, 91, 2158-2184. Google Scholar | Crossref | ISI | |
|
Charles, C. Z., Dinwiddie, G., Massey, D. S. (2004). The continuing consequences of segregation: Family stress and college academic performance. Social Science Quarterly, 85, 1353-1373. Google Scholar | Crossref | ISI | |
|
Chicago Area Fair Housing Alliance . (2013). Chicago from home to school: Why segregation persists and current reforms may only make things worse. Chicago, IL: Author. Google Scholar | |
|
Chicago Public Schools . (2011). Chicago Public Schools space utilization standards. Retrieved from http://www.cps.edu/About_CPS/Policies_and_guidelines/Documents/SpaceUtilizationStandards.pdf Google Scholar | |
|
Chicago Public Schools . (2012a). Guidelines for school actions 2012-2013 school year. Retrieved from http://www.cps.edu/About_CPS/Policies_and_guidelines/Documents/2012_2013SchoolActionsGuidelines.pdf Google Scholar | |
|
Chicago Public Schools . (2012b). New schools and programs. Retrieved from http://cps.edu/NewSchools/Pages/ONS.aspx/ Google Scholar | |
|
Chicago Public Schools . (2013a). CPS fact check on consolidating underutilized schools. Retrieved from http://cps.edu/qualityschools/documents/factcheck.pdf Google Scholar | |
|
Chicago Public Schools . (2013b). Guidelines for school actions 2013-2014 school year. Retrieved from http://www.cps.edu/SiteCollectionDocuments/GUIDELINES_FOR_SCHOOL_ACTIONS_2013-14.pdf Google Scholar | |
|
Chicago Public Schools . (2013c, March 21). Media briefing. Retrieved from http://www.cps.edu/qualityschools/Documents/briefing.pdf Google Scholar | |
|
Chicago Public Schools . (2013d, March 21). Significant new investments to provide quality, 21st century education for CPS students transitioning from underutilized schools this fall. Retrieved from http://cps.edu/News/Press_releases/Pages/3_21_2013_PR2.aspx Google Scholar | |
|
Cingranelli, D. L. (1981). Race, politics and elites: Testing alternative models of municipal service distribution. American Journal of Political Science, 25, 664-692. Google Scholar | Crossref | ISI | |
|
Clark, W., Ware, J. (1997). Trends in residential integration by socioeconomic status in southern California. Urban Affairs Review, 32, 825-843. Google Scholar | SAGE Journals | ISI | |
|
Cohn, E. (1968). Economies of scale in Iowa high school operations. The Journal of Human Resources, 3, 422-434. Google Scholar | Crossref | ISI | |
|
Commission on School Utilization . (2013). Final report. Chicago, IL: The Chicago Public Schools. Google Scholar | |
|
Coulton, C. J., Pandey, S. (1992). Geographic concentration of poverty and risk to children in urban neighborhoods. American Behavioral Scientist, 35, 238-257. Google Scholar | SAGE Journals | ISI | |
|
Council of Organizations and Others for Education About Parochiaid, Inc. v. John Engler, Governor of the State of Michigan, 455 Mich. 557 (1997). Google Scholar | |
|
Cutler, D. M., Glaeser, E. L. (1997). Are ghettos good or bad? The Quarterly Journal of Economics, 112, 827-872. Google Scholar | Crossref | ISI | |
|
Darden, J., Kamel, S. (2000). Black residential segregation in the city and suburbs of Detroit: Does socioeconomic status matter? Journal of Urban Affairs, 22, 1-13. Google Scholar | Crossref | ISI | |
|
Davey, M. (2013, August 26). In Chicago, campaign to provide Safe Passage on way to school. The New York Times. Retrieved from http://www.nytimes.com/2013/08/27/education/in-chicago-campaign-to-provide-safe-passage-on-way-to-school.html?_r=0 Google Scholar | |
|
Davidoff, T. (2005). Income sorting: Measurement and decomposition. Journal of Urban Economics, 58, 289-303. Google Scholar | Crossref | ISI | |
|
Davis, T., Oakley, D. (2013). Linking charter school emergence to urban revitalization and gentrification: A socio-spatial analysis of three cities. Journal of Urban Affairs, 35, 81-102. Google Scholar | Crossref | ISI | |
|
Dean, J. (1983). Neighborhood impacts of school closings: The case in New York City. Education and Urban Society, 15, 245-254. Google Scholar | SAGE Journals | ISI | |
|
de la Torre, M., Gwynne, J. (2009). When schools close: Effects on displaced students in Chicago Public Schools. Chicago, IL: Consortium on Chicago School Research, University of Chicago. Google Scholar | |
|
Denton, N. A., Massey, D. S. (1988). Residential segregation of Blacks, Hispanics, and Asians by socioeconomic status and generation. Social Science Quarterly, 69, 797-817. Google Scholar | ISI | |
|
Dillon, E. (2008). Plotting school choice: The challenges of crossing district lines (Education Sector). Washington, DC: Education Sector. Google Scholar | |
|
Dorling, D. (1996). Area cartograms: Their use and creation. Norwich, UK: Environmental Publications, University of East Anglia. Google Scholar | |
|
Dowdall, E. (2011). Closing public schools in Philadelphia. Philadelphia, PA: Pew Charitable Trusts. Google Scholar | |
|
Dufur, M. J., Parcel, T. L., Troutman, K. P. (2013). Does capital at home matter more than capital at school? Social capital effects on academic achievement. Research in Social Stratification and Mobility, 31, 1-21. Google Scholar | Crossref | ISI | |
|
Duncombe, W., Miner, J., Ruggiero, J. (1995). Potential cost savings from school district consolidation: A case study of New York. Economics of Education Review, 14, 265-284. Google Scholar | Crossref | ISI | |
|
Duncombe, W., Yinger, J. (2007). Does school district consolidation cut costs? Education Finance and Policy, 2, 341-375. Google Scholar | Crossref | ISI | |
|
Engberg, J., Gill, B., Zamarro, G., Zimmer, R. W. (2012). Closing schools in a shrinking district: Do student outcomes depend on which schools are closed? Journal of Urban Economics, 71, 189-203. Google Scholar | Crossref | ISI | |
|
Eyre, E., Finn, S. (2002, August). Closing costs. Charleston Gazette. Retrieved from http://www.wvgazettemail.com/News/ClosingCosts/200208300003 Google Scholar | |
|
Farley, R., Allen, W. R., National Committee for Research on the 1980 Census . (1989). The color line and the quality of life in America. New York, NY: Oxford University Press. Google Scholar | |
|
Fyhri, A., Hjorthol, R., Mackett, R. L., Fotel, T. N., Kyttä, M. (2011). Children’s active travel and independent mobility in four countries: Development, social contributing trends and measures. Transport Policy, 18, 703-710. Google Scholar | Crossref | ISI | |
|
Galster, G. C., Mikelsons, M. (1995). The geography of metropolitan opportunity: A case study of neighborhood conditions confronting youth in Washington, DC. Housing Policy Debate, 6, 73-102. Google Scholar | Crossref | ISI | |
|
Gastner, M. T., Newman, M. E. (2004). Diffusion-based method for producing density-equalizing maps. Proceedings of the National Academy of Sciences of the United States of America, 101, 7499-7504. Google Scholar | Crossref | Medline | ISI | |
|
Gautier, P. A., Zenou, Y. (2010). Car ownership and the labor market of ethnic minorities. Journal of Urban Economics, 67, 392-403. Google Scholar | Crossref | ISI | |
|
Glaeser, E. L., Gyourko, J., Saks, R. E. (2005). Urban growth and housing supply. Journal of Economic Geography, 6, 71-89. Google Scholar | Crossref | ISI | |
|
Hennig, B. D. (2013). Rediscovering the world: Map transformations of human and physical space. London, England: Springer. Google Scholar | Crossref | |
|
Henriques, R., Bação, F., Lobo, V. (2009). Carto-SOM: Cartogram creation using self-organizing maps. International Journal of Geographical Information Science, 23, 483-511. Google Scholar | Crossref | ISI | |
|
Howley, C., Johnson, J., Petrie, J. (2011). Consolidation of schools and districts. Boulder, CO: National Education Policy Center. Google Scholar | |
|
Iceland, J., Weinberg, D. H., Steinmetz, E. (2002). Racial and ethnic residential segregation in the United States (No. CENSR-3). Washington, DC: U.S. Census Bureau. Google Scholar | |
|
Kain, J. F. (1968). Housing segregation, Negro employment and metropolitan decentralization. The Quarterly Journal of Economics, 83, 175-197. Google Scholar | Crossref | ISI | |
|
Kain, J. F. (1969). Housing segregation, Negro employment and metropolitan decentralization. The Quarterly Journal of Economics, 83, 175-197. Google Scholar | ISI | |
|
Kain, J. F. (1992). The spatial mismatch hypothesis: Three decades later. Housing Policy Debate, 3, 371-460. Google Scholar | Crossref | |
|
Kain, J. F. (2004). A pioneer’s perspective on the spatial mismatch literature. Urban Studies, 41, 7-32. Google Scholar | SAGE Journals | ISI | |
|
Kearns, R. A., Lewis, N., McCreanor, T., Witten, K. (2009). “The status quo is not an option”: Community impacts of school closure in South Taranaki, New Zealand. Journal of Rural Studies, 25, 131-140. Google Scholar | Crossref | ISI | |
|
Kenny, L. W. (1983). Economies of scale in schooling. Economics of Education Review, 2, 1-24. Google Scholar | Crossref | |
|
Kirk, D. S., Sampson, R. J. (2012). Juvenile arrest and collateral educational damage in the transition to adulthood. Sociology of Education, 86, 36-62. Google Scholar | SAGE Journals | ISI | |
|
Knox, P. L. (1978). The intraurban ecology of primary medical care: Patterns of accessibility and their policy implications. Environment and Planning A, 10, 415-435. Google Scholar | SAGE Journals | ISI | |
|
Krivo, L. J., Peterson, R. D. (1996). Extremely disadvantaged neighborhoods and urban crime. Social Forces, 75, 619-648. Google Scholar | Crossref | ISI | |
|
Krivo, L. J., Peterson, R. D., Kuhl, D. (2009). Segregation, racial structure, and neighborhood violent crime. American Journal of Sociology, 114, 1765-1802. Google Scholar | Crossref | Medline | ISI | |
|
Kuziemko, I. (2006). Using shocks to school enrollment to estimate the effect of school size on student achievement. Economics of Education Review, 25, 63-75. Google Scholar | Crossref | ISI | |
|
Kwan, M. P. (1998). Space-time and integral measures of individual accessibility: A comparative analysis using a point-based framework. Geographical Analysis, 30, 191-216. Google Scholar | Crossref | ISI | |
|
Langford, M., Fry, R., Higgs, G. (2012). Measuring transit system accessibility using a modified two-step floating catchment technique. International Journal of Geographical Information Science, 26, 193-214. Google Scholar | Crossref | ISI | |
|
Larsen, K., Gilliland, J. (2008). Mapping the evolution of “food deserts” in a Canadian city: Supermarket accessibility in London, Ontario, 1961–2005. International Journal of Health Geographics, 7, Article 16. Google Scholar | Crossref | Medline | ISI | |
|
Lawyers’ Committee for Better Housing . (2013). Housing instability for renters continues: Chicago responds with adoption of New Tenant Protections. Chicago, IL: Author. Google Scholar | |
|
Lee, J., Joo, H. (2012, April). The invisible wall between schools: An exploratory report on repeated low performing schools in the Chicago Public Schools. Presented at the annual meeting of the American Educational Research Association, Vancouver, British Columbia, Canada. Google Scholar | |
|
Lindbom, A. (2010). School choice in Sweden: Effects on student performance, school costs, and segregation. Scandinavian Journal of Educational Research, 54, 615-630. Google Scholar | Crossref | ISI | |
|
Lipman, P. (2002). Making the global city, making inequality: The political economy and cultural politics of Chicago school policy. American Educational Research Journal, 39, 379-419. Google Scholar | SAGE Journals | ISI | |
|
Lipman, P. (2008). Mixed-income schools and housing: Advancing the neoliberal urban agenda. Journal of Education Policy, 23, 119-134. Google Scholar | Crossref | ISI | |
|
Lipman, P. (2009). The cultural politics of mixed-income schools and housing: A racialized discourse of displacement, exclusion, and control. Anthropology & Education Quarterly, 40, 215-236. Google Scholar | Crossref | ISI | |
|
Lubienski, C. (2005). School choice as a civil right: District responses to competition and equal educational opportunity. Equity & Excellence in Education, 38, 331-341. Google Scholar | Crossref | |
|
Lubienski, C., Gulosino, C., Weitzel, P. (2009). School choice and competitive incentives: Mapping the distribution of educational opportunities across local education markets. American Journal of Education, 115, 601-647. Google Scholar | Crossref | ISI | |
|
Luo, W., Qi, Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15, 1100-1107. Google Scholar | Crossref | Medline | ISI | |
|
Luo, W., Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: Synthesis and a case study in the Chicago region. Environment and Planning B: Planning and Design, 30, 865-884. Google Scholar | SAGE Journals | ISI | |
|
Lytton, M. (2011). Have all the costs of closing a school been considered? (No. 2011/08). CELE Exchange. doi:10.1787/5kg5c8bwqs31-en Google Scholar | |
|
Martin, S., Carlson, S. (2005). Barriers to children walking to or from school-United States, 2004. Journal of the American Medical Association, 294, 2160-2162. Google Scholar | Crossref | ISI | |
|
Massey, D. S., Denton, N. A. (1989). Hypersegregation in U.S. metropolitan areas: Black and Hispanic segregation along five dimensions. Demography, 26, 373-391. Google Scholar | Crossref | Medline | ISI | |
|
Massey, D. S., Denton, N. A. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press. Google Scholar | |
|
Massey, D. S., Fischer, M. J. (1999). Does rising income bring integration? New results for blacks, Hispanics, and Asians in 1990. Social Science Research, 28, 316-326. Google Scholar | Crossref | ISI | |
|
Mayer, C. J. (1996). Does location matter? New England Economic Review, May/June, 26-40. Google Scholar | |
|
McDonald, N. C. (2007). Active transportation to school: Trends among U.S. schoolchildren, 1969–2001. American Journal of Preventive Medicine, 32, 509-516. Google Scholar | Crossref | Medline | ISI | |
|
McDonald, N. C. (2008). Children’s mode choice for the school trip: The role of distance and school location in walking to school. Transportation, 35, 23-35. Google Scholar | Crossref | ISI | |
|
McDonald, N. C., Aalborg, A. E. (2009). Why parents drive children to school: Implications for safe routes to school programs. Journal of the American Planning Association, 75, 331-342. Google Scholar | Crossref | ISI | |
|
McDonald, N. C., Brown, A. L., Marchetti, L. M., Pedroso, M. S. (2011). U.S. school travel, 2009. American Journal of Preventive Medicine, 41, 146-151. Google Scholar | Crossref | Medline | ISI | |
|
McGrail, M. R., Humphreys, J. S. (2009). Measuring spatial accessibility to primary care in rural areas: Improving the effectiveness of the two-step floating catchment area method. Applied Geography, 29, 533-541. Google Scholar | Crossref | ISI | |
|
McLafferty, S. L. (1982). Urban structure and geographical access to public services. Annals of the Association of American Geographers, 72, 347-354. Google Scholar | Crossref | |
|
McMillan, T., Day, K., Boarnet, M., Alfonzo, M., Anderson, C. (2006). Johnny walks to school-Does Jane? Sex differences in children’s active travel to school. Children, Youth & Environments, 16, 75-89. Google Scholar | |
|
Mladenka, K. R. (1989). The distribution of an urban public service: The changing role of race and politics. Urban Affairs Review, 24, 556-583. Google Scholar | SAGE Journals | ISI | |
|
Morrill, R. L., Symons, J. (1977). Efficiency and equity aspects of optimum location. Geographical Analysis, 9, 215-225. Google Scholar | Crossref | ISI | |
|
Müller, S. (2011). Assessment of school closures in urban areas by simple accessibility measures. Erdkunde, 65, 401-414. Google Scholar | Crossref | ISI | |
|
New York Appleseed . (2013). Within our reach: Segregation in NYC district elementary schools and what we can do about it: School-to-school diversity. New York: Author. Google Scholar | |
|
Nicholls, S. (2001). Measuring the accessibility and equity of public parks: A case study using GIS. Managing Leisure, 6, 201-219. Google Scholar | Crossref | |
|
Nitta, K. A., Holley, M. J., Wrobel, S. L. (2010). A phenomenological study of rural school consolidation. Journal of Research in Rural Education, 25(2), 1-19. Google Scholar | |
|
Bard, J., Gardener, C., Wieland, R. (2006). Rural school consolidation report: History, research summary, conclusions, and recommendations. The Rural Educator, 27(2), 40–48. Google Scholar | |
|
Oh, K., Jeong, S. (2007). Assessing the spatial distribution of urban parks using GIS. Landscape and Urban Planning, 82, 25-32. Google Scholar | Crossref | ISI | |
|
Pacione, M. (1989). Access to urban services: The case of secondary schools in Glasgow. Scottish Geographical Magazine, 105, 12-18. Google Scholar | Crossref | ISI | |
|
Pais, J., South, S., Crowder, K. (2012). Metropolitan heterogeneity and minority neighborhood attainment: Spatial assimilation or place stratification? Social Problems, 59, 258-281. Google Scholar | Crossref | Medline | ISI | |
|
Pattillo-McCoy, M. (1999). Black picket fences. Chicago, IL: University of Chicago Press. Google Scholar | |
|
Peipins, L. A., Graham, S., Young, R., Lewis, B., Foster, S., Flanagan, B., Dent, A. (2011). Time and distance barriers to mammography facilities in the Atlanta metropolitan area. Journal of Community Health, 36, 675-683. Google Scholar | Crossref | Medline | ISI | |
|
Pendall, R. (2012). Housing markets in six metropolitan areas and their main central cities. LISC Institute for Comprehensive Community Development. Retrieved from http://www.instituteccd.org/uploads/iccd/documents/pendall_housing_markets_paper.pdf Google Scholar | |
|
Peshkin, A. (1982). The imperfect union: School consolidation & community conflict. Chicago, IL: The University of Chicago Press. Google Scholar | |
|
Pinch, S. (1987). The changing geography of preschool services in England between 1977 and 1983. Environment and Planning C: Government and Policy, 5, 469-480. Google Scholar | SAGE Journals | |
|
Podmolik, M. E. (1998, May 10). Downtown spreading out as residents pour in. Chicago Sun-Times, p. A20. Google Scholar | |
|
Post, D., Stambach, A. (1999). District consolidation and rural school closure: E pluribus unum? Journal of Research in Rural Education, 15, 106-117. Google Scholar | |
|
Radke, J., Mu, L. (2000). Spatial decompositions, modeling and mapping service regions to predict access to social programs. Geographic Information Sciences, 6, 105-112. Google Scholar | |
|
Rotberg, I. C. (2014, February). Charter schools and the risk of increased segregation. Phi Delta Kappan, 95, 26-30. Google Scholar | SAGE Journals | ISI | |
|
Rothwell, J. T., Massey, D. S. (2010). Density zoning and class segregation in U.S. metropolitan areas. Social Science Quarterly, 91, 1123-1143. Google Scholar | Crossref | Medline | ISI | |
|
Scott, H. J. (1983). Desegregation in Nashville: Conflicts and contradictions in preserving schools in Black communities. Education and Urban Society, 15, 235-244. Google Scholar | SAGE Journals | ISI | |
|
Sell, R. S., Leistritz, F. L. (1997). Socioeconomic impacts of school consolidation on host and vacated communities. Community Development Society, 28, 186-205. Google Scholar | Crossref | |
|
Sharkey, P. (2010). The acute effect of local homicides on children’s cognitive performance. Proceedings of the National Academy of Sciences, 107, 11733-11738. Google Scholar | Crossref | Medline | ISI | |
|
Shavers, A. W. (2005). Closing the school doors in the pursuit of equal education opportunity: A comment on Montoy v. State, 2003 WL 22902963 (Kan. Dist. Ct. 2003). Nebraska Law Review, 83, 901-914. Google Scholar | |
|
Shen, Q. (1998). Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environment and Planning B: Planning and Design, 25, 345-365. Google Scholar | SAGE Journals | |
|
Smith, D. M., Cummins, S., Taylor, M., Dawson, J., Marshall, D., Sparks, L., Anderson, A. S. (2010). Neighbourhood food environment and area deprivation: Spatial accessibility to grocery stores selling fresh fruit and vegetables in urban and rural settings. International Journal of Epidemiology, 39, 277-284. Google Scholar | Crossref | Medline | ISI | |
|
South, S. J., Crowder, K. D. (1997). Residential mobility between cities and suburbs: Race, suburbanization, and back-to-the-city moves. Demography, 34, 525-538. Google Scholar | Crossref | Medline | ISI | |
|
Streifel, J. S., Foldesy, G., Holman, D. M. (1991). The financial effects of consolidation. Journal of Research in Rural Education, 7(2), 13-20. Google Scholar | |
|
Swanstrom, T., Dreier, P., Mollenkopf, J. H. (2002). Economic inequality and public policy: The power of place. City & Community, 1, 349-372. Google Scholar | Crossref | |
|
Taeuber, K. E., Taeuber, A. F. (1965). Negroes in cities: Residential segregation and neighborhood change. Chicago, IL: Aldine Publishing. Google Scholar | |
|
Talen, E. (1997). The social equity of urban service distribution: An exploration of park access in Pueblo, Colorado, and Macon, Georgia. Urban Geography, 18, 521-541. Google Scholar | Crossref | ISI | |
|
Talen, E. (2001). School, community, and spatial equity: An empirical investigation of access to elementary schools in West Virginia. Annals of the Association of American Geographers, 91, 465-486. Google Scholar | Crossref | |
|
Talen, E., Anselin, L. (1998). Assessing spatial equity: An evaluation of measures of accessibility to public playgrounds. Environment and Planning A, 30, 595-613. Google Scholar | SAGE Journals | ISI | |
|
Teske, P. (2012). Creating savvy choosers: Informing families about school choices. In Lake, R. J., Gross, B. (Eds.), Hopes, fears, & reality: A balanced look at American charter schools in 2011 (pp. 79-90). Seattle, WA: National Charter School Resource Center. Google Scholar | |
|
Timperio, A., Ball, K., Salmon, J., Roberts, R., Giles-Corti, B., Simmons, D., . . . Crawford, D. (2006). Personal, family, social, and environmental correlates of active commuting to school. American Journal of Preventive Medicine, 30, 45-51. Google Scholar | Crossref | Medline | ISI | |
|
Tobler, W. (2004). Thirty five years of computer cartograms. Annals of the Association of American Geographers, 94, 58-73. Google Scholar | Crossref | |
|
Truelove, M. (1993). Measurement of spatial equity. Environment and Planning C, 11, 19-34. Google Scholar | SAGE Journals | ISI | |
|
Turley, R. N. L. (2009). College proximity: Mapping access to opportunity. Sociology of Education, 82, 126-146. Google Scholar | SAGE Journals | ISI | |
|
Uetricht, M. (2013, March 27). Chicago is ground zero for disastrous “free market” reforms of education. The Guardian. Retrieved from http://www.theguardian.com/commentisfree/2013/mar/27/chicago-teacher-strike-against-school-closures-and-privatization Google Scholar | |
|
Ulfarsson, G. F., Shankar, V. N. (2008). Children’s travel to school: Discrete choice modeling of correlated motorized and nonmotorized transportation modes using covariance heterogeneity. Environment and Planning B: Planning and Design, 35, 195-206. Google Scholar | SAGE Journals | |
|
Valencia, R. R. (2013). The school closure issue and the Chicano community: A follow-up study of the Angeles case. The Urban Review, 16, 145-163. Google Scholar | Crossref | |
|
Van Valey, T. L., Roof, W. C., Wilcox, J. E. (1977). Trends in residential segregation: 1960-1970. American Journal of Sociology, 82, 826-844. Google Scholar | Crossref | ISI | |
|
Villanueva v. Carere, 85 F.3d 481 . (1994). Google Scholar | |
|
Wall, J. T. (1998). The establishment of charter schools: A guide to legal issues for legislatures. Brigham Young University Education and Law Journal, 1998, 69-102. Google Scholar | |
|
Wallace, R. (1991). Expanding coupled shock fronts of urban decay and criminal behavior: How US cities are becoming “hollowed out.” Journal of Quantitative Criminology, 7, 333-356. Google Scholar | Crossref | |
|
Wan, N., Zhan, F. B., Zou, B., Chow, E. (2012). A relative spatial access assessment approach for analyzing potential spatial access to colorectal cancer services in Texas. Applied Geography, 32, 291-299. Google Scholar | Crossref | ISI | |
|
Wang, F., Luo, W. (2005). Assessing spatial and nonspatial factors for healthcare access: Towards an integrated approach to defining health professional shortage areas. Health & Place, 11, 131-146. Google Scholar | Crossref | Medline | ISI | |
|
Williams, D. R., Collins, C. (2001). Racial residential segregation: A fundamental cause of racial disparities in health. Public Health Reports, 116, 404-416. Google Scholar | SAGE Journals | ISI | |
|
Wilson, E. J., Marshall, J. D., Wilson, R. D., Krizek, K. J. (2010). By foot, bus or car: Children’s school travel and school choice policy. Environment and Planning A, 42, 2168-2185. Google Scholar | SAGE Journals | ISI | |
|
Wilson, W. J. (1987). The truly disadvantaged. Chicago, IL: University of Chicago Press. Google Scholar | |
|
Wilson, W. J. (2012). The truly disadvantaged: The inner city, the underclass, and the public policy. Chicago, IL: University of Chicago Press. Google Scholar | Crossref | |
|
Witten, K., Exeter, D., Field, A. (2003). The quality of urban environments: Mapping variation in access to community resources. Urban Studies, 40, 161-177. Google Scholar | SAGE Journals | ISI | |
|
Witten, K., McCreanor, T., Kearns, R., Ramasubramanian, L. (2001). The impacts of a school closure on neighbourhood social cohesion: Narratives from Invercargill, New Zealand. Health & Place, 7, 307-317. Google Scholar | Crossref | Medline | ISI | |
|
Zars, B. (1998). Long rides, tough hides: Enduring long school bus rides. Randolph, VT: Rural Challenge Policy Program. Google Scholar | |
|
Zenk, S. N., Schulz, A. J., Israel, B. A., James, S. A., Bao, S., Wilson, M. L. (2005). Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. American Journal of Public Health, 95, 660-667. Google Scholar | Crossref | Medline | ISI | |
|
Zimmer, T., DeBoer, L., Hirth, M. (2009). Examining economies of scale in school consolidation: Assessment of Indiana school districts. Journal of Education Finance, 35, 103-127. Google Scholar | Crossref |
Author Biographies
Jin Lee is a graduate student in the Department of Education Policy, Organization, and Leadership at the University of Illinois. She is currently completing her doctoral program in education policy studying equity and access.
Christopher Lubienski is a professor of the Department of Education Policy, Organization, and Leadership, and the director of the Forum on the Future of Public Education at the University of Illinois. His current work examines organizational responses to competitive conditions in local education markets, including geo-spatial analyses of charter schools.