Narrowing Pathways? Exploring the Spatial Dynamicsof Postsecondary STEM Preparation in Philadelphia,Pennsylvania

Kimberly A. Edmunds • Hamil Pearsall •

Laura K. Porterfield

� Springer Science+Business Media New York 2014

Abstract This paper explores geographical factors associated with the postsec-

ondary science, technology, engineering, and mathematics (STEM) preparation of

students from underrepresented groups in the School District of Philadelphia from

middle to high school during 2008 and 2011. We analyze Pennsylvania state

assessment data for mathematics in conjunction with data from the American

Community Survey using correlation analysis, cluster analysis, ordinary least

squares regression, and geographically-weighted regression. Our analyses find

strong relationships among math performance, a key indicator of college readiness

for courses of study in STEM, and neighborhood factors within school catchment

areas. For example, high percentages of unemployed residents are negatively cor-

related to math performance, while high median household income is positively

correlated with math performance. These relationships vary spatially across middle

and high school catchment areas. The results of this research can foster discussions

about school reform towards more nuanced, spatially-informed STEM policies that

focus on improving the educational outcomes of students who are traditionally

underrepresented in STEM fields, particularly for those youth living in economi-

cally disadvantaged communities.

Keywords Neighborhood factors � STEM � High schools � Urban schools

K. A. Edmunds (&)

Research for Action, Philadelphia, PA, USA

e-mail: kedmunds@researchforaction.org

H. Pearsall

Department of Geography and Urban Studies, Temple University, Philadelphia, PA, USA

L. K. Porterfield

Department of Educational Foundations, University of Wisconsin-Whitewater, Whitewater, WI,

USA

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DOI 10.1007/s11256-014-0285-6

Introduction

Students with limited access to high-quality educational opportunities during K-12

may lack adequate preparation, or readiness, to pursue science, technology,

engineering, and mathematics (STEM) fields in higher education. Our research aims

to better understand the geography of these ‘‘narrowing pathways’’ in middle and

high school, educational periods that studies have shown to be difficult for youth

living in urban areas, particularly for youth from groups that are underrepresented in

STEM fields (Gold et al. 2010a, b; McIntosh et al. 2008; Alspaugh 1998). Existing

literature has illuminated the complex relationships among neighborhood factors

and educational outcomes (e.g., Sampson 2012; Fotheringham et al. 2001; Rothwell

2012), yet few studies explore how these relationships vary across space and at

different periods of time.

Our work explores spatial variations in the relationship between 8th and 11th

grade group math performance, grades that correspond with critical testing

periods for measuring student achievement at the middle and high school levels,

and neighborhood characteristics to identify structural factors that may facilitate

or hinder college preparation in STEM. Performance data are based on

neighborhood schools, which draw students from surrounding catchment areas.

We use the example of Philadelphia, Pennsylvania, a large American city that

can provide insight about the geography of opportunity in other urban areas

where pathways to twenty-first century careers may be constricted for members

of historically disenfranchised communities. The primary questions that we

examine are:

What is the relationship between socioeconomic neighborhood factors and 8th

grade (2007–2008) and 11th grade (2010–2011) school math performance in

Philadelphia? What are the differences between school math performance in

8th grade and 11th grade?

Our study uses math performance data from the Pennsylvania System of School

Assessment (PSSA). We investigate the relationship between neighborhood

factors, such as residents’ household income and educational attainment, and

school math performance at the 8th and 11th grades through several spatial and

statistical techniques, including correlation analysis, cluster analysis, and global

and local regression. While it is likely that many of the students who attended

8th grade at their neighborhood middle school in 2007–2008 attended their

neighborhood high school as 11th graders in 2010–2011, our use of school-level

data does not allow us to conduct analyses based on a cohort sample. As a result,

we aim to examine these school–neighborhood relationships at two different

levels and at two different time periods. We find that neighborhood factors are

associated with school math performance at both levels, and further, that this

relationship varies across Philadelphia County. The results of this study can

inform policy discussions about improving the educational outcomes of

economically disadvantaged students.

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Literature Review

In the following section, we provide an overview of the literature on postsecondary

participation in STEM among underrepresented populations. We also discuss the

link between neighborhood characteristics and educational outcomes to highlight

the need for additional research on these complex relationships.

Postsecondary STEM Readiness

We focus on the importance of math preparation to improve college readiness,

which supports student retention in postsecondary STEM programs–an ongoing

recommendation to policymakers (President’s Council of Advisors on Science and

Technology [PCAST] 2012; National Academy of Sciences [NAS] 2011). In

general, about 60 % of all American students enter college without the math skills

needed to persist in STEM degree programs (PCAST 2012). The global

competiveness of the nation’s high school students has been a salient topic in

education reform since the release of David Gardner’s (1983) seminal report ‘‘A

Nation at Risk’’ to the U.S. Secretary of Education. The Department of Education

recently released data from its Civil Rights Data Collection showing vast

inequalities in the advanced math course offerings across the nation’s schools

(Lewin 2012).

Recent research has uncovered the need for an education policy agenda that

targets economically disadvantaged students (Maxwell 2012; Reardon 2011).

Stanford University sociologist Sean Reardon found that the achievement gap

between rich and poor children has widened and is now nearly double the gap

between Black and White children (Reardon 2011; Tavernise 2012). Moreover,

researchers at the University of Michigan concluded that the division between rich

and poor children in college completion, the strongest predictor of workforce

success, has grown by about 50 % since the late 1980s (Tavernise 2012).

PCAST conveys that there needs to be a sense of urgency to better prepare

individuals from underrepresented populations, which includes students from

economically disadvantaged communities, considering that they constitute the

fastest growing groups in the country and the least represented in STEM education

and professions. In 2009–2010, 55 % of high schools with low Black and Latino

enrollment offered calculus; the figure was 29 % for schools with high enrollment

of those subgroups (Lewin 2012). Only about 20 % of African-American, Latino,

and Native American (AALANA) aspirants complete STEM degrees (Hrabowski

2011). In 2006, AALANA minorities constituted only 9 % of the nation’s science

and engineering labor force, while accounting for nearly 30 % of the population

(Hrabowski 2011).

With the heightened political and civic interest in Philadelphia around public

education in recent years, this city presents an excellent geographic example for

exploring the national issue of postsecondary STEM readiness among underrepre-

sented groups. Philadelphia Mayor Michael Nutter announced a goal of cutting the

rate of student dropout in half by 2014 (Gold et al. 2010a). While the School District

of Philadelphia’s on-time high school graduation rate has increased recently to 61 %

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(Socolar 2012), there are massive numbers of Black and Latino students who may

never engage in postsecondary experiences. Despite progress and higher expecta-

tions, in 2013 the district faced an unprecedented budget crisis that almost prevented

the on-time opening of schools. Philadelphia remains deeply challenged by a state

and local funding formula that falls short on supporting a high-quality educational

experience to all public school students.

Neighborhood Factors and Educational Outcomes

We already know that youth of color living in urban communities often attend low-

performing schools and have disproportionately low participation in postsecondary

STEM fields. As Schmidt et al. (2011) point out, ‘‘Few may actually question

whether learning opportunities are the same or equal across the United States, but

what many may not realize is the extent to which differences in students’ learning

opportunities are embedded in and are a function of the very structure of the U.S.

education system’’ (p. 400). Ainsworth (2002) found that not only do neighborhood

characteristics predict educational outcomes, but that the strength of the predictions

often rivals that of the more commonly cited family and school related factors.

Geographic context is an important unit of analysis for understanding school

performance. Research indicates that feeder patterns between middle and high

school impact students’ academic trajectories (Schiller 1999). In addition, ‘‘…a

student’s mathematics learning opportunities related to content coverage are deeply

affected by where the student lives…In those districts where a large percentage of

students are of low socioeconomic status the content coverage is typically less

demanding’’ (Schmidt et al. 2011, p. 423). In fact, Catsambis and Beveridge (2001)

found that being from a low-income neighborhood and attending a school with a

higher proportion of students receiving free or reduced price lunch were both

strongly associated with reduced math achievement. They thusly argue for

‘‘bringing neighborhoods in’’ to the research conversation on student achievement

and the socioeconomic factors impacting that achievement.

Research has shown that racial and economic segregation at the neighborhood

level impacts achievement considerably. What to do about the racial and economic

segregation of students and their families has been the topic of recurring debate for

more than half a century in this country (Massey and Denton 1993; Coleman 1966).

Mickelson et al. (2013) conducted the first ever meta-analysis of school racial

composition on K-12 math outcomes. They found that, ‘‘The emergence and

widening of the race gaps as students move through the grades suggest that the

association of racial segregation with mathematics performance compounds over

time’’ (p. 121). Neighborhood context often mediates parental involvement

(Catsambis and Beveridge 2001), and in some cases, school poverty level

(Ainsworth 2002) in math achievement. Where a student lives, and the resources

that he or she is exposed to by virtue of neighborhood location, can overcome

curricular disadvantages he or she may face in school (Ainsworth 2002). Other

researchers have found that neighborhood bonding social capital is positively linked

to math performance (Woolley et al. (2008). And as Owens (2010) points out, ‘‘If

neighborhoods still have a significant association with educational attainment,

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regardless of school composition, the efficacy of changing the school a student

attends without changing his or her neighborhood characteristics is questionable’’

(p. 288). This does not mean that school-level changes are unnecessary to combat

the effects of racial and economic segregation on school performance, but rather

that making these changes without considering the context in which they occur

considerably limits their impact.

Summary of the Literature

Students across the country have inequitable access to primary and secondary

school experiences that sufficiently prepare them for courses of study in STEM

fields. Despite awareness of this chronic problem, school policy has failed to

respond to the needs of historically disadvantaged school communities and improve

educational opportunities for students who are consistently underprepared. Previous

research has demonstrated that neighborhood factors, including poverty and racial

and economic segregation, influence school performance. However, little research

has explored how neighborhood factors may have variable impacts across schools

within the same district. In developing context for our study, we examine these

issues with regard to the Philadelphia region.

Philadelphia Geographic and Educational Context

Neighborhood demographics in Philadelphia have changed over several decades

due to multiple factors, including white flight to the suburbs, loss of certain

industries, the influx of Latino and Asian immigrants, the growth of the downtown

business district, the gentrification of downtown and nearby residential neighbor-

hoods, and the expansion of university properties, among other factors. In 2011, The

Pew Charitable Trusts (Pew) announced that the city had experienced an increase in

the overall population from 2000 to 2010, which was attributed to growth in the

Latino (46 %) and Asian (42 %) populations.

Poverty and a lack of economic opportunity have compounded demographic

changes across the city. Data collected for the 2010 census revealed that poverty

rose in Philadelphia County as well as surrounding counties between 2000 and 2010

(Lubrano 2012). In 2011, median household income ranked the second lowest in the

country among the 25 largest cities (Lubrano 2012). Unemployment and low-wage

jobs are contributing factors in the high levels of poverty across the city (Lubrano

2012; Von Bergen 2012). Perna (2013) notes that in metropolitan areas like

Philadelphia, there is a dramatic mismatch between people’s educational credentials

and the educational requirements of current and future jobs–and that the number of

unskilled workers far exceeds the number of unskilled jobs available. Notably, both

north and west Philadelphia, neighborhoods with relatively high concentrations of

poverty (Pew 2011), are also home to three of the city’s largest university systems:

Temple University, Drexel University, and the University of Pennsylvania. Chad

Womack of the Philadelphia STEM Innovation Center has pointed out that ‘‘in

Philadelphia, universities are islands of wealth and innovation surrounded by oceans

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of poverty and disconnected communities…’’ (2012, p. 44). This dichotomy is

highlighted by the economic and spatial tensions at work on the neighborhood level.

Philadelphia’s district-run high schools are sprawled across this dichotomous

landscape, operating within a controversial school choice system. The School

District of Philadelphia manages three types of traditional public high schools: non-

selective neighborhood, citywide, and special admission public schools. The non-

selective, neighborhood schools admit all students within their feeder pattern or

catchment area. They tend to have a reputation for being academically underper-

forming and unsafe, which factor into higher incidents of teacher turnover (Gold

et al. 2010a). During the 2007–2008 school year, most 8th graders participated in

the district’s high school application process, yet less than half (45 %) were

admitted to and enrolled in one of their schools of choice (Gold et al. 2010a). In

reference to selective schools, ‘‘White and Asian applicants were admitted and

enrolled at significantly higher rates (56 and 68 %, respectively) than Black (45 %)

and Latino (39 %) applicants’’ (Gold et al. 2010a, p. 24). About two-thirds of the

8th graders who participated in the application process attended their feeder high

school (Gold et al. 2010a). Black and Latino students were overrepresented at these

schools—as were students eligible for free or reduced price lunch. These structural

factors influence students’ educational opportunities and school performance across

Philadelphia. Necessarily, they frame our approach to and analysis of the data.

Data and Methods

School Performance Data

We examined 8th and 11th grade groups at neighborhood middle and high schools in

Philadelphia and their corresponding catchment areas. We excluded citywide and

special admission schools from our analyses because, unlike neighborhood schools,

their student populations are not limited to surrounding catchment areas. As the unit of

analysis in this study, the school catchment area constitutes a neighborhood boundary.

Elementary schools are contained in middle school catchments and middle schools are

contained in high school catchments. Catchment areas and school feeder patterns are

subject to change based on shifts in population size, school reconfigurations, and

closings. We acknowledge that by also excluding charter schools, which in recent

years have been rapidly expanding in Philadelphia, as well as private and parochial

schools, we limit the analyses to a small subset of educational institutions. However,

the specific focus of our work is to look at the neighborhood schools, which serve the

most vulnerable student populations in the city.

We drew on the Pennsylvania System of School Assessment (PSSA) data in math

from 2007 to 2008 (Grade 8) and 2010–2011 (Grade 11). Students in Pennsylvania

take the PSSA for reading and math in grades 3–8 and 11. We aimed to examine two

time periods overlapping with the important transition from middle to high school.

It is likely that, due to such factors as student mobility and dropout, the 11th grade

students differed from the 8th students; however we did not have this information

available to factor into our analyses.

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For student performance, we used data on the percentage of students with scores

in the ‘‘advanced’’ and ‘‘proficient’’ categories for math (Table 1). The comparison

between the 8th grade (Fig. 1) and 11th grade (Fig. 2) PSSA data indicates that

math performance declined dramatically during this time period and that most high

schools had an especially small percentage of 11th graders scoring at or above

proficiency in math. School performance also varied spatially at both the 8th and

11th grade levels (Figs. 3a, 4a).

High school data also include average SAT Reasoning Test (SAT) scores for

math and the percentage of students enrolled in Advanced Placement (AP) courses

during the 2010–2011 school year. High school students, mostly 11th and 12th

graders, complete the SAT as a requirement to apply for many colleges and

Table 1 Descriptive statistics for the variables

Variable Mean SD Minimum Maximum

8th grade performance data (n = 121)

#PSSATested 87.05 77.09 15.0 412.0

%MathProAdv 48.85 18.59 14.8 96.50

%ELL 5.16 7.08 0.00 35.29

%IEP 16.94 7.58 0.00 46.67

%EconDis 87.92 21.86 23.08 100.00

%NonWhAs 83.82 22.62 12.68 100.00

%HSDiplomaOnly 35.87 9.91 7.93 57.40

%BachelorsDegree 11.90 7.89 0.31 34.95

MedianHIncome 32,259.74 16,752.36 5,095.56 117,593.00

%Unemployed 7.72 2.95 1.71 15.23

%HiDensHouse 4.57 5.16 0.00 24.34

#ParkAcres 78.13 191.12 0.00 1,401.02

11th performance data (n = 25)

#PSSATested 193.00 129.90 56.00 588.00

%MathProAdv 22.50 13.50 7.20 63.20

#SATTested 109.52 82.2 0.00 403.00

SATMathAvgScore 344.52 77.61 0.00 452.00

%APEnrolled 11.59 8.63 0.00 32.40

%ELL 7.00 8.10 0.00 28.70

%IEP 13.40 7.50 1.10 28.60

%EconDis 87.10 19.10 37.50 100.0

%NonWhAs 87.70 18.00 45.10 100.0

%HSDiplomaonly 35.48 7.37 22.90 48.07

%BachelorsDegree 12.15 6.01 3.42 23.81

MedianHIncome 32,779.37 12,726.57 14,355.20 63,757.65

%Unemployed 8.39 2.14 4.96 12.65

%HiDensHouse 3.75 2.30 0.80 8.90

#ParkAcres 378.40 474.90 8.93 1,482.80

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universities, providing one indicator of college preparation. During 11th and/or 12th

grade is also typically the time when students enroll in AP courses as a way to gain

more preparation for college-level work and, in some cases, to earn college credits.

These high school variables serve to provide additional information about levels of

college readiness across the neighborhood high schools in Philadelphia. Although

the AP course participation variable is not exclusive to math courses and both

variables are not exclusive to 11th graders, these variables allowed us to link 11th

grade high school math performance to key indicators of college preparation.

Fig. 1 Histogram: 8th grade math, percentage of students scoring proficient or advanced by middleschool

Fig. 2 Histogram: 11th grade math, percentage of students scoring proficient or advanced by high school

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Demographic Data by School

We included demographic information on the students taking the PSSA exam: race/

ethnicity, English language learners (ELL), special education (students with an

individualized education plan, or IEP), and economically disadvantaged (eligible for

free or reduced price lunch). Race/ethnicity is closely related to socioeconomic

status, such that House and Williams (2000) suggest that race/ethnicity can predict

Fig. 3 a Percentage of 8th grade students scoring proficient or advanced in math by middle schoolcatchment area, 2007–2008; b Percentage of non-White and non-Asian students; c Percentage of specialeducation students; and d Percentage of economically disadvantaged students (by middle schoolcatchment area, displayed in quartiles)

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socioeconomic status. For race/ethnicity, we calculated a variable for the percentage

of students not White and not Asian. White and Asian-American students have

historically demonstrated higher academic achievement on standardized measures

as compared to Black and Latino students and are not underrepresented in STEM

fields. These variables based on grade group data align well with neighborhood

characteristics since most students attending neighborhood schools live in close

proximity to the school. These school demographic variables differ across

Philadelphia County (Figs. 3b–d, 4b–d).

Fig. 4 a Percentage of 11th grade students scoring proficient or advanced in math by high schoolcatchment area, 2010–2011; b Percentage of non-White and non-Asian students; c Percentage of specialeducation students; and d Percentage of economically disadvantaged students (by high school catchmentarea, displayed in quartiles)

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Neighborhood Data

To correspond with the school performance data, we used American Community

Survey (ACS) data 5-year estimates from roughly corresponding years, based on the

data availability (2005–2009 and 2007–2011). Based on our review of the literature

(Rothwell 2012; Schott Foundation 2012; Wilson 2009; Conley 1999), we used the

following variables from the ACS to explore neighborhood factors: median

household income, unemployment, and educational attainment (high school

diplomas and bachelor degrees for adults aged 25 and over) (Figs. 5, 6). Each

educational attainment variable represents the highest level of education achieved.

Fig. 5 Socio-economic variables from the 2005–2009 ACS, including median household income,unemployment, and educational attainment (by middle school catchment area, displayed in quartiles)

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Household income has been linked to educational outcomes in numerous studies

(Rothwell 2012; Duncan and Murnane 2011; Wilson 2009; Tate 2008; Eamon 2005;

Conley 1999; Yancey and Saporito 1995; Davis 1949). A recent study by the Schott

Foundation (2012) indicated that student performance is linked more closely to

location than student ability and that wealth contributes to inequalities in the

educational system. Indicators of educational attainment, i.e., the percentages of

residents holding a high school diploma or bachelor’s degree, and unemployment,

were also included based on previous research that suggests that absolute levels of

neighborhood resources, including the educational attainment of neighbors, can

Fig. 6 Socioeconomic variables from the 2007–2011 ACS, including median household income,unemployment, and educational attainment (by high school catchment area, displayed in quartiles)

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predict students’ educational performance (Owens 2010). A variable on housing

density was included based on Rothwell’s (2012) finding that cities with the most

anti-density or exclusionary zoning had the highest test scores. Restrictive zoning

that ensures low-density housing is linked to a house-cost gap, or economic

segregation, which is also associated with school test score gaps. The housing

density variable included the percentage of each school catchment area that was

covered by high-density housing. We also used park acreage as an additional

neighborhood characteristic, frequently associated with quality of life (Sampson

2012).

Spatial Joining and Apportioning

All data were aggregated to the school catchment boundaries. For instance, the

percentage of catchment area covered by high-density residential uses was

calculated after overlaying the catchment area and high-density residential area in

a geographic information system (GIS). The census data were aggregated to the

boundaries of the catchment areas using areal interpolation with mask area

weighting because the boundaries of the census tracts did not match those of the

catchment areas. Data from the census tracts were reallocated to the catchment area

boundaries. Areal interpolation weights geographical areas based on the proportion

of the area that falls within the catchment area. For instance, if a census tract

covered 50 % of a catchment area, that percentage was used to weight the

demographic variable. Areas that were known to be unoccupied, including water

bodies and parks, were removed from the analysis prior to the interpolation to

provide a more accurate representation of the distribution of socioeconomic

variables. The final sum is an area weighted average of all census tracts that

intersect a catchment area.

Correlation, Cluster Analysis, Ordinary Least Squares, and Geographically-

Weighted Regression

Quantitative and spatial analyses explored the relationship between school math

performance and neighborhood factors and the geographic distribution of these results to

detect patterns at two grade levels (8th and 11th) at two different points in time (2008 and

2011, respectively). We used Pearson’s r correlation analyses to detect linear

relationships between math performance and school catchment characteristics. Due to

the small sample size of 25, we did not conduct other statistical analyses using the high

school data. The relatively large size of high school catchment areas considerably

reduced our capability to examine local and global phenomena associated with PSSA

math performance. At the middle school level (n = 121) we utilized the Local Moran’s

I tool in ArcGIS 10.0 to examine spatial clustering of school performance based on an

inverse distance weighted conceptualization of the spatial relationship that weights

nearby features more heavily than features that are farther away from the target feature.

We used two regression models to examine the relationship between neighbor-

hood factors and school performance. Ordinary least squares (OLS) regression

(stepwise regression) was employed to understand the relationship between

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neighborhood factors and school math performance at the citywide scale.

Geographically-weighted regression (GWR) (Fotheringham et al. 2001) was

employed to allow for further examination of differences across Philadelphia at

the local level that may have been obscured by the global trends. GWR is a local

regression technique that allows for the examination of non-stationarity of variables

across a region. This approach has been increasingly used for a wide range of

applied studies (e.g., Pearsall and Christman 2012; Ogneva-Himmelberger et al.

2009; Brunsdon et al. 2001; Fotheringham et al. 2001) to provide a better fit to the

data when spatial patterns are hypothesized.

Results

Middle School Cluster Analysis, Correlation, and Regression (OLS and GWR)

Results

The results of the Local Moran’s I analysis indicate that school performance at the

8th grade level for math is not randomly distributed across Philadelphia (Fig. 7).

Three clusters of high-scoring catchment areas appear in three different regions in

Philadelphia, in the northwest, northeast, and central east. Clusters of low-scoring

catchment areas appear in north Philadelphia and parts of west Philadelphia. There

are also several isolated catchment areas that are high-scoring catchments

surrounded by low-scoring catchment areas. These areas are found in parts of

north Philadelphia and west Philadelphia. These spatial patterns suggest that scores

are linked to geographic phenomena at the neighborhood scale.

The following correlation analysis sheds some light on the relationship between

math performance and neighborhood factors. Ten of the fourteen school

demographic and neighborhood variables are significantly correlated with the

percentage of students scoring advanced or proficient in math at the 8th grade level

(Table 2). From the school demographic variables, %NonWhAs (Fig. 3b), %IEP

(Fig. 3c), and %EconDis (Fig. 3d) are strongly and significantly correlated to math

performance. Each of these variables is negatively correlated to math performance.

From the neighborhood variables, MedianHIncome, %Unemployed, %HSDiplo-

maOnly, and %BachelorsDegree are significantly correlated. MedianHIncome and

%BachelorsDegree are positively associated, while %Unemployed and %HSDiplo-

maOnly are negatively associated.

The results of the OLS regression indicate that the multiple regression model

explains just over 35 % of the variation in school math performance for 8th grade

(Table 3). The residuals were tested for spatial autocorrelation using Global

Moran’s I in ArcGIS 10.0. The results of the Moran’s I test indicate that the

residuals are randomly distributed. The results of the GWR model indicate minor

improvements in explanatory power and fit over the OLS model, with an adjusted r2

of 0.365. However, the purpose of this analysis is not solely to compare the two

regression models based on model fit.

The benefit of the local regression lies in the local coefficients that provide

insight into variations in the relationships among the dependent and independent

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variables across the region. For instance, in the global regression model results,

there is a single, fixed relationship between math performance and the independent

variables. In the local model, however, the results allow for the exploration of

variations in the strength of the relationships (Fig. 8). The fit of the model varies

dramatically across Philadelphia, and the coefficient maps each reveal spatial

variations in the strength of the relationship with the dependent variable. For

instance, r2 values vary from 0.329 to 0.441, with the stronger values occurring in

the northeast and northwest areas of Philadelphia. The use of GWR enables us to see

that the influence of median household income on school math performance is not

random across the region but linked to place-based phenomena.

High School Visual Analysis and Correlation Results

As explained above, the high school dataset included PSSA data and corresponding

catchment areas for 25 neighborhood high schools. In order to illuminate

neighborhood factors that may be related to postsecondary STEM readiness, in

Fig. 7 Clustering of math school performance at the 8th grade level, 2007–2008

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this section we (1) describe the results of a visual analysis of the math performance

of 11th grade students attending the high schools in 2011; (2) examine the

relationships between math performance and particular school catchment area

characteristics; and (3) examine the relationships between other indicators of

college readiness and school catchment characteristics.

In general, math performance at the high school level is poor across Philadelphia.

On average, less than 25 % of the 11th graders at neighborhood high schools scored

at proficiency in math in 2011 (See Table 1). Figure 4a shows that there are a few

regions in the county in which math performance appears to be clustered.

Contiguous school catchments in the same class (i.e., the same shade of gray) reveal

regions in which a school’s math performance is similar or related to the

Table 2 Correlations between

advanced/proficient math

variable and geographic and

school demographic variables

for 8th grade

* p \ 0.10; ** p \ 0.05;

*** p \ 0.01

Variable Correlation with advanced/

proficient math,

8th grade (n = 121)

School demographics

%ELL -0.05

%IEP -0.23*

%EconDis -0.28**

%Black -0.35**

%Latino -0.05

%White 0.49**

%Asian 0.30**

%NonWhAs -0.51**

Neighborhood variables

%HSDiplomaOnly -0.24**

%BachelorsDegree 0.39**

MedianHIncome 0.39**

%Unemployed -0.46**

%HiDensHouse -0.02

#ParkAcres 0.13

Table 3 Results of OLS and GWR regression models for 8th grade

Advanced/proficient math Unstandardized coefficients Standardized coefficients Significance

OLS—Intercept 101.77 0.00

OLS—%NonWhAs -0.35 -0.42 0.00

OLS—PercUnemp -1.81 0.50 0.00

OLS—%IEP -0.44 -0.18 0.02

OLS—ELL_Per -0.24 0.18 0.02

OLS—adj. R2 0.38 GWR—adj. R2 0.37

OLS—AICc 1,000.74 GWR—AICc 1,004.08

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performance of nearby schools. Typically, high school students living in the

northeast and southern regions of the city perform better than students living in

other areas. At those schools, up to 63 % of 11th graders scored proficient or

advanced in math. High school students living in the north central and western areas

of the city performed especially poorly as compared to students in the rest of the

city. In those areas, no more than 15 % of students attending the neighborhood high

school scored at proficient or above. Evident spatial autocorrelation of math

performance by high school catchment area suggests that student proficiency in

math was not evenly distributed across the county but rather influenced by school-

level and neighborhood factors, such as the socioeconomic characteristics of the

communities.

To further examine the relationships between math performance, a key indicator

of college readiness for courses of study in STEM, and school catchment area

characteristics, we use a correlation analysis. As shown in Table 4, six of the 14

variables are significantly correlated. The six significantly correlated variables are

from the school demographics category. The correlations between rates of

proficiency in math and catchment area characteristics differ in direction and

strength, the results of which align with our review of the literature.

The strongest correlations occur between math performance and the school

demographic variables, which are based on aggregated student-level data for

students taking the PSSA exam. School demographic variables with a significant

positive relationship to math performance include the percentages of ELL students,

White students, and Asian students. Student variables with a negative relationship to

math performance include the percentages of special education students (Fig. 4c),

Table 4 Correlations between

advanced/proficient math

variable and geographic and

school demographic variables

for 11th grade

* p \ 0.10; ** p \ 0.05;

*** p \ 0.01

Variable Correlation with advanced/

proficient math,

11th grade (n = 25)

School demographics

%ELL 0.65***

%IEP -0.51***

%EconDis -0.24

%Black -0.45**

%Latino -0.07

%White 0.54***

%Asian 0.79***

%NonWhAs -0.78***

Neighborhood variables

%HSDiplomaOnly -0.08

%BachelorsDegree 0.30

MedianHIncome 0.18

%Unemployed -0.24

%HiDensHouse -0.03

#ParkAcres 0.08

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economically disadvantaged students (Fig. 4d), Black students, and Latino students

taking the PSSA exam. Notably, as is the case for the 8th grade dataset, the variable

measuring the percentage of students taking the test who were non-White and non-

Asian (Fig. 4b) has a significant negative correlation to math performance (-0.776).

Figure 4b displays the high concentration of non-White and non-Asian students

in most of the neighborhood high schools. These schools also have high

concentrations of economically disadvantaged students, and the variable for

percentage of economically disadvantaged students is linked negatively, yet less

strongly, to math performance. This suggests that poverty is a weaker hindrance to

math performance for schools with majority White and Asian populations. There is

little variation among the high schools on these variables: just three catchment areas

in which the percentage of Black and Latino students was below 50 % and two

catchment areas in which the percentage of economically disadvantaged students

taking the test was below 50 %.

Neighborhood variables positively associated with math performance in the

catchment area include median household income, percentage of the population

with a bachelor’s degree as highest level of education, and the number of park acres

(Table 4). However, these variables are not significantly correlated and are weakly

associated with proficiency in math performance in the high school catchment area.

The spatial distributions for income and degree completion provided in Fig. 6a, d

show that socioeconomic status, based on median household income and

educational attainment, in the northeast, northwest, and southeast is higher than

in other regions of the city.

Neighborhood variables with negative associations to math performance include

percentage of the population aged 25 and older with a high school diploma only,

percentage unemployed, and high-density housing. Correlation results for high

school completion suggest that high school graduation rates among the catchment

area population are inversely related to proficiency in math at the high school level.

As indicated above, this was also the case with regard to the 8th grade dataset. As

shown in Fig. 6b, unemployment is higher in particular areas of the city, especially

in the north central region. There are noticeably lower rates of unemployment in the

southern, northwestern, and northeastern areas.

We found that other indicators of preparation for college entry, i.e., average SAT

scores for math and participation in Advanced Placement (AP) courses, were

strongly and significantly correlated (0.62; p \ 0.01 and 0.69; p \ 0.01, respec-

tively) with 11th grade PSSA math performance and correlated in similar ways to

school catchment area characteristics (Table 5). These results suggest that PSSA

math performance is related to college readiness in general. As provided in Table 1,

on average a mere 13.9 % of students in neighborhood high schools took AP

courses in 2011. In addition, the mean SAT score for math was 358.9 on a scale of

200–800. These educational outcomes do not evoke confidence in the ability of

neighborhood high schools in Philadelphia to provide college readiness opportu-

nities to the students they serve.

The variables measuring educational attainment—percentage of the population

earning a high school diploma only and percentage holding a bachelor’s degree—

were weakly, and contrarily, correlated with the two indicators of college readiness

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(Table 5). SAT performance and AP enrollment were negatively associated with

diploma earning while positively associated with bachelor’s degree attainment.

These results suggest that, individually, the educational attainment variables, based

on the small dataset of high school catchment area characteristics, are not useful in

assessing the influence of neighborhood factors on these indicators of college

readiness.

The results of the visual and correlation analyses of the high school catchment

data reveal patterns of spatial dependence in math proficiency across the city of

Philadelphia. Students attending school with high percentages of Black and Latino

classmates are especially vulnerable to graduating without the skills needed to excel

as a STEM major in college. While the neighborhood variables used in the

correlation analysis yielded weaker relationships to math performance than school-

level demographic characteristics, they too demonstrated spatial variation and

dependence.

Differences in Math Performance at the Middle School and High School Levels

Despite an upward trend in recent years, Philadelphia’s neighborhood middle and

high schools continue to perform poorly in math, typically with less than 50 % of

students at these schools scoring proficient or advanced.1 Further, proficiency in

math declines from neighborhood middle schools to high schools. In 2008, the 8th

grade mean percentage of students performing at the proficient or advanced level

was 48.9 %, with a standard deviation of approximately 19 percentage points. In

2011, 11th grade mean percentage was 22.5 %, with a standard deviation of 13.5. In

most catchments, there was a double-digit drop in the percentage of students scoring

proficient or advanced between the middle school and its feeder high school.

Proficiency levels increased in only 10 of 121 middle school cases (Fig. 9). In three

of those 10 cases, there was a double-digit percentage point increase in the rate of

proficiency from middle to high school, signifying meaningful growth. This

Table 5 Correlations between average SAT score and AP course enrollment for 11th grade

Variable SATMathAvgScore (n = 24) %APEnrolled (n = 23)

%MathProvAdv 0.62*** 0.69**

#ParkAcres 0.27 0.05

%HSDiplomaOnly -0.01 -0.20

%BachelorsDegree 0.25 0.24

MedianHIncome 0.45* 0.18

%Unemployed -0.25 -0.29

%HiDensHouse -0.06 -0.32

* p \ 0.10; ** p \ 0.05; *** p \ 0.01

1 http://www.portal.state.pa.us/portal/server.pt/community/school_assessments/7442.

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Fig. 8 Results of geographically-weighted regression (GWR) for 8th grade data, including local r2

values and local coefficients

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subsample of schools was found clustered in one high school catchment area in

south Philadelphia. The darkest shade of gray in Fig. 9 signifies the largest positive

differences in math performance from middle to high school.

The disproportionate number of middle school catchments versus high school

catchments over the same area makes it difficult to compare neighborhood and

school characteristics from 2008 to 2011. In addition, as indicated above, the use of

school-level data does not enable a cohort analysis. Nonetheless, Figs. 3b and 4b

show that the percentage of non-White and non-Asian students taking the PSSA

exam was quite similar at the middle and high schools during those years.

Neighborhood levels of bachelor’s degree attainment and median household income

were also comparable (See Figs. 5a and 6a, 5d and 6d, respectively). Overall,

correlation results were consistent from middle to high school (See Tables 2, 4), but

there was variation in the strength of the relationships between math performance

and the other variables. For example, the strength of the correlation with math

performance increased from the middle school level to high school level for the IEP

and Non-White/Non-Asian variables, yet it decreased for other variables. These

observations suggest that students often transition to high schools that are similar in

student composition to their middle schools and that neighborhood characteristics

Fig. 9 Middle school catchment area difference in the percentage of students scoring proficient oradvanced in math as compared to feeder high school performance

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tend to endure over time. Further analysis using student-level data from additional

years would be useful to see if these patterns persisted.

Discussion and Conclusion

Policy Implications

Numerous studies have provided evidence that neighborhood factors are linked to

school performance. Our intention was to examine the relationship of these

neighborhood factors with school math performance across space and at two

different testing periods to draw attention to the role of neighborhood factors in

math performance at the middle and high school levels in the city of Philadelphia.

As noted above, math proficiency is a major indicator of aptitude in STEM fields.

Our study has considerable policy implications and highlights the need for

additional research on broadening pathways to STEM pursuits.

To begin, math performance is related to neighborhood factors, such as

household income and educational attainment; however, our findings indicate that

these relationships vary spatially. For instance, the 8th grade geographically-

weighted regression results show that the strength and direction of the relationship

between math performance and median household income differ across Philadel-

phia. In some places the influence of income, a wealth indicator, on math

achievement seems to matter more than in other places. Because grouping together

high percentages of poor students of color in schools appears to be problematic for

student achievement in Philadelphia (Edmunds 2010), education policy should

strive to address the underlying causes of this pattern and do a better job of closing

the ‘‘opportunity gap’’ (Carter and Welner 2013). Policymakers should be wary of

imposing ‘‘one-size-fits-all’’ remedies. At the least, they should be context-oriented

and place-specific in order to generally enhance access to high-quality educational

opportunities and to broaden the participation of traditionally underrepresented

students in STEM fields.

Locational factors play a role in the academic trajectories of Philadelphia youth

and their STEM participation. An Executive Branch initiative includes policies to

recruit, support, retain, and reward 100,000 STEM educators in 10 years (PCAST

2012). A comprehensive approach would address how to transform low-performing

schools into places where educators want to work and students want to learn. In

addition, students and parents may not want to depend on neighborhood schools to

provide the type of math preparation required to excel in STEM. They may need to

look outside of the school to access services that will meet their needs. These may

include subsidized college preparation programs happening after school or during

the weekend. Accessing external resources involves a level of social capital that

may be lacking in many communities. In addition, the services may not be available,

which points to the need for cultivating public–private partnerships among the

school district, community-based organizations, local colleges and universities, and

other stakeholders.

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Areas for Future Research

While this research has drawn attention to the narrowing pathways to STEM among

public school students in Philadelphia, we have identified several ways in which the

study could be extended and elaborated to expand our understanding of neighbor-

hood effects on STEM readiness. First, we use publicly-available, aggregated data

to illuminate geographic phenomena linking academic performance to the

socioeconomic characteristics of school catchment areas. It is difficult to attribute

neighborhood factors to math performance, considering that there are numerous

factors at multiple levels that influence this academic outcome. We were not able to

control for those many factors, such as teacher effectiveness and students’ prior

performance in math. A more comprehensive research design would incorporate

student-level data on math performance and residence and include students

attending charter, special admission, private and parochial schools. That would

enable analyses of students’ math performance by controlling for neighborhood

variables (based on their residence), school variables (based on their middle schools

and high schools), and individual characteristics. It would also increase the number

of data points at the high school level, allowing for more robust analyses. Moreover,

additional measures of STEM readiness could be incorporated. Our study relies on

math performance as an indicator of postsecondary STEM readiness, but a

multidimensional approach to measuring STEM readiness would provide a more

complete and accurate assessment.

Our unequal samples sizes did not allow for direct comparison between the 8th

grade and 11th grade math performance. However, we found that math performance

declined from 8th grade to 11th grade in Philadelphia, or between 2008 and 2011,

calling for the need for further research on this transition. Expanding the sample size

and tracking students from 8th grade to 11th grade would allow for sufficient

representation of the transition and would allow us to better understand the changing

pathway to courses of study in STEM.

Our literature review and research suggest that the high school selection system

in Philadelphia further disenfranchises economically disadvantaged students and

youth of color aspiring to gain expertise in STEM-related fields that will enable

them to be highly-skilled, successful members of the American workforce. The

policy implications of this study are clear for broadening participation in STEM; the

structural barriers that are associated with academic achievement impede the ability

of a growing population of students to enter STEM fields. As acknowledged by

multiple scholars (NAS 2011), only a comprehensive approach that involves all

stakeholders and addresses all factors that impact these students’ success in STEM

will result in sustainable progress.

Acknowledgments Research for this paper was supported, in part, by the National Science Foundation

(NSF) through a grant (Award 1061028) to the Association of American Geographers (AAG). Any

opinions, findings, and conclusions or recommendations expressed in this material are those of the

author(s) and do not necessarily reflect the views of the NSF or the AAG. We would like to acknowledge

May Yuan, University of Oklahoma, for her generous guidance and feedback.

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