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


This paper explores geographical factors associated with the postsecondary 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 correlated 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 economically disadvantaged communities.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.


  1. Ainsworth, J. W. (2002). Why does it take a village? The mediation of neighborhood effects on educational achievement. Social Forces, 81(1), 117–152.

    Article  Google Scholar 

  2. Alspaugh, J. W. (1998). Achievement loss associated with the transition to middle school and high school. The Journal of Educational Research, 92(1), 20–25.

    Article  Google Scholar 

  3. Brunsdon, C., McClatchey, J., & Unwin, D. J. (2001). Spatial variations in the average rainfall-altitude relationship in Great Britain: An approach using geographically weighted regression. International Journal of Climatology, 21, 455–466.

    Article  Google Scholar 

  4. Carter, P. L., & Welner, K. G. (Eds.). (2013). Closing the opportunity gap: What America must do to give every child an even chance. New York, NY: Oxford University Press.

    Google Scholar 

  5. Catsambis, S., & Beveridge, A. A. (2001). Does neighborhood matter? Family, neighborhood, and school influences on eighth-grade mathematics achievement. Sociological Focus, 34(4), 435–457.

    Article  Google Scholar 

  6. Coleman, J. S. (1966). Equality of educational opportunity. Washington, DC: National Center for Educational Statistics (DHEW/OE). Retrieved from ERIC database.

  7. Conley, D. (1999). Being black, living in the red: Race, wealth, and social policy in America. Berkeley, CA: University of California Press.

    Google Scholar 

  8. Davis, A. (1949). Social-class influences upon learning. Cambridge, MA: Harvard University Press.

    Google Scholar 

  9. Duncan, G. J., & Murnane, R. J. (Eds.). (2011). Whither opportunity? Rising inequality, schools, and children’s life chances. New York, NY: Russell Sage Foundation.

    Google Scholar 

  10. Eamon, M. K. (2005). Social-demographic, school, neighborhood, and parenting influences on the academic achievement of Latino young adolescents. Journal of Youth and Adolescence, 34(2), 163–174.

    Article  Google Scholar 

  11. Edmunds, K. (2010). Looking from the outside in: A spatial analysis of students’ neighborhood characteristics and school performance in Philadelphia. In Paper presented at the ESRI International Education User Conference, San Diego, CA, July 2010.

  12. Fotheringham, A. S., Charlton, M., & Brunsdon, C. (2001). Spatial variations in school performance: A local analysis using geographically weighted regression. Geographical & Environmental Modelling, 5(1), 43–66.

    Article  Google Scholar 

  13. Gardner, D. P. (1983). A nation at risk: The imperative for educational reform. An open letter to the American people. A Report to the Nation and the Secretary of Education. Washington, DC: National Commission on Excellence in Education. Retrieved from ERIC database.

  14. Gold, E., Evans, S., Haxton, C., Maluk, H., Mitchell, C., Simon, E., et al. (2010a). Context, conditions, and consequences: Freshman year transition in Philadelphia. Philadelphia: Research for Action.

    Google Scholar 

  15. Gold, E., Evans, S., Haxton, C., Maluk, H., Mitchell, C., Simon, E., et al. (2010b). Transition to high school: School ‘choice’ and freshman year in Philadelphia. Philadelphia: Research for Action.

    Google Scholar 

  16. House, J. S., & Williams, D. R. (2000). Understanding and reducing socioeconomic and racial/ethnic disparities in health. In B. D. Smedley & S. L. Syme (Eds.), Promoting health: Intervention strategies from social and behavioral research (pp. 81–125). Washington, DC: National Academy Press.

    Google Scholar 

  17. Hrabowski, F. A. (2011). Editorial: Boosting minorities in science. Science, 331, 125.

    Article  Google Scholar 

  18. Lewin, T. (2012). Black students face more discipline, data suggests. The New York Times. Retrieved from

  19. Lubrano, A. (2012). Poverty rises in Phila., suburbs, census study finds. Philadelphia Inquirer. Retrieved from

  20. Massey, D., & Denton, N. (1993). American apartheid: Segregation and the making of the underclass. Boston, MA: Harvard University Press.

    Google Scholar 

  21. Maxwell, L. A. (2012). Growing gaps bring focus on poverty’s role in schooling. Education Week. Retrieved from

  22. McIntosh, K., Flannery, K. B., Sugai, G., Braun, D. H., & Cochrane, K. L. (2008). Relationships between academics and problem behavior in the transition from middle school to high school. Journal of Positive Behavior Interventions, 10(4), 243–255.

    Article  Google Scholar 

  23. Mickelson, R. A., Bottia, M. C., & Lambert, R. (2013). Effects of school racial composition on K-12 mathematics outcomes: A metaregression analysis. Review of Educational Research, 83(1), 121–158.

    Article  Google Scholar 

  24. National Academy of Sciences (2011). Expanding underrepresented minority participation: America’s science and technology talent at the crossroads. Washington, DC: National Academies Press. Summary retrieved from

  25. Ogneva-Himmelberger, Y., Pearsall, H., & Rakshit, R. (2009). Concrete evidence & geographically weighted regression: A regional analysis of wealth and the land cover in Massachusetts. Applied Geography, 29(4), 478–487.

    Article  Google Scholar 

  26. Owens, A. (2010). Neighborhoods and schools as competing and reinforcing contexts for educational attainment. Sociology of Education, 83(4), 287–311.

    Article  Google Scholar 

  27. Pearsall, H., & Christman, Z. (2012). Tree-lined lanes or vacant lots? Evaluating non-stationarity between urban greenness and socio-economic conditions in Philadelphia, Pennsylvania, USA at multiple scales. Applied Geography, 35(1), 257–264.

    Article  Google Scholar 

  28. Perna, L. W. (Ed.). (2013). Preparing today’s students for tomorrow’s jobs in metropolitan America. Philadelphia: University of Pennsylvania Press.

    Google Scholar 

  29. President’s Council of Advisors on Science and Technology. (2012). Summary. Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Retrieved from

  30. Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. Murnane (Eds.), Whither opportunity? Rising inequality, schools and children’s life chances (pp. 91–116). New York, NY: Russell Sage Foundation.

    Google Scholar 

  31. Rothwell, J. (2012). Housing costs, zoning, and access to high-scoring schools. Washington, DC: Brookings Institution.

    Google Scholar 

  32. Sampson, R. (2012). Great American city: Chicago and the enduring neighborhood effect. Chicago: The University of Chicago Press.

    Google Scholar 

  33. Schiller, K. (1999). Effects of feeder patterns on students’ transition to high school. Sociology of Education, 72(4), 216–233.

    Article  Google Scholar 

  34. Schmidt, W., Cogan, L., Houang, R., & McKnight, C. (2011). Content coverage differences across districts/states: A persisting challenge for U.S. American Journal of Education, 117(3), 399–427.

    Article  Google Scholar 

  35. Schott Foundation for Public Education. (2012). A rotting apple: Education redlining in New York City. Executive summary. Cambridge, MA: Schott Foundation for Public Education.

  36. Socolar, P. (2012). District on-time graduation rate surpasses 60 percent. The Public School Notebook, 19(4). Retrieved from Figure is the percentage of students who entered 9th grade in fall 2007 and finished high school by 2011.

  37. Tate, W. F. (2008). Geography of opportunity: Poverty, place, and educational outcomes. Educational Researcher, 37, 397–411.

    Article  Google Scholar 

  38. Tavernise, S. (2012). Education gap grows between rich and poor, studies say. The New York Times. Retrieved from

  39. The Pew Charitable Trusts. (2011). Philadelphia 2011: The state of the city. Philadelphia: The pew charitable trusts. Retrieved from

  40. Von Bergen, J. (2012). Where are jobs for young people? Philadelphia Inquirer. Retrieved from

  41. Wilson, W. J. (2009). More than just race: Being Black and poor in the inner city. New York, NY: W. W. Norton & Co.

    Google Scholar 

  42. Womack, C. (2012). PowerPoint presented during the Philadelphia STEM Stakeholder Summit, June 20, 2012, School District of Philadelphia.

  43. Woolley, M. E., Grogan-Kaylor, A., Gilster, M. E., Karb, R. A., Gant, L. M., Reischl, T. M., et al. (2008). Neighborhood social capital, poor physical conditions, and school achievement. Children & Schools, 30(3), 133–145.

    Article  Google Scholar 

  44. Yancey, W. L., & Saporito, S. J. (1995). Racial and economic segregation and educational outcomes: One tale, two cities. In Paper originally presented at the Conference of the National Center on Education in the Inner Cities, Philadelphia, PA, October 1994. Retrieved from ERIC database.

Download references


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.

Author information



Corresponding author

Correspondence to Kimberly A. Edmunds.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Edmunds, K.A., Pearsall, H. & Porterfield, L.K. Narrowing Pathways? Exploring the Spatial Dynamics of Postsecondary STEM Preparation in Philadelphia, Pennsylvania. Urban Rev 47, 1–25 (2015).

Download citation


  • Neighborhood factors
  • STEM
  • High schools
  • Urban schools