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Geospatial Analysis in Higher Education Research

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Higher Education: Handbook of Theory and Research

Part of the book series: Higher Education: Handbook of Theory and Research ((HATR,volume 32))

Abstract

Geography offers a useful but under-utilized lens to examine a number of topics within the field of higher education. This chapter presents examples where geospatial analysis is applied to higher education contexts, and the chapter’s goal is to encourage researchers to expand, extend, and critique how geography can be more useful to the field. Through examples and illustrations, it introduces readers to a wide range of techniques for conducting geospatial analysis including descriptive maps, geostatistics, and distance elasticity. It also highlights how geography can be useful in designing quasi-experimental studies and for building upon theories of college choice. The chapter discusses a number of georeferenced data sources that can be merged with existing higher education databases to integrate geography more systematically into higher education research. It concludes with reflections on how the field of higher education can continue to incorporate geography and geospatial analysis into its scholarship. Doing so can generate new knowledge about the causes and consequences of educational inequality, while also developing new theories and lines of inquiry that have not yet been fully explored.

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    Nicholas W. Hillman

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    Michael B. Paulsen

Appendix A

Appendix A

Stata commands to create maps found in this chapter.

// Download shape files to working directory from the following Census website https://www.census.gov/geo/maps -data/data/cbf/cbf_counties.html

// Install geospatial commands: ssc install spmap; ssc install shp2dta

// Set working directory cd “C:\Users\Maps”

// Convert shape file to Stata using shp2dta command shp2dta using cb_2015_us_county_500k, data(US_data) coor(US_coordinates) genid(id) use US_data.dta, clear destring GEOID STATEFP, replace

// Merge additional county-level data merge 1:1 GEOID using “C:\Users\Maps\ed_attain.dta” drop _merge replace hs_less = hs_less/100 format %5.0g hs_less

merge 1:1 GEOID using “C:\Users\Maps\county_center.dta” drop _merge

// Creating “Michiana” dummy variable to shade state map gen michiana = 0 replace michiana = 1 if inlist(GEOID,18039,18141,18099,26021,26027) label define michiana_label 0 “Not Michiana” 1 “Michiana” label values michiana michiana_label

// Base map of Michigan and Indiana with “Michiana” coded (Fig. 11.1) spmap michiana if (STATEFP==18|STATEFP==26) using US_coordinates.dta, id(id) clnumber(2) legenda(off) fcolor(white gray)

// County map of Michiana with point location of colleges (Fig. 11.1) spmap if michiana==1 using US_coordinates.dta, id(id) clnumber(3) legstyle(2) point(data(“IPEDS.dta”)select(keep if inlist(GEOID,18039,18141,18099,26021,26027)) x(lon) y(lat) by(sector2) fcolor(white gray black) ocolor(black ..) size(*0.8) legenda(on) legcount) label(x(intptlong)y(intptlat) label(NAME)select(keep if inlist(GEOID,18039,18141,18099,26021,26027)))

// County map of Michigan with educational attainment (Fig. 11.2) spmap hs_less if michiana==1 using US_coordinates.dta, id(id) clnumber(3) legstyle(2) label(x(intptlong)y(intptlat) label(NAME) select(keep if michiana==1)) legtitle(“Pct H.S. diploma or less”)

// County map of cross-border commuting zones (Fig. 11.4). Not shown are steps to identify cross-border commuting zones and the variable “singlestate,” which are available upon request. spmap singlestate if (state~=“AK” & state~=“HI”) using US_coordinates.dta, fcolor(white gray) id(id) clnumber(3) legenda(off)

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Hillman, N.W. (2017). Geospatial Analysis in Higher Education Research. In: Paulsen, M. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-48983-4_11

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