Changing Geographic Units and the Analytical Consequences: An Example of Simpson's Paradox

Ron Wilson
U.S. Department of Housing and Urban Development
University of Maryland, Baltimore County

SpAM
SpAM (Spatial Analysis and Methods) presents short articles on the use of spatial statistical techniques for housing or urban development research. Through this department of Cityscape, the Office of Policy Development and Research introduces readers to the use of emerging spatial data analysis methods or techniques for measuring geographic relationships in research data. Researchers increasingly use these new techniques to enhance their understanding of urban patterns but often do not have access to short demonstration articles for applied guidance. If you have an idea for an article of no more than 3,000 words presenting an applied spatial data analysis method or technique, please send a one-paragraph abstract to ronald.e.wilson@hud.gov for review.

The rapidly degrading housing market of the mid-2000s caused local governments to be concerned about the multitude of problems foreclosures could wreak on their jurisdictions (Wilson and Paulsen, 2008). One concern was the escalation of crime and disorder in neighborhoods with concentrated foreclosures. Several researchers who examined the relationship between foreclosure and crime had conflicting results (Arnio and Baumer, 2012; Arnio, Baumer, and Wolff, 2012; Baumer, Wolff, and Arnio, 2012; Cui, 2010; Ellen, Lacoe, and Sharygin, 2011; Goodstein and Lee, 2010; Immergluck and Smith, 2006; Jones and Pridemore, 2012; Katz, Wallace, and Hedberg, 2011; Kirk and Hyra, 2012; Stucky, Ottensmann, and Payton, 2012; Wallace, Hedberg, and Katz, 2012). The assortment of geographic units used in these studies is extensive, consisting of property locations, block faces, census block groups, census tracts, customized local geographies, grid cells, cities, counties, and metropolitan statistical areas. The variety of factors, constructs, and variables the researchers used in these studies certainly contributed to their conflicting results, but the range of geographies likely played a role in the outcome differences, because the underlying data were aggregated to different geographic scales.