Academic journal article Cityscape

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

Academic journal article Cityscape

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

Article excerpt

The views expressed in this article are those of the author and do not represent the official positions or policies of the Office of Policy Development and Research or the U.S. Department of Housing and Urban Development.

(ProQuest: ... denotes formulae omitted.)

Foreclosures and Crime

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 trated 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.

Conflicting results are common in social science research from the use of different geographic units of analysis (Coulton et al., 2001; Hipp, 2007; Macintyre, Ellaway, and Cummins, 2002; Rengert and Lockwood, 2009; Taylor, 2012). None of the cited studies, though, included tests of the foreclosure and crime relationship with multiple geographic units to gauge the effect on results. I illustrate in this article how changing geographic units can produce converse results with an example of foreclosure and crime modeling drawn from Wilson and Behlendorf (2013). I also conduct a spatial analysis to identify which geographic unit is best for modeling foreclosures and crime in the Wilson and Behlendorf (2013) example, using several spatial analysis techniques.

Modeling Foreclosures and Neighborhood Crime in Charlotte and Mecklenburg County

Wilson and Behlendorf (2013) analyzed the relationship between foreclosures and neighborhood crime in the city of Charlotte and Mecklenburg County, North Carolina, with four crime constructs1 for the years 2006 and 2007. Point-level crime2 and single-family foreclosure3 locations were aggregated to census block group and tract geographies to compare results. Several demographic, economic, and environmental variables were included to form a set of explanatory factors (concentrated disadvantage, neighborhood quality, residential stability, and immigration concentration) known to be associated with neighborhood crime. The spatial proximity of other foreclosures and the temporal occurrence of crime were also included as controls to account for concurrent events in nearby places and time that may have an influence on the outcome. A negative binomial regression count model was used:

...(1)

The central finding from Wilson and Behlendorf (2013) was that the rate of foreclosures had a positive and significant association with crime increases in 2006 and 2007, but results differed between geographic units. The full output for the two geographies is shown in exhibits 1 (tracts) and 2 (block groups), but I focus on the residential instability factor and the spatial lag variable for the remainder of this analysis.

The residential stability coefficients changed dramatically between tracts and block groups. Residential stability represents the level of social connections between neighborhood residents. Stable neighborhoods have a constancy of residents who remain in their homes over long periods of time and they know, trust, like, and communicate with their neighbors. …

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