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Abstract
We investigate spatial patterns of residential and nonresidential land use for 257 United States metropolitan areas in 1990 and 2000, measured with 14 empirical indices. We find that metropolitan areas became denser during the 1990s but developed in more sprawl-like patterns across all other dimensions, on average. By far, the largest changes in our land use metrics occurred in the realm of employment, which became more prevalent per unit of geographic area, but less spatially concentrated and farther from the historical urban core, on average. Our exploratory factor analyses reveal that four factors summarize land use patterns in both years, and remained relatively stable across the two years: intensity, compactness, mixing, and core-dominance. Mean factor scores vary by metropolitan population, water proximity, type, and Census region. Improved measurement of metropolitan land use patterns can facilitate policy and planning decisions intended to minimize the most egregious aspects of urban sprawl.
Notes
1. For other “sprawl” studies of the 1990s using residential metrics and conventional Census-derived boundaries, see Burchfield et al. (Citation2006), Lopez and Hynes (Citation2003), Theobald (Citation2001).
2. For a thorough discussion of the issue of the appropriate scale of a metropolitan region, see Dahmann and Fitzsimmons (Citation1999) and Adams, Van Drasek and Phillips (Citation1999).
3. Personal communication, Michael Ratcliffe, United States Census Bureau, September 21, 2009.
4. Because our grid cells are smaller than a square mile, we calculated a kernel density function for each grid cell that compiled housing units up to a mile in each direction, which smoothes the density surface to avoid small breaks. Thus, the EUA boundaries included a contiguous area surrounding the UA and adjacent cells meeting our selection criteria, and included detached areas surrounding the core but still meeting the selection criteria, such as for bedroom communities.
5. Worker location was coded by the Census Bureau for the respondent’s primary employment location, even if respondents had multiple jobs. Thus, while we use the shorthand “jobs” throughout the document, in reality the data are for workers that were surveyed by the Census Bureau and likely undercount total jobs in some locations.
6. We made several changes in how we operationalized measurement of metropolitan land use patterns compared to our prototype work with 50 EUAs (Cutsinger & Galster, Citation2006; Cutsinger et al., Citation2005; Wolman et al., Citation2005). Thus, readers should not compare land use indices computed as part of that earlier work with those reported here.
7. For complete descriptions and visual representations of each conceptual dimension, please see Cutsinger and Galster (Citation2006). See Appendix 3 for measurement equations.
8. For 1990, we used the UA boundaries that had been redefined using the same selection criteria used to define the 2000 UAs, allowing appropriate comparisons over time.
9. Our previous work identified ice, water, and wetlands as three classes of land cover that should be excluded as “undevelopable” land for the purposes of measuring land use patterns (Wolman et al., Citation2005). Here, we clipped the block group boundaries to its “developable” land area using data on surface water and wetlands from the United States Geological Survey (USGS), as of 2001. The surface water data layer includes oceans, bays, lakes, reservoirs, rivers, canals, streams, glaciers, and swamp or marsh areas.
10. This measure is equivalent to a Dissimilarity index often employed in segregation research.
11. Two areas did not have any nuclei that met the four standard deviation criteria in 1990, although they did have one employment nucleus each in 2000 (Dover, DE, and Grand Forks, ND). To retain these areas in our sample, we imputed a value of 1 for nuclearity in 1990.
12. Factors with eigenvalues less than one were retained only if the solution coincides closely with other criteria. Factors with eigenvalues before the first level occurs in the scree plot were retained. Generally, retained factors should account for at least 70% of the total variability. Finally, the reproduced correlations compared to the observed correlations should only have a small percentage of residuals greater than the absolute value of 0.05 to be selected for the most appropriate solution.
13. These findings were confirmed using ANOVA tests using the Scheffe adjustment for groups of unequal variance. Only statistically significant results are reported. The results are presented for the year 2000, although similar patterns are evident for both years; detailed results are available from the authors.
14. The density and exposure values were significantly and positively skewed across the entire sample in both 1990 and 2000. A log transformation was performed on these four indices.
15. The large change in mean job centrality may be a result of missing job data for 1990 in some outer counties of some EUAs, which may be unduly influencing job centrality scores. Even so, the changes in job centrality among the ones with complete job data exhibit similar declining trends in centrality, suggesting that the finding is not entirely the result of missing job data.
16. We also experimented with factor analysis of the absolute change in indices for 1990–2000. We found that the results were difficult to interpret as the units are different across the indices and change depend on starting values. We explore other ways to analyze dynamics and drivers of land use change in an upcoming paper.
17. After comparing our factor scores to the sprawl scores reported by Ewing et al. (Citation2002) for 81 metropolitan areas with reported data from both data sets, we find that our compactness factor is moderately correlated with their centeredness factor (r = 0.55); our intensity factor is moderately correlated with their street connectivity and mixed-use factors (r = 0.54 and r = 0.56, respectively), and with their mixed-use factor (r = 0.36); our mixing factor is well correlated with their residential density factor (r = 0.77) and street connectivity factor (r = 0.51), less well correlated with their mixed-use factor (r = 0.37) and with their centeredness factor (r = 0.29); and that our core-dominance factor is modestly correlated with their centeredness and mixed-use factors (r = 0.34 and r = 0.30, respectively). Thus, it is clear that our factors are measuring quite different things than theirs, even when similar labels might imply that we are measuring the same underlying dimension of land use.