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  1. Change in Visible Impervious Surface Area in Southeastern Michigan Before and After the “Great Recession” Courtney Wilson Daniel G. Brown

  2. Dedicated to the Memory of Courtney Wilson

  3. Michigan NCRN Node • The Michigan node aims to improve survey measurements of economic and demographic data and potentially supplement or replace surveys with statistics based on administrative, Web-based, and geospatial data.  • Objective 4 focuses on developing techniques to use geospatial data to improve estimates of population and migration for small geographic areas and small demographic groups.

  4. Outline • Background • Urban Remote Sensing • Spatial differentiation in urban land-cover change • Study area • Methods • Sub-pixel mapping, compositing and change • Statistical analysis • Results • Conclusions and Implications

  5. Landsat Program • Civilian satellite programs date to 1972 launch of ERTS-1 satellite (aka Landsat 1). • Landsat 8 launched 2013 • Moderate resolution (30 m) multi-spectral images collected every 16 days globally • Finer and coarser resolution images collected under different programs (MODIS, commercial) • Combination of global acquisition strategy and free access (beginning 2009) of all processed images has expanded potential applications.

  6. Ridd’s (1995) V-I-S Model of Urban Land Cover

  7. Sub-Pixel Composition and VISA • Fine scale of urban land covers means that any given pixel includes a mixture. • Semi-automated methods permit estimating composition. • The visible amount of impervious surface area (VISA) from satellites during growing season is affected by both ISA and any obstructing vegetation canopy. Image source:

  8. Spatial Differentiation in Urban Change • Rates of urban growth and change are spatially differentiated and associated the social and economic characteristics of areas. • Understanding how these patterns are reflected in spatial differences in rates of land-cover change requires combining remote sensing over time with social data. • Has implications for the if and how remote sensing can support surveys, e.g., through identification of hard-to-count areas.

  9. Research questions • We were interested in • If and how rates of land-cover change were associated with community socioeconomic characteristics at the census tract level in Southeastern Michigan, and • How patterns of association were affected by the “Great Recession.” • Naïvely, we might think the GR had uniformly negative effects on indicators of development (like VISA), with no effect on spatial patterns. • Methodologically, we are interested in the potential for urban land-cover change information to support social science and survey.

  10. Hypotheses • We might expect to see greater indications of development (more rapid increase in VISA) in areas with higher SES. • Differences between high and low SES areas may be diminished. • Just as high-growth Sunbelt cities were particularly hard hit by the Great Recession, areas with populations of higher socioeconomic status within Southeastern Michigan might also have experienced sharper declines in economic activity, and therefore land-cover changes related to development. • Additionally, these same areas may have less investment through public-sector investment associated with the ARRA.

  11. Study Area • The region had low overall growth prior to the recession but a high degree of internal heterogeneity and social segregation. • Consists of 769 census tracts 2010. • Census Tracts with darker tones have more urban land covers (NLCD).

  12. Time periods Pre-Recession Period Post-Recession Period ARRA • American Recovery and Reinvestment Act (ARRA), ultimately, resulted in >$4B spending in the study area.

  13. Methods SubPixel Analysis Map DVISA, Aggregate to CT Spatial Regression Annual Compositing Factor Analysis

  14. Image Processing • Selected 102 images between years 2001 and 2011 with minimal cloud and snow cover. • Applied atmospheric correction. • Identify areas on high-resolution imagery with light, medium, medium-brown, and dark impervious surfaces each year to identify spectral signatures. • Estimate sub-pixel fractions for each material using a non-parametric supervised subpixel spectral classification approach in ERDAS Imagine.

  15. Annual Maximum Composite Image Example

  16. Comparison with NLCD NLCD 2006 LandSat 2006 with Subpixel Analysis

  17. D Visible Impervious Surface Area(DVISA) • Using annual composites of VISA we calculated the average rate of change (DVISA) for the five years before and during/after the GR. • Calculated as the best-fit line through the 5-year time series for each pixel. • Averaged DVISA for all pixels in census tracts. 2009 2007 2008 2010 2011

  18. D VISA pre-Recession post-Recession

  19. Factor Analysis • Deprivation(20 percent of the variation) • + % black, unemployed, below the national poverty line, with a high school education or less, and low income (<$25,000), %homes vacant • - % white, married, and had a high income (>$75,000). • Rurality(13 to 14 percent of the variation) - control • + %agriculture, forest, wetlands, shrub, and grass, and size of the census tract • - %developed • Wealth/Education (7 to 8 percent of the variation) • + %completing a bachelor’s degree or higher, %high income (>$75,000), %living in houses valued at greater than $300,000, and %Asian. • - % with a high school degree or less • Ethnicity(7 percent of variation) • + % linguistically isolated and %Hispanic. • - % black and %English-only speaking households • Families(6 to 7 percent of variation) • + % married, living in single-family dwellings, and under age 18 • - % homes that were rental units.

  20. Spatial Regression Results pre-Recession • post-Recession Model 1 = null Model 2 = control only Model 3 = full ** - p<0.001, * - p<0.01, † - p<0.05 Spatial Autoregressive (SAR) models with queen connectivity and weights for tract area

  21. Interpretation 1 • Results support hypothesis that, controlling for a measure of land availability (rurality), VISA was more likely to increase in areas with less deprivation and more wealth and education. • These relationships were consistent both before and after the GR.

  22. Difference in D VISA

  23. Results for Difference in DVISA ** - p<0.001, * - p<0.01, † - p<0.05

  24. Interpretation 2 • Results support the hypothesis that the GR reduced spatial differences. • Deprivation was associated with increases in slope (DVISA) and Wealth and Education with decreases. • Decreases in private-sector investment in wealthy, less-deprived areas a likely cause, but cannot rule out possible effects of public-sector investment in less wealthy, more deprived areas.

  25. Pattern of ARRA Investments

  26. Conclusions • Temporally detailed information on urban land-cover changes can be meaningfully related socioeconomic variability. • The Great Recession reduced observed spatial differences in rates of land-cover change. • Similar measures of land-cover change could be useful supplement to surveys, like ACS, to identify hard-to-count (e.g., rapidly changing) areas.

  27. Thank You and Nicole Sholtz Dr. Amy Burnicki Shannon Brines Steve Herskovitz Dr. Ken Sylvester NSF and US Census Bureau Paper in press, Population and Environment