Change in Visible Impervious Surface Area in Southeastern Michigan Before and After the

“Great Recession”

Courtney Wilson Daniel G. Brown

Dedicated to the Memory of Courtney Wilson

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.

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

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.

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

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: http://clear.uconn.edu/projects/landscape/

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.

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.

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.

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).

Time periods

• American Recovery and Reinvestment Act (ARRA), ultimately, resulted in >$4B spending in the study area.

Pre-Recession PeriodPost-Recession

Period

ARRA

Methods

SubPixelAnalysis

Spatial Regression

Factor Analysis

Annual Compositing

Map DVISA, Aggregate to CT

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.

Annual Maximum Composite Image Example

Comparison with NLCDNLCD 2006 LandSat 2006 with Subpixel Analysis

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.

20092007 2008 2010 2011

pre-Recession

post-Recession

D VISA

Factor AnalysisDeprivation (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.

Spatial Regression Results

Model 1 = nullModel 2 = control onlyModel 3 = full

Model 1 Model 2 Model 3

Intercept 0.081 -0.909** -1.017**

Rurality 0.304** 0.308**

Deprivation -0.233**

Wealth/Education 0.157**

Ethnicity -0.229**

Families 0.004

Lambda 0.844** 0.782** 0.790**

AIC 1795.3 1689.8 1576.1

Model 1 Model 2 Model 3

Intercept 0.367* -0.262† -0.423**

Rurality 0.124** 0.186**

Deprivation -0.121**

Wealth/Education 0.075*

Ethnicity 0.050

Families -0.110**

Lambda 0.911** 0.884** 0.877**

AIC 1518.8 1496.0 1456.5

** - p<0.001, * - p<0.01, † - p<0.05

pre-Recession post-Recession

Spatial Autoregressive (SAR) models with queen connectivity and weights for tract area

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.

Difference in D VISA

Results for Difference in DVISA

Model 1 Model 2 Model 3

Intercept 0.050 0.141 0.154†

Rurality -0.035

Deprivation 0.279**

Wealth/Education -0.092*

Ethnicity 0.208**

Families -0.030

Lambda 0.726** 0.738** 0.747**

AIC 1996.6 1997.6 1927.6

** - p<0.001, * - p<0.01, † - p<0.05

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.

Pattern of ARRA Investments

http://www.recovery.gov/arra/Transparency/RecoveryData/Pages/RecipientReportedDataMap.aspx

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.

Thank Youand

Nicole SholtzDr. Amy Burnicki

Shannon Brines Steve Herskovitz Dr. Ken Sylvester

NSF and US Census Bureau

Paper in press, Population and Environment