courtney wilson daniel g. brown
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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. - PowerPoint PPT PresentationTRANSCRIPT
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