1. Quantitatively Estimating the
Population Segregation in Buffalo, NY
from Remote Sensing Perspective
Chenyang Wei, Le Wang
Department of Geography
University at Buffalo
Presented at AAG Annual Meeting 2016
San Francisco, CA ~ March 30, 2016

2. Introduction
Extreme population segregation could cause some very serious
social problems.
According to the census data, the Buffalo metropolitan area,
New York was ranked the nation’s 12th most segregated
region in 1980, 10th in 1990, and 8th in 2000.
Population segregation gradually formed in Buffalo, NY after
1940, while the recent censuses show that the segregation
degree there declined to some extent from 1990 to 2000.
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3. Socio-economic
Factors
What happened after that?
Population
Segregation
Racial
Discrimination
Own-race
Preference
Economic
Difference
Physical
Environmental
Characteristics
Census Data
Remote Sensing
Approach

4. Objectives
1. To quantify the change of population segregation in
Buffalo, NY from 2000 to 2010 based on the census
data.
2. To estimate the level of population segregation in
Buffalo, NY from the remote sensing perspective.
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5. QUANTIFICATION
Part A:

6. Boundary
Adjustment
on
Block Groups
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7. Quantification
Local segregation index in block group 𝑖 (Wong, 1996):
𝐿𝑆𝐼𝑖 =
𝑛
2
𝑎𝑖
𝐴
−
𝑤𝑖
𝑊
𝑎𝑖: African-American population in block group 𝑖;
𝑤𝑖: White population in block group 𝑖;
𝐴: total population of African Americans in Buffalo;
𝑊: total population of Whites in Buffalo;
𝑛: total number of block groups in Buffalo.
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(Scale: from −
𝑛
2
to +
𝑛
2
)

9. Quantification
Local segregation change in block group 𝑖:
𝐿𝑆𝐶𝑖 = 𝐿𝑆𝐼𝑖,10 − 𝐿𝑆𝐼𝑖, 00
𝐿𝑆𝐼𝑖,10: local segregation index in block group 𝑖 of 2010;
𝐿𝑆𝐼𝑖,00: local segregation index in block group 𝑖 of 2000.
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10. Local
Segregation
Change
10
LSC < 0:
54.42%.

11. Population
Change
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12. ESTIMATION
Part B:

13. Estimation
Data:
Landsat-5 TM imagery (for free, temporal resolution: 16
days, spatial resolution: 30 meters).
Problems:
1. Can we acquire more detailed information within each
pixel?
2. Which remote sensing factors (RSFs) should we choose?
3. How to establish the spatial model at local level?
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14. Methods
1. Spectral Mixture Analysis
To describe the surface composition in each pixel of an image using several
endmembers (Wu & Murray, 2003). Each endmember represents a pure land
cover type.
2. Vegetation-Impervious Surface-Soil (V-I-S) Model
To parameterize biophysical composition of urban environments from both
perspectives of physical geography and human geography (Ridd, 1995).
3. Geographically Weighted Regression (GWR)
To expand standard regression for use with spatial data. It allows the
regression coefficients to vary across space at local level (Yu & Wu, 2004).
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15. 15
Image 1
(09/06/2000)
Image 2
(08/01/2010)
RSFs in 2000 RSFs in 2010
GWR Model
Estimated LSI in 2010
LSI in 2000 𝐿𝑆𝐼𝑖,00
= 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡𝑖,00 + 𝐶𝑜𝑒𝑓𝐼𝑖,00 × 𝐼𝑖,00
+ 𝐶𝑜𝑒𝑓𝑆𝑖,00 × 𝑆𝑖,00 + 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑖,00
𝐿𝑆𝐼𝑖,10
= 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡𝑖,00 + 𝐶𝑜𝑒𝑓𝐼𝑖,00 × 𝐼𝑖,10
+ 𝐶𝑜𝑒𝑓𝑆𝑖,00 × 𝑆𝑖,10 + 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑖,00

17. Accuracy Assessment
Estimation error in block group 𝑖:
𝑒𝑖 =
𝐸𝐿𝑆𝐼𝑖 − 𝐿𝑆𝐼𝑖
𝐿𝑆𝐼𝑖
𝐸𝐿𝑆𝐼𝑖: estimated local segregation index in block group 𝑖;
𝐿𝑆𝐼𝑖: actual local segregation index in block group 𝑖.
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18. Accuracy
Assessment
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ei<0.5: 66.08%;
RMSE = 0.3024.

19. Conclusions
1. In over 54% block groups of Buffalo, NY, the
population segregation level decreases in some
degree from 2000 to 2010.
2. The population segregation in Buffalo, NY could be
estimated by the remote sensing approach in
combination with the census data. The RMSE of
estimation is approximately 0.30 in 2010. In around
66% area, the estimation error is below 0.5.
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20. References
Ridd, M. K. (1995). Exploring a V-I-S (vegetation-impervious surface-soil) model
for urban ecosystem analysis through remote sensing: comparative anatomy
for cities. International Journal of Remote Sensing, 16, 2165-2185.
Wong, D. (1996). Enhancing segregation studies using GIS. Computers,
Environment, and Urban Systems, 20, 99-109.
Wu, C., & Murray, A. T. (2003). Estimating impervious surface distribution by
spectral mixture analysis. Remote Sensing of Environment, 84, 493-505.
Yu, D. & Wu, C. (2004). Understanding population segregation from Landsat
ETM+ imagery: a geographically weighted regression approach. GIScience
and Remote Sensing, 41, 187-206.

21. Thank You
Chenyang Wei
E-mail: cwei5@buffalo.edu

22. 1. Extracting the remote sensing variables (RSVs) from
Landsat imagery of 2000 and 2010.
2. Establishing the geographically weighted
regression (GWR) model between RSVs and LSI in
2000.
3. Estimating LSI in 2010 by introducing 2010 RSVs
into 2000 GWR model.
Question - Estimation
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Data: Landsat-5 TM images (September 6th, 2000 & August
1st, 2010).
Method: spectral mixture analysis.
Model: vegetation-impervious surface-soil (V-I-S) model
RSF: average fractions of V-I-S in each block group.
𝐿𝑆𝐼𝑖,00
= 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡𝑖,00 + 𝐶𝑜𝑒𝑓𝐼𝑖,00 × 𝐼𝑖,00 + 𝐶𝑜𝑒𝑓𝑆𝑖,00 × 𝑆𝑖,00
+ 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑖,00
𝐿𝑆𝐼𝑖,10
= 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡𝑖,00 + 𝐶𝑜𝑒𝑓𝐼𝑖,00 × 𝐼𝑖,10 + 𝐶𝑜𝑒𝑓𝑆𝑖,00 × 𝑆𝑖,10
+ 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑖,00

23. 23

24. 24

25. 25

26. Estimation
of 2015
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27. Estimated
Local
Segregation
Change
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