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Transcript of GIScience 2000 Raster Data Pixels as Modifiable Areal Units E. Lynn Usery U.S. Geological Survey...
![Page 1: GIScience 2000 Raster Data Pixels as Modifiable Areal Units E. Lynn Usery U.S. Geological Survey University of Georgia.](https://reader035.fdocuments.net/reader035/viewer/2022070401/56649f175503460f94c2d99e/html5/thumbnails/1.jpg)
GIScience 2000
Raster Data Pixels as Modifiable Areal Units
E. Lynn Usery
U.S. Geological Survey
University of Georgia
![Page 2: GIScience 2000 Raster Data Pixels as Modifiable Areal Units E. Lynn Usery U.S. Geological Survey University of Georgia.](https://reader035.fdocuments.net/reader035/viewer/2022070401/56649f175503460f94c2d99e/html5/thumbnails/2.jpg)
GIScience 2000
Outline
• MAUP Concepts from Socioeconomic Data
• Raster Resolution as MAUP
• Experimental Approach
• Results
• Conclusions
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GIScience 2000
Objectives
• Relate raster resolution effects to MAUP
• Analyze effects of resolution on computation of parameters for water models
• Develop empirical base for deciding appropriate resolution for particular modeling result
• Examine pixels as modifiable units in database projection
![Page 4: GIScience 2000 Raster Data Pixels as Modifiable Areal Units E. Lynn Usery U.S. Geological Survey University of Georgia.](https://reader035.fdocuments.net/reader035/viewer/2022070401/56649f175503460f94c2d99e/html5/thumbnails/4.jpg)
GIScience 2000
MAUP Concepts
• Individuals in spatial analysis are often zones
• Scientific study - definition of objects precedes measurement.
• Not true for spatial data - areas are aggregated after data collected for one set of entities
• Farm fields aggregated to counties for statistical analysis
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GIScience 2000
MAUP Concepts
• No rules for aggregation; no standards; no international convention
• Areal units for geographic study are arbitrary, modifiable, and subjective
• Possible m zones from n individuals is combinatorial
• 1000 objects (individuals) in 20 groups (zones) = 101260
• Does it matter?
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GIScience 2000
MAUP Scale Problem
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GIScience 2000
MAUP Scale Problem
Male juvenile delinquency vs income based on 252 Census tracts (Gehlke and Biehl, 1934).
Number of Units Correlation Coefficient
252 -0.5020 175 -0.5800 125 -0.6620 50 -0.6850 25 -0.7650
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GIScience 2000
MAUP Aggregation Problem
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GIScience 2000
MAUP Aggregation Problem
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4
A=2x=2y=4 A=4
x=2y=4
A=8x=2y=4
R = 0.7150 R = 0.5000R = 0.8750
A.H. Robinson - grouping scheme correlations
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GIScience 2000
MAUP Solutions?
• An insoluble problem; if so, ignore it
• Problem that can be assumed away; work at individual level
• Powerful analytical device; manipulate aggregations to get optimal zoning
• Ruzycki (1994) - Used GIS to create 1000's of aggregations of census block groups in Milwaukee and calculated 3 indices of racial segregation for each aggregation; statistically analyzed results.
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GIScience 2000
Application of MAUP Concepts to Raster Data
• Pixel is zone.
• Various resolutions (pixel sizes) corresponds to scale problem of MAUP
• Grouping of pixels in different ways to form larger units corresponds to the aggregation problem of MAUP
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GIScience 2000
Land Cover Example
• Classify land cover from different image sources for same area using same classification system– Landsat TM (30 m)– SPOT MX (20 m)– Ikonos (4 m)
• Do you get same percentages of land cover in each category?
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GIScience 2000
Water Modeling Example
• Data collected at 30 m resolution– DEM– Land cover from TM
• Aggregate data to get 10 acre (210 m) cells for parameter determination for AGNPS
• How to aggregate?
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GIScience 2000
Experimental Approach
• Analysis requires DEM, slope, and land cover at 30, 60, 120, 210, 240, 480, 960, 1920 m cells
• Starting point is 30 m DEM and land cover
• Calculate slope at 30 m cell size from DEM
• Resample land cover
• How to generate slope at 60 m and larger cell sizes? How to aggregate land cover?
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GIScience 2000
Method of Calculation
• Slope calculated from DEM– 30, 60, 120, 210, 240, 480, 960, 1920 m cells
• Compute slope from 30 DEM
• Aggregate DEM from 30 m to each lower resolution
• Compute slope from aggregated elevation data
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GIScience 2000
30 m DEM 120 m DEM 120 m slope
60 m slope
30 m DEM 30 m slope 60 m slope
30 m DEM 60 m DEM
30 m DEM 30 m slope 120 m slope
Sample of Slope Generation Approaches
compute aggregate
aggregate
aggregate
aggregate
compute
compute
compute
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GIScience 2000
Results - DEM
Regression Output:0.980539Constant3.105509Std Err of Y Est0.959085R Squared
34No. of Observations32Degrees of Freedom
0.983164X Coefficient(s)0.035898Std Err of Coef.
120-210m30-210m76766153464978767578464569707167575660636465606038385152
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GIScience 2000
Results - DEM
Regression Output:-1.38617Constant2.274152Std Err of Y Est0.97968R Squared
10No. of Observations8Degrees of Freedom
1.010755X Coefficient(s)0.051466Std Err of Coef.
210-480m30-480m65636365404061614849787756623334616132335356
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GIScience 2000
Image Results -- DEM
30-480 m Pixels 210-480 m Pixels
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GIScience 2000
Results -- Slope
Slope %30 to 480m
Pixels
7.8816 7.8232 7.5870 7.8251 8.1604 8.5415 8.2065 7.9530 7.7434 7.7092
Slope %210 to 480m
Pixels
7.9514 7.8969 7.6244 7.7855 8.1263 8.5087 8.2157 7.8606 7.6390 7.6081
Regression Output:
Constant 0.2762 Std Err of Y Est 1.1626 R Squared 0.7690 No. of Observations 500 Degrees of Freedom 498
X Coefficient(s) 0.8860
Std Err of Coef. 0.0218
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GIScience 2000
Results -- Slope
• Slope– Method of calculation affects results– Higher resolution aggregation directly to large
pixel sizes yields better results than multistage aggregation (e.g., 30 m to 960 m is better than 30 m to 60 m to 120 m to 240 m to 480 m to 960 m)
– Even multiples of pixels hold results while odd pixel sizes introduce error
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GIScience 2000
Slope Image Comparison
30 m to 480 m pixels 210 m to 480 m pixels
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GIScience 2000
Sample of Land Cover Aggregation Approaches
30 m LC 210 m LC 480 m LC
210m LC
30 m LC 60 m LC 120 m LC
30 m LC 120 m LC
30 m LC 960 m LC 1920 m LC
aggregate aggregate
aggregate aggregate
aggregate aggregate
aggregate aggregate
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GIScience 2000
Results - Land Cover -- 120 M Pixels
30_original
2360.07
14026.41
8667.72
8607.87
17203.86
4669.65
14773.41
25133.67
5554.08
583.83
22166.55
120_30res
2466.72
14224.32
8786.88
8627.04
17343.36
4743.36
14860.8
25509.6
5705.28
593.28
22432.32
30-120 %
-4.52
-1.41
-1.37
-0.22
-0.81
-1.58
-0.59
-1.50
-2.72
-1.62
-1.20
Land Cover Category
Pecan Groves
Recently Disturbed Land / Harvested Cropland
Pastures
Cypress Dominant Weltands
Mature Deciduous
Young Planted Pine
Mature Planted Pine
Mixed Dominant Deciduous / Pine
Roads / Urban Complex
Open Water
Crops (Cotton, Peanuts)
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GIScience 2000
Results - Land Cover -- 210 m Pixels
210_30res
2424.048
14632.4352
8492.98272
8625.20352
17536.88544
4689.43104
15527.12928
25465.72608
5641.4208
612.62304
22213.0944
210_120res
2500.71948
14413.31792
8679.74592
8812.05912
17169.84292
4600.08892
14894.05588
25624.6564
5680.64672
648.33468
22171.28188
210 % diff
-3.16
1.50
-2.20
-2.17
2.09
1.91
4.08
-0.62
-0.70
-5.83
0.19
Land Cover Category
Pecan Groves
Recently Disturbed Land / Harvested Cropland
Pastures
Cypress Dominant Weltands
Mature Deciduous
Young Planted Pine
Mature Planted Pine
Mixed Dominant Deciduous / Pine
Roads / Urban Complex
Open Water
Crops (Cotton, Peanuts)
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GIScience 2000
Results - Land Cover -- 480 m Pixels
210-240d30-240d30-210d480_240res480_210res480_30res
-36.45-10.4419.062764.80002026.30562503.3376
8.773.29-6.0013570.560014874.925214032.4704
-5.332.507.438755.20008312.45828979.8624
6.511.98-4.858847.36009463.76829025.7952
-7.010.346.8717372.160016233.471017431.4976
-8.06-11.70-3.364976.64004605.24004455.4816
3.11-4.35-7.7015505.920016003.209014859.2608
0.65-0.23-0.8925735.680025904.475025676.4352
6.98-8.04-16.145483.52005894.70725075.5744
-30.51-20.387.76691.2000529.6026574.1600
-0.992.973.9222440.960022220.283023127.1648
Land Cover Category
Pecan Groves
Recently Disturbed Land / Harvested Cropland
Pastures
Cypress Dominant Weltands
Mature Deciduous
Young Planted Pine
Mature Planted Pine
Mixed Dominant Deciduous / Pine
Roads / Urban Complex
Open Water
Crops (Cotton, Peanuts)
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GIScience 2000
Results-Land Cover -- 960 m Pixels
Land Cover Category
Pecan Groves
Recently Disturbed Land / Harvested Cropland
Pastures
Cypress Dominant Weltands
Mature Deciduous
Young Planted Pine
Mature Planted Pine
Mixed Dominant Deciduous / Pine
Roads / Urban Complex
Open Water
Crops (Cotton, Peanuts)
210-480d30-480d30-210d960_480res960-210res960_30res
-19.69-3.1213.842755.974 2302.61752672.64
11.542.93-9.7413688.0042 15473.589614100.48
-18.79-12.415.389737.7748 8197.31838663.04
0.26-12.68-12.989554.0432 9578.88888478.72
-9.94-0.208.8617821.9652 16210.427217786.88
17.7611.60-7.484317.6926 5249.96794884.48
9.015.76-3.5714331.0648 15749.903715206.4
0.942.651.7326916.6794 27170.886527648
21.4223.773.004777.0216 6078.91026266.88
40.1640.190.06275.597460.5235460.8
-8.52-6.741.6324987.523026.17523408.64
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GIScience 2000
Image Results - Land Cover
30-480 m Pixels 240-480 m Pixels
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GIScience 2000
Image Results - Land Cover
30-210 m Pixels 120-210 m Pixels
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GIScience 2000
Resampling Asia Land Cover
• Land cover data (21 categories) at 1 km pixel size for Asia
• Resample to 2,4,8,16,25, and 50 km pixels
• Tabulate land cover percentages at each resolution to assess scale effects
• Aggregate in various ways and retabulate to assess aggregation effects
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GIScience 2000
Asia Land Cover Lambert Azimuthal Equal Area Projection, 8 km pixels
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GIScience 2000
Scale Effect ResultsAsia Land Cover
50 km25 km16 km8 km4 km2 kmLand Cover Category
-6.09-13.91-16.18-13.85-15.1816.20Urban & Built-Up Land
1.870.480.990.800.71-0.56Dryland Cropland & Pasture
-2.93-3.93-3.76-4.21-4.314.22Irrigated Cropland & Pasture
-1.78-2.52-2.50-2.46-2.232.22Cropland/Grassland Mosaic
-2.74-6.63-5.48-5.47-5.765.69Cropland/Woodland Mosaic
-1.37-0.58-1.25-1.12-1.041.00Grassland
2.871.421.752.031.69-1.61Shrubland
-1.08-4.35-5.21-4.70-4.194.17Mixed Shrubland/Grassland
16.0315.6513.2312.5513.43-13.02Savanna
0.05-1.33-0.23-1.95-1.641.86Deciduous Broadleaf Forest
10.653.930.540.17-0.250.62Deciduous Needleleaf Forest
3.603.041.493.152.24-2.19Evergreen Broadleaf Forest
4.3512.509.8211.1610.65-10.40Evergreen Needleleaf Forest
2.003.872.732.171.83-2.10Mixed Forest
14.78-12.128.965.055.33-4.78Herbaceous Wetland
62.6114.7825.1212.4010.32-9.00Wooded Wetland
3.656.126.786.316.32-6.25Barren or Sparsely Vegetated
21.9629.139.5919.7423.42-18.84Herbaceous Tundra
-8.362.065.243.842.67-2.70Wooded Tundra
-4.3519.5715.896.273.700.03Mixed Tundra
-4.35-18.69-15.18-19.03-17.4817.91Snow or Ice
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GIScience 2000
Aggregation Effect ResultsAsia Land Cover
25f825f4225f225f1Land Cover Category
5.56-6.94-6.94-8.33Urban & Built-Up Land
-0.48-1.14-1.95-1.36Dryland Cropland & Pasture
0.20-0.370.31-1.03Irrigated Cropland & Pasture
-3.59-2.051.38-0.75Cropland/Grassland Mosaic
-3.32-2.59-2.76-4.00Cropland/Woodland Mosaic
1.290.180.430.81Grassland
-1.99-0.65-1.06-1.41Shrubland
-4.13-1.89-3.77-3.30Mixed Shrubland/Grassland
-4.980.70-2.46-0.32Savanna
-1.99-1.99-2.84-1.38Deciduous Broadleaf Forest
-4.21-7.36-5.84-6.07Deciduous Needleleaf Forest
1.800.09-1.98-0.54Evergreen Broadleaf Forest
10.639.388.757.81Evergreen Needleleaf Forest
0.520.121.421.83Mixed Forest
-31.25-29.69-20.31-23.44Herbaceous Wetland
-41.18-19.12-16.18-29.41Wooded Wetland
3.962.082.332.38Barren or Sparsely Vegetated
14.7120.5914.715.88Herbaceous Tundra
8.5910.358.3311.36Wooded Tundra
10.005.005.0025.00Mixed Tundra
-7.50-3.33-14.17-15.00Snow or Ice
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GIScience 2000
Conclusions
• MAUP affects remotely sensed data
• Resolution of images corresponds to MAUP scale problem
• Resampling corresponds to MAUP aggregation problem
• Higher resolution data are more accurate (scale effect)
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GIScience 2000
Conclusions
• Areas of land cover vary significantly (up to 30 %) based on aggregation method– Nearest neighbor resampling leads to inaccurate
aggregations based on modal category concepts
• Continuous data (DEM and slope) retain values better through aggregation because of averaging (bilinear) during resampling.
• Continental land cover datasets shows significant effects on land cover areas resulting from categorical (nearest neighbor) resampling.