# Presentation: 696922

## Raster Data Pixels as Modifiable Areal Units

- E. Lynn Usery
- U.S. Geological Survey
- University of Georgia

## Outline

- MAUP Concepts from Socioeconomic Data
- Raster Resolution as MAUP
- Experimental Approach
- Results
- Conclusions

## 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

## 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

## 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?

## MAUP Scale Problem

## 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

## MAUP Aggregation Problem

## MAUP Aggregation Problem

- A.H. Robinson - grouping scheme correlations

## 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.

## 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

## 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?

## 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?

## 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?

## 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

## Sample of Slope Generation Approaches

- 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

- compute

- aggregate

- aggregate

- aggregate

- aggregate

- compute

- compute

- compute

## Results - DEM

## Results - DEM

## Image Results -- DEM

- 30-480 m Pixels

- 210-480 m Pixels

## 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

## 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

## Slope Image Comparison

- 30 m to 480 m pixels

- 210 m to 480 m pixels

## 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

## Results - Land Cover -- 120 M Pixels

## Results - Land Cover -- 210 m Pixels

## Results - Land Cover -- 480 m Pixels

## Results-Land Cover -- 960 m Pixels

## Image Results - Land Cover

- 30-480 m Pixels

- 240-480 m Pixels

## Image Results - Land Cover

- 30-210 m Pixels

- 120-210 m Pixels

## 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

- Asia Land Cover Lambert Azimuthal Equal Area Projection,
- 8 km pixels

## Scale  Effect ResultsAsia Land Cover

## Aggregation Effect ResultsAsia Land Cover

## 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)

## 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.