Effect of Category Aggregation on Map Comparison

  • Robert Gilmore PontiusJr.
  • Nicholas R. Malizia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3234)


This paper investigates the influence of category aggregation on measurement of land-use and land-cover change. To date, research concerning data aggregation has examined primarily the effects of modifying the unit of observation (i.e., the modifiable areal unit problem and the ecological inference problem); here, we examine the effects of changing the categorical definition, such as the conversion from many, detailed Anderson Level II classes to fewer, broader Anderson Level I classes. Cross-tabulation matrices are used to analyze the change between two times for aggregated and unaggregated versions of identical landscapes. A mathematical technique partitions the Total change as the sum of Net (i.e., quantity change) and Swap (i.e., location change). This paper shows that the Total and Net exhibited by maps between two points in time can be substantially reduced through land-use category aggregation, but cannot be increased. Swap, however, can be reduced or increased by the aggregation of categories. We derive five principles that dictate the effect of aggregation and illustrate the principles using both simplified examples and empirical data. The empirical data are from three Human Environment Regional Observatory sites. The principles are mathematical facts that apply to the analysis of any categorical variable.


Modifiable Areal Unit Problem Identical Landscape Category Aggregation Barren Category Warm Season Crop 
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  1. 1.
    Anderson, J., Hardy, E., Roach, J., Witmer, R.: A land use and land cover classification system for use with remote sensor data. USGS Professional Paper 964, Souix Falls, SD (1976)Google Scholar
  2. 2.
    Gehlke, C., Biehl, K.: Certain effects of grouping on the size of the correlation coefficient in census tract material. Journal of the American Statistical Association Supplement 29, 169–170 (1934)CrossRefGoogle Scholar
  3. 3.
    Helmer, E., Ramos, O., Lopez, T., Quinones, M., Diaz, W.: Mapping the forest type and land cover of Puerto Rico, a component of the Caribbean biodiversity hotspot. Caribbean Journal of Science 38, 165–183 (2002)Google Scholar
  4. 4.
    King, G.: A solution to the ecological inference problem. Princeton University Press, Princeton (1997)Google Scholar
  5. 5.
    Massachusetts Geographic Information Systems (MASSGIS).: Land Use (2002),
  6. 6.
    Meyer, W., Turner, B. (eds.): Changes in land use and land cover: A global perspective. Cambridge University Press, Cambridge (1994)Google Scholar
  7. 7.
    National Research Council (NRC).: Grand challenges in the environmental sciences. National Academy of Sciences Press, Washington, DC (2000)Google Scholar
  8. 8.
    Openshaw, S., Taylor, P.: A million or so correlation coefficients: three experiments on the modifiable areal unit problem. In: Wrigley, N. (ed.) Statistical Applications in the Spatial Sciences, Pion, London, pp. 127–144 (1979)Google Scholar
  9. 9.
    Openshaw, S.: The Modifiable Areal Unit Problem. GeoBooks, Norwich (1984)Google Scholar
  10. 10.
    Pontius Jr., R., Shusas, E., McEachern, M.: Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems and Environment 101, 251–268 (2004)CrossRefGoogle Scholar
  11. 11.
    Quattrochi, D., Goodchild, M.: Scale in Remote Sensing and GIS. CRC Press, Boca Raton (1997)Google Scholar
  12. 12.
    University Consortium for Geographic Information Science (UCGIS): Scale: Research White Paper (1998),
  13. 13.
    Wong, D., Amrhein, C.: Research on the MAUP: old wine in a new bottle or real breakthrough? Geographical Systems 3, 73–76 (1996)Google Scholar
  14. 14.
    Yule, G., Kendall, M.: An Introduction to the Theory of Statistics. Griffin, London (1950)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Robert Gilmore PontiusJr.
    • 1
  • Nicholas R. Malizia
    • 1
  1. 1.Graduate School of Geography, George Perkins Marsh Institute, and, Department of International Development, Community, and EnvironmentClark UniversityWorcesterUSA

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