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Recognizing MAUP in Research

Celestine Kemah

The Openshaw article on modifiable areal unit problem (1984) establishes a keen relationship with the Bishop B and Cushing R. article; The Big Sort (2008). It demonstrates how both articles establish the relationship in the quantitative study of spatial data for areal unit such as residential or commercial zones. It presents an argument that the MAUP is a fundamental geographical problem that is endemic to all studies of spatially aggregated data (Openshaw, 1984), and that it is a geographical fact of life that the results of spatial study will always depend on the areal units that are being studied.The MAUP has established some level of significance that it presents an opportunity for the development of new geographical techniques like spatial autocorrelation that can be used for the analysis of thematic units like census tracks The article also examines MAUP from three different perspectives: An insoluble problem, a problem that can be assumed away, and a very powerful analytical device which happens to be the most appropriate one for geographers to structure data and reduce geographical bias, i.e. reduce spatial dependence. Most geographical studies have employed spatial aggregations based on contiguous arrangements of zones, something referred to as a zoning system. Bishop and Cushing described the natural phenomenon of people sorting themselves into communities of interest, a confluence of social, political, and economic trend (1970-2004) that caused like-minded people to cluster and to exclude others who are different Finally, it is noted that the MAUP is also closely involved in what is known as the ecological fallacy problem. An ecological fallacy occurs when it is inferred that results based on aggregate zonal (or grouped) data can be applied to the individuals who form the zones or groups being studied. In a geographical context the individuals can either be zones prior to a subsequent aggregation or non-modifiable entities. The ecological fallacy problem has also been studied further. The principal problem here is that a detailed investigation requires access to large spatially referenced individual data sets and it is only quite recently that sufficiently powerful computers have become available to handle these. Openshaw used some aggregation experiments like Random aggregation and the correlation coefficient, Random aggregation and other statistics which concerned the effects of randomly aggregating zonal data which have already been aggregated at least once previously, and Random aggregation experiments with once aggregated data in order to provide possible solutions to the boundary and pattern problems. He finally provided possible solutions to MAUP by using the optimal zoning approach to test hypotheses by manipulating the aggregation process.