Rethink Uncertainty in GIS research

The first step for uncertainty management is the identification or quantitative measurement of uncertainty. In the paper published by Foody 2003, uncertainty is generally categorized into two types: ambiguity and vagueness. The former is the problem of making choice, while the latter is the challenges of making distinction. Then authors talks about the contemporary research in uncertainty, and they also give analysis about the failure to recognize uncertainty. But I think the most important challenge in this filed is how to quantitatively measure uncertainty, with probability theories, statistical tool, to mention a few here. Later author mentions data mining with uncertainty analysis, and we still need a systematic approach to measure the uncertainty in GIS data mining.

If we consider the process of geospatial data collection, data storage, data analysis and visualization, uncertainty analysis can be applied to each of them. Sometimes, transferring a large number of files across Internet can incur great errors, due to the uncertainty on the network and storage. Therefore, this kind of uncertainty in uncertainty should also be taken into account, especially when high reliability is required. Of course, how to analyze uncertainty in uncertainty is another great challenge.

Uncertainty can change with respect to different study areas or different research projects. For example, tiny vagueness in classification process may cause unpredicted problems, while its impact in visualization can be well handled. However, uncertainty should be handled very carefully, as ClimateNYC mentioned in his blog posts.



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