Foody’s article offers a fairly general overview of uncertainty and its impact on GIS analyses. I did not find this an enjoyable read as it seemed to touch on various implications of uncertainty on decision-making, but did not go in-depth on ways to handle uncertainty in a GI system. The chief insight brought out by Foody concerned the paradigmatic shift from absolute accuracy of data to its fitness or appropriateness to the research question.
Sah mentions the importance of making uncertainty explicit at each step. To me, fuzzy sets and fuzzy logic (things can partially belong to different categories, or a percentage likelihood of being a certain class) seem one of the most intuitive tools to computer users to represent spatial uncertainty. “Stratified partitions” have also been used in other cases to track this uncertainty through different scales1. Additionally, fuzzy sets are most valuable for those transitional zones (which analyses tend to be most interested in for emerging phenomena) where uncertainty is highest. Despite these kinds of parametric solutions however, there remain ontological issues of deciding what categories are included in the fuzzy set. Given the increasing amount of data available through data mining, uncertainty needs to be handled in a more robust way that is more interoperable than fuzzy sets with predefined classes. Finally, to naïve users who aren’t familiar with the bits, bytes, and algorithms behind the scenes that drive classification and visualization on a GIS, is fuzzy logic an intuitive way of representing uncertainty?
– Madskiier_JWong
1. Beaubouef, T., Petry, F. (2010) “Fuzzy and Rough Set Approaches for Uncertainty in Spatial Data” http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA530497&Location=U2&doc=GetTRDoc.pdf
Tags: uncertainty