Scale, Uncertainty, and Spatial Data Libraries

In the paper published by Goodchild et al. in 1998, authors presented the definition of spatial data libraries and demonstrated how user access the information by specifying multidimensional keys. Footprint was studied in details and authors also demonstrated how to model fuzzy regions in spatial data libraries. The corresponding implementations were discussed, as well as the visualization. Finally, the goodness of fit was delineated.

I find that the fuzzy modeling is directly related to the previous topics in our class, scale and uncertainty analysis. Most of the geospatial information in the spatial data libraries is modeled with probability, which contains uncertainty. But the magnitude of the uncertainty is largely (not completely) determined by the scale, including the query scale, the segmentation scale, the data analysis scale, visualization scale and others. Therefore, fuzzy modeling may change with respect to different scale and uncertainty.

For example, if we request the spatial information about “south China” in the CHGIS digital GIS library of Harvard University, the uncertainty in the footprint “south China” will cause unexpected results. Since there is no standard interpretation of “south China”, the places that different users choose to represent “south China” maybe different from each other to large extent. Moreover, since the scale in “south China” is not clearly specified, one may choose a city, a province, or even several provinces to represent “south China”. Therefore, we can see both scale and uncertainty play pivotal role in spatial data library queries, which should be taken into consideration with the design of spatial data libraries.


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