Soft Boundaries, Scale and Geolibraries

The article by Goodchild et al (1998) mainly dealt with finding a way to figure out to what degree a footprint conceived by the users matches with one that exists in the geolibrary. The difficulty is how to include ill-defined areas into the gazetteer since their boundaries are not precise yet they hold significance in people’s lives. The author sums it up nicely by declaring “effective digital libraries will need to decouple these 2 issues of official recognition and ability to search, by making it possible for users to construct queries for both ill-defined and well defined regions, and for librarians to build catalog entries for data sets about ill-defined regions.” (207). I agree with ClimateNYC. This was the exact problem for researchers building landscape ontology and displaying features that have “gradual” boundaries such as towns, beaches, forests and mountains. Field representations seem a viable option. However, if a neighborhood, for example, have a range of “soft” boundaries, I would argue in favor of having one of the more inclusive one (so that a point that is considered only 30% to be part of Area A will also be included in the query) be taken into consideration by the gazetteer and thus giving the user the opportunity to filter through the data himself.
Hierarchical nature of space is also an interesting topic raised by the authors. Should a search for Quebec also return datasets about Montreal? In addition to listing all well- and ill-defined places, it might also be favorable to separate the datasets into relevant scales. For instance, a user querying Quebec (or even Eastern Canada) is most likely looking for datasets at smaller (cartographic) scales than someone who is querying Montreal. For instance, a search for Eastern Canada in the ADL brought me directly to Fredericton when I would be expecting the whole area between Quebec and Newfoundland. Returning data at the wrong scale would be very inappropriate.


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