Deriving Locational Reference through Implicit Information Retrieval

Thursday Sep 29   11:10 AM to 11:30 AM (20 minutes)
GIScienceGeographic Information RetrievalImplicit Spatial Data
The convoluted and fragmented process of online spatial data retrieval remains a barrier to domain scientists interested in spatial analysis. Scientists often do not have access to the data necessary for particular studies and are limited to interacting with simplistic web tools or downloading preconstructed administrative datasets. While there is a wealth of hidden spatial information online, without prior experience querying web APIs (Application Programming Interface) or scraping web documents, scientists cannot extract potentially valuable implicit data from unconventional sources. In an attempt to broaden the spectrum of exploitable spatial data sources, this paper proposes an extensible, locational reference deriving model that shifts extraction and encoding logic from the user to preprocessing software. To test this idea, we implement a software mediation layer that: collects data through web APIs and scrapers, determines locational reference as geometries or toponyms, and re-encodes the data as explicit spatial information, usable in spatial analysis tools, such as those in R or ArcGIS.
Center for Spatial Studies @ UCSB
University of California, Santa Barbara

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