Abstract
The characterization of place and its representation in current Geographic Information System (GIS) has become a prominent research topic. This paper concentrates on places that are cognitive regions, and presents a computational framework to derive the geographic footprint of these regions. The main idea is to use Natural Language Processing (NLP) tools to identify unique geographic features from User Generated Content (UGC) sources consisting of textual descriptions of places. These features are used to detect on a map an initial area that the descriptions refer to. A semantic representation of this area is extracted from a GIS and passed over to a Machine Learning (ML) algorithm that locates other areas according to semantic similarity. As a case study, we employ the proposed framework to derive the geographic footprint of the historic center of Vienna and validate the results by comparing the derived region against a historical map of the city.
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Notes
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The correct spelling in German language is Kärntner Straße. This comment has been retrieved from the web and is purposely reported in its original, wrongly spelled, form.
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The Levenshtein distance is a string metric that measures similarity by the minimal number of required editing steps to transform one string into the other string.
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The bag-of-words model is typically used for text classification. A text is represented as the bag (multiset) of its words and the frequency of occurrence of each word is used as a feature vector for training a classifier.
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We are working on an extension to select activities from textual descriptions.
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The cell size can be shrinked or enlarged to obtain finer-grained or coarser results, respectively.
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Acknowledgments
We acknowledge the work of \(\copyright \) OpenStreetMap contributors (http://www.openstreetmap.org/copyright), and Leaflet (http://leafletjs.com). This research was partially funded by the Vienna University of Technology through the Doctoral College Environmental Informatics.
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Hobel, H., Fogliaroni, P., Frank, A.U. (2016). Deriving the Geographic Footprint of Cognitive Regions. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_5
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DOI: https://doi.org/10.1007/978-3-319-33783-8_5
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