Deriving the Geographic Footprint of Cognitive Regions. Hobel, H.; Fogliaroni, P.; and Frank, A. U In Sarjakoski, T.; Santos, M. Y.; and Sarjakoski, T., editors, International Conference on Geographic Information Science (AGILE 2016), Helsinki (Finland), of Lecture Notes in Geoinformation and Cartography, pages 67–84, 2016. Springer.
Deriving the Geographic Footprint of Cognitive Regions [link]Paper  doi  abstract   bibtex   
The characterization of place and its representation in current Geographic Information System (GIS) has become a prominent research topic. This paper focus 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
@inproceedings{Hoebel2016deriving_footprints,
abstract = {The characterization of place and its representation in current Geographic Information System (GIS) has become a prominent research topic. This paper focus 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},
author = {Hobel, Heidelinde and Fogliaroni, Paolo and Frank, Andrew U},
booktitle = {International Conference on Geographic Information Science (AGILE 2016), Helsinki (Finland)},
doi = {10.1007/978-3-319-33783-8_5},
editor = {Sarjakoski, Tapani and Santos, Maribel Yasmina and Sarjakoski, Tiina},
file = {:Users/tremity/Dropbox/0.CurrentWork/Research(dropbox)/Publications/Published/2016.Agile/Heidi/pre-proofreading/GeoFootprintCognitiveRegions{\_}pre{\_}proofreading.pdf:pdf},
pages = {67--84},
publisher = {Springer},
series = {Lecture Notes in Geoinformation and Cartography},
title = {{Deriving the Geographic Footprint of Cognitive Regions}},
url = {http://link.springer.com/10.1007/978-3-319-33783-8{\_}5},
year = {2016}
}
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