TY - GEN
T1 - Sonet
T2 - 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, GeoHumanities 2019
AU - Palumbo, Rachel
AU - Thompson, Laura
AU - Thakur, Gautam
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2019/11/5
Y1 - 2019/11/5
N2 - Scalability, standardization, and management are important issues when working with very large Volunteered Geographic Information (VGI). VGI is a rich and valuable source of Points of Interest (POI) information, but its inherent heterogeneity in content, structure, and scale across sources present major challenges for interlinking data sources for analysis. To be useful at scale, the raw information needs to be transformed into a standardized schema that can be easily and reliably used by data analysts. In this work, we tackle the problem of unifying POI categories (e.g. restaurants, temple, and hotel) across multiple data sources to aid in improving land use maps and population distribution estimation as well as support data analysts wishing to fuse multiple data sources with the OpenStreetMap (OSM) mapping platform or working with projects that are already configured in the OSM schema and wish to add additional sources of information. Graph theory and its implementation through the SONET graph database, provides a programmatic way to organize, store, and retrieve standardized POI categories at multiple levels of abstraction. Additionally, it addresses category heterogeneity across data sources by standardizing and managing categories in a way that makes cross-domain analysis possible.
AB - Scalability, standardization, and management are important issues when working with very large Volunteered Geographic Information (VGI). VGI is a rich and valuable source of Points of Interest (POI) information, but its inherent heterogeneity in content, structure, and scale across sources present major challenges for interlinking data sources for analysis. To be useful at scale, the raw information needs to be transformed into a standardized schema that can be easily and reliably used by data analysts. In this work, we tackle the problem of unifying POI categories (e.g. restaurants, temple, and hotel) across multiple data sources to aid in improving land use maps and population distribution estimation as well as support data analysts wishing to fuse multiple data sources with the OpenStreetMap (OSM) mapping platform or working with projects that are already configured in the OSM schema and wish to add additional sources of information. Graph theory and its implementation through the SONET graph database, provides a programmatic way to organize, store, and retrieve standardized POI categories at multiple levels of abstraction. Additionally, it addresses category heterogeneity across data sources by standardizing and managing categories in a way that makes cross-domain analysis possible.
KW - Big data
KW - Graph database
KW - Ontology
KW - Openstreetmap
KW - Points of interest
UR - http://www.scopus.com/inward/record.url?scp=85075607296&partnerID=8YFLogxK
U2 - 10.1145/3356991.3365474
DO - 10.1145/3356991.3365474
M3 - Conference contribution
AN - SCOPUS:85075607296
T3 - Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, GeoHumanities 2019
BT - Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, GeoHumanities 2019
A2 - Martins, Bruno
A2 - Moncla, Ludovic
A2 - Murrieta-Flores, Patricia
PB - Association for Computing Machinery, Inc
Y2 - 5 November 2019
ER -