Constructing gazetteers from volunteered Big Geo-Data based on Hadoop. (January 2017)

• Record Type: Journal Article
• Authors: Gao, Song; Li, Linna; Li, Wenwen; Janowicz, Krzysztof; Zhang, Yue
• Is Part Of: Computers, environment and urban systems. Volume 61:Part B(2017)
• Page Start: 172
• Page End: 186
• Publication Date: 2017-01
• Subjects: Gazetteers -- Volunteered geographic information -- Hadoop -- Scalable geoprocessing workflow -- Big Geo-Data -- CyberGIS; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2014.02.004
• Languages: English
• ISSNs: 0198-9715

Abstract

Highlights: A data-driven approach to construct gazetteers from volunteered geographic information was introduced. We built a high-performance Hadoop-based geoprocessing platform to facilitate gazetteer research. It connects spatial analysis to the cloud computing environment for Big Geo-Data analytics. Abstract: Traditional gazetteers are built and maintained by authoritative mapping agencies. In the age of Big Data, it is possible to construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI) from the Web. In this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem to harvest crowd-sourced gazetteer entries. Using experiments based on geotagged datasets in Flickr, we find that the MapReduce-based workflow running on the spatially enabled Hadoop cluster can reduce the processing time compared with traditional desktop-based operations by an order of magnitude. We demonstrate how to use such a novel spatial-computing infrastructure to facilitate gazetteer research. In addition, we introduce a provenance-based trust model for quality assurance. This work offers new insights on enriching future gazetteers with the use of Hadoop clusters, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analytics.