Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop

摘要

Traditional gazetteers are built and maintained by authoritative mappingagencies. In the age of Big Data, it is possible to construct gazetteers in adata-driven approach by mining rich volunteered geographic information (VGI)from the Web. In this research, we build a scalable distributed platform and ahigh-performance geoprocessing workflow based on the Hadoop ecosystem toharvest crowd-sourced gazetteer entries. Using experiments based on geotaggeddatasets in Flickr, we find that the MapReduce-based workflow running on thespatially enabled Hadoop cluster can reduce the processing time compared withtraditional desktop-based operations by an order of magnitude. We demonstratehow to use such a novel spatial-computing infrastructure to facilitategazetteer research. In addition, we introduce a provenance-based trust modelfor quality assurance. This work offers new insights on enriching futuregazetteers with the use of Hadoop clusters, and makes contributions inconnecting GIS to the cloud computing environment for the next frontier of BigGeo-Data analytics.