  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.
