Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop

TL;DR

This paper presents a scalable Hadoop-based platform for constructing geospatial gazetteers from volunteered geographic information, significantly reducing processing time and enhancing data quality assurance.

Contribution

It introduces a novel distributed geoprocessing workflow on Hadoop for efficient gazetteer construction from Big Geo-Data, including a provenance-based trust model.

Findings

MapReduce workflow reduces processing time by an order of magnitude

Hadoop cluster effectively handles large geospatial datasets

Provenance-based trust model improves data quality assurance

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.