# Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop

arXiv: https://arxiv.org/abs/1311.7676 · HTML: https://mlsanity.com/paper/1311.7676/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2013.

## Built on by (most cited first, 2 of 2)

- [Artificial Intelligence and Human Geography](https://mlsanity.com/paper/2312.08827/) (2023)
- [Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution](https://mlsanity.com/paper/2508.06584/) (2025)

## Abstract (arXiv)

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.