Geo-Text Data and Data-Driven Geospatial Semantics

TL;DR

This paper reviews how geo-text data, linking geographic locations with natural language texts, can be processed and utilized for geospatial semantics, highlighting challenges and opportunities in data-driven research.

Contribution

It systematically reviews existing studies on geo-text data, proposes a generalized workflow, and discusses key challenges for future research in this domain.

Findings

Identification of various types of knowledge from geo-text data

A generalized workflow for geo-text data analysis

Key challenges and future directions in geo-text data research

Abstract

Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts. These links can also be place mentions, such as those in news articles, travel blogs, or historical archives, in which texts are implicitly connected to the mentioned places. This kind of data is referred to as geo-text data. The availability of large amounts of geo-text data brings both challenges and opportunities. On the one hand, it is challenging to automatically process this kind of data due to the unstructured texts and the complex spatial footprints of some places. On the other hand, geo-text data offers unique research opportunities through the rich information contained in texts and the special links between texts and geography. As a result, geo-text data facilitates various studies especially those in data-driven geospatial semantics. This paper discusses geo-text data and related concepts. With a focus on data-driven research, this paper systematically reviews a large number of studies that have discovered multiple types of knowledge from geo-text data. Based on the literature review, a generalized workflow is extracted and key challenges for future work are discussed.