Area-Based Topic Modeling and Visualization of Social Media for Qualitative GIS

Abstract

Qualitative geographic information systems (GIS) has progressed in meaningful ways since early calls for a qualitative GIS in the 1990s. From participatory methods to the invention of the participatory geoweb and finally to geospatial social media sources, the amount of information available to nonquantitative GIScientists has grown tremendously. Recently, researchers have advanced qualitative GIS by taking advantage of new data sources, like Twitter, to illustrate the occurrence of various phenomena in the data set geospatially. At the same time, computer scientists in the field of natural language processing have built increasingly sophisticated methods for digesting and analyzing large text-based data sources. In this article, the authors implement one of these methods, topic modeling, and create a visualization method to illustrate the results in a visually comparative way, directly onto the map canvas. The method is a step toward making the advances in natural language processing available to all GIScientists. The article discusses the ways in which geography plays an important part in understanding the results presented from the model and visualization, including issues of place and space.