Social Spatial: A Qualitative GIS for Social Big Data Investigations
conference contributionposted on 17.09.2019 by Michael Martin
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
In this paper we introduce Social Spatial, a qualitative GIS for social media and big data research. This software enables GIScience researchers to build social media corpus
that reflects a phenomenon being researched and implement methods that analyse that corpus. Natural language processing methods are integrated into Social Spatial, and the code framework has been designed to allow for easy integration of further algorithms. The software builds upon the knowledge of the researcher to identify new ways that phenomena are expressed and see where these posts are geospatially. The software was released open-source with thorough internal documentation to a collaborative code repository that encourages contribution. This program seeks to demystify the analysis process of qualitative social media exploration by use of settings files that expose parameters – word lists, model coefficients, and stop words. These files enhance transparency in qualitative social media research methods and the code/spaces they
were enacted within without increasing the burden of research documentation.