GIS and Computational Social Sciences

Abstract

A restless intellectual dynamic has been witnessed in the field of data-driven social science in the past two decades, especially where computational social science meets GIS (geographic information science). Emerging theoretical viewpoints, data sources, and methodological advances have offered novel venues to examine both spatial and semantic information in high-dimensional and heterogeneous social datasets. Space and place can integrate social science disciplines both theoretically and methodologically. A recent surge of interests in computational social science research has been witnessed due to the increasingly availability of fine-scale human behavior and social network data. GIS-based computational social science thus emphasizes the spatio-temporal social relationships and movements ranging from micro- (individual) to macro- (social group or administrative unit) perspectives. This entry opens with GIS and Computational Social Science: History and Status and the frameworks on which it is based. We have identified three interrelated themes and one trend: (1) Spatial Regression and Simulation, (2) Spatial Social Networking, (3) Human Mobility, and (4) integrated conceptualizations, analytical methods, and open source packages toward synthesis and convergence. The convergence between GIS and Computational Social Science can be achieved through these human-centered and context-based interrelated themes. With the increasingly available detailed individual-level data and analytical tools, the cross-fertilisationbetween GIS and computational social science brings the human factor to the former while situating the latter in the spatial context. Finally, the implications of such trends in terms of achieving convergence and synthesis between GIS and computational social science are presented.