Geography GIS and Computational Social Sciences
Xinyue Ye
  • LAST MODIFIED: 21 February 2023
  • DOI: 10.1093/obo/9780199874002-0256


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.

GIS and Computational Social Science: History and Status

Linking the spatial and social spaces lies at the heart of quantitative and spatial social science. Goodchild, et al. 2000 states that space and place integrate social science disciplines both theoretically and methodologically. The Center for Spatially Integrated Social Science was funded in 1999 by the National Science Foundation under its program to stimulate research infrastructure throughout the social and behavioral sciences. It aims to implement and provide packages (such as GeoDa) and best practices that will enhance the spatial analytic capabilities of researchers across the social sciences. As a popular term, spatially integrated social science has been used and investigated by researchers and organizations in various fields from very diverse angles, with particular attention to location, spatial structure, and spatial processes. Advancements in location-aware technology, information and communication technology, and mobile technology during the past two decades have transformed the focus and need for urban research from mostly static assessments to multi-scalar assessments that span spatial, temporal, and dynamic relationships and integrate human behaviors across various environments (natural, built, and virtual). Lazer, et al. 2009 further notes that the human behaviors and interactions have been unprecedentedly observed and recorded at fine spatial, temporal, and digital resolutions in both virtual and physical worlds, rendering the opportunity for computable social and behavioral dynamics. Kitts, et al. 2020 claims that computational social science flourishes at the intersection of social science and computational science, redirecting attention from structural patterns to social processes. Batty 2013 suggests developing and implementing spatiotemporal and network methods to better understand inequality, mobility, and diffusion in our social systems composed of flows and networks. Shaw, et al. 2016 offers thoughts on human-centered GIS. Ye and Liu 2018 promotes computational social science in an urban setting from the spatial perspective, while Ye and Andris 2021 presents the research framework for social network analytics within the context of geospatial big data deluge. Despite all the substantial growth in the field of computational social science, Lazer, et al. 2020 points out that the institutional structures such as research ethics, pedagogy, and data infrastructure still need to be strengthened.

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    Serves as an editorial to a special issue on integration of network and spatial analytic strategies.

  • Batty, M. The New Science of Cities. Cambridge, MA: MIT Press, 2013.

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    Proposes a bottom-up way of understanding cities and their design not as artifacts but as systems composed of flows and networks, which is a metaphor of social network and relationships.

  • Goodchild, M. F., L. Anselin, R. P. Appelbaum, and B. H. Harthorn. “Toward Spatially Integrated Social Science.” International Regional Science Review 23.2 (2000): 139–159.

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    Recognizes the mounting importance of space, spatiality, location, and place in social science studies through the vision of research community building.

  • Kitts, J. A., E. Quintane, and E. S. M. T. Berlin. “Rethinking Social Networks in the Era of Computational Social Science.” In The Oxford Handbook of Social Network Analysis. Edited by Ryan Light, and James Moody, 71–97. Oxford: Oxford University Press, 2020.

    Integrates social network analysis and computational social science from structural patterns to social processes.

  • Lazer, D., A. S. Pentland, L. Adamic, et al. “Life in the Network: The Coming Age of Computational Social Science.” Science 323.5915 (2009): 721–723.

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    Discusses both potential opportunities and barriers to the emergence of a computational social science.

  • Lazer, D. M., A. Pentland, D. J. Watts, et al. “Computational Social Science: Obstacles and Opportunities.” Science 369.6507 (2020): 1060–1062.

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    Suggest opportunities to facilitate the alignment between the organization of the 20th-century university and the intellectual requirements of computational social science.

  • Shaw, S- L., M- H. Tsou, and X. Ye, eds. “Editorial.” In Special Issue: Human Dynamics in the Mobile and Big Data Era. International Journal of Geographical Information Science 30.9 (2016): 1687–1693.

    DOI: 10.1080/13658816.2016.1164317

    Sevres as an editorial to a special issue on human dynamics, an emerging topic in human-centered GIS.

  • Tsou, M- H. “Research Challenges and Opportunities in Mapping Social Media and Big Data.” Cartography and Geographic Information Science 42.supp. 1 (2015): 70–74.

    DOI: 10.1080/15230406.2015.1059251

    Addresses important research challenges and major opportunities for cartographers to process and visualize big social data.

  • Ye, X. “Spatial Econometrics.” In The International Encyclopedia of Geography. Edited by Douglas Richardson, Noel Castree, Michael F. Goodchild, Audrey Kobayashi, Weidong Liu, and Richard A. Marston, 6644–6654. Chichester, UK: John Wiley and Sons, 2017.

    Reviews the theoretical thoughts, applications, and software development in spatial econometrics, especially highlighting the spatiotemporally integrated social science.

  • Ye, X., and X. Liu. “Editorial.” In Special Issue: Integrating Social Network and Spatial Analyses of the Built Environment. Environment and Planning B 45.3 (2018).

    DOI: 10.1177/2399808318772381

    Serves as an editorial to a special issue on spatial social network analytics in the urban setting, an emerging topic in computational social science from the spatial perspective.

  • Ye, X., and C. Andris. “Editorial.” In Special Issue: Spatial Social Networks in Geographic Information Science. International Journal of Geographical Information Science 35.12 (2021): 1–5.

    DOI: 10.1080/13658816.2021.2001722

    Serves as an editorial to a special issue on social network analytics in the GIS context, driven by novel conceptualizations, unprecedented data sources, AI-driven network science methods, and open-source toolbox environments.

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