Spatial Networks: The synergy of computational geography and geospatial Big Data for uncovering geo-complexity in human-urban environment interactions
About the talk
Understanding detailed spatial and temporal human activity patterns concerning how citizens interact with their surrounding urban environments is of great importance to urban planning and its applications. This presentation illustrates how we can utilize computational geography approaches and geospatial social media Big Data to model and uncover unique human activity patterns in navigating through the urban spaces. By utilizing complex network theory and methods, coupling with large-scale mobility data, people’s activities in interacting with the urban environments can be represented as spatial networks. Two case studies are introduced in this presentation. The first study reveals strongly connected urban regions in the form of communities in the network space provides a clear delineation of urban geography delineated by the collective human activities. It further explains the role of spatial proximity plays in affecting the interaction intensity across space. The second study characterizes people’s daily activity patterns in the urban environment with a mobility network approach with geographic context-aware Twitter data. The integration of geographic context from the synthesis of geo-located Twitter data with land use parcels enables us to reveal unique activity motifs that form the fundamental elements embedded in complex urban activities. Finally, this presentation discusses the potential issues and how they can be addressed to encourage interdisciplinary research and enable a wider range of social science applications.
About the speaker
Junjun Yin, is an Assistant Research Professor at the Social Science Research Institute and an ICDS Associate at the Institute for Computational and Data Sciences, the Pennsylvania State University. Before joining Penn State, he was a postdoctoral research fellow at the CyberGIS Center for Advanced Digital and Spatial Studies at the National Center for Supercomputing Applications, the University of Illinois at Urbana-Champaign. He obtained his Ph.D. degree in Spatial Information Science from Dublin Institute of Technology, Ireland, which is a field in conjunction with Computer Science and Geographic Information Science (GIScience).
His research interests center on GIScience with a focus on understanding human dynamics in the urban environment. His main research agenda employs computational geography approaches and geospatial Big Data to model human-urban environment interactions and their applications about urban environmental sustainability, resilience, and mobility. One of his current research themes is using Twitter data as a geospatial Big Data source for addressing social problems and societal issues. His work in using Big Data for social science research is enabled by state-of-the-art high-performance computing environments supported by the Extreme Science and Engineering Discovery Environment (XSEDE) and the Institute for Computational and Data Sciences at Penn State.
- Yin, J. and Chi, G., 2021. Characterizing People’s Daily Activity Patterns in the Urban Environment: A Mobility Network Approach with Geographic Context-Aware Twitter Data. Annals of the American Association of Geographers, 111(7), pp.1967-1987.
- Yin, J., Soliman, A., Yin, D. and Wang, S., 2017. Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data. International Journal of Geographical Information Science, 31(7), pp.1293-1313.
- (optional) Soliman, A., Soltani, K., Yin, J., Padmanabhan, A. and Wang, S., 2017. Social sensing of urban land use based on analysis of Twitter users’ mobility patterns. PloS one, 12(7), p.e0181657.