Research Committee, Working Group 4

Research Initiative on a Synergistic Integration of GIS and Public Health


  • Enki Yoo (Leader), University at Buffalo
  • Yongmei Lu (Co-Leader), Texas State University
  • Ling Bian, University at Buffalo  
  • Yingjie Hu, University at Buffalo
  • Wenwen Li, Arizona State University
  • Jeremy Mennis, Temple University
  • Shih-Lung Shaw, University of Tennessee
  • Daoqin Tong, Arizona State University
  • Ming-Hsing Tsou, San Diego State University
  • Zhe Zhang, Texas A & M University
  • Lei Zou, Texas A & M University


Geographic Information Systems and Science (GIS) is increasingly recognized as vital for understanding health problems and finding ways to address them. The importance of GIS in spatial epidemiology and public health has become more apparent with the emergence of global epidemics, such as COVID-19, and the growth of noncommunicable diseases (NCDs). Meanwhile, advances in public health and spatial epidemiology also offer new ways to explore fundamental issues in GIS. We believe that the increased transdisciplinary collaboration among broad scientific disciplines, including GIS and environmental science, health and medical geography, epidemiology, exposure science, and computer science, will advance knowledge in both GIS and public health.

Through this research initiative, we hope to nurture transdisciplinary activities from the UCGIS community and beyond that will lead to the synergistic integration of GIS and Public Health with a keen focus in the following aims:

1. to identify and examine the various factors that influence NCDs at multiple scales, from individuals to communities and countries, using increasingly available geospatial and location-aware technologies. We believe that these efforts will enable the assessment of exposures or their proxies over long time frames, which can yield valuable measures of the exposome (e.g. the totality of an individual’s environmental and lifestyle exposures over the life course).

2. to develop data-driven, mathematical models that simulate, explain, and predict spatiotemporal dynamics of communicable disease at the local community, regional, national and global levels, and to link such spatiotemporal dynamics with socio-cultural, behavior and life-style, and public health policy interventions.

3. to design and develop public health services and interventions to improve access to healthcare services, assess locational impacts of health policy, and facilitate community participation in addressing local health concerns.