A. Stewart Fotheringham

Alexander Stewart Fotheringham (born February 2, 1954) is a British-American geographer known for his contributions to quantitative geography, geographic information science (GIScience), and spatial analysis. He has contributed to the literature surrounding spatial analysis and spatial statistics, particularly in the development of geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR).

Education Fotheringham received a BSc in geography from the University of Aberdeen in 1976. He received an M.A. in 1978 and Ph.D. in 1980, both in geography from McMaster University. His research focuses on developing and applying spatial statistics, mathematical, and computational methods within the discipline of quantitative geography. He has worked both on the theoretical and applied side of quantitative geography. His applied research interests include crime, public health, and human migration. From 1993 to 1994, Fotheringham worked as an assistant chair in the Department of Geography at the State University of New York. In 2024, Fotheringham began his tenure as a professor of geography at Florida State University.

Fotheringham published more than 200 peer-reviewed journal articles and book chapters during his career. This committee seeks to organize research and inform on methods to use spatial data ethically to inform policy and benefit society. Fotheringham has continued to be involved in researching expanding upon GWR, and its applications, in the years since. In GWR, regression coefficients (parameters) are estimated locally for each geographic location or point, allowing for the modeling of spatial heterogeneity.

Geographical and Temporal Weighted Regression Time is recognized as significant to spatial analysis, with a substantial amount of literature within the discipline of time geography. However, incorporating both space and time is a significant challenge for researchers. Fotheringham addressed this problem in his 2015 paper titled "Geographical and Temporal Weighted Regression (GTWR)."

Multiscale Geographically Weighted Regression Multiscale Geographically Weighted Regression (MGWR) builds upon GWR by allowing for the comparison of variables at different spatial scales| This is accomplished by allowing for different neighborhood bandwidths for each variable. Fortheringham spoke at UCGIS on applying MGWR in a webinar titled Measuring the "Unmeasurable: Models of Geographical Context."

Awards and honors

Member, National Academy of Sciences Fellow, American Association of Geographers, 2023 Distinguished Scholarship Honors, American Association of Geographers, 2019