Stewart Fotheringham

British-American geographer

Stewart Fotheringham

Born|

Alexander Stewart Fotheringham

(1954-02-02) 2 February 1954 (age 69)

Yorkshire, England

Citizenship| United Kingdom, US
Alma mater| University of Aberdeen
McMaster University
Scientific career
Institutions| University at Buffalo
University of Newcastle
University of St Andrews
Arizona State University
Thesis| Spatial Structure, Spatial Interaction, and Distance-Decay Parameters (1980)
Doctoral advisor| Michael J. Webber

Alexander Stewart Fotheringham (born February 2, 1954), or A. Stewart Fotheringham is a British-American geographer known for his contributions to quantitative geography and geographic information science (GIScience).[1] He holds a PhD in geography from McMaster University, and is currently a Regents professor of Computational Spatial Science in the School of Geographical Sciences and Urban Planning at Arizona State University.[2][3] 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).[2][4][5]

## Education and field[edit]

Fotheringham has three degrees in geography: His BSc from the University of Aberdeen in 1976, his M.A. in 1978, and Ph.D. in 1980, both from McMaster University, Canada.[2][3][6] 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.[7] His applied research interests include crime, public health, and human migration.[2]

## Career[edit]

### University positions[edit]

In his early career years after obtaining his PhD in 1980, he worked as a professor at University at Buffalo, becoming a full professor in 1988.[1] From 1991 to 1992, he held the position of Professor of Quantitative Geography at the University of Newcastle, setting the stage for his future endeavors.[8] From 1993 to 1994, Fotheringham worked as an Assistant Chair in the Department of Geography at the State University of New York.[8]

In 1994, he returned to the University of Newcastle as a professor of Quantitative Geography. Notably, during this time, he also took on the role of Director of the North-East Regional Research Laboratory. He remained in this position until 2004.[8]

Fotheringham became a Visiting Research Fellow at the University of Leeds, where he remained until 2006.[8] Simultaneously, from 2004 to 2011, he assumed the SFI Research Professor and Director role at the National University of Ireland.[8]

Between 2011 and 2014, Fotheringham served as the Director of the Centre for GeoInformatics and held the Professor of Quantitative Geography position at the University of St Andrews.[8]

In 2014, Fotheringham began his tenure as a professor of Computational Spatial Science at Arizona State University.

### Other academic positions[edit]

From 1995 to 1998, Fotheringham was elected as the Chair of the Quantitative Methods Study Group of the Royal Geographical Society.[8] In 2009, he was appointed as Ireland's representative on the Governance Committee of the EU Joint Planning Initiative on Urban Europe, giving him an active involvement in shaping urban planning initiatives.[8]

In 2014, Fotheringham was selected as a member of the National Academy of Sciences’ Mapping Science Committee.[9][10] This committee seeks to organize research and inform on methods to use spatial data ethically to inform policy and benefit society.[9]

## Geographically Weighted Regression family of statistics[edit]

### Geographically Weighted Regression[edit]

Fotheringham contributed to GIScience and spatial statistics with his work in developing Geographically Weighted Regression (GWR).[4] GWR was first developed as a statistical technique in the 1990s by Fotheringham, Chris Brundson, and Martin Charloton.[5][11][12] Fotheringham has continued to be involved in researching expanding upon GWR, and its applications, in the years since.[12]

GWR is designed to address the limitations of traditional global regression models, such as Ordinary Least Squares (OLS), which assume that relationships between variables are global; that is, constant across space.[13] GWR recognizes that relationships between variables are non-stationary; that is, they can vary from one location to another within a geographic area. In this way, it is comparable to other local spatial statistics, such as the univariate Getis-Ord Gi* and Local Moran's I, as compared to their global spatial statistic equivalents Getis-Ord General G and Global Moran's I.

In GWR, regression coefficients (parameters) are estimated locally for each geographic location or point, allowing for the modeling of spatial heterogeneity.[5] This means that GWR considers the spatial context of data and generates separate regression models for different locations, considering the varying relationships between dependent and independent variables. It provides insights into how these relationships change as you move across a geographic area, making it valuable for understanding spatial patterns and identifying areas with unique relationships.[5]

Geographically Weighted Regression is a cornerstone of GIS and spatial analysis, and is built into ArcGIS, as a package for the R (programming language), and as a plugin for QGIS.[14][15][16]

### Geographical and Temporal Weighted Regression[edit]

Time is recognized as significant to spatial analysis, with a substantial amount of literature within the discipline of time geography.[17] 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)."[17] GTWR builds upon GWR by incorporating the dimension of time into the analysis.[17] This is accomplished by deriving both spatial and temporal bandwidths and using them to construct a weighted matrix.[17] GTWR is available as packages in R, such as GWmodelS.[18]

### Multiscale Geographically Weighted Regression[edit]

Multiscale Geographically Weighted Regression (MGWR) builds upon GWR by allowing for the comparison of variables at different spatial scales|[7][19] This is accomplished by allowing for different neighborhood bandwidths for each variable.[7][19] MGWR is available both within ArcGIS, and as Python scripts published by a team of researchers including Fotheringham.[19][20][21] Fortheringham spoke at UCGIS on applying MGWR in a webinar titled Measuring the "Unmeasurable: Models of Geographical Context."[22]

## Awards[edit]

* Fellow, American Association of Geographers, 2023[23]
* Distinguished Scholarship Honors, American Association of Geographers, 2019[4]
* Outstanding Achievement Award, GEOIDE Network Centre of Excellence, 2000[8]
* The Warren G. Nystrom Award, Association of American Geographers, 1981[8]

## Publications[edit]

Fotheringham published more than 200 peer-reviewed journal articles and book chapters during his career.[2][3] He has authored, or served as volume editor, for numerous books including:

* Haynes, Kingsley E.; Fotheringham, A. Stewart (1985). Gravity and Spatial Interaction Models. Scientific Geography. SAGE Publishing.
* Fotheringham, Alexander Stewart; Knudsen, Daniel C. (1987). Goodness-of-Fit Statistics (PDF). Geo Books. ISBN 0-86094-222-8.
* Fotheringham, Stewart; Rogerson, Peter (1994). Spatial Analysis and GIS: Technical Issues in Geographic Information Systems. CRC Press. ISBN 0-7484-0104-0.
* Fotheringham, A. Stewart (1999). Spatial Models and GIS: New and Potential Models (Gisdata). CRC Press. ISBN 978-0748408467.
* Fotheringham, A. Stewart; Brunsdon, Chris; Charlton, Martin (2000). Quantitative Geography: Perspectives on Spatial Data Analysis. Sage Publishing. ISBN 978-0-7619-5948-9.
* Fotheringham, A. Stewart; Brunsdon, Chris; Charlton, Martin (2002). Geographically Weighted Regression: the analysis of spatially varying relationships. John Wiley & Sons. ISBN 0-471-49616-2.
* Wilson, John P.; Fotheringham, A. Stewart (2007). The Handbook of Geographic Information Science. John Wiley & Sons. ISBN 9781405107952.
* Fotheringham, A. Stewart; Rogerson, Peter A. (2008). The SAGE Handbook of Spatial Analysis. SAGE Publishing. ISBN 9781412910828.
* Carswell, James D.; Fotheringham, A. Stewart; McArdle, Gavin (2009). Web and Wireless Geographical Information Systems: 9th International Symposium. Springer Science+Business Media. doi:10.1007/978-3-642-10601-9. ISBN 9783642106002.
* Fotheringham, A. Stewart; Oshan, Taylor M.; Li, Ziqi (2023). Multiscale Geographically Weighted Regression: Theory and Practice. CRC Press. doi:10.1201/9781003435464. ISBN 9781003435464. S2CID 262209577.

## See also[edit]

* Arthur Getis – American geographer and spatial statistician
* George F. Jenks – American geographer and cartographer
* Michael DeMers – American geographer and fiction writer
* Michael Frank Goodchild – British-American geographer
* National Center for Geographic Information and Analysis
* Technical geography – Study of using and creating tools to manage spatial information
* Waldo Tobler – American geographerPages displaying short descriptions of redirect targets

## References[edit]

1. ^ a b "Alexander Stewart Fotheringham – Biography". The Academy of Europe. Retrieved 17 October 2023.
2. ^ a b c d e "Stewart Fotheringham". Arizona State University. Retrieved 17 October 2023.
3. ^ a b c "Stewart Fotheringham". Arizona State University: Global Institute of Sustainability and Innovation. Retrieved 17 October 2023.
4. ^ a b c "2019 AAG Distinguished Scholarship Honors". American Association of Geographers. 4 December 2018. Retrieved 17 October 2023.
5. ^ a b c d Fotheringham, A. Stewart; Brunsdon, Chris; Charlton, Martin (2002). Geographically Weighted Regression: the analysis of spatially varying relationships. John Wiley & Sons. ISBN 0-471-49616-2.
6. ^ "Stewart Fotheringham". National Academy of Sciences. Retrieved 17 October 2023.
7. ^ a b c Fotheringham, A. Stewart; Oshan, Taylor M.; Li, Ziqi (2023). Multiscale Geographically Weighted Regression: Theory and Practice (1 ed.). doi:10.1201/9781003435464. ISBN 9781003435464. S2CID 262209577. Retrieved 17 October 2023.
8. ^ a b c d e f g h i j "Alexander Stewart Fotheringham". Academia Europaea. Retrieved 18 October 2023.
9. ^ a b "Mapping Science Committee". National Academy of Sciences. Retrieved 17 October 2023.
10. ^ Martin, Megan (15 February 2017). "Fotheringham selected for National Academy of Sciences committee on mapping science". ASU News. Arizona State University. Retrieved 17 October 2023.
11. ^ "How Geographically Weighted Regression (GWR) works". ArcGIS Pro. Retrieved 17 October 2023.
12. ^ a b Mitchell, Andy (2009). The ESRI Guide to GIS Analysis, Volume 2. Esri Press. ISBN 978-1-58948-116-9.
13. ^ Páez, A.; Wheeler, D.C. "Geographically Weighted Regression". International Encyclopedia of Human Geography. Retrieved 17 October 2023.
14. ^ "Geographically Weighted Regression (GWR) (Spatial Statistics)". ArcGIS Pro. Retrieved 17 October 2023.
15. ^ Bivand, Roger. "Geographically Weighted Regression". Comprehensive R Archive Network (CRAN). Retrieved 17 October 2023.
16. ^ Xiangyang, Song; Jiawei, Gao. "GWR(Processing)". QGIS Python Plugins Repository. Retrieved 17 October 2023.
17. ^ a b c d Fotheringham, A. Stewart; Crespo, Ricardo; Yao, Jing (9 March 2015). "Geographical and Temporal Weighted Regression (GTWR)" (PDF). Geographic Analysis. 47 (4): 431–452. Bibcode:2015GeoAn..47..431F. doi:10.1111/gean.12071.
18. ^ Lu, Binbin; Hu, Yigong; Yang, Dongyang; Liu, Yong; Liao, Liuqi; Yin, Zuoyao; Xia, Tianyang; Dong, Zheyi; Harris, Paul; Brunsdon, Chris; Comber, Lex; Dong, Guanpeng (February 2023). "GWmodelS: A software for geographically weighted models". SoftwareX. 21. Bibcode:2023SoftX..2101291L. doi:10.1016/j.softx.2022.101291.
19. ^ a b c "Multiscale Geographically Weighted Regression (MGWR) (Spatial Statistics)". ArcGIS Pro. Retrieved 17 October 2023.
20. ^ Oshan, T. M.; Kang, Li, Z.; Wolf, W.; Fotheringham, Alexander Stewart (2019). "mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale". ISPRS International Journal of Geo-Information. 8 (6): 269. Bibcode:2019IJGI....8..269O. doi:10.3390/ijgi8060269.`{{cite journal}}`: CS1 maint: multiple names: authors list (link)
21. ^ "Multiscale Geographically Weighted Regression (MGWR)". GitHub. Retrieved 18 October 2023.
22. ^ "Webinars & Workshops". University Consortium for Geographic Information Science. Retrieved 18 October 2023.
23. ^ "The AAG Fellows". American Association of Geographers. Retrieved 17 October 2023.

## External links[edit]

* Arizona State University official website
* Stewart Fotheringham publications indexed by Google Scholar