Ziqi Li
Assistant Professor of Quantitative Geography
Department of Geography
Florida State university
Ziqi.Li [at] fsu.edu
Bio
My research focuses on the methodological development of spatially explicit and interpretable statistical/machine learning models to investigate human behavior across space and place. I am one of the primary developers of Multi-scale Geographically Weighted Regression (MGWR) and Python Spatial Analysis Library (PySAL). Prior to FSU, I was a permenat faculty (2021-2023) at the University of Glasgow UK (and remain as a Honorary Research Fellow), and a visiting faculty (2020-2021) at the University of Illinois, Urbana-Champaign. I earned my PhD in Geography from Arizona State University.
Research interests
Explainable AI for spatial data
Spatial statistical methods
Spatial AI fairness
Urban/social inequalities
Open-source software
News
02/2024: I am happy to join the editorial board of Social Science Computer Review (SAGE) and Social Sciences & Humanities Open (Elsevier).
11/2023: Our Book on MGWR has been finally published and it is available to order. Link
10/2023: New paper: Equalizing urban agriculture access in Glasgow: A spatial optimization approach. Our paper has been reported by the University of Glasgow News and The Herald.
09/2023: New confernece paper being published in GIScience 2023. Link.
09/2023: New co-authored paper (On the local modeling of count data: multiscale geographically weighted Poisson regression) being published in IJGIS. Link.
09/2023: I will be giving a talk at SDS the Fourth Spatial Data Science Symposium (SDSS 2023) on Sep 6. Link.
08/2023: I am thrilled to join the Department of Geography at Florida State Univeristy!
07/2023: I am co-chairing AGILE 2024 which will take place in Glasgow June 2024. The Call for papers is online now.
07/2023: New paper (Measuring the Unmeasurable: Models of Geographical Context) being published in the Annals of American Association of Geographers. Link
06/2023: I will be giving a seminar talk at Harvard Center for Geographical Analysis on June 16th. Details
06/2023: Our upcoming book Multiscale Geographically Weighted Regression: Theory and Practice is undergoing production with CRC Press and will be published in Nov 2023. Link
04/2023: We organised/concluded a successful GISRUK 2023!
04/2023: Sui Zhang will present his accepted GISRUK 2023 conference paper on Geographically Weighted Cronbach's Alpha.
03/2023: I presented my work on GeoShapley at AAG 2023 Denver. More to come.
02/2023: We are hosting a workshop on March 13 with goverment partners on flooding risk management and mitigation: Eventbrite
02/2023: I am in the organsing committee of CPGIS 2023. Look forward to seeing everyone in London!
02/2023: I am in the organising committee of City+2023 and organising a session on "spatial methods".
02/2023: I am in the organsing committee of GISRUK 2023. Look forward to seeing everyone in Glasgow!
01/2023: New paper out in Travel Behaviour and Society: Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing.
News (past - 2022)
11/2022: I will present a workshop on MGWR at FOSS4G:UK Local in Glasgow. Registration is available here.
09/2022: I join the GIScience Research Group at Royal Geographical Society comittee and will be the Secretary for the next three year.
09/2022: New paper out in the Annals of GIS: Understanding public perspectives on fracking in the United States using social media big data
07/2022: I will give a seminar talk at the Urban Big Data Centre. Link
06/2022: New paper accepted in CEUS: Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost.
05/2022: I will give a short talk on geospatial XAI at CoSE Zoomposium.
04/2022: I will present my work on modelling ridesharing willingness at GISRUK 2022 in Liverpool.
03/2022: I will present my work on interpretable machine learning at AI UK 2022 hosted by the Alan Turing Institute.
02/2022: I presented my work: An investigation of using SHAP to interpret machine learning models of spatial data at the AAG 2022. The recordings can be found here.
02/2022: I received a Post-doctoral Enrichment Award from the Alan Turing Institute to investigate the utility of local interpretable machine learning and explainable AI for spatial data analysis and modelling.
01/2022: New paper accepted with Fotheringham et al. in IJGIS : On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processe.
01/2022: New paper out with Schedeva et al: Do Places Have Value? Quantifying the Intrinsic Value of Housing Neighborhoods Using MGWR.
12/2021: New paper out with Fotheringham: The spatial and temporal dynamics of voter preference determinants in four US presidential elections (2008–2020).
10/2021: New paper with Zhao et al is published in Health and Place: Understanding the Interaction between Human Activities and Physical Health under Extreme Heat Environment in Phoenix, Arizona.
09/2021: I joined the School of Geographical and Earth Sciences in the University of Glasgow as a Lecturer in GIScience. Check out my staff page.
09/2021: Our US NSF grant with PIs from Arizona State University and the University of Maryland is awared.
06/2021: New paper with Sachdeva et al is published in Geographical Analysis: Are We Modelling Spatially Varying Processes or Non-linear Relationships?
06/2021: New paper with Rey et al is published in Geographical Analysis: The PySAL Ecosystem: Philosophy and Implementation
05/2021: I am listed as Teachers Ranked as Excellent from the University of Illinois in Fall 2020 and Spring 2021. Link
05/2021: New paper with Kurji et al is published in BMC Health Services Research: Spatial variability in factors influencing maternal health service use in Jimma Zone, Ethiopia: a geographically-weighted regression analysis.
04/2021: I received the J. Warren Nystrom Award from the the American Associatetion of Geographers. ASUNews
01/2021: New paper with Fotheringham et al is published in the Annals of American Associatetion of Geographers: Scale, Context, and Heterogeneity: A Spatial Analytical Perspective on the 2016 U.S. Presidential Election.