Dr Ziqi Li

  • Lecturer in Geospatial Information Science (GIS) (School of Geographical & Earth Sciences)

Biography

My research focuses on the methodological development of spatially explicit statistical and machine learning models. I am one of the primary developers of multi-scale geographically weighted regression (MGWR). I am also broadly interested in the application of advanced spatial analysis and modeling in the fields of public health, urban analytics, political geography, and remote sensing.

Prior to joining Glasgow in 2021, I was a Visiting Assistant Professor at the University of Illinois, Urbana-Champaign. I earned my PhD in Geography from Arizona State University in 2020. I also hold a MA in Geography from George Washington University, a BSc in Geomatics from the University of Waterloo, and a BEng in Remote Sensing from Wuhan University.

Personal Website

I'm happy to supervise PhD students and visiting students/scholars in my research areas. If our interests align, please contact me via email.

Research interests

Research interests

  • Spatially-explicit statistical learning
  • Interpretable Machine Learning and AI
  • Urban and health geography
  • Open-source software development

Research profiles:

Publications

List by: Type | Date

Jump to: 2022 | 2021 | 2020 | 2019 | 2018 | 2015
Number of items: 22.

2022

Gong, X., Lu, Y., Beene, D., Li, Z. , Hu, T., Morgan, M. and Lin, Y. (2022) Understanding public perspectives on fracking in the United States using social media big data. Annals of GIS, (doi: 10.1080/19475683.2022.2121856) (Early Online Publication)

Fotheringham, A. S., Yu, H., Wolf, L. J., Oshan, T. M. and Li, Z. (2022) On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processes. International Journal of Geographical Information Science, 36(8), pp. 1485-1502. (doi: 10.1080/13658816.2022.2034829)

Li, Z. (2022) Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845. (doi: 10.1016/j.compenvurbsys.2022.101845)

Rey, S. J. et al. (2022) The PySAL ecosystem: philosophy and implementation. Geographical Analysis, 54(3), pp. 467-487. (doi: 10.1111/gean.12276)

Li, Z. and Fotheringham, A. S. (2022) The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008-2020). Transactions in GIS, 26(3), pp. 1609-1628. (doi: 10.1111/tgis.12880)

Wang, C., Li, Z. , Clay Mathews, M., Praharaj, S., Karna, B. and Solís, P. (2022) The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States. International Journal of Environmental Health Research, 32(5), pp. 1147-1154. (doi: 10.1080/09603123.2020.1847258) (PMID:33228411)

Li, Z. and Xu, T. (2022) Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning. In: GISRUK 2022, Liverpool, UK, 05-08 Apr 2022, (Accepted for Publication)

Sachdeva, M., Fotheringham, S. and Li, Z. (2022) Do places have value?: Quantifying the intrinsic value of housing neighborhoods using MGWR. Journal of Housing Research, 31(1), pp. 24-52. (doi: 10.1080/10527001.2021.2003505)

2021

Zhao, Q. , Li, Z. , Shah, D., Fischer, H., Solís, P. and Wentz, E. (2021) Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona. Health and Place, (doi: 10.1016/j.healthplace.2021.102691) (PMID:34656430) (In Press)

Sachdeva, M., Fotheringham, A. S., Li, Z. and Yu, H. (2021) Are we modelling spatially varying processes or non-linear relationships? Geographical Analysis, (doi: 10.1111/gean.12297) (Early Online Publication)

Kurji, J., Thickstun, C., Bulcha, G., Taljaard, M., Li, Z. and Kulkarni, M. A. (2021) Spatial variability in factors influencing maternal health service use in Jimma Zone, Ethiopia: a geographically-weighted regression analysis. BMC Health Services Research, 21, 454. (doi: 10.1186/S12913-021-06379-3) (PMID:33980233) (PMCID:PMC8117568)

Fotheringham, A. S., Li, Z. and Wolf, L. J. (2021) Scale, context, and heterogeneity: a spatial analytical perspective on the 2016 U.S. presidential election. Annals of the American Association of Geographers, 111(6), pp. 1602-1621. (doi: 10.1080/24694452.2020.1835459)

2020

Li, Z. , Fotheringham, A. S., Oshan, T. M. and Wolf, L. J. (2020) Measuring bandwidth uncertainty in multiscale geographically weighted regression using Akaike weights. Annals of the American Association of Geographers, 110(5), pp. 1500-1520. (doi: 10.1080/24694452.2019.1704680)

Yu, H., Fotheringham, A. S., Li, Z. , Oshan, T. and Wolf, L. J. (2020) On the measurement of bias in geographically weighted regression models. Spatial Statistics, 38, 100453. (doi: 10.1016/j.spasta.2020.100453)

Li, Z. and Fotheringham, A. S. (2020) Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), pp. 1378-1397. (doi: 10.1080/13658816.2020.1720692)

Yu, H., Fotheringham, A. S., Li, Z. , Oshan, T., Kang, W. and Wolf, L. J. (2020) Inference in multiscale geographically weighted regression. Geographical Analysis, 52(1), pp. 87-106. (doi: 10.1111/gean.12189)

2019

Fotheringham, A. S., Yue, H. and Li, Z. (2019) Examining the influences of air quality in China's cities using multi‐scale geographically weighted regression. Transactions in GIS, 23(6), pp. 1444-1464. (doi: 10.1111/tgis.12580)

Oshan, T., Wolf, L. J., Fotheringham, A. S., Kang, W., Li, Z. and Yu, H. (2019) A comment on geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 33(7), pp. 1289-1299. (doi: 10.1080/13658816.2019.1572895)

Oshan, T. M., Li, Z. , Kang, W., Wolf, L. J. and Fotheringham, A. S. (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. (doi: 10.3390/ijgi8060269)

Li, Z. , Fotheringham, A. S., Li, W. and Oshan, T. (2019) Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1), pp. 155-175. (doi: 10.1080/13658816.2018.1521523)

2018

Li, Z. (2018) NoSQL databases. Geographic Information Science and Technology Body of Knowledge, 2018(Q2), (doi: 10.22224/gistbok/2018.2.10)

2015

Li, Z. , Zhang, Z. and Davey, K. (2015) Estimating geographical PV potential using LiDAR data for buildings in downtown San Francisco. Transactions in GIS, 19(6), pp. 930-963. (doi: 10.1111/tgis.12140)

This list was generated on Sat Oct 1 10:27:37 2022 BST.
Number of items: 22.

Articles

Gong, X., Lu, Y., Beene, D., Li, Z. , Hu, T., Morgan, M. and Lin, Y. (2022) Understanding public perspectives on fracking in the United States using social media big data. Annals of GIS, (doi: 10.1080/19475683.2022.2121856) (Early Online Publication)

Fotheringham, A. S., Yu, H., Wolf, L. J., Oshan, T. M. and Li, Z. (2022) On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processes. International Journal of Geographical Information Science, 36(8), pp. 1485-1502. (doi: 10.1080/13658816.2022.2034829)

Li, Z. (2022) Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845. (doi: 10.1016/j.compenvurbsys.2022.101845)

Rey, S. J. et al. (2022) The PySAL ecosystem: philosophy and implementation. Geographical Analysis, 54(3), pp. 467-487. (doi: 10.1111/gean.12276)

Li, Z. and Fotheringham, A. S. (2022) The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008-2020). Transactions in GIS, 26(3), pp. 1609-1628. (doi: 10.1111/tgis.12880)

Wang, C., Li, Z. , Clay Mathews, M., Praharaj, S., Karna, B. and Solís, P. (2022) The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States. International Journal of Environmental Health Research, 32(5), pp. 1147-1154. (doi: 10.1080/09603123.2020.1847258) (PMID:33228411)

Sachdeva, M., Fotheringham, S. and Li, Z. (2022) Do places have value?: Quantifying the intrinsic value of housing neighborhoods using MGWR. Journal of Housing Research, 31(1), pp. 24-52. (doi: 10.1080/10527001.2021.2003505)

Zhao, Q. , Li, Z. , Shah, D., Fischer, H., Solís, P. and Wentz, E. (2021) Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona. Health and Place, (doi: 10.1016/j.healthplace.2021.102691) (PMID:34656430) (In Press)

Sachdeva, M., Fotheringham, A. S., Li, Z. and Yu, H. (2021) Are we modelling spatially varying processes or non-linear relationships? Geographical Analysis, (doi: 10.1111/gean.12297) (Early Online Publication)

Kurji, J., Thickstun, C., Bulcha, G., Taljaard, M., Li, Z. and Kulkarni, M. A. (2021) Spatial variability in factors influencing maternal health service use in Jimma Zone, Ethiopia: a geographically-weighted regression analysis. BMC Health Services Research, 21, 454. (doi: 10.1186/S12913-021-06379-3) (PMID:33980233) (PMCID:PMC8117568)

Fotheringham, A. S., Li, Z. and Wolf, L. J. (2021) Scale, context, and heterogeneity: a spatial analytical perspective on the 2016 U.S. presidential election. Annals of the American Association of Geographers, 111(6), pp. 1602-1621. (doi: 10.1080/24694452.2020.1835459)

Li, Z. , Fotheringham, A. S., Oshan, T. M. and Wolf, L. J. (2020) Measuring bandwidth uncertainty in multiscale geographically weighted regression using Akaike weights. Annals of the American Association of Geographers, 110(5), pp. 1500-1520. (doi: 10.1080/24694452.2019.1704680)

Yu, H., Fotheringham, A. S., Li, Z. , Oshan, T. and Wolf, L. J. (2020) On the measurement of bias in geographically weighted regression models. Spatial Statistics, 38, 100453. (doi: 10.1016/j.spasta.2020.100453)

Li, Z. and Fotheringham, A. S. (2020) Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), pp. 1378-1397. (doi: 10.1080/13658816.2020.1720692)

Yu, H., Fotheringham, A. S., Li, Z. , Oshan, T., Kang, W. and Wolf, L. J. (2020) Inference in multiscale geographically weighted regression. Geographical Analysis, 52(1), pp. 87-106. (doi: 10.1111/gean.12189)

Fotheringham, A. S., Yue, H. and Li, Z. (2019) Examining the influences of air quality in China's cities using multi‐scale geographically weighted regression. Transactions in GIS, 23(6), pp. 1444-1464. (doi: 10.1111/tgis.12580)

Oshan, T., Wolf, L. J., Fotheringham, A. S., Kang, W., Li, Z. and Yu, H. (2019) A comment on geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 33(7), pp. 1289-1299. (doi: 10.1080/13658816.2019.1572895)

Oshan, T. M., Li, Z. , Kang, W., Wolf, L. J. and Fotheringham, A. S. (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. (doi: 10.3390/ijgi8060269)

Li, Z. , Fotheringham, A. S., Li, W. and Oshan, T. (2019) Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1), pp. 155-175. (doi: 10.1080/13658816.2018.1521523)

Li, Z. (2018) NoSQL databases. Geographic Information Science and Technology Body of Knowledge, 2018(Q2), (doi: 10.22224/gistbok/2018.2.10)

Li, Z. , Zhang, Z. and Davey, K. (2015) Estimating geographical PV potential using LiDAR data for buildings in downtown San Francisco. Transactions in GIS, 19(6), pp. 930-963. (doi: 10.1111/tgis.12140)

Conference Proceedings

Li, Z. and Xu, T. (2022) Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning. In: GISRUK 2022, Liverpool, UK, 05-08 Apr 2022, (Accepted for Publication)

This list was generated on Sat Oct 1 10:27:37 2022 BST.

Grants

  • Ziqi Li (2022). Investigating the utility of local interpretable machine learning and explainable AI for spatial data analysis and modelling. Alan Turing Institute Post-doctoral Enrichment Award (£2000). 
  • Stewart Fotheringham, Taylor Oshan and Ziqi Li. (2021 - 2024). Advancing Methods for Spatial Analysis in Local Modeling (Total amount $399,920). Human-Environment and Geographical Sciences Program, US National Science Foundation. Award #2117455.

 

Teaching

  • GEOG5015 Web and Mobile Mapping
  • GEOG5018 Principles of Cartographic Design
  • GEOG5026 Visualisation and Map Use

Professional activities & recognition

Prizes, awards & distinctions

  • 2021: J. Warren Nystrom Award (American Association of Geographers)
  • 2020: John Odland Award (Spatial Analysis and Modeling Group, American Association of Geographers)

Professional & learned societies

  • current: Member, Scottish Alliance for Geoscience, Environment and Society
  • current: Member, The American Association of Geographers
  • current: Member, Chinese Professional in Geographic Information Systems
  • current: Fellow, Royal Geographical Society
  • current: Secretary, GIScience Research Group, Royal Geographical Society