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

 

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: 2021 | 2020 | 2019 | 2018 | 2015
Number of items: 17.

2021

Li, Z. and Fotheringham, A. S. (2021) The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008-2020). Transactions in GIS, (Accepted for Publication)

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)

Rey, S. J. et al. (2021) The PySAL ecosystem: philosophy and implementation. Geographical Analysis, (doi: 10.1111/gean.12276) (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

Wang, C., Li, Z. , Clay Mathews, M., Praharaj, S., Karna, B. and Solís, P. (2020) The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States. International Journal of Environmental Health Research, (doi: 10.1080/09603123.2020.1847258) (Early Online Publication)

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 Wed Dec 1 11:36:35 2021 GMT.
Jump to: Articles
Number of items: 17.

Articles

Li, Z. and Fotheringham, A. S. (2021) The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008-2020). Transactions in GIS, (Accepted for Publication)

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)

Rey, S. J. et al. (2021) The PySAL ecosystem: philosophy and implementation. Geographical Analysis, (doi: 10.1111/gean.12276) (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)

Wang, C., Li, Z. , Clay Mathews, M., Praharaj, S., Karna, B. and Solís, P. (2020) The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States. International Journal of Environmental Health Research, (doi: 10.1080/09603123.2020.1847258) (Early Online Publication)

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)

This list was generated on Wed Dec 1 11:36:35 2021 GMT.

Grants

  • 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.

 

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)

Additional information

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