2023

Fotheringham, A. S., Oshan, T. and Li, Z. (2023) Multiscale Geographically Weighted Regression: Theory and Practice. CRC Press. (In Press)

Fotheringham, A. S. and Li, Z. (2023) Measuring the unmeasurable: models of geographical context. Annals of the American Association of Geographers, 113(10), pp. 2269-2286. (doi: 10.1080/24694452.2023.2227690)

Russell, A., Li, Z. and Wang, M. (2023) Equalizing urban agriculture access in Glasgow: a spatial optimization approach. International Journal of Applied Earth Observation and Geoinformation, 124, 103525. (doi: 10.1016/j.jag.2023.103525)

Li, Z. (2023) Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing. Travel Behaviour and Society, 31, pp. 284-294. (doi: 10.1016/j.tbs.2022.12.006)

Gong, X., Lu, Y., Beene, D., Li, Z. , Hu, T., Morgan, M. and Lin, Y. (2023) Understanding public perspectives on fracking in the United States using social media big data. Annals of GIS, 29(1), pp. 21-35. (doi: 10.1080/19475683.2022.2121856)

Zhao, Q. , Li, Z. , Shah, D., Fischer, H., Solís, P. and Wentz, E. (2023) Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona. Health and Place, 79, 102691. (doi: 10.1016/j.healthplace.2021.102691) (PMID:34656430)

2022

Sachdeva, M., Fotheringham, A. S., Li, Z. and Yu, H. (2022) Are we modelling spatially varying processes or non-linear relationships? Geographical Analysis, 54(4), pp. 715-738. (doi: 10.1111/gean.12297)

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, (doi: 10.5281/zenodo.6411504)

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

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)

Articles

Fotheringham, A. S. and Li, Z. (2023) Measuring the unmeasurable: models of geographical context. Annals of the American Association of Geographers, 113(10), pp. 2269-2286. (doi: 10.1080/24694452.2023.2227690)

Russell, A., Li, Z. and Wang, M. (2023) Equalizing urban agriculture access in Glasgow: a spatial optimization approach. International Journal of Applied Earth Observation and Geoinformation, 124, 103525. (doi: 10.1016/j.jag.2023.103525)

Li, Z. (2023) Leveraging explainable artificial intelligence and big trip data to understand factors influencing willingness to ridesharing. Travel Behaviour and Society, 31, pp. 284-294. (doi: 10.1016/j.tbs.2022.12.006)

Gong, X., Lu, Y., Beene, D., Li, Z. , Hu, T., Morgan, M. and Lin, Y. (2023) Understanding public perspectives on fracking in the United States using social media big data. Annals of GIS, 29(1), pp. 21-35. (doi: 10.1080/19475683.2022.2121856)

Zhao, Q. , Li, Z. , Shah, D., Fischer, H., Solís, P. and Wentz, E. (2023) Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona. Health and Place, 79, 102691. (doi: 10.1016/j.healthplace.2021.102691) (PMID:34656430)

Sachdeva, M., Fotheringham, A. S., Li, Z. and Yu, H. (2022) Are we modelling spatially varying processes or non-linear relationships? Geographical Analysis, 54(4), pp. 715-738. (doi: 10.1111/gean.12297)

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)

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)

Books

Fotheringham, A. S., Oshan, T. and Li, Z. (2023) Multiscale Geographically Weighted Regression: Theory and Practice. CRC Press. (In Press)

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, (doi: 10.5281/zenodo.6411504)