Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models

References

• Brunsdon, C., A. S. Fotheringham, and M. Charlton. 1999. Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science 39 (3):497–524. doi: 10.1111/0022-4146.00146.
   Web of Science ®Google Scholar
• Brunsdon, C., A. S. Fotheringham, and M. Charlton. 1996. Geographically weighted regression: A method for exploring spatial non-stationarity. Geographical Analysis 28 (4):281–98. doi: 10.1111/j.1538-4632.1996.tb00936.x.
   Web of Science ®Google Scholar
• Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: A practical information-theoretic approach. 2nd ed. New York: Springer.
   Google Scholar
• Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65 (1):23–35. doi: 10.1007/s00265-010-1029-6.
   Web of Science ®Google Scholar
• Chen, V. Y. J., T. C. Yang, and H. L. Jian. 2022. Geographically weighted regression modeling for multiple outcomes. Annals of the American Association of Geographers 112 (5):1278–95. doi: 10.1080/24694452.2021.1985955.
   Web of Science ®Google Scholar
• Chen, Y. X., M. Chen, B. Huang, C. Wu, and W. J. Shi. 2021. Modeling the spatiotemporal association between COVID-19 transmission and population mobility using geographically and temporally weighted regression. GeoHealth 5 (5):e2021GH000402. doi: 10.1029/2021GH000402.
   Web of Science ®Google Scholar
• Chu, H. J., S. J. Kong, and C. H. Chang. 2018. Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression. International Journal of Applied Earth Observation and Geoinformation 65:1–11. doi: 10.1016/j.jag.2017.10.001.
   Web of Science ®Google Scholar
• Dubé, J., and D. Legros. 2013. Dealing with spatial data pooled over time in statistical models. Letters in Spatial and Resource Sciences 6 (1):1–18. doi: 10.1007/s12076-012-0082-3.
   Google Scholar
• Du, Z. H., S. S. Wu, F. Zhang, R. Y. Liu, and Y. Zhou. 2018. Extending geographically and temporally weighted regression to account for both spatiotemporal heterogeneity and seasonal variations in coastal seas. Ecological Informatics 43:185–99. doi: 10.1016/j.ecoinf.2017.12.005.
   Web of Science ®Google Scholar
• Fan, J. Q., C. M. Zhang, and J. Zhang. 2001. Generalized likelihood ratio statistics and Wilks phenomenon. The Annals of Statistics 29 (1):153–93. doi: 10.1214/aos/996986505.
   Web of Science ®Google Scholar
• Fotheringham, A. S., R. Crespo, and J. Yao. 2015. Geographical and temporal weighted regression (GTWR). Geographical Analysis 47 (4):431–52. doi: 10.1111/gean.12071.
   Web of Science ®Google Scholar
• Fotheringham, A. S., and T. M. Oshan. 2016. Geographically weighted regression and multicollinearity: Dispelling the myth. Journal of Geographical Systems 18 (4):303–29. doi: 10.1007/s10109-016-0239-5.
   Web of Science ®Google Scholar
• Fotheringham, A. S., W. B. Yang, and W. Kang. 2017. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers 107 (6):1247–65. doi: 10.1080/24694452.2017.1352480.
   Web of Science ®Google Scholar
• Guo, Y. X., Q. H. Tang, D. Y. Gong, and Z. Y. Zhang. 2017. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment 198:140–49. doi: 10.1016/j.rse.2017.06.001.
   Web of Science ®Google Scholar
• Hong, Z. M., C. L. Mei, H. H. Wang, and W. L. Du. 2021. Spatiotemporal effects of climate factors on childhood hand, foot, and mouth disease: A case study using mixed geographically and temporally weighted regression models. International Journal of Geographical Information Science 35 (8):1611–33. doi: 10.1080/13658816.2021.1882681.
   Web of Science ®Google Scholar
• Huang, B., B. Wu, and M. Barry. 2010. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science 24 (3):383–401. doi: 10.1080/13658810802672469.
   Web of Science ®Google Scholar
• Hurvich, C. M., J. S. Simonoff, and C. L. Tsai. 1998. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60 (2):271–93. doi: 10.1111/1467-9868.00125.
   Web of Science ®Google Scholar
• Kiefer, J. 1953. Sequential minimax search for a maximum. Proceedings of the American Mathematical Society 4 (3):502–06. doi: 10.1090/S0002-9939-1953-0055639-3.
   Web of Science ®Google Scholar
• Leung, Y., C. L. Mei, and W. X. Zhang. 2000. Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A: Economy and Space 32 (1):9–32. doi: 10.1068/a3162.
   Web of Science ®Google Scholar
• Liu, J. P., Y. Y. Zhao, Y. Yang, S. H. Xu, F. H. Zhang, X. L. Zhang, L. H. Shi, and A. Qiu. 2017. A mixed geographically and temporally weighted regression: Exploring spatial-temporal variations from global and local perspectives. Entropy 19 (2):53. doi: 10.3390/e19020053.
   Web of Science ®Google Scholar
• Liu, Y., K. F. Lam, J. T. Wu, and T. T. Y. Lam. 2018. Geographically weighted temporally correlated logistic regression model. Scientific Reports 8 (1):1417. doi: 10.1038/s41598-018-19772-6.
   PubMedGoogle Scholar
• Mei, C. L., M. Xu, and N. Wang. 2016. A bootstrap test for constant coefficients in geographically weighted regression models. International Journal of Geographical Information Science 30 (8):1622–43. doi: 10.1080/13658816.2016.1149181.
   Web of Science ®Google Scholar
• Murakami, D., N. Tsutsumida, T. Yoshida, T. Nakaya, and B. Lu. 2021. Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels. Annals of the American Association of Geographers 111 (2):459–80. doi: 10.1080/24694452.2020.1774350.
   Web of Science ®Google Scholar
• Peng, Y. D., W. S. Li, X. B. Luo, and H. Li. 2019. A geographically and temporally weighted regression model for spatial downscaling of Modis land surface temperatures over urban heterogeneous regions. IEEE Transactions on Geoscience and Remote Sensing 57 (7):5012–27. doi: 10.1109/TGRS.2019.2895351.
   Web of Science ®Google Scholar
• Wang, N., C. L. Mei, and X. D. Yan. 2008. Local linear estimation of spatially varying coefficient models: An improvement on the geographically weighted regression technique. Environment and Planning A: Economy and Space 40 (4):986–1005. doi: 10.1068/a3941.
   Web of Science ®Google Scholar
• Wang, Q., Y. A. Wang, W. Chen, X. Zhou, and M. J. Zhao. 2021. Factors affecting industrial land use efficiency in China: Analysis from government and land market. Environment, Development and Sustainability 23 (7):10973–93. doi: 10.1007/s10668-020-01100-6.
   Web of Science ®Google Scholar
• Wang, W. T., and D. K. Li. 2017. Structure identification and variable selection in geographically weighted regression models. Journal of Statistical Computation and Simulation 87 (10):2050–68. doi: 10.1080/00949655.2017.1311896.
   Web of Science ®Google Scholar
• Wheeler, D., and M. Tiefelsdorf. 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems 7 (2):161–87. doi: 10.1007/s10109-005-0155-6.
   Google Scholar
• Wu, B., R. Li, and B. Huang. 2014. A geographically and temporally weighted autoregressive model with application to housing prices. International Journal of Geographical Information Science 28 (5):1186–1204. doi: 10.1080/13658816.2013.878463.
   Web of Science ®Google Scholar
• Wu, C., F. Ren, W. Hu, and Q. Y. Du. 2019. Multiscale geographically and temporally weighted regression: Exploring the spatiotemporal determinants of housing prices. International Journal of Geographical Information Science 33 (3):489–511. doi: 10.1080/13658816.2018.1545158.
   Web of Science ®Google Scholar
• Xiao, Y. T., Z. Tian, and W. Y. Guo. 2014. A bootstrap test for non-stationarity of geographically and temporally weighted regression models (in Chinese). Statistics and Decision 9:8–12.
   Google Scholar
• Xuan, H. Y., S. Li, and M. Amin. 2015. Statistical inference of geographically and temporally weighted regression model. Pakistan Journal of Statistics 31 (3):307–25.
   Google Scholar
• Yuan, M., and Y. Lin. 2006. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68 (1):49–67. doi: 10.1111/j.1467-9868.2005.00532.x.
   Web of Science ®Google Scholar
• Zhang, W. Y., S. Y. Lee, and X. Y. Song. 2002. Local polynomial fitting in semivarying coefficient models. Journal of Multivariate Analysis 82 (1):166–88. doi: 10.1006/jmva.2001.2012.
   Web of Science ®Google Scholar
• Zhang, Z., J. Li, T. Fung, H. Y. Yu, C. L. Mei, Y. Leung, and Y. Zhou. 2021. Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing. International Journal of Geographical Information Science 35 (11):2262–86. doi: 10.1080/13658816.2021.1912348.
   Web of Science ®Google Scholar