Exploring Multiscale Spatial Interactions: Multiscale Geographically Weighted Negative Binomial Regression

References

• Bai, H., H. Wang, D. Li, and Y. Ge. 2023. Information consistency-based measures for spatial stratified heterogeneity. Annals of the American Association of Geographers 113 (10):2512–24. doi: 10.1080/24694452.2023.2223700.
   Google Scholar
• Barrios, J. M., W. W. Verstraeten, P. Maes, J. M. Aerts, J. Farifteh, and P. Coppin. 2012. Using the gravity model to estimate the spatial spread of vector-borne diseases. International Journal of Environmental Research and Public Health 9 (12):4346–64. doi: 10.3390/ijerph9124346.
   PubMed Web of Science ®Google Scholar
• Batten, D. F., and D. E. Boyce. 1987. Spatial interaction, transportation, and interregional commodity flow models. In Handbook of regional and urban economics, vol. 1, ed. P. Nijkamp, 357–406. Amsterdam, The Netherlands: Elsevier.
   Google Scholar
• Batty, M. 2013. The new science of cities. Cambridge, MA: MIT Press.
   Google Scholar
• Berhanu, G. 2004. Models relating traffic safety with road environment and traffic flows on arterial roads in Addis Ababa. Accident Analysis and Prevention 36 (5):697–704. doi: 10.1016/j.aap.2003.05.002.
   PubMed Web of Science ®Google Scholar
• Burger, M., F. Van Oort, and G. J. Linders. 2009. On the specification of the gravity model of trade: Zeros, excess zeros and zero-inflated estimation. Spatial Economic Analysis 4 (2):167–90. doi: 10.1080/17421770902834327.
   Web of Science ®Google Scholar
• Cameron, A. C., and P. K. Trivedi. 1986. Econometric models based on count data: Comparisons and applications of some estimators and tests. Journal of Applied Econometrics 1 (1):29–53. doi: 10.1002/jae.3950010104.
   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
• Congdon, P. 1993. Approaches to modelling overdispersion in the analysis of migration. Environment and Planning A: Economy and Space 25 (10):1481–1510. doi: 10.1068/a251481.
   Web of Science ®Google Scholar
• Da Silva, A. R., and T. C. V. Rodrigues. 2014. Geographically weighted negative binomial regression—Incorporating overdispersion. Statistics and Computing 24:769–83.
   Web of Science ®Google Scholar
• Fischer, M. M., and A. Reggiani. 2005. Spatial interaction models: From the gravity to the neural network approach. In Urban dynamics and growth: Advances in urban economics, ed. R. Capello and P. Nijkamp, 319–46. Bingley, UK: Emerald Group.
   Google Scholar
• Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2002. Geographically weighted regression: The analysis of spatially varying relationships. Chichester, UK: Wiley.
   Google Scholar
• Fotheringham, A. S., and Z. Li. 2023. Measuring the unmeasurable: Models of geographical context. Annals of the American Association of Geographers 113 (10): 2269–86.
   Google Scholar
• Fotheringham, A. S., Z. Li, and L. J. Wolf. 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):1–20. doi: 10.1080/24694452.2020.1835459.
   Web of Science ®Google Scholar
• Fotheringham, A. S., and M. E. O’Kelly. 1989. Spatial interaction models: Formulations and applications. London: Kluwer Academic.
   Google Scholar
• Fotheringham, A. S., W. 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
• Gourieroux, C., A. Monfort, and A. Trognon. 1984. Pseudo-maximum likelihood methods: Applications to Poisson models. Econometrica 52 (3):701. doi: 10.2307/1913472.
   Web of Science ®Google Scholar
• Griffith, D. A. 2009. Modeling spatial autocorrelation in spatial interaction data: Empirical evidence from 2002 Germany journey-to-work flows. Journal of Geographical Systems 11 (2):117–40. doi: 10.1007/s10109-009-0082-z.
   Web of Science ®Google Scholar
• Gu, H., H. Yu, M. Sachdeva, and Y. Liu. 2021. Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression. Growth and Change 52 (1):443–59. doi: 10.1111/grow.12453.
   Web of Science ®Google Scholar
• Guo, F., X. Wang, and M. A. Abdel-Aty. 2010. Modeling signalized intersection safety with corridor-level spatial correlations. Accident; Analysis and Prevention 42 (1):84–92. doi: 10.1016/j.aap.2009.07.005.
   PubMed Web of Science ®Google Scholar
• Guo, L., Z. Ma, and L. Zhang. 2008. Comparison of bandwidth selection in application of geographically weighted regression: A case study. Canadian Journal of Forest Research 38 (9):2526–34. doi: 10.1139/X08-091.
   Web of Science ®Google Scholar
• Hastie, T., and R. Tibshirani. 1986. Generalized Additive Models. Statistical Science 1 (3): 297–310.
   Google Scholar
• Haynes, K. E., and A. S. Fotheringham. 1984. Gravity and spatial interaction models, vol. 2. Beverly Hills, CA: Sage.
   Google Scholar
• Kordi, M., and A. S. Fotheringham. 2016. Spatially weighted interaction models (SWIM). Annals of the American Association of Geographers 106 (5):990–1012. doi: 10.1080/24694452.2016.1191990.
   Web of Science ®Google Scholar
• Li, Z., and A. S. Fotheringham. 2020. Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science 34 (7):1378–97. doi: 10.1080/13658816.2020.1720692.
   Web of Science ®Google Scholar
• Ma, Z., and Z. Huang. 2023. A Bayesian implementation of the multiscale geographically weighted regression model with INLA. Annals of the American Association of Geographers 113 (6):1501–15. doi: 10.1080/24694452.2023.2187756.
   Web of Science ®Google Scholar
• Murakami, D., N. Tsutsumida, T. Yoshida, T. Nakaya, and B. Lu. 2020. 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
• Nakaya, T. 2001. Local spatial interaction modelling based on the geographically weighted regression approach. GeoJournal 53 (4):347–58. doi: 10.1023/A:1020149315435.
   Google Scholar
• Nakaya, T., A. S. Fotheringham, C. Brunsdon, and M. Charlton. 2005. Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine 24 (17): 2695–717.
   Google Scholar
• Nissi, E., and A. Sarra. 2011. Detecting local variations in spatial interaction models by means of geographically weighted regression. Journal of Applied Sciences 11 (4):630–38. doi: 10.3923/jas.2011.630.638.
   Google Scholar
• Oliveto, P. S., and C. Witt. 2014. On the runtime analysis of the simple genetic algorithm. Theoretical Computer Science 545:2–19. doi: 10.1016/j.tcs.2013.06.015.
   Web of Science ®Google Scholar
• Oshan, T. 2022. Spatial interaction modeling. In Handbook of spatial analysis in the social sciences, ed. S. J. Rey and R. S. Franklin, 208–22. Cheltenham, UK: Edward Elgar.
   Google Scholar
• Páez, A., T. Uchida, and K. Miyamoto. 2002a. A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity. Environment and Planning A: Economy and Space 34 (4):733–54. doi: 10.1068/a34110.
   Web of Science ®Google Scholar
• Páez, A., T. Uchida, and K. Miyamoto. 2002b. A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests. Environment and Planning A: Economy and Space 34 (5):883–904. doi: 10.1068/a34133.
   Web of Science ®Google Scholar
• Roy, J. R. 2004. Spatial interaction modelling: A regional science context. Berlin, Germany: Springer.
   Google Scholar
• Roy, J. R., and J. C. Thill. 2003. Spatial interaction modelling. Papers in Regional Science 83 (1):339–61. doi: 10.1007/s10110-003-0189-4.
   Web of Science ®Google Scholar
• Sachdeva, M., A. S. Fotheringham, and Z. Li. 2022. Do places have value? Quantifying the intrinsic value of housing neighborhoods using MGWR. Journal of Housing Research 31 (1):24–52. doi: 10.1080/10527001.2021.2003505.
   Google Scholar
• Sachdeva, M., A. S. Fotheringham, Z. Li, and H. Yu. 2023. On the local modeling of count data: Multiscale geographically weighted Poisson regression. International Journal of Geographical Information Science 37 (10):2238–61. doi: 10.1080/13658816.2023.2250838.
   Web of Science ®Google Scholar
• Sen, A., and T. Smith. 1995. Gravity models of spatial interaction behavior. Berlin, Germany: Springer.
   Google Scholar
• Song, J., H. Yu, and Y. Lu. 2021. Spatial-scale dependent risk factors of heat-related mortality: A multiscale geographically weighted regression analysis. Sustainable Cities and Society 74:103159. doi: 10.1016/j.scs.2021.103159.
   Web of Science ®Google Scholar
• Thurston, S. W., M. P. Wand, and J. K. Wiencke. 2000. Negative binomial additive models. Biometrics 56 (1):139–44. doi: 10.1111/j.0006-341x.2000.00139.x.
   PubMed Web of Science ®Google Scholar
• Vlašić, I., M. Ðurasević, and D. Jakobović. 2019. Improving genetic algorithm performance by population initialisation with dispatching rules. Computers & Industrial Engineering 137:106030. doi: 10.1016/j.cie.2019.106030.
   Web of Science ®Google Scholar
• Wilson, A. G. 1967. A statistical theory of spatial distribution models. Transportation Research 1 (3):253–69. doi: 10.1016/0041-1647(67)90035-4.
   Google Scholar
• Wilson, A. G. 1971. A family of spatial interaction models, and associated developments. Environment and Planning A: Economy and Space 3 (1):1–32. doi: 10.1068/a030001.
   Web of Science ®Google Scholar
• Wolf, L. J., L. Anselin, D. Arribas-Bel, and L. R. Mobley. 2021. On spatial and platial dependence: Examining shrinkage in spatially dependent multilevel models. Annals of the American Association of Geographers 111 (6):1–13. doi: 10.1080/24694452.2020.1841602.
   Web of Science ®Google Scholar
• Wu, B., J. Yan, and K. Cao. 2023. 10-Norm variable adaptive selection for geographically weighted regression model. Annals of the American Association of Geographers 113 (5):1190–1206. doi: 10.1080/24694452.2022.2161988.
   Web of Science ®Google Scholar
• Yacim, J. A., and D. G. B. Boshoff. 2019. A comparison of bandwidth and kernel function selection in geographically weighted regression for house valuation. Architecture 10 (1):58–68.
   Google Scholar
• Yu, H., A. S. Fotheringham, Z. Li, T. Oshan, W. Kang, and L. J. Wolf. 2019. Inference in multiscale geographically weighted regression. Geographical Analysis 52 (1):87–106. doi: 10.1111/gean.12189.
   Web of Science ®Google Scholar
• Yu, H., A. S. Fotheringham, Z. Li, T. Oshan, and L. J. Wolf. 2020. On the measurement of bias in geographically weighted regression models. Spatial Statistics 38:100453. doi: 10.1016/j.spasta.2020.100453.
   Web of Science ®Google Scholar
• Zhang, L., J. Cheng, C. Jin, and H. Zhou. 2019. A multiscale flow-focused geographically weighted regression modelling approach and its application for transport flows on expressways. Applied Sciences 9 (21):4673. doi: 10.3390/app9214673.
   Google Scholar
• Zhang, W. B., Y. Ge, H. Bai, Y. Jin, A. Stein, and P. M. Atkinson. 2023. Spatial association from the perspective of mutual information. Annals of the American Association of Geographers 113 (8):1960–76. doi: 10.1080/24694452.2023.2209629.
   Web of Science ®Google Scholar