On the local modeling of count data: multiscale geographically weighted Poisson regression

References

• Agnew, J., 1996. Mapping politics: how context counts in electoral geography. Political Geography, 15 (2), 129–146.
   Web of Science ®Google Scholar
• Agnew, J.A., 2014. Place and politics: the geographical mediation of state and society. London: Routledge.
   Google Scholar
• Agresti, A. 2002. Categorical data analysis. 2nd ed. New York: John Wiley & Sons, Inc., 320–332. http://dx.doi.org/10.1002/0471249688
   Google Scholar
• Buja, A., Hastie, T., and Tibshirani, R., 1989. Linear smoothers and additive models. The Annals of Statistics, 17 (2), 453–510.
   Web of Science ®Google Scholar
• Cardozo, O.D., García-Palomares, J.C., and Gutiérrez, J., 2012. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34, 548–558.
   Web of Science ®Google Scholar
• Cupido, K., Fotheringham, A.S., and Jevtic, P., 2021. Local modelling of U.S. mortality rates: a multiscale geographically weighted regression approach. Population, Space and Place, 27 (1), e2379.
   Web of Science ®Google Scholar
• da Silva, A.R., and Fotheringham, A.S., 2016. The Multiple Testing Issue in Geographically Weighted Regression. Geographical Analysis, 48 (3), 233–247.
   Web of Science ®Google Scholar
• DiMaggio, C., et al., 2020. Black/African American Communities are at highest risk of COVID-19: spatial modeling of New York City ZIP Code-level testing results. Annals of Epidemiology, 51, 7–13.
   PubMed Web of Science ®Google Scholar
• Everitt, B.S., 2005. Generalized additive model. In: Encyclopedia of statistics in behavioral science. John Wiley & Sons, Ltd.
   Google Scholar
• Fotheringham, A.S., Brunsdon, C., and Charlton, M., 2002. Geographically weighted regression: the analysis of spatially varying relationships. Hoboken: Wiley.
   Google Scholar
• Fotheringham, A.S. and Sachdeva, M., 2021. Modelling spatial processes in quantitative human geography. Annals of GIS, 28 (1), 5–14.
   Web of Science ®Google Scholar
• Fotheringham, A.S. and Sachdeva, M., 2022. On the importance of thinking locally for statistics and society. Spatial Statistics, 50, 100601.
   Web of Science ®Google Scholar
• Fotheringham, A.S., 2020. Local modelling: one size does not fit all. Journal of Spatial Information Science, 21 (21), 83–87.
   Google Scholar
• 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), 1–20.
   Web of Science ®Google Scholar
• Fotheringham, A.S., Yang, W., and Kang, W., 2017. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107 (6), 1247–1265.
   Web of Science ®Google Scholar
• 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), 1444–1464.
   Google Scholar
• Gao, S., et al., 2020. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12 (1), 16–26.
   Google Scholar
• Golledge, R.G., 1997. Spatial behavior: a geographic perspective. New York: Guilford Press.
   Google Scholar
• Goodchild, M.F., 2011. Formalizing place in geographic information systems. In: L.M. Burton, S. A. Matthews, et al., eds. Communities, neighborhoods, and health: expanding the boundaries of place. New York: Springer, 21–33.
   Google Scholar
• Hastie, T. and Tibshirani, R., 1986. Generalized additive models. Statistical Science, 1 (3), 297–310.
   Google Scholar
• Hauser, R.M., 1970. Context and consex: a cautionary tale. American Journal of Sociology, 75 (4, Part 2), 645–664.
   Web of Science ®Google Scholar
• Hughes, M.M., et al., 2021. County-level COVID-19 vaccination coverage and social vulnerability—United States, December 14, 2020–March 1, 2021. MMWR. Morbidity and Mortality Weekly Report, 70 (12), 431–436.
   PubMed Web of Science ®Google Scholar
• Kedron, P., et al., 2022. A replication of DiMaggio et al. (2020) in Phoenix, AZ. Annals of Epidemiology, 74, 8–14.
   PubMed Web of Science ®Google Scholar
• Khazanchi, R., et al., 2020. County-level association of social vulnerability with COVID-19 cases and deaths in the USA. Journal of General Internal Medicine, 35 (9), 2784–2787.
   PubMed Web of Science ®Google Scholar
• King, G., 1996. Why context should not count. Political Geography, 15 (2), 159–164.
   Web of Science ®Google Scholar
• Li, Z. and Fotheringham, A.S., 2020. Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34 (7), 1378–1397.
   Web of Science ®Google Scholar
• Li, Z., et al., 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), 155–175.
   Web of Science ®Google Scholar
• Li, Z., et al., 2020. Measuring bandwidth uncertainty in multiscale geographically weighted regression using Akaike weights. Annals of the American Association of Geographers, 110 (5), 1500–1520.
   Web of Science ®Google Scholar
• Liu, C., Liu, Z., and Guan, C., 2021. The impacts of the built environment on the incidence rate of COVID-19: a case study of King County, Washington. Sustainable Cities and Society, 74, 103144.
   PubMed Web of Science ®Google Scholar
• Malczewski, J. and Poetz, A., 2005. Residential burglaries and neighborhood socioeconomic context in London, Ontario: global and local regression analysis. The Professional Geographer, 57 (4), 516–529.
   Web of Science ®Google Scholar
• Marhuenda, Y., Morales, D., and Pardo, M.C., 2014. Information criteria for Fay–Herriot model selection. Computational Statistics & Data Analysis, 70, 268–280.
   Web of Science ®Google Scholar
• Maroko, A.R., et al., 2009. The complexities of measuring access to parks and physical activity sites in New York City: a quantitative and qualitative approach. International Journal of Health Geographics, 8 (1), 34.
   PubMed Web of Science ®Google Scholar
• McCullagh, P. and Nelder, J.A., 2019. Generalized linear models. 2nd ed. New York: Routledge.
   Google Scholar
• Nakaya, T., et al., 2005. Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine, 24 (17), 2695–2717.
   PubMed Web of Science ®Google Scholar
• Oshan, T.M., Smith, J.P., and Fotheringham, A.S., 2020. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal of Health Geographics, 19 (1), 11.
   PubMed Web of Science ®Google Scholar
• Poole, M.A. and O’Farrell, P.N., 1971. The assumptions of the linear regression model. Transactions of the Institute of British Geographers, 52 (52), 145–158.
   Google Scholar
• Relph, E.C., 1976. Place and placelessness. London: Pion.
   Google Scholar
• Sachdeva, M., and Fotheringham, A.S., 2023. A geographical perspective on Simpson's paradox. Journal of Spatial Information Science, (26), 1–25.
   Web of Science ®Google Scholar
• 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), 24–52.
   Google Scholar
• Wang, S., et al., 2018. Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: a global and local regression perspective. Journal of Cleaner Production, 203, 143–152.
   Web of Science ®Google Scholar
• Yu, H., et al., 2020. Inference in multiscale geographically weighted regression. Geographical Analysis, 52 (1), 87–106.
   Web of Science ®Google Scholar
• Zhang, L., et al., 2004. Modeling spatial variation in tree diameter–height relationships. Forest Ecology and Management, 189 (1–3), 317–329.
   Web of Science ®Google Scholar
• Zhu, C., et al., 2020. Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecological Indicators, 117, 106654.
   Web of Science ®Google Scholar