Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model

References

• Anselin, L., 2010. Thirty years of spatial econometrics. Papers in Regional Science, 89 (1), 3–25.
   Web of Science ®Google Scholar
• Azmi, D.I., and Karim, H.A., 2012. Implications of walkability towards promoting sustainable urban neighbourhood. Procedia - Social and Behavioral Sciences, 50, 204–213.
   Google Scholar
• Banerjee, S., Carlin, B.P., and Gelfand, A.E., 2014. Hierarchical modeling and analysis for spatial data. Boca Raton, FL: CRC Press.
   Google Scholar
• Besag, J., York, J., and Mollié, A., 1991. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43 (1), 1–20.
   Web of Science ®Google Scholar
• Bo, Y.-C., et al., 2014. Using an autologistic regression model to identify spatial risk factors and spatial risk patterns of hand, foot and mouth disease (HFMD) in Mainland China. BMC Public Health, 14, 358.
   PubMed Web of Science ®Google Scholar
• Brooks, S.P., and Gelman, A., 1998. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7 (4), 434–455.
   Web of Science ®Google Scholar
• Brownson, R.C., et al., 2009. Measuring the built environment for physical activity: state of the science. American Journal of Preventive Medicine, 36 (4 Suppl), S99–S123.
   PubMed Web of Science ®Google Scholar
• Brunsdon, C., Fotheringham, A.S., and Charlton, M.E., 2010. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28 (4), 281–298.
   Web of Science ®Google Scholar
• Chhetri, P., Stimson, R., and Western, J., 2006. Modeling the factors of neighborhood attractiveness reflected in residential location decision choices. Chiikigaku Kenkyu (Studies in Regional Science), 36 (2), 393–417.
   Google Scholar
• Congdon, P., 2014. Bayesian statistical modelling. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Cressie, N., 1996. Change of support and the modifiable areal unit problem. Geographical Systems, 3 (2–3), 159–180.
   Google Scholar
• Davies, K.F., et al., 2005. Spatial heterogeneity explains the scale dependence of the native-exotic diversity relationship. Ecology, 86 (6), 1602–1610.
   Web of Science ®Google Scholar
• Fang, Z., et al., 2017. Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns. International Journal of Geographical Information Science, 31 (11), 2119–2141.
   Web of Science ®Google Scholar
• Finley, A.O., 2011. Comparing spatially‐varying coefficients models for analysis of ecological data with nonstationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2 (2), 143–154.
   Web of Science ®Google Scholar
• Fischer, M.M., and Wang, J., 2012. Spatial data analysis models, methods and techniques. Berlin: Springer.
   Google Scholar
• Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2003. Geographically weighted regression: the analysis of spatially varying relationships. Chichester, UK: John Wiley & Sons.
   Google Scholar
• Fotheringham, A.S., Charlton, M.E., and Brunsdon, C., 1998. Geographically weighted regression a natural evolution of the expansion method for spatial data analysis. Environment and Planning A: Economy and Space, 30 (11), 1905–1927.
   Web of Science ®Google Scholar
• Fotheringham, A.S., 2009. “The problem of spatial autocorrelation" and local spatial statistics. Geographical Analysis, 41 (4), 398–403.
   Web of Science ®Google Scholar
• Gelfand, A.E., et al., 2003. Spatial modeling with spatially varying coefficient processes. Journal of the American Statistical Association, 98 (462), 387–396.
   Web of Science ®Google Scholar
• Gelfand, A.E., and Dey, D.K., 1994. Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society: Series B (Methodological), 56 (3), 501–514.
   Google Scholar
• Gelman, A., 2006. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1 (3), 515–533.
   Web of Science ®Google Scholar
• Gelman, A., and Rubin, D.B., 1992. Inference from iterative simulation using multiple sequences. Statistical Science, 7 (4), 457–472.
   Google Scholar
• Goodchild, M.F., and Haining, R.P., 2003. GIS and spatial data analysis: converging perspectives. Papers in Regional Science, 83 (1), 363–385.
   Web of Science ®Google Scholar
• Griffith, D.A., 2008. Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR). Environment and Planning A: Economy and Space, 40 (11), 2751–2769.
   Web of Science ®Google Scholar
• Haining, R.P., 2003. Spatial data analysis: theory and Practice. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Hansell, A.L., et al., 2013. Aircraft noise and cardiovascular disease near Heathrow airport in London: small area study. BMJ, 347 (oct08 3), f5432–f5432.
   PubMedGoogle Scholar
• Huang, B., et al., 2020. Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study. Environment and Planning B: Urban Analytics and City Science, 47 (9), 1543–1559.
   Google Scholar
• Huang, Q., and Wong, D.W.S., 2016. Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? International Journal of Geographical Information Science, 30 (9), 1873–1898.
   Web of Science ®Google Scholar
• Jacobs, J., 1961. The death and life of Great American cities. New York: Vintage Book Company.
   Google Scholar
• Jacobs-Crisioni, C., et al., 2014. Evaluating the impact of land-use density and mix on spatiotemporal urban activity patterns: an exploratory study using mobile phone data. Environment and Planning A: Economy and Space, 46 (11), 2769–2785.
   Web of Science ®Google Scholar
• Jost, L., 2006. Entropy and diversity. Oikos, 113 (2), 363–375.
   Web of Science ®Google Scholar
• Lawson, A.B., 2013. Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Boca Raton, FL: CRC Press.
   Google Scholar
• Lawson, A.B., and Hill, J., 2006. Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.
   Google Scholar
• Lee, D., 2011. A comparison of conditional autoregressive models used in Bayesian disease mapping. Spatial and Spatio-Temporal Epidemiology, 2 (2), 79–89.
   PubMedGoogle Scholar
• Liu, X., et al., 2017. Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31 (8), 1675–1696.
   Web of Science ®Google Scholar
• Liu, Y., et al., 2015. Social sensing: a new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105 (3), 512–530.
   Web of Science ®Google Scholar
• Lunn, D.J., et al., 2000. WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10 (4), 325–337.
   Web of Science ®Google Scholar
• Montgomery, J., 1998. Making a city: urbanity, vibrancy and urban design. Journal of Urban Design, 3 (1), 93–116.
   Google Scholar
• Murakami, D., et al., 2017. A Moran coefficient-based mixed effects approach to investigate spatially varying relationships. Spatial Statistics, 19, 68–89.
   Web of Science ®Google Scholar
• Pei, T., et al., 2014. A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28 (9), 1988–2007.
   Web of Science ®Google Scholar
• Shenzhen Municipal Statistics Bureau, 2020. Shenzhen statistical yearbook. Beijing, China: China Statistics Press.
   Google Scholar
• Shoff, C., Chen, V.Y., and Yang, T.C., 2014. When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization. Geospatial Health, 8 (2), 557–568.
   PubMed Web of Science ®Google Scholar
• Spiegelhalter, D.J., et al., 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64 (4), 583–616.
   Web of Science ®Google Scholar
• Sulis, P., et al., 2018. Using mobility data as proxy for measuring urban vitality. Journal of Spatial Information Science, 2018 (16), 137–162.
   Google Scholar
• Tranos, E., and Nijkamp, P., 2015. Mobile phone usage in complex urban systems: a space–time, aggregated human activity study. Journal of Geographical Systems, 17 (2), 157–185.
   Web of Science ®Google Scholar
• Truong, P.N., and Stein, A., 2018. A hierarchically adaptable spatial regression model to link aggregated health data and environmental data. Spatial Statistics, 23, 36–51.
   Web of Science ®Google Scholar
• Tu, W., et al., 2020. Portraying the spatial dynamics of urban vibrancy using multisource urban big data. Computers, Environment and Urban Systems, 80, 101428.
   Web of Science ®Google Scholar
• Wang, J., Zhang, T., and Fu, B., 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67, 250–256.
   Web of Science ®Google Scholar
• Wang, J., et al., 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region. International Journal of Geographical Information Science, 24 (1), 107–127.
   Web of Science ®Google Scholar
• Wang, J., and Xu, C., 2017. Geodetector: principle and prospective. Acta Geographica Sinica, 72 (01), 116–134.
   Google Scholar
• Wang, Z., et al., 2014. Analysis of the spatial variation of hospitalization admissions for hypertension disease in Shenzhen, China. International Journal of Environmental Research and Public Health, 11 (1), 713–733.
   PubMed Web of Science ®Google Scholar
• Wang, Z., et al., 2021. A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data. International Journal of Geographical Information Science, 35 (1), 116–134.
   Web of Science ®Google Scholar
• Wheeler, D.C., and Waller, L.A., 2009. Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests. Journal of Geographical Systems, 11 (1), 1–22.
   Web of Science ®Google Scholar
• Wu, C., et al., 2018. Check-in behaviour and spatio-temporal vibrancy: an exploratory analysis in Shenzhen, China. Cities, 77, 104–116.
   Web of Science ®Google Scholar
• Wu, J., et al., 2018. Urban form breeds neighborhood vibrancy: a case study using a GPS-based activity survey in suburban Beijing. Cities, 74, 100–108.
   Web of Science ®Google Scholar
• Ye, Y., Li, D., and Liu, X., 2018. How block density and typology affect urban vibrancy: an exploratory analysis in Shenzhen. Urban Geography, 39 (4), 631–652.
   Web of Science ®Google Scholar
• Yuan, J., Zheng, Y., and Xie, X., 2012. Discovering regions of different functions in a city using human mobility and POIs. In: ACM SIGKDD international conference on knowledge discovery and data mining ACM, 186–194.
   Google Scholar
• Yue, Y., et al., 2017. Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science, 31 (4), 658–675.
   Web of Science ®Google Scholar
• Zheng, Y., et al., 2014. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5 (3), 1–55.
   Web of Science ®Google Scholar