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Articles

Periodicity and Variability in Daily Activity Satisfaction: Toward a Space-Time Modeling of Subjective Well-Being

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Pages 1918-1938 | Received 03 Jan 2020, Accepted 07 Mar 2023, Published online: 22 May 2023

References

  • Ambrey, C., and C. Fleming. 2014. Public greenspace and life satisfaction in urban Australia. Urban Studies 51 (6):1290–1321. doi: 10.1177/0042098013494417.
  • Anselin, L. 1988. Spatial econometrics: Methods and models. Dordrecht, The Netherlands: Kluwer Academic.
  • Banerjee, S., B. P. Carlin, and A. E. Gelfand. 2014. Hierarchical modeling and analysis for spatial data. Boca Raton, FL: Chapman and Hall/CRC.
  • Bates, D., M. Mächler, M. B. Bolker, and S. Walker. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67 (1):1–48. doi: 10.18637/jss.v067.i01.
  • Birenboim, A. 2018. The influence of urban environments on our subjective momentary experiences. Environment and Planning B: Urban Analytics and City Science 45 (5):915–32. doi: 10.1177/2399808317690149.
  • Brereton, F., P. Clinch, and S. Ferreira. 2008. Happiness, geography and the environment. Ecological Economics 65 (2):386–96. doi: 10.1016/j.ecolecon.2007.07.008.
  • Brink, M. 2011. Parameters of well-being and subjective health and their relationship with residential traffic noise exposure a representative evaluation in Switzerland. Environment International 37 (4):723–33. doi: 10.1016/j.envint.2011.02.011.
  • Burnham, K. P., and D. R. Anderson. 2004. Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research 33 (2):261–304. doi: 10.1177/0049124104268644.
  • Cao, X. J. 2016. How does neighborhood design affect life satisfaction? Evidence from twin cities. Travel Behaviour and Society 5:68–76. doi: 10.1016/j.tbs.2015.07.001.
  • Chai, Y., Z. Chen, Y. Liu, N. Ta, and X. Ma. 2014. Space-time behavior survey for smart travel planning in Beijing, China. In Mobile technologies for activity-travel data collection and analysis, 79–90. Hershey, PA: IGI Global.
  • Cressie, N. 1993. Statistics for spatial data. New York: Wiley.
  • Diener, E., W. Ng, J. Harter, and R. Arora. 2010. Wealth and happiness across the world: Material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling. Journal of Personality and Social Psychology 99 (1):52–61. doi: 10.1037/a0018066.
  • Diener, E., S. Oishi, and L. Tay. 2018. Advances in subjective well-being research. Nature Human Behaviour 2 (4):253–60. doi: 10.1038/s41562-018-0307-6.
  • Diggle, P. J., I. Sousa, and O. Asar. 2015. Real-time monitoring of progression towards renal failure in primary care patients. Biostatistics 16 (3):522–36. doi: 10.1093/biostatistics/kxu053.
  • Doherty, S. T., C. J. Lemieux, and C. Canally. 2014. Tracking human activity and well-being in natural environments using wearable sensors and experience sampling. Social Science & Medicine 106:83–92. doi: 10.1016/j.socscimed.2014.01.048.
  • Dong, G., and R. Harris. 2015. Spatial autoregressive models for geographically hierarchical data structures. Geographical Analysis 47 (2):173–91. doi: 10.1111/gean.12049.
  • Dong, G., J. Ma, R. Harris, and G. Pryce. 2016. Spatial random slope multilevel modeling using multivariate conditional autoregressive models: A case study of subjective travel satisfaction in Beijing. Annals of the American Association of Geographers 106 (1):19–35. doi: 10.1080/00045608.2015.1094388.
  • Dong, G., J. Ma, M. P. Kwan, Y. Wang, and Y. Chai. 2018. Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data. International Journal of Geographical Information Science 32 (11):2189–2208. doi: 10.1080/13658816.2018.1504219.
  • Dong, G., J. Ma, D. Lee, M. Chen, G. Pryce, and Y. Chen. 2020. Developing a locally adaptive spatial multilevel logistic model to analyze ecological effects on health using individual census records. Annals of the American Association of Geographers 110 (3):739–57. doi: 10.1080/24694452.2019.1644990.
  • Dong, H., and B. Qin. 2017. Exploring the link between neighborhood environment and mental wellbeing: A case study in Beijing, China. Landscape and Urban Planning 164:71–80. doi: 10.1016/j.landurbplan.2017.04.005.
  • Ettema, D., T. Gärling, L. E. Olsson, and M. Friman. 2010. Out-of-home activities, daily travel, and subjective well-being. Transportation Research Part A: Policy and Practice 44 (9):723–32. doi: 10.1016/j.tra.2010.07.005.
  • Ettema, D., and M. Schekkerman. 2016. How do spatial characteristics influence well-being and mental health? Comparing the effect of objective and subjective characteristics at different spatial scales. Travel Behaviour and Society 5:56–67. doi: 10.1016/j.tbs.2015.11.001.
  • Ferreira, S., and M. Moro. 2010. On the use of subjective well-being data for environmental valuation. Environmental and Resource Economics 46 (3):249–73. doi: 10.1007/s10640-009-9339-8.
  • Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2000. Quantitative geography: Perspectives on spatial data analysis. London: Sage.
  • Golder, S. A., and M. W. Macy. 2011. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333 (6051):1878–81. doi: 10.1126/science.1202775.
  • Goldstein, H. 2011. Multilevel statistical models. New York: Wiley.
  • Grinberger, A. Y., and N. Shoval. 2015. A temporal-contextual analysis of urban dynamics using location-based data. International Journal of Geographical Information Science 29 (11):1969–87. doi: 10.1080/13658816.2015.1049951.
  • Haining, R. 2003. Spatial data analysis: Theory and practice. Cambridge, UK: Cambridge University Press.
  • Harris, R., G. Dong, and W. Zhang. 2013. Using contextualized geographically weighted regression to model the spatial heterogeneity of land prices in Beijing, China. Transactions in GIS 17 (6):901–19. doi: 10.1111/tgis.12020.
  • Headey, B., R. Muffels, and G. G. Wagner. 2010. Long-running German panel survey shows that personal and economic choices, not just genes, matter for happiness. Proceedings of the National Academy of Sciences of the United States of America 107 (42):17922–26. doi: 10.1073/pnas.1008612107.
  • Iacus, S., and N. Yoshida. 2018. Simulation and inference for stochastic process with YUIMA. New York: Springer.
  • Jacobs, J. 1961. The death and life of great American cities. New York: Random House.
  • Jebb, A. T., L. Tay, E. Diener, and S. Oishi. 2018. Happiness, income satiation and turning points around the world. Nature Human Behaviour 2 (1):33–38. doi: 10.1038/s41562-017-0277-0.
  • Jennrich, R., and M. Schluchter. 1986. Unbalanced repeated-measures models with structured covariance matrices. Biometrics 42 (4):805–20. doi: 10.2307/2530695.
  • Kahneman, D., A. B. Krueger, D. A. Schkade, N. Schwarz, and A. A. Stone. 2004. A survey method for characterizing daily life experience: The day reconstruction method. Science 306 (5702):1776–80. doi: 10.1126/science.1103572.
  • Krizek, K. J. 2003. Neighborhood services, trip purpose, and tour-based travel. Transportation 30 (4):387–410. doi: 10.1023/A:1024768007730.
  • Kwan, M. P. 2012. The uncertain geographic context problem. Annals of the Association of American Geographers 102 (5):958–68. doi: 10.1080/00045608.2012.687349.
  • Kwan, M. P. 2018. The limits of the neighborhood effect: Contextual uncertainties in geographic, environmental health, and social science research. Annals of the American Association of Geographers 108 (6):1482–90. doi: 10.1080/24694452.2018.1453777.
  • Larsen, R. J. 1987. The stability of mood variability: A spectral analytic approach to daily mood assessments. Journal of Personality and Social Psychology 52 (6):1195–1204. doi: 10.1037/0022-3514.52.6.1195.
  • Larsen, R. J., and M. Kasimatis. 1990. Individual differences in entrainment of mood to the weekly calendar. Journal of Personality and Social Psychology 58 (1):164–71. doi: 10.1037//0022-3514.58.1.164.
  • Leyden, K. M., A. Goldberg, and P. Michelbach. 2011. Understanding the pursuit of happiness in ten major cities. Urban Affairs Review 47 (6):861–88. doi: 10.1177/1078087411403120.
  • Lioy, P. J. 2010. Exposure science: A view of the past and milestones for the future. Environmental Health Perspectives 118 (8):1081–90. doi: 10.1289/ehp.0901634.
  • Liu, X., and Y. Long. 2016. Automated identification and characterization of parcels with OpenStreetMap and points of interest. Environment and Planning B: Planning and Design 43 (2):341–60. doi: 10.1177/0265813515604767.
  • Liu, Y., M. Dijst, and S. Geertman. 2017. The subjective well-being of older adults in Shanghai: The role of residential environment and individual resources. Urban Studies 54 (7):1692–1714. doi: 10.1177/0042098016630512.
  • Lu, B., Y. Hu, D. Yang, Y. Liu, L. Liao, Z. Yin, T. Xia, Z. Dong, P. Harris, C. Brunsdon, et al. 2023. GWmodelS: A software for geographically weighted models. SoftwareX 21:101291. doi: 10.1016/j.softx.2022.101291.
  • Lucas, R. E. 2007. Adaptation and the set-point model of subjective well-being: Does happiness change after major life events? Current Directions in Psychological Science 16 (2):75–79. doi: 10.1111/j.1467-8721.2007.00479.x.
  • Ma, J., G. Dong, Y. Chen, and W. Zhang. 2018. Does satisfactory neighbourhood environment lead to a satisfying life? An investigation of the association between neighbourhood environment and life satisfaction in Beijing. Cities 74:229–39. doi: 10.1016/j.cities.2017.12.008.
  • Ma, J., J. Rao, M. P. Kwan, and Y. Chai. 2020. Examining the effects of mobility-based air and noise pollution on activity satisfaction. Transportation Research Part D: Transport and Environment 89:102633. doi: 10.1016/j.trd.2020.102633.
  • Ma, J., Y. Tao, M. P. Kwan, and Y. Chai. 2020. Assessing mobility-based real-time air pollution exposure in space and time using smart sensors and GPS trajectories in Beijing. Annals of the American Association of Geographers 110 (2):434–48. doi: 10.1080/24694452.2019.1653752.
  • MacKerron, G., and S. Mourato. 2013. Happiness is greater in natural environments. Global Environmental Change 23 (5):992–1000. doi: 10.1016/j.gloenvcha.2013.03.010.
  • Morrison, P. S. 2011. Local expressions of subjective well-being: The New Zealand experience. Regional Studies 45 (8):1039–58. –doi: 10.1080/00343401003792476.
  • Mouratidis, K., and W. Poortinga. 2020. Built environment, urban vitality and social cohesion: Do vibrant neighborhoods foster strong communities? Landscape and Urban Planning 204:103951. doi: 10.1016/j.landurbplan.2020.103951.
  • Nieuwenhuijsen, M. 2016. Urban and transport planning, environmental exposures and health: New concepts, methods and tools to improve health in cities. Environmental Health 15 (Suppl. 1):723– 33. doi: 10.1186/s12940-016-0108-1.
  • Park, Y. M., and M. P. Kwan. 2017. Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health & Place 43:85–94. doi: 10.1016/j.healthplace.2016.10.002.
  • R Core Team. 2017. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Raudenbush, S. W., and A. S. Bryk. 2002. Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage.
  • Reis, H. T., K. M. Sheldon, S. L. Gable, J. Roscoe, and R. M. Ryan. 2000. Daily well-being: The role of autonomy, competence, and relatedness. Personality and Social Psychology Bulletin 26 (4):419–35. doi: 10.1177/0146167200266002.
  • Rietveld, C. A., D. Cesarini, D. J. Benjamin, P. D. Koellinger, J.-E. De Neve, H. Tiemeier, M. Johannesson, P. K. E. Magnusson, N. L. Pedersen, R. F. Krueger, et al. 2013. Molecular genetics and subjective well-being. Proceedings of the National Academy of Sciences of the United States of America 110 (24):9692–97. doi: 10.1073/pnas.1222171110.
  • Schwanen, T., and D. Wang. 2014. Well-being, context, and everyday activities in space and time. Annals of the Association of American Geographers 104 (4):833–51. doi: 10.1080/00045608.2014.912549.
  • Shen, Y., M. P. Kwan, and Y. Chai. 2013. Investigating commuting flexibility with GPS data and 3d geovisualization: A case study of Beijing, China. Journal of Transport Geography 32:1–11. doi: 10.1016/j.jtrangeo.2013.07.007.
  • Shreve, S. E. 2004. Stochastic calculus for finance II: Continuous-time models. New York: Springer.
  • Sigrist, F., H. R. Künsch, and W. A. Stahel. 2015. Stochastic partial differential equation based modelling of large spacetime data sets. Journal of the Royal Statistical Society Series B: Statistical Methodology 77 (1):3–33. doi: 10.1111/rssb.12061.
  • Ta, N., M. P. Kwan, and Y. Chai. 2016. Urban form, car ownership and activity space in inner suburbs: A comparison between Beijing (China) and Chicago (United States). Urban Studies 53 (9):1784–1802. doi: 10.1177/0042098015581123.
  • Ta, N., H. Li, Y. Chai, and J. Wu. 2021. The impact of green space exposure on satisfaction with active travel trips. Transportation Research Part D: Transport and Environment 99:103022. doi: 10.1016/j.trd.2021.103022.
  • Taylor, J. M., W. Cumberland, and J. Sy. 1994. A stochastic model for analysis of longitudinal aids data. Journal of the American Statistical Association 89 (427):727–36. doi: 10.1080/01621459.1994.10476806.
  • Tobler, W. R. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46:234–40. doi: 10.2307/143141.
  • Tonne, C., C. Mil, D. Fecht, M. Alvarez, J. Gulliver, J. Smith, S. Beevers, R. Anderson, and F. Kelly. 2018. Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London. Environment International 115:170–79. doi: 10.1016/j.envint.2018.03.023.
  • Uhlenbeck, G. E., and L. S. Ornstein. 1930. On the theory of the Brownian motion. Physical Review 36 (5):823–41. doi: 10.1103/PhysRev.36.823.
  • Welsch, H. 2006. Environment and happiness: Valuation of air pollution using life satisfaction data. Ecological Economics 58 (4):801–13. doi: 10.1016/j.ecolecon.2005.09.006.
  • Wikle, N. B., E. M. Hanks, L. Henneman, and C. M. Zigler. 2022. A mechanistic model of annual sulfate concentrations in the United States. Journal of the American Statistical Association 117 (539):1082–93. doi: 10.1080/01621459.2022.2027774.
  • Wood, S. N. 2017. Generalized additive models: An introduction with R. Boca Raton, FL: Chapman and Hall/CRC.
  • Yoo, E., C. Rudra, M. Glasgow, and L. Mu. 2015. Geospatial estimation of individual exposure to air pollutants: Moving from static monitoring to activity-based dynamic exposure assessment. Annals of the Association of American Geographers 105 (5):915–26. doi: 10.1080/00045608.2015.1054253.
  • Yue, Y., Y. Zhuang, A. G. Yeh, J. Xie, C. Ma, and Q. Li. 2017. Measurements of POI based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science 31 (4):658–75. doi: 10.1080/13658816.2016.1220561.
  • Zheng, S., J. Wang, C. Sun, X. Zhang, and M. E. Kahn. 2019. Air pollution lowers Chinese urbanites expressed happiness on social media. Nature Human Behaviour 3 (3):237–43. doi: 10.1038/s41562-018-0521-2.
  • Zivin, J. G., and M. Neidell. 2018. Air pollution’s hidden impacts. Science 359 (6371):39–40. doi: 10.1126/science.aap7711.