Neighborhood Social Disadvantage and Bicycling Behavior: A Big Data-Spatial Approach Based on Social Indicators

  • Shukui Tan
  • Yi ZhaoEmail author
  • Wenke Huang
Original Research


Characterizing the travel behavior of disadvantaged neighborhoods is of significance for social inequality elimination and public well-being promotion. However, rather few studies have empirically been conducted in respect of bicycling, especially in developing countries. Using the case of Shenzhen in China, this paper explores bicycling behavior associated with neighborhood social disadvantage (NSD). Data of 3 explained variables (bicycling frequency, travel duration and trip distance) for bicycling behavior is crawled from mobile application programming interface. Based on a set of 16 potential social indicators, we apply principal component analysis to construct NSD index and it produces 5 sub-indices including income, housing, occupation, education and population. Results of spatial regression demonstrate that NSD generally is positive with bicycling frequency and trip distance, while negative with travel duration. Specifically, income and population disadvantaged neighborhoods tend to bicycling for higher frequency, less time and longer distance. Housing disadvantage and education disadvantage reduce the likelihood of bicycling, while occupation disadvantage encourages bicycling frequency and trip distance. The variance decomposition shows that income disadvantage contributes most for bicycling frequency, housing disadvantage is more important for travel duration. For trip distance, education disadvantage is more influential. Our attempt provides an innovative insight into social indicators research.


Neighborhood social disadvantage Bicycling behavior Spatial approach Big data Principle component analysis 



This paper is funded by the National 985 Project of Nontraditional Security at Huazhong University of Science and Technology, P.R. China. We also thank two anonymous reviewers for their valuable comments. Any remaining errors are the authors own responsibility.


  1. Alvarado, S. E. (2016). Neighborhood disadvantage and obesity across childhood and adolescence: Evidence from the nlsy children and young adults cohort (1986–2010). Social Science Research, 57, 80.Google Scholar
  2. Anselin, L. (1988). Spatial econometrics: Methods and models. Economic Geography, 65(2), 160–162.Google Scholar
  3. Anselin, L., Syabri, I., & Kho, Y. (2006). Geoda: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22.Google Scholar
  4. Barnes, T. L., Colabianchi, N., Hibbert, J. D., Porter, D. E., Lawson, A. B., & Liese, A. D. (2016). Scale effects in food environment research: Implications from assessing socioeconomic dimensions of supermarket accessibility in an eight-county region of south carolina. Applied Geography, 68, 20–27.Google Scholar
  5. Castillo-Manzano, J. I., López-Valpuesta, L., & Sánchez-Braza, A. (2016). Going a long way? On your bike! Comparing the distances for which public bicycle sharing system and private bicycles are used. Applied Geography, 71, 95–105.Google Scholar
  6. Cervero, R. (1996). Mixed land-uses and commuting: Evidence from the American Housing Survey. Transportation Research Part A: Policy and Practice, 30(5), 361–377.Google Scholar
  7. Chen, J., Guo, F., & Wu, Y. (2011). One decade of urban housing reform in china: Urban housing price dynamics and the role of migration and urbanization, 1995–2005. Habitat International, 35(1), 8.Google Scholar
  8. Duvarci, Y., Yigitcanlar, T., & Mizokami, S. (2015). Transportation disadvantage impedance indexing: A methodological approach to reduce policy shortcomings. Journal of Transport Geography, 48, 61–75.Google Scholar
  9. Ettema, D., Schwanen, T., & Timmermans, H. (2007). The effect of location, mobility and socio-demographic factors on task and time allocation of households. Transportation, 34(1), 89–105.Google Scholar
  10. Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation Research Record, 1780(1), 87–114.Google Scholar
  11. Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306–314.Google Scholar
  12. Faghih-Imani, A., Hampshire, R., Marla, L., & Eluru, N. (2017). An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville. Transportation Research Part A: Policy and Practice, 97, 177–191.Google Scholar
  13. Fam, S. F., Ismail, N., & Jemain, A. A. (2017). Geographical and socio-economic analysis in Peninsular Malaysia. The Social Sciences, 12(9), 1695–1704.Google Scholar
  14. Fam, S. F., Ismail, N., Maukar, A. L., Yanto, H., Prastyo, D. D., Jemain, A. A., et al. (2018). Weighting methods in the construction of area deprivation indices. Journal of Fundamental and Applied Sciences, 10(6S), 2655–2668.Google Scholar
  15. Fam, S. F., Jemain, A. A., & Zin, W. Z. W. (2011). Spatial analysis of socioeconomic deprivation in Peninsular Malaysia. International Journal of Arts & Sciences, 4(17), 241.Google Scholar
  16. Gupta, K., Kumar, P., Pathan, S. K., & Sharma, K. P. (2012). Urban neighborhood green index—A measure of green spaces in urban areas. Landscape & Urban Planning, 105(3), 325–335.Google Scholar
  17. Hanson, M. D., & Chen, E. (2007). Socioeconomic status and health behaviors in Adolescence: A review of the literature. Journal of Behavioral Medicine, 30(3), 263.Google Scholar
  18. Heesch, K. C., Giles-Corti, B., & Turrell, G. (2014). Cycling for transport and recreation: Associations with socio-economic position, environmental perceptions, and psychological disposition. Preventive Medicine, 63, 29–35.Google Scholar
  19. Heinen, E., van Wee, B., & Maat, K. (2010). Commuting by bicycle: An overview of the literature. Transport reviews, 30(1), 59–96.Google Scholar
  20. Janmaimool, P. (2017). Investigating pro-environmental behaviors of well-educated people in thailand: Implications for the development of environmental communication. International Journal of Sociology & Social Policy, 37(10), 788–807.Google Scholar
  21. Li, H., & Liu, Y. (2016). Neighborhood socioeconomic disadvantage and urban public green spaces availability: A localized modeling approach to inform land use policy. Land Use Policy, 57, 470–478.Google Scholar
  22. Lin, T., Wang, D., & Zhou, M. (2018). Residential relocation and changes in travel behavior: What is the role of social context change? Transportation Research Part A: Policy and Practice, 111, 360–374.Google Scholar
  23. Liu, C. W., Lin, K. H., & Kuo, Y. M. (2003). Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science of the Total Environment, 313(1–3), 77–89.Google Scholar
  24. Luo, J., Zhang, X., Wu, Y., Shen, J., Shen, L., & Xing, X. (2018). Urban land expansion and the floating population in china: For production or for living? Cities, 74, 219–228.Google Scholar
  25. Ma, L., Dill, J., & Mohr, C. (2014). The objective versus the perceived environment: What matters for bicycling? Transportation, 41(6), 1135–1152.Google Scholar
  26. Mateo-Babiano, I., Bean, R., Corcoran, J., & Pojani, D. (2016). How does our natural and built environment affect the use of bicycle sharing? Transportation Research Part A: Policy and Practice, 94, 295–307.Google Scholar
  27. Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, 10(2), 243–251.Google Scholar
  28. Moudon, A. V., Lee, C., Cheadle, A. D., Collier, C. W., Johnson, D., Schmid, T. L., et al. (2005). Cycling and the built environment, a US perspective. Transportation Research Part D: Transport and Environment, 10(3), 245–261.Google Scholar
  29. Parkin, J., Wardman, M., & Page, M. (2008). Estimation of the determinants of bicycle mode share for the journey to work using census data. Transportation, 35(1), 93–109.Google Scholar
  30. Pi, J., Sun, Y., Xu, M., Su, S., & Weng, M. (2018). Neighborhood social determinants of public health: Analysis of three prevalent non-communicable chronic diseases in Shenzhen, China. Social Indicators Research, 135, 1–16.Google Scholar
  31. Plaut, P. O. (2005). Non-motorized commuting in the US. Transportation Research Part D: Transport and Environment, 10(5), 347–356.Google Scholar
  32. Pucher, J., & Buehler, R. (2008). Making cycling irresistible: Lessons from the Netherlands, Denmark and Germany. Transport Reviews, 28(4), 495–528.Google Scholar
  33. Pucher, J., Komanoff, C., & Schimek, P. (1999). Bicycling renaissance in North America?: Recent trends and alternative policies to promote bicycling. Transportation Research Part A: Policy and Practice, 33(7), 625–654.Google Scholar
  34. Pyrialakou, V. D., Gkritza, K., & Fricker, J. D. (2016). Accessibility, mobility, and realized travel behavior: Assessing transport disadvantage from a policy perspective. Journal of Transport Geography, 51, 252–269.Google Scholar
  35. Rao, Y., & Dai, D. (2017). Creative class concentrations in shanghai, china: What is the role of neighborhood social tolerance and life quality supportive conditions? Social Indicators Research, 132(3), 1–10.Google Scholar
  36. Rietveld, P., & Daniel, V. (2004). Determinants of bicycle use: Do municipal policies matter? Transportation Research Part A: Policy and Practice, 38(7), 531–550.Google Scholar
  37. Ryley, T. (2006). Use of non-motorised modes and life stage in edinburgh. Journal of Transport Geography, 14(5), 367–375.Google Scholar
  38. Sahlqvist, S. L., & Heesch, K. C. (2012). Characteristics of utility cyclists in queensland, australia: An examination of the associations between individual, social, and environmental factors and utility cycling. Journal of Physical Activity & Health, 9(6), 818–828.Google Scholar
  39. Shay, E., Combs, T. S., Findley, D., Kolosna, C., Madeley, M., & Salvesen, D. A. (2016). Identifying transportation disadvantage: Mixed-methods analysis combining GIS mapping with qualitative data. Transport Policy, 48, 129–138.Google Scholar
  40. Sherwin, H., Chatterjee, K., & Jain, J. (2014). An exploration of the importance of social influence in the decision to start bicycling in England. Transportation Research Part A: Policy and Practice, 68, 32–45.Google Scholar
  41. Sisson, S. B., Lee, S. M., Burns, E. K., & Tudor-Locke, C. (2006). Suitability of commuting by bicycle to arizona elementary schools. American Journal of Health Promotion Ajhp, 20(3), 210.Google Scholar
  42. Stinson, M., & Bhat, C. (2004). Frequency of bicycle commuting: Internet-based survey analysis. Transportation Research Record Journal of the Transportation Research Board, 1878(1), 122–130.Google Scholar
  43. Su, S., Chen, W., Hu, Y., & Cai, Z. (2016a). Characterizing geographical preferences of international tourists and the local influential factors in china using geo-tagged photos on social media. Applied Geography, 73, 26–37.Google Scholar
  44. Su, S., Gong, Y., Tan, B., Pi, J., Weng, M., & Cai, Z. (2017a). Area social deprivation and Public Health: Analyzing the spatial non-stationary associations using geographically weighed regression. Social Indicators Research, 133(3), 819–832.Google Scholar
  45. Su, S., Li, Z., Xu, M., Cai, Z., & Weng, M. (2017b). A geo-big data approach to intra-urban food deserts: Transit-varying accessibility, social inequalities, and implications for urban planning. Habitat International, 64, 22–40.Google Scholar
  46. Su, S., Pi, J., Xie, H., Cai, Z., & Weng, M. (2017c). Community deprivation, walkability, and public health: Highlighting the social inequalities in land use planning for health promotion. Land Use Policy, 67, 315–326.Google Scholar
  47. Su, S., Wang, Y., Luo, F., Mai, G., & Pu, J. (2014). Peri-urban vegetated landscape pattern changes in relation to socioeconomic development. Ecological Indicators, 46, 477–486.Google Scholar
  48. Su, S., Zhang, Q., Pi, J., Wan, C., & Weng, M. (2016b). Public health in linkage to land use: Theoretical framework, empirical evidence, and critical implications for reconnecting health promotion to land use policy. Land Use Policy, 57, 605–618.Google Scholar
  49. Tan, S., Li, Y., Song, Y., Luo, X., Zhou, M., Zhang, L., et al. (2017). Influence factors on settlement intention for floating population in urban area: A china study. Quality & Quantity, 51(1), 147–176.Google Scholar
  50. Titze, S., Stronegger, W. J., Janschitz, S., & Oja, P. (2007). Environmental, social, and personal correlates of cycling for transportation in a student population. Journal of Physical Activity & Health, 4(1), 66–79.Google Scholar
  51. Titze, S., Stronegger, W. J., Janschitz, S., & Oja, P. (2008). Association of built-environment, social-environment and personal factors with bicycling as a mode of transportation among Austrian city dwellers. Preventive Medicine, 47(3), 252–259.Google Scholar
  52. Trost, S. G., Owen, N., Bauman, A. E., Sallis, J. F., & Brown, W. (2002). Correlates of adults’ participation in physical activity: Review and update. Medicine and Science in Sports and Exercise, 34(12), 1996–2001.Google Scholar
  53. Wan, C., & Su, S. (2016). Neighborhood housing deprivation and public health: Theoretical linkage, empirical evidence, and implications for urban planning. Habitat International, 57, 11–23.Google Scholar
  54. Wan, C., & Su, S. (2017). China’s social deprivation: Measurement, spatiotemporal pattern and urban applications. Habitat International, 62, 22–42.Google Scholar
  55. Wang, D., & Lin, T. (2013). Built environments, social environments, and activity-travel behavior: A case study of Hong Kong. Journal of Transport Geography, 31(7), 286–295.Google Scholar
  56. Weng, M., Pi, J., Tan, B., Su, S., & Cai, Z. (2017). Area deprivation and liver cancer prevalence in Shenzhen, China: a spatial approach based on social indicators. Social Indicators Research, 133(1), 317–332.Google Scholar
  57. Winters, M., Brauer, M., Setton, E. M., & Teschke, K. (2010). Built environment influences on healthy transportation choices: Bicycling versus driving. Journal of Urban Health, 87(6), 969–993.Google Scholar
  58. Winters, M., Davidson, G., Kao, D., & Teschke, K. (2011). Motivators and deterrents of bicycling: Comparing influences on decisions to ride. Transportation, 38(1), 153–168.Google Scholar
  59. Wüstemann, H., Kalisch, D., & Kolbe, J. (2017). Access to urban green space and environmental inequalities in Germany. Landscape and Urban Planning, 164, 124–131.Google Scholar
  60. Xiao, R., Wang, G., & Wang, M. (2018). Transportation disadvantage and neighborhood sociodemographics: A composite indicator approach to examining social inequalities. Social Indicators Research, 137(1), 29–43.Google Scholar
  61. Xu, M., Xin, J., Su, S., Weng, M., Cai, Z., & Witlox, F. (2017). Social inequalities of park accessibility in Shenzhen, China: The role of park quality, transport modes, and hierarchical socioeconomic characteristics. Journal of Transport Geography, 62, 38–50.Google Scholar
  62. You, H. (2016). Characterizing the inequalities in urban public green space provision in Shenzhen, China. Habitat International, 56, 176–180.Google Scholar
  63. Zhao, P. (2013). The impact of the built environment on bicycle commuting: Evidence from Beijing. Urban Studies, 51(5), 1019–1037.Google Scholar
  64. Zhou, M., Tan, S., Tao, Y., Lu, Y., Zhang, Z., Zhang, L., et al. (2017). Neighborhood socioeconomics, food environment and land use determinants of public health: Isolating the relative importance for essential policy insights. Land Use Policy, 68, 246–253.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Public AdministrationHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Civil Engineering and TransportationShenzhen UniversityShenzhenChina

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