IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i19p5534-d274110.html
   My bibliography  Save this article

Scissors Difference of Socioeconomics, Travel and Space Consumption Behavior of Rural and Urban Households and Its Impact on Modeling Accuracy and Data Requirements

Author

Listed:
  • Ming Zhong

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan430063, China
    Engineering Research Center of Transportation Safety, Ministry of Education, Wuhan 430063, China
    National Engineering Research Center for Water Transportation Safety, Wuhan 430063, China)

  • Qi Tang

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan430063, China
    Engineering Research Center of Transportation Safety, Ministry of Education, Wuhan 430063, China
    National Engineering Research Center for Water Transportation Safety, Wuhan 430063, China)

  • Xiaofeng Ma

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan430063, China
    Engineering Research Center of Transportation Safety, Ministry of Education, Wuhan 430063, China
    National Engineering Research Center for Water Transportation Safety, Wuhan 430063, China)

  • John Douglas Hunt

    (Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

Abstract

It is believed that the “scissors difference” of socioeconomics between rural and urban households in typical municipalities of China is significant. This may result in differences in their behavior and has important implications for urban land use and transportation planning policies, as well as related modeling accuracy and data requirements. However, detailed analyses regarding such “scissors differences” between rural and urban groups in China have not been done before. In this study, travel survey data collected from the City of Wuhan in 2008 is used to study if rural and urban households are statistically different in terms of household income, household size, space consumption, highest household mobility and travel distance. A set of statistical tests, such as the Kolmogorov–Smirnov test, Mann–Whitney U test and Kruskal–Wallis H test, are applied to the study data. The study results show that the “scissors difference” is found to be statistically significant in terms of household size (HS), household income (HI), building area (BA) consumed and household mobility (except for travel distance) between rural and urban households. Conversely, analyses applied to travel distance of urban and rural household subgroups (categorized by HS and HI) reveal that the urban and rural counterparts show almost exactly opposite behavior. The study results also suggest that such differences should be explicitly considered in relevant modeling exercises by separately setting up urban and rural household groups, but the number of household groups used should be determined based on a balance between modeling accuracy and data required/modeling workload.

Suggested Citation

  • Ming Zhong & Qi Tang & Xiaofeng Ma & John Douglas Hunt, 2019. "Scissors Difference of Socioeconomics, Travel and Space Consumption Behavior of Rural and Urban Households and Its Impact on Modeling Accuracy and Data Requirements," Sustainability, MDPI, vol. 11(19), pages 1-18, October.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5534-:d:274110
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/19/5534/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/19/5534/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liming Zhao & Ling Li & Yujie Wu, 2017. "Research on the Coupling Coordination of a Sea–Land System Based on an Integrated Approach and New Evaluation Index System: A Case Study in Hainan Province, China," Sustainability, MDPI, vol. 9(5), pages 1-25, May.
    2. Jain, Manisha & Korzhenevych, Artem & Hecht, Robert, 2018. "Determinants of commuting patterns in a rural-urban megaregion of India," Transport Policy, Elsevier, vol. 68(C), pages 98-106.
    3. Madhu Sehrawat & A. Giri, 2016. "Financial development, poverty and rural-urban income inequality: evidence from South Asian countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 577-590, March.
    4. Wang, Siliang & Tan, Shukui & Yang, Shengfu & Lin, Qiaowen & Zhang, Lu, 2019. "Urban-biased land development policy and the urban-rural income gap: Evidence from Hubei Province, China," Land Use Policy, Elsevier, vol. 87(C).
    5. Ulfarsson, Gudmundur F. & Steinbrenner, Anne & Valsson, Trausti & Kim, Sungyop, 2015. "Urban household travel behavior in a time of economic crisis: Changes in trip making and transit importance," Journal of Transport Geography, Elsevier, vol. 49(C), pages 68-75.
    6. Liddle, Brantley, 2012. "The Systemic, Long-run Relation among Gasoline Demand, Gasoline Price, Income, and Vehicle Ownership in OECD Countries: Evidence from Panel Cointegration and Causality Modeling," MPRA Paper 52081, University Library of Munich, Germany.
    7. Paulley, Neil & Balcombe, Richard & Mackett, Roger & Titheridge, Helena & Preston, John & Wardman, Mark & Shires, Jeremy & White, Peter, 2006. "The demand for public transport: The effects of fares, quality of service, income and car ownership," Transport Policy, Elsevier, vol. 13(4), pages 295-306, July.
    8. Jan K. Brueckner, 2000. "Urban Sprawl: Diagnosis and Remedies," International Regional Science Review, , vol. 23(2), pages 160-171, April.
    9. Dargay, Joyce & Gately, Dermot, 1999. "Income's effect on car and vehicle ownership, worldwide: 1960-2015," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(2), pages 101-138, February.
    10. Niny Khor & John Pencavel, 2010. "Income inequality, income mobility, and social welfare for urban and rural households of China and the United States," Research in Labor Economics, in: Jobs, Training, and Worker Well-being, pages 61-106, Emerald Group Publishing Limited.
    11. Zhong & Shan & Du & Lu, 2015. "A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa, Florida," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(5), pages 517-533, July.
    12. Jayne Hutchinson & Piran White & Hilary Graham, 2014. "Differences in the social patterning of active travel between urban and rural populations: findings from a large UK household survey," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 59(6), pages 993-998, December.
    13. He, Wei & Tang, Bu-long, 2012. "Development Mode of Mid-small Cities in Northern Jiangsu Based on the Growth Pole Theory," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 4(06), pages 1-4, June.
    14. Choudhary, Ravi & Vasudevan, Vinod, 2017. "Study of vehicle ownership for urban and rural households in India," Journal of Transport Geography, Elsevier, vol. 58(C), pages 52-58.
    15. Madhu Sehrawat & A.K. Giri, 2016. "Panel data analysis of financial development, economic growth and rural-urban income inequality," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 43(10), pages 998-1015, October.
    16. Seton, Francis, 2000. "Scissor crises, value-prices, and the movement of value-prices under technical change," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 13-24, July.
    17. Pengjun Zhao & Bin Lu & Gert de Roo, 2011. "The impact of urban growth on commuting patterns in a restructuring city: Evidence from Beijing," Papers in Regional Science, Wiley Blackwell, vol. 90(4), pages 735-754, November.
    18. John Pucher & John Renne, 2005. "Rural mobility and mode choice: Evidence from the 2001 National Household Travel Survey," Transportation, Springer, vol. 32(2), pages 165-186, March.
    19. Yue, Dongxia & Xu, Xiaofeng & Li, Zizhen & Hui, Cang & Li, Wenlong & Yang, Hequn & Ge, Jianping, 2006. "Spatiotemporal analysis of ecological footprint and biological capacity of Gansu, China 1991-2015: Down from the environmental cliff," Ecological Economics, Elsevier, vol. 58(2), pages 393-406, June.
    20. Pengjun Zhao & Bin Lü & Gert de Roo, 2010. "Urban Expansion and Transportation: The Impact of Urban form on Commuting Patterns on the City Fringe of Beijing," Environment and Planning A, , vol. 42(10), pages 2467-2486, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bi, Guohua & Yang, Qingyuan, 2023. "The spatial production of rural settlements as rural homestays in the context of rural revitalization: Evidence from a rural tourism experiment in a Chinese village," Land Use Policy, Elsevier, vol. 128(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Pengjun & Wan, Jie, 2021. "Land use and travel burden of residents in urban fringe and rural areas: An evaluation of urban-rural integration initiatives in Beijing," Land Use Policy, Elsevier, vol. 103(C).
    2. Zhiqiang Zhang & Ling Li & Qiuyu Guo, 2022. "The Interactive Relationships between the Tourism-Transportation-Ecological Environment System of Provinces along the ‘Silk Road Economic Belt’ in China," Sustainability, MDPI, vol. 14(5), pages 1-33, March.
    3. Singh, Shivendu Shekhar & Sarkar, Basudatta, 2022. "Transport accessibility and affordability as the determinant of non-motorized commuting in rural India," Transport Policy, Elsevier, vol. 118(C), pages 101-111.
    4. Ulfarsson, Gudmundur F. & Steinbrenner, Anne & Valsson, Trausti & Kim, Sungyop, 2015. "Urban household travel behavior in a time of economic crisis: Changes in trip making and transit importance," Journal of Transport Geography, Elsevier, vol. 49(C), pages 68-75.
    5. Curl, Angela & Clark, Julie & Kearns, Ade, 2018. "Household car adoption and financial distress in deprived urban communities: A case of forced car ownership?," Transport Policy, Elsevier, vol. 65(C), pages 61-71.
    6. Xiaoquan Wang & Chunfu Shao & Chaoying Yin & Chengxiang Zhuge & Wenjun Li, 2018. "Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    7. Ali Enes Dingil & Federico Rupi & Domokos Esztergár-Kiss, 2021. "An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    8. Echeverría, Lucía & Giménez-Nadal, J. Ignacio & Alberto Molina, José, 2022. "Who uses green mobility? Exploring profiles in developed countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 247-265.
    9. Souche, Stéphanie, 2010. "Measuring the structural determinants of urban travel demand," Transport Policy, Elsevier, vol. 17(3), pages 127-134, May.
    10. Zhiheng Yang & Nengneng Shen & Yanbo Qu & Bailin Zhang, 2021. "Association between Rural Land Use Transition and Urban–Rural Integration Development: From 2009 to 2018 Based on County-Level Data in Shandong Province, China," Land, MDPI, vol. 10(11), pages 1-22, November.
    11. Yu, Zhao & Zhao, Pengjun, 2021. "The factors in residents' mobility in rural towns of China: Car ownership, road infrastructure and public transport services," Journal of Transport Geography, Elsevier, vol. 91(C).
    12. Welch, Timothy F., 2013. "Equity in transport: The distribution of transit access and connectivity among affordable housing units," Transport Policy, Elsevier, vol. 30(C), pages 283-293.
    13. Hacievliyagil Nuri & Eksi Ibrahim Halil, 2019. "A Micro Based Study on Bank Credit and Economic Growth: Manufacturing Sub-Sectors Analysis," South East European Journal of Economics and Business, Sciendo, vol. 14(1), pages 72-91, June.
    14. Quaglione, Davide & Cassetta, Ernesto & Crociata, Alessandro & Marra, Alessandro & Sarra, Alessandro, 2019. "An assessment of the role of cultural capital on sustainable mobility behaviours: Conceptual framework and empirical evidence," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 24-34.
    15. Yang, Zhenshan & Jia, Peng & Liu, Weidong & Yin, Hongchun, 2017. "Car ownership and urban development in Chinese cities: A panel data analysis," Journal of Transport Geography, Elsevier, vol. 58(C), pages 127-134.
    16. Zhao, Pengjun, 2014. "Private motorised urban mobility in China’s large cities: the social causes of change and an agenda for future research," Journal of Transport Geography, Elsevier, vol. 40(C), pages 53-63.
    17. Ferreira, João-Pedro & Barata, Eduardo & Ramos, Pedro Nogueira & Cruz, Luis, 2014. "Economic, social, energy and environmental assessment of inter-municipality commuting: The case of Portugal," Energy Policy, Elsevier, vol. 66(C), pages 411-418.
    18. Zhao, Pengjun & Yu, Zhao, 2020. "Investigating mobility in rural areas of China: Features, equity, and factors," Transport Policy, Elsevier, vol. 94(C), pages 66-77.
    19. Liming Zhao & Ling Li & Yujie Wu, 2017. "Research on the Coupling Coordination of a Sea–Land System Based on an Integrated Approach and New Evaluation Index System: A Case Study in Hainan Province, China," Sustainability, MDPI, vol. 9(5), pages 1-25, May.
    20. Shanshan Guo & Yinghong Wang & Huping Hou & Changyue Wu & Jing Yang & Wei He & Lan Xiang, 2020. "Natural Capital Evolution and Driving Forces in Energy-Rich and Ecologically Fragile Regions: A Case Study of Ningxia Province, China," Sustainability, MDPI, vol. 12(2), pages 1-16, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5534-:d:274110. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.