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Street network structure and household activity spaces

Author

Listed:
  • Pavithra Parthasarathi

    (CDM Smith, USA)

  • Hartwig Hochmair

    (University of Florida, USA)

  • David Levinson

    (University of Minnesota, USA)

Abstract

This research analyses the influence of street network structure on household travel patterns, as measured by activity spaces. The analysis uses street network and travel survey data from the Minneapolis – St. Paul (Twin Cities) and Miami – Ft. Lauderdale (South Florida) metropolitan areas. Various measures of street network structure are used to quantify street network structure. The activity space polygon for each household in the travel survey data set is identified by combining the destinations reached by all household members on the given travel day including the household location. Statistical regression models are then estimated for each study area to test the relationship between street network structure and household activity space. The results show that network structure has a significant influence on household travel patterns, after controlling for other non-network variables such as accessibility to jobs and shops, and car ownership.

Suggested Citation

  • Pavithra Parthasarathi & Hartwig Hochmair & David Levinson, 2015. "Street network structure and household activity spaces," Urban Studies, Urban Studies Journal Limited, vol. 52(6), pages 1090-1112, May.
    • Handle: RePEc:sae:urbstu:v:52:y:2015:i:6:p:1090-1112
    DOI: 10.1177/0042098014537956
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    References listed on IDEAS

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    Cited by:

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    2. Geoff Boeing, 2020. "Planarity and street network representation in urban form analysis," Environment and Planning B, , vol. 47(5), pages 855-869, June.
    3. Xiubin B. Wang & Xiaowei Cao & Kai Yin & Teresa M. Adams, 2017. "Modeling Vehicle Miles Traveled on Local Roads Using Classification Roadway Spatial Structure," Networks and Spatial Economics, Springer, vol. 17(3), pages 713-735, September.
    4. Boeing, Geoff, 2019. "Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood," SocArXiv 7fxjz, Center for Open Science.
    5. Jie Song & Zhong-Ren Peng & Liyuan Zhao & Chih-Hung Hsu, 2016. "Developing a theoretical framework for integrated vulnerability of businesses to sea level rise," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 1219-1239, November.
    6. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    7. Chua, Vincent & Axhausen, Kay W. & Tan, Teresa, 2018. "Who do you know, where? Social investments in faraway contacts," Research in Transportation Economics, Elsevier, vol. 68(C), pages 38-45.
    8. Shen, Yue & Luo, Xueyao, 2023. "Linking spatial and temporal contexts to multi-contextual segregation by hukou status in urban China," Journal of Transport Geography, Elsevier, vol. 107(C).
    9. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    10. Boeing, Geoff, 2017. "Methods and Measures for Analyzing Complex Street Networks and Urban Form," SocArXiv 93h82, Center for Open Science.
    11. Boeing, Geoff, 2018. "Urban Spatial Order: Street Network Orientation, Configuration, and Entropy," SocArXiv qj3p5, Center for Open Science.
    12. Barrington-Leigh, Christopher Paul & Millard-Ball, Adam, 2019. "A global assessment of street network sprawl," OSF Preprints 6vp8j, Center for Open Science.

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