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A Spatial-Indexing Model for Measuring Neighbourhood-Level Land-Use and Transport Integration

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  • Fatih Dur
  • Tan Yigitcanlar
  • Jonathan Bunker

Abstract

Significant attention has been given in urban policy literature to the integration of land-use and transport planning and policies—with a view to curbing sprawling urban form and diminishing externalities associated with car-dependent travel patterns. By taking land-use and transport interaction into account, this debate mainly focuses on how a successful integration can contribute to societal well-being, providing efficient and balanced economic growth while accomplishing the goal of developing sustainable urban environments and communities. The integration is also a focal theme of contemporary urban development models, such as smart growth, liveable neighbourhoods, and new urbanism. Even though available planning policy options for ameliorating urban form and transport-related externalities have matured—owing to growing research and practice worldwide—there remains a lack of suitable evaluation models to reflect on the current status of urban form and travel problems or on the success of implemented integration policies. In this study we explore the applicability of indicator-based spatial indexing to assess land-use and transport integration at the neighbourhood level. For this, a spatial index is developed by a number of indicators compiled from international studies and trialled in Gold Coast, Queensland, Australia. The results of this modelling study reveal that it is possible to propose an effective metric to determine the success level of city plans considering their sustainability performance via composite indicator methodology. The model proved useful in demarcating areas where planning intervention is applicable, and in identifying the most suitable locations for future urban development and plan amendments. Lastly, we integrate variance-based sensitivity analysis with the spatial indexing method, and discuss the applicability of the model in other urban contexts.

Suggested Citation

  • Fatih Dur & Tan Yigitcanlar & Jonathan Bunker, 2014. "A Spatial-Indexing Model for Measuring Neighbourhood-Level Land-Use and Transport Integration," Environment and Planning B, , vol. 41(5), pages 792-812, October.
    • Handle: RePEc:sae:envirb:v:41:y:2014:i:5:p:792-812
    DOI: 10.1068/b39028
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    References listed on IDEAS

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    1. Bhat, Chandra R. & Guo, Jessica Y., 2007. "A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 506-526, June.
    2. Hanafizadeh, Mohammad Reza & Saghaei, Abbas & Hanafizadeh, Payam, 2009. "An index for cross-country analysis of ICT infrastructure and access," Telecommunications Policy, Elsevier, vol. 33(7), pages 385-405, August.
    3. Frank, Lawrence Douglas & Saelens, Brian E. & Powell, Ken E. & Chapman, James E., 2007. "Stepping towards causation: Do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity?," Social Science & Medicine, Elsevier, vol. 65(9), pages 1898-1914, November.
    4. Tan Yigitcanlar & Fatih Dur, 2010. "Developing a Sustainability Assessment Model: The Sustainable Infrastructure, Land-Use, Environment and Transport Model," Sustainability, MDPI, vol. 2(1), pages 1-20, January.
    5. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
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    Cited by:

    1. Shaopei Chen & Dachang Zhuang, 2020. "Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    2. Sajida Perveen & Md. Kamruzzaman & Tan Yigitcanlar, 2017. "Developing Policy Scenarios for Sustainable Urban Growth Management: A Delphi Approach," Sustainability, MDPI, vol. 9(10), pages 1-27, October.
    3. Amirafshar Vaeztavakoli & Azadeh Lak & Tan Yigitcanlar, 2018. "Blue and Green Spaces as Therapeutic Landscapes: Health Effects of Urban Water Canal Areas of Isfahan," Sustainability, MDPI, vol. 10(11), pages 1-20, November.

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