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An Algorithmic Definition of the Axial Map

Author

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  • Alasdair Turner
  • Alan Penn
  • Bill Hillier

Abstract

The fewest-line axial map, often simply referred to as the ‘axial map’, is one of the primary tools of space syntax. Its natural language definition has allowed researchers to draw consistent maps that present a concise description of architectural space; it has been established that graph measures obtained from the map are useful for the analysis of pedestrian movement patterns and activities related to such movement: for example, the location of services or of crime. However, the definition has proved difficult to translate into formal language by mathematicians and algorithmic implementers alike. This has meant that space syntax has been criticised for a lack of rigour in the definition of one of its fundamental representations. Here we clarify the original definition of the fewest-line axial map and show that it can be implemented algorithmically. We show that the original definition leads to maps similar to those currently drawn by hand, and we demonstrate that the differences between the two may be accounted for in terms of the detail of the algorithm used. We propose that the analytical power of the axial map in empirical studies derives from the efficient representation of key properties of the spatial configuration that it captures.

Suggested Citation

  • Alasdair Turner & Alan Penn & Bill Hillier, 2005. "An Algorithmic Definition of the Axial Map," Environment and Planning B, , vol. 32(3), pages 425-444, June.
  • Handle: RePEc:sae:envirb:v:32:y:2005:i:3:p:425-444
    DOI: 10.1068/b31097
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    Cited by:

    1. Lebendiger, Yonatan & Lerman, Yoav, 2019. "Applying space syntax for surface rapid transit planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 59-72.
    2. Sadia Afrose & Ahmad Mojtoba Riyadh & Afsana Haque, 2019. "Cores of Dhaka city: area delimitation and comparison of their characteristics," Asia-Pacific Journal of Regional Science, Springer, vol. 3(2), pages 521-560, June.
    3. Su, Shiliang & Zhou, Hao & Xu, Mengya & Ru, Hu & Wang, Wen & Weng, Min, 2019. "Auditing street walkability and associated social inequalities for planning implications," Journal of Transport Geography, Elsevier, vol. 74(C), pages 62-76.
    4. Isti Hidayati & Claudia Yamu & Wendy Tan, 2019. "The Emergence of Mobility Inequality in Greater Jakarta, Indonesia: A Socio-Spatial Analysis of Path Dependencies in Transport–Land Use Policies," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    5. Peixue Liu & Xiao Xiao & Jie Zhang & Ronghua Wu & Honglei Zhang, 2018. "Spatial Configuration and Online Attention: A Space Syntax Perspective," Sustainability, MDPI, vol. 10(1), pages 1-15, January.
    6. Li, Yuan & Xiao, Longzhu & Ye, Yu & Xu, Wangtu & Law, Andrew, 2016. "Understanding tourist space at a historic site through space syntax analysis: The case of Gulangyu, China," Tourism Management, Elsevier, vol. 52(C), pages 30-43.
    7. Valerio Cutini & Valerio Di Pinto & Antonio Maria Rinaldi & Francesco Rossini, 2020. "Proximal Cities: Does Walkability Drive Informal Settlements?," Sustainability, MDPI, vol. 12(3), pages 1-20, January.

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