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Measuring Streetscape Complexity Based on the Statistics of Local Contrast and Spatial Frequency

Author

Listed:
  • André Cavalcante
  • Ahmed Mansouri
  • Lemya Kacha
  • Allan Kardec Barros
  • Yoshinori Takeuchi
  • Naoji Matsumoto
  • Noboru Ohnishi

Abstract

Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.

Suggested Citation

  • André Cavalcante & Ahmed Mansouri & Lemya Kacha & Allan Kardec Barros & Yoshinori Takeuchi & Naoji Matsumoto & Noboru Ohnishi, 2014. "Measuring Streetscape Complexity Based on the Statistics of Local Contrast and Spatial Frequency," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0087097
    DOI: 10.1371/journal.pone.0087097
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    Cited by:

    1. Colin G. Johnson & Jon McCormack & Iria Santos & Juan Romero, 2019. "Understanding Aesthetics and Fitness Measures in Evolutionary Art Systems," Complexity, Hindawi, vol. 2019, pages 1-14, March.
    2. Marianne Gatti & Markus Nollert & Elena Pibernik, 2022. "Regulating Façade Length for Streetscapes of Human Scale," Land, MDPI, vol. 11(12), pages 1-27, December.
    3. Jing Zhao & Qi Guo, 2022. "Intelligent Assessment for Visual Quality of Streets: Exploration Based on Machine Learning and Large-Scale Street View Data," Sustainability, MDPI, vol. 14(13), pages 1-24, July.
    4. Boeing, Geoff, 2018. "Measuring the Complexity of Urban Form and Design," SocArXiv bxhrz, Center for Open Science.
    5. Boeing, Geoff, 2017. "Methods and Measures for Analyzing Complex Street Networks and Urban Form," SocArXiv 93h82, Center for Open Science.

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