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The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai

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Book cover Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13725))

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Abstract

Recent development in Street View Imagery (SVI), Computer Vision (CV) and Machine Learning (ML) has allowed scholars to quantitatively measure human perceived street characteristics and perceptions at an unprecedented scale. Prior research has measured street perceptions either objectively or subjectively. However, there is little agreement on measuring these concepts. Fewer studies have systematically investigated the coherence and divergence between objective and subjective measurements of perceptions. Large divergence between the two measurements over the same perception can lead to different and even opposite spatial implications. Furthermore, what street environment features can cause the discrepancies between objectively and subjectively measured perceptions remain unexplained. To fill the gap, five pairwise (subjectively vs objectively measured) perceptions (i.e., complexity, enclosure, greenness, imageability, and walkability) are quantified based on Street View Imagery (SVI) and compared their overlap and disparity both statistically and through spatial mapping. With further insights on what features can explain the differences in each pairwise perceptions, and urban-scale mapping of street scene perceptions, this research provides valuable guidance on the future improvement of models.

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Author information

Authors and Affiliations

  1. IBI Group, Toronto, ON, M5V 3T5, Canada

    Qiwei Song

  2. Shenzhen Technology University, Shenzhen, 518118, Guangdong, China

    Meikang Li

  3. Cornell University, Ithaca, NY, 14850, USA

    Waishan Qiu

  4. The University of Tokyo, Tokyo, 113-8654, Japan

    Wenjing Li

  5. University of Queensland, St. Lucia, QLD, 4072, Australia

    Dan Luo

Authors
  1. Qiwei Song
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  2. Meikang Li
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  3. Waishan Qiu
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  4. Wenjing Li
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  5. Dan Luo
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Corresponding author

Correspondence to Qiwei Song .

Editor information

Editors and Affiliations

  1. The University of Adelaide, Adelaide, SA, Australia

    Weitong Chen

  2. The University of New South Wales, Sydney, NSW, Australia

    Lina Yao

  3. Macquarie University, Sydney, NSW, Australia

    Taotao Cai

  4. Griffith University, Brisbane, QLD, Australia

    Shirui Pan

  5. Microsoft, Beijing, China

    Tao Shen

  6. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

Appendix

Appendix

General descriptive statistics of perceptions.

Neighbourhood attributes

Count

Mean

Std. Dev.

Min

Max

Data source

Subjective streetscape attributes

S1_CMPLX

Subjectively perceived complexity

40,159

0.6

0.0

0.5

0.9

Predicted by ML models from Baidu SVIs

S2_ENCLS

Subjectively perceived enclosure

40,159

0.7

0.1

0.3

0.9

S3_GREEN

Subjectively perceived greenness

40,159

0.8

0.0

0.4

0.9

S4_IMBLT

Subjectively perceived imageability

40,159

0.7

0.1

0.3

0.9

S5_WALKB

Subjectively perceived walkability

40,159

0.6

0.1

0.4

0.8

Objective streetscape attributes

O1_CMPLX

Objectively calculated complexity

40,159

0.3

0.1

0.0

0.6

Recombined selected physical feature view indices

O2_ENCLS

Objectively calculated enclosure

40,159

0.6

0.0

0.1

0.7

O3_GREEN

Objectively calculated greenness

40,159

0.4

0.1

0.0

0.8

O4_IMBLT

Objectively calculated imageability

40,159

0.6

0.1

0.0

0.9

O5_WALKB

Objectively calculated walkability

40,159

0.6

0.1

0.2

0.7

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Song, Q., Li, M., Qiu, W., Li, W., Luo, D. (2022). The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_19

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  • DOI: https://doi.org/10.1007/978-3-031-22064-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22063-0

  • Online ISBN: 978-3-031-22064-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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