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C-IMAGE: city cognitive mapping through geo-tagged photos

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Abstract

Traditional research categorizes people’s perceptions towards city into Kevin Lynch’s five elements: node, path, edge, district, and landmark. However, enabled by the proliferation of crowd sourced data this paper utilizes geo-tagged photos to detect, measure, and analyze people’s perceptions. This paper introduces a project called C-IMAGE, which analyzes the interactions between city and human perception through the massive amount of photos taken in 26 different cities: one based on the metadata and the other based on image content. Important discoveries through them include that (1) C-IMAGE can partially confirm Kevin Lynch’s city image efficiently; (2) There are mainly four prototypes among the tested 26 cities, based on the 7 urban perceptions based C-IMAGE; (3) C-IMAGE shows the gap between subjective perceptions and objective environment while compared to traditional urban indicators.

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Notes

  1. In their paper, the author partitions a city into some disjoint regions using major roads. And they project the taxi trajectories of each day into these regions and detect the salient region pairs having traffic beyond its capacity. When the overloaded region pairs are repeatedly detected across many days, they define such a situation as a “flawed planning”. Here the word “planning” they used is somehow weird to the normal one in urban planning area.

  2. The city boundary data is downloaded from GADM, http://www.gadm.org.

  3. JSON, which is an open standard format that uses human-readable text to transit data objects consisting of attribute-value pairs. (Wikipedia, http://en.wikipedia.org/wiki/JSON).

  4. For those two maps, lease refer to Fig. 1.

  5. Please refer to the legend of Fig. 5.

  6. The complete list of the 102 attributes and their relation to the 7 urban perceptions is provided in the “Appendix 2”.

  7. Package of “ggmap”: http://cran.r-project.org/web/packages/ggmap/ggmap.pdf (page 6 describe how to use the “get_cloudmap” to create the C-IMAGE in this section).

  8. Unfortunately, CloudMade has shut down its service for Map Tile, Geocoding, Routing and Vector Stream since May 1, 2014.

  9. All the 21 cities’ urban perception C-IMAGEs are included in the “Appendix 2”.

  10. The downtown of New York City is different, which will be discussed later.

  11. The complete set of all the histograms of the 26 cities can be found in “Appendix 2”.

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Author information

Authors and Affiliations

  1. China Academy of Urban Planning and Design, 168 Linhong Rd, Shanghai, People’s Republic of China

    Liu Liu

  2. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Bolei Zhou

  3. Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA

    Jinhua Zhao

  4. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA

    Brent D. Ryan

Authors
  1. Liu Liu
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  2. Bolei Zhou
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  3. Jinhua Zhao
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  4. Brent D. Ryan
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Corresponding author

Correspondence to Liu Liu.

Appendices

Appendix 1

Larger map of of Fig. 4.

figure a

Larger map of Fig. 5.

figure b

Larger map of Fig. 12 (left).

figure c

Larger map of Fig. 12 (right).

figure d

Reference map for Table 5 (problem 1 to 9).

figure e

Reference map for Table 5 (problem 9 to 18).

figure f

Reference map for Table 5 (problem 19 to 26).

figure g

Appendix 2

The 7 urban perceptions (colored rectangles) derived from the 102 attributes (left) and the histogram of all the photos of each perception in LondonFootnote 11 (right).

figure h

The infographics based on the seven perceptions of 21 cities.

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Liu, L., Zhou, B., Zhao, J. et al. C-IMAGE: city cognitive mapping through geo-tagged photos. GeoJournal 81, 817–861 (2016). https://doi.org/10.1007/s10708-016-9739-6

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  • DOI: https://doi.org/10.1007/s10708-016-9739-6

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