Crowd-sourced cognitive mapping: A new way of displaying people's cognitive perception of urban space

PLoS One. 2019 Jun 20;14(6):e0218590. doi: 10.1371/journal.pone.0218590. eCollection 2019.


By utilizing cognitive mapping and leveraging georeferenced text data, this paper aims to suggest a new visualization method that combines the advantages of both conventional and state-of-the-art research techniques to depict the collective identity of place in a single image. The study addressed two research questions: (1) Can crowd-sourced text data be utilized in representing place identity? (2) Can collective place identity be expressed in the form of a cognitive map? By confirming that text data gathered from social media effectively demonstrate people's behaviors and perceptions related to places, we propose a novel method to create a visual representation of urban identity-a "crowd-sourced cognitive map". In particular, to improve the conventional cognitive mapping method to depict the collective identity of a city, we draw cognitive maps of Bundang and Ilsan developed in the 1990s, as well as Songdo and Dongtan developed in the 2000s, just outside of the administrative boundaries of Seoul in Korea, through a computational method based on crowd-sourced opinions collected from social media. We open the possibility for the use of social media text data to capture the identity of cities and suggest a graphical image through which people without prior information could also easily apprehend the overall image of a city. The work in this paper is expected to provide a methodological technique for appropriate decision-making and the evaluation of urban identity to shape a more unique and imageable city.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cognition*
  • Humans
  • Models, Psychological*
  • Perception*
  • Social Identification*
  • Social Media
  • Spatial Behavior
  • Urban Renewal / methods*

Associated data

  • figshare/10.6084/m9.figshare.7603907.v1

Grant support

This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Science and ICT for convergent research in EDucation-research Integration through Simulation On the Net (EDISON) (NRF-2017M3C1A6075020) and by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport of Korea (19CTAP-C142170-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.