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.

Advancements in remote sensing techniques and urban data analysis tools have enabled the successful monitoring and detection of green spaces in a city. This study aims to develop an index called the urban green accessibility (UGA) index, which measures people’s accessibility to green space and represents the citywide or local characteristics of the distribution pattern of green space. The index is defined as the sum of pedestrians’ accessibility to all vegetation points, which consists of the normalized difference vegetation index (NDVI) with integration and choice values from angular segment analysis. In this study, the proposed index is tested with cases of New York, NY, and San Francisco, CA, in the US. The results reveal differences based on the significance of streets. When analysis ranges are on a neighborhood scale, a few hotspots appear in well-known green areas on commonly accessible streets and in local neighborhood parks on residential blocks. The appearance of high-accessibility points in low-NDVI areas implies the potential of the efficient and proper distribution of green spaces for pedestrians. The proposed measure is expected to help in planning and managing green areas in cities, taking people’s accessibility and spatial relationships into consideration.