|Title||C-IMAGE: City Cognitive Mapping through Geo-tagged Photos|
|Year of Publication||2014|
|Academic Department||Dept. of Urban Studies and Planning|
|Degree||Master of City Planning|
|University||Massachusetts Institute of Technology|
Traditional research categorizes people's perceptions towards the city environment with Kevin Lynch's five elements: node, path, edge, district, and landmark. His method has been a keystone in guiding both urban design and urban study for decades. However, enabled by the proliferation of crowd sourcing technology, this thesis tries another angle to detect, measure, and analyze people's perceptions through geo-tagged photos. Using Python scripting language, the project downloads photos of 26 cities (an average of 100,000 photos per city) with geographic information, in North America, Europe, and Asia, from Panoramio and partially from Flickr. The process of image analysis is built on a computer vision technique - scene understanding, which categorizes the photos into 102 scenes by content. This paper aims to introduce photos collected at the massive scale as an additional method of city image. The project name of C-IMAGE reflects the interaction between city computation and city cognition. This thesis implies different roles of C-IMAGE in three applications for urban planning: a monitor for city forms, an evaluator for planning strategies, and a reference for urban functions. Important discoveries through these applications include that 1) C-IMAGE can partially confirm Kevin Lynch's city image efficiently; 2) C-IMAGE can disclose both agent-led and agent-less urban changes; 3) There are mainly four prototypes among the tested 26 cities, based on a seven-category C-IMAGE patterns; 4) C-IMAGE can evaluate planning suitability because its indicators are associated with the real activity; 5) Part of land use is statistically predictable in the case of Manhattan. Three impacts on urban planning are highlighted: First C-IMAGE is a method to pull data from social media to describe vividly the collective perception from the public. Second it computationally extracts information from photos at a deeper level for the study of urban space and activities. Third, C-IMAGE benefits from the multidisciplinary cooperation between planning and computer science to open up a new channel and provide analytical tools for further research on the cognitive mapping of urban spaces. Keywords: City Image, Cognitive Mapping, Urban Computing, Geo-tagged Photos, Crowd Sourcing.
Supervised by Brent Ryan, Reader: Jinhua Zhao