Invisible cities: exploring psychological urban data

• Ano de defesa: 2014
• Autor(a) principal: João Paulo Barros Cotta Pesce
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Informática urbana; Análise espacial; Urban informatics; Social media; Web science; Future cities; Mídias sociais; Computação; Análise espacial (Estatística)
• Link de acesso: http://hdl.handle.net/1843/ESBF-9TEPKF

Planners and social psychologists have suggested that the recognizability of the urban environment is linked to peoples socio-economic well-being. We build a web game that puts the recognizability of Londons streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgrams). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We nd that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do signicantly suer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life o- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare.We then use the results of this experiment, along with other urban data, to tackle the problem of identifying interesting and memorable pictures in photo sharing sites. Past proposals for identifying such pictures have relied on either metadata (e.g., likes) or visual features. In practice, techniques based on those two inputs do not always work: metadata is sparse (only few pictures have considerable number of likes), and extracting visual features is computationally expensive. In mobile solutions, georeferenced content becomes increasingly important. The premise behind this work is that pictures of a neighborhood is linked to the way the neighborhood is perceived by people: whether it is, for instance, distinctive and beautiful or not. Since 1970s, urban theories proposed by Lynch, Milgram and Peterson aimed at systematically capturing the way people perceive neighborhoods. Here we tested whether those theories could be put to use for automatically identifying appealing city pictures. We did so by gathering geo-referenced Flickr pictures in the city of London; selecting six urban qualities associated with those urban theories; computing proxies for those qualities from online social media data; and ranking Flickr pictures based on those proxies. We nd that our proposal enjoys three main desirable properties: it is eective, scalable, and aware of contextual changes such as time of day and weather condition. All this suggests new promising research directions for multi-modal learning approaches that automatically identify appealing city pictures