The Death and Life of Great American Cities was written in 1961 and is now one of the most influential book in city planning. In it, Jane Jacobs proposed four conditions that promote life in a city. However, these conditions have not been empirically tested until recently. This is mainly because it is hard to collect data about "city life". The city of Seoul recently collected pedestrian activity through surveys at an unprecedented scale, with an effort spanning more than a decade, allowing researchers to conduct the first study successfully testing Jacobs's conditions. In this paper, we identify a valuable alternative to the lengthy and costly collection of activity survey data: mobile phone data. We extract human activity from such data, collect land use and socio-demographic information from the Italian Census and Open Street Map, and test the four conditions in six Italian cities. Although these cities are very different from the places for which Jacobs's conditions were spelled out (i.e., great American cities) and from the places in which they were recently tested (i.e., the Asian city of Seoul), we find those conditions to be indeed associated with urban life in Italy as well. Our methodology promises to have a great impact on urban studies, not least because, if replicated, it will make it possible to test Jacobs's theories at scale.
Researchers also investigated which urban elements people use to visually judge a street to be safe, wealthy, and attractive using web crowdsourcing games @cite_41 @cite_43 @cite_10 , and they also studied how to identify walkable streets using the social media data of Flickr and Foursquare (e.g., unsafe streets tended to be photographed during the day, while walkable streets were tagged with walkability-related keywords @cite_22 ).
@cite_41 @cite_43 @cite_10 @cite_22
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Cities' visual appearance plays a central role in shaping human perception and response to the surrounding urban environment. For example, the visual qualities of urban spaces affect the psychological states of their inhabitants and can induce negative social outcomes. Hence, it becomes critically important to understand people's perceptions and evaluations of urban spaces. Previous works have demonstrated that algorithms can be used to predict high level attributes of urban scenes (e.g. safety, attractiveness, uniqueness), accurately emulating human perception. In this paper we propose a novel approach for predicting the perceived safety of a scene from Google Street View Images. Opposite to previous works, we formulate the problem of learning to predict high level judgments as a ranking task and we employ a Convolutional Neural Network (CNN), significantly improving the accuracy of predictions over previous methods. Interestingly, the proposed CNN architecture relies on a novel pooling layer, which permits to automatically discover the most important areas of the images for predicting the concept of perceived safety. An extensive experimental evaluation, conducted on the publicly available Place Pulse dataset, demonstrates the advantages of the proposed approach over state-of-the-art methods. In the 1960s, Lynch's 'The Image of the City' explored what impression US city neighborhoods left on its inhabitants. The scale of urban perception studies until recently was considerably constrained by the limited number of study participants. We here present a crowdsourcing project that aims to investigate, at scale, which visual aspects of London neighborhoods make them appear beautiful, quiet, and or happy. We collect votes from over 3.3K individuals and translate them into quantitative measures of urban perception. In so doing, we quantify each neighborhood's aesthetic capital. By then using state-of-the-art image processing techniques, we determine visual cues that may cause a street to be perceived as being beautiful, quiet, or happy. We identify effects of color, texture and visual words. For example, the amount of greenery is the most positively associated visual cue with each of three qualities; by contrast, broad streets, fortress-like buildings, and council houses tend to be associated with the opposite qualities (ugly, noisy, and unhappy). A traveler visiting Rio, Manila or Caracas does not need a report to learn that these cities are unequal; she can see it directly from the taxicab window. This is because in most cities inequality is conspicuous, but also, because cities express different forms of inequality that are evident to casual observers. Cities are highly heterogeneous and often unequal with respect to the income of their residents, but also with respect to the cleanliness of their neighborhoods, the beauty of their architecture, and the liveliness of their streets, among many other evaluative dimensions. Until now, however, our ability to understand the effect of a city's built environment on social and economic outcomes has been limited by the lack of quantitative data on urban perception. Here, we build on the intuition that inequality is partly conspicuous to create quantitative measure of a city's contrasts. Using thousands of geo-tagged images, we measure the perception of safety, class and uniqueness; in the cities of Boston and New York in the United States, and Linz and Salzburg in Austria, finding that the range of perceptions elicited by the images of New York and Boston is larger than the range of perceptions elicited by images from Linz and Salzburg. We interpret this as evidence that the cityscapes of Boston and New York are more contrasting, or unequal, than those of Linz and Salzburg. Finally, we validate our measures by exploring the connection between them and homicides, finding a significant correlation between the perceptions of safety and class and the number of homicides in a NYC zip code, after controlling for the effects of income, population, area and age. Our results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities. Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.