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The Collaborative Image of The City: Mapping the Inequality of Urban Perception

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TLDR
The results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities, using thousands of geo-tagged images to measure the perception of safety, class and uniqueness.
Abstract
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.

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Proceedings ArticleDOI

Streetscore -- Predicting the Perceived Safety of One Million Streetscapes

TL;DR: The predictive power of commonly used image features is studied using support vector regression, finding that Geometric Texton and Color Histograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape.
Posted Content

The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

TL;DR: In this paper, the authors use data from a crowd-sourcing platform that shows two street scenes in London and a user votes on which one looks more beautiful, quiet, and happy.
Journal ArticleDOI

Measuring human perceptions of a large-scale urban region using machine learning

TL;DR: A deep learning model, which has been trained on millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image and can help to map the distribution of the city-wide human perception for a new urban region.
Proceedings ArticleDOI

The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city

TL;DR: This work uses data from a crowd-sourcing platform to quantify the extent to which urban locations are pleasant, and finds that the recommended routes add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy.
References
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Book

The Death and Life of Great American Cities

Jane Jacobs
TL;DR: The conditions for city diversity, the generators of diversity, and the need for mixed primary uses are discussed in this paper, with a focus on the use of small blocks for small blocks.

Image of the city

Abstract: What does the city's form actually mean to the people who live there? What can the city planner do to make the city's image more vivid and memorable to the city dweller? To answer these questions, Mr. Lynch, supported by studies of Los Angeles, Boston, and Jersey City, formulates a new criterion -- imageability -- and shows its potential value as a guide for the building and rebuilding of cities. The wide scope of this study leads to an original and vital method for the evaluation of city form. The architect, the planner, and certainly the city dweller will all want to read this book.
Journal ArticleDOI

Notes on continuous stochastic phenomena.

TL;DR: Two problems arising in the two and three-dimensional cases of stochastic phenomena which are distributed in space of two or more dimensions are considered.