The Collaborative Image of The City: Mapping the Inequality of Urban Perception
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{"title"=>"The Collaborative Image of The City: Mapping the Inequality of Urban Perception", "type"=>"journal", "authors"=>[{"first_name"=>"Philip", "last_name"=>"Salesses", "scopus_author_id"=>"55015851100"}, {"first_name"=>"Katja", "last_name"=>"Schechtner", "scopus_author_id"=>"14070744800"}, {"first_name"=>"César A.", "last_name"=>"Hidalgo", "scopus_author_id"=>"8362331000"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"369437974", "issn"=>"19326203", "pmid"=>"23894301", "scopus"=>"2-s2.0-84880765337", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0068400", "sgr"=>"84880765337"}, "id"=>"42753397-56f3-3aaa-9fa2-1569f198af27", "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.", "link"=>"http://www.mendeley.com/research/collaborative-image-city-mapping-inequality-urban-perception", "reader_count"=>170, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>31, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>2, "Student > Master"=>34, "Other"=>9, "Student > Bachelor"=>16, "Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>31, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>54, "Student > Postgraduate"=>2, "Student > Master"=>34, "Other"=>9, "Student > Bachelor"=>16, "Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>2, "Arts and Humanities"=>8, "Business, Management and Accounting"=>1, "Computer Science"=>57, "Earth and Planetary Sciences"=>7, "Economics, Econometrics and Finance"=>2, "Engineering"=>17, "Environmental Science"=>14, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Design"=>12, "Physics and Astronomy"=>1, "Psychology"=>8, "Social Sciences"=>34, "Sports and Recreations"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>34}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>8}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>14}, "Arts and Humanities"=>{"Arts and Humanities"=>8}, "Design"=>{"Design"=>12}, "Engineering"=>{"Engineering"=>17}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>7}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>57}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}}, "reader_count_by_country"=>{"United States"=>10, "Japan"=>2, "United Kingdom"=>3, "Switzerland"=>1, "Spain"=>4, "New Zealand"=>1, "Canada"=>1, "Czech Republic"=>1, "Luxembourg"=>1, "Taiwan"=>1, "Denmark"=>1, "Brazil"=>2, "South Africa"=>1, "Mexico"=>1, "Italy"=>1, "France"=>2, "Chile"=>1, "Germany"=>3}, "group_count"=>10}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1129948"], "description"=>"<p><b>A–D.</b> Locations from which images were collected for: <b>A</b> Boston, <b>B</b> New York City, <b>C</b> Salzburg and <b>D</b> Linz. We note that for many locations, more than one image was collected (with the camera looking in different directions).</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics"], "article_id"=>754354, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Images_used_in_the_study_/754354", "title"=>"Images used in the study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129949"], "description"=>"<p><b>A.</b> The website used to collect votes. Participants were presented a random pair of images and voted by clicking on one in response to the question. <b>B</b>. Robustness of the urban perception metric (Q). <i>B</i> is the square of the Pearson correlation between two disjoint subsets of votes of size <i>v</i> containing the same number of images.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics"], "article_id"=>754355, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.g002", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_Collection_Methods_/754355", "title"=>"Data Collection Methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129950"], "description"=>"<p><b>A.</b> High and low scoring images for safety <b>B</b>. social-class and <b>C</b>. uniqueness. <b>D</b>. Scatter plot of Q-scores for safety and social-class with four examples illustrating images with different combinations of evaluative criteria. <b>E</b>. Same as <b>D</b>, but for safety and uniqueness. <b>G</b>. Same as <b>D</b>, but for social-class and uniqueness.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics", "places"], "article_id"=>754356, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Identifying_places_associated_with_different_urban_perceptions_/754356", "title"=>"Identifying places associated with different urban perceptions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129951"], "description"=>"<p><b>A.</b> Scatter plot showing the Q-scores obtained for each image, city and question. Top and bottom whiskers represent one standard deviation. <b>B</b>. Moran's I z-scores for each city and question (all p-values<0.01, see SM). <b>C</b>. Spatial correlograms showing the decay of spatial autocorrelation as a function of distance. <b>D–F</b>. Map of NYC showing statistically significant clusters of high -and low- Q-scores for the perception of safety, class and uniqueness according to Getis Gi* statistic. Green shows clusters of positive perceptions (high Q-scores) and red shows clusters of negative perceptions (low Q-scores).</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics"], "article_id"=>754357, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contrasts_in_urban_perception_/754357", "title"=>"Contrasts in urban perception.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129952"], "description"=>"<p><b>A</b> Comparison between the location of crimes in NYC and the predictions of urban perception, area and population (model <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068400#pone.0068400-Robinson1\" target=\"_blank\">[4]</a>). <b>B</b>. Demographics (model <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068400#pone.0068400-Lynch1\" target=\"_blank\">[1]</a>). <b>C</b>. All variables (model <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068400#pone.0068400-Howard1\" target=\"_blank\">[5]</a>).</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics"], "article_id"=>754358, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.g005", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Urban_perception_and_violent_crime_/754358", "title"=>"Urban perception and violent crime.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129953"], "description"=>"<p>Comparison between the means and standard deviations of the urban perception recorded for each city and question.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics", "means", "deviations", "recorded"], "article_id"=>754359, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.t002", "stats"=>{"downloads"=>6, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_the_means_and_standard_deviations_of_the_urban_perception_recorded_for_each_city_and_question_/754359", "title"=>"Comparison between the means and standard deviations of the urban perception recorded for each city and question.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129954"], "description"=>"<p>Getis Spatially Filtered Regression including variables for demographic and urban perception.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics", "spatially", "filtered", "regression", "variables"], "article_id"=>754360, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.t003", "stats"=>{"downloads"=>4, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Getis_Spatially_Filtered_Regression_including_variables_for_demographic_and_urban_perception_/754360", "title"=>"Getis Spatially Filtered Regression including variables for demographic and urban perception.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129955"], "description"=>"<p>Means and Standard Deviations of the Q-scores obtained for each city and question.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics", "deviations", "q-scores"], "article_id"=>754361, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068400.t001", "stats"=>{"downloads"=>7, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Means_and_Standard_Deviations_of_the_Q_scores_obtained_for_each_city_and_question_/754361", "title"=>"Means and Standard Deviations of the Q-scores obtained for each city and question.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-24 06:46:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1129956", "https://ndownloader.figshare.com/files/1129957"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "geoinformatics", "geostatistics", "Remote sensing imagery", "Spatial autocorrelation", "Information technology", "databases", "Civil engineering", "structures", "Human factors engineering", "Applied mathematics", "Complex systems", "Interdisciplinary physics", "collaborative", "inequality"], "article_id"=>754362, "categories"=>["Information And Computing Sciences", "Mathematics", "Engineering", "Physics"], "users"=>["Philip Salesses", "Katja Schechtner", "César A. Hidalgo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0068400.s001", "https://dx.doi.org/10.1371/journal.pone.0068400.s002"], "stats"=>{"downloads"=>29, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Collaborative_Image_of_The_City_Mapping_the_Inequality_of_Urban_Perception_/754362", "title"=>"The Collaborative Image of The City: Mapping the Inequality of Urban Perception", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-07-24 06:46:00"}

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