Streetscore -- Predicting the Perceived Safety of One Million Streetscapes
Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/92811 https://orcid.org/0000-0002-6031-5982 https://orcid.org/0000-0002-9894-8865 https://orcid.org/0000-0002-3254-3224 |
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| author | Raskar, Ramesh Naik, Nikhil Deepak Philipoom, Jade D. Hidalgo Ramaciotti, Cesar A. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Raskar, Ramesh Naik, Nikhil Deepak Philipoom, Jade D. Hidalgo Ramaciotti, Cesar A. |
| author_sort | Raskar, Ramesh |
| collection | MIT |
| description | Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features 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. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring ~1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception. |
| first_indexed | 2024-09-23T09:29:47Z |
| format | Article |
| id | mit-1721.1/92811 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:29:47Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/928112022-09-30T14:47:42Z Streetscore -- Predicting the Perceived Safety of One Million Streetscapes Raskar, Ramesh Naik, Nikhil Deepak Philipoom, Jade D. Hidalgo Ramaciotti, Cesar A. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Naik, Nikhil Deepak Philipoom, Jade D. Raskar, Ramesh Hidalgo, Cesar A. Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features 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. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring ~1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception. MIT Media Lab Consortium Google (Firm). Living Labs Tides Foundation 2015-01-13T13:44:49Z 2015-01-13T13:44:49Z 2014-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-4308-1 http://hdl.handle.net/1721.1/92811 Naik, Nikhil, Jade Philipoom, Ramesh Raskar, and Cesar Hidalgo. “Streetscore -- Predicting the Perceived Safety of One Million Streetscapes.” 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (June 2014). https://orcid.org/0000-0002-6031-5982 https://orcid.org/0000-0002-9894-8865 https://orcid.org/0000-0002-3254-3224 en_US http://dx.doi.org/10.1109/CVPRW.2014.121 Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Raskar, Ramesh Naik, Nikhil Deepak Philipoom, Jade D. Hidalgo Ramaciotti, Cesar A. Streetscore -- Predicting the Perceived Safety of One Million Streetscapes |
| title | Streetscore -- Predicting the Perceived Safety of One Million Streetscapes |
| title_full | Streetscore -- Predicting the Perceived Safety of One Million Streetscapes |
| title_fullStr | Streetscore -- Predicting the Perceived Safety of One Million Streetscapes |
| title_full_unstemmed | Streetscore -- Predicting the Perceived Safety of One Million Streetscapes |
| title_short | Streetscore -- Predicting the Perceived Safety of One Million Streetscapes |
| title_sort | streetscore predicting the perceived safety of one million streetscapes |
| url | http://hdl.handle.net/1721.1/92811 https://orcid.org/0000-0002-6031-5982 https://orcid.org/0000-0002-9894-8865 https://orcid.org/0000-0002-3254-3224 |
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