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      How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View

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          Abstract

          Extensive evidence has revealed that street greenery, as a quality-of-life component, is important for oxygen production, pollutant absorption, and urban heat island effect mitigation. Determining how green our streets are has always been difficult given the time and money consumed using conventional methods. This study proposes an automatic method using an emerging online street-view service to address this issue. This method was used to analyze street greenery in the central areas (28.3 km 2 each) of 245 major Chinese cities; this differs from previous studies, which have investigated small areas in a given city. Such a city-system-level study enabled us to detect potential universal laws governing street greenery as well as the impact factors. We collected over one million Tencent Street View pictures and calculated the green view index for each picture. We found the following rules: (1) longer streets in more economically developed and highly administrated cities tended to be greener; (2) cities in western China tend to have greener streets; and (3) the aggregated green view indices at the municipal level match with the approved National Garden Cities of China. These findings can prove useful for drafting more appropriate policies regarding planning and engineering practices for street greenery.

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          Learning hierarchical features for scene labeling.

          Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a $(320\times 240)$ image labeling in less than a second, including feature extraction.
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            Using Google Street View to audit neighborhood environments.

            Research indicates that neighborhood environment characteristics such as physical disorder influence health and health behavior. In-person audit of neighborhood environments is costly and time-consuming. Google Street View may allow auditing of neighborhood environments more easily and at lower cost, but little is known about the feasibility of such data collection. To assess the feasibility of using Google Street View to audit neighborhood environments. This study compared neighborhood measurements coded in 2008 using Street View with neighborhood audit data collected in 2007. The sample included 37 block faces in high-walkability neighborhoods in New York City. Field audit and Street View data were collected for 143 items associated with seven neighborhood environment constructions: aesthetics, physical disorder, pedestrian safety, motorized traffic and parking, infrastructure for active travel, sidewalk amenities, and social and commercial activity. To measure concordance between field audit and Street View data, percentage agreement was used for categoric measures and Spearman rank-order correlations were used for continuous measures. The analyses, conducted in 2009, found high levels of concordance (≥80% agreement or ≥0.60 Spearman rank-order correlation) for 54.3% of the items. Measures of pedestrian safety, motorized traffic and parking, and infrastructure for active travel had relatively high levels of concordance, whereas measures of physical disorder had low levels. Features that are small or that typically exhibit temporal variability had lower levels of concordance. This exploratory study indicates that Google Street View can be used to audit neighborhood environments. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
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              The Relationship between Natural Park Usage and Happiness Does Not Hold in a Tropical City-State

              Previous studies have shown that contact with urban green spaces can produce positive effects on people's stress, health and well-being levels. However, much of this research has been conducted in the temperate regions of Europe or North America. Additionally, most studies have only compared the effects of urban and natural areas on health and well-being, but not made a finer distinction between different types of urban green spaces. We tested the relationship between well-being and the access or use of different types of green spaces among young adults in Singapore, a tropical city-state. The results showed that extraversion and emotional stability increased subjective well-being, positive affect and life satisfaction and decreased stress and negative affect. In addition, we found that level of physical activity increased positive affect and health problems increased negative affect. Neither access to green spaces nor the use of green spaces in Singapore significantly affected the well-being metrics considered, contradicting findings in the temperate regions of the world. We hypothesize that the differences in temperature and humidity and the higher greenery and biodiversity levels outside parks in Singapore could explain this phenomenon. Our results thus question the universality of the relationship between well-being and park usage and highlight the need for more research into the multifaceted effects of green spaces on well-being in the tropics.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 February 2017
                2017
                : 12
                : 2
                : e0171110
                Affiliations
                [1 ]School of Architecture and Hang Lung Center for Real Estate, Tsinghua University, Beijing, China
                [2 ]China Academy of Urban Planning and Design, Shanghai, China
                Beihang University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: YL LL.

                • Data curation: YL LL.

                • Formal analysis: YL LL.

                • Funding acquisition: YL.

                • Investigation: YL LL.

                • Methodology: YL LL.

                • Project administration: YL.

                • Resources: YL LL.

                • Software: YL LL.

                • Supervision: YL LL.

                • Validation: YL.

                • Visualization: YL.

                • Writing – original draft: YL LL.

                • Writing – review & editing: YL LL.

                Author information
                http://orcid.org/0000-0002-8657-6988
                Article
                PONE-D-16-27428
                10.1371/journal.pone.0171110
                5308808
                28196071
                4485f11b-02ad-43d3-886e-f08dde9946e0
                © 2017 Long, Liu

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 July 2016
                : 15 January 2017
                Page count
                Figures: 6, Tables: 5, Pages: 18
                Funding
                The first author would like to acknowledge the funding of the National Natural Science Foundation of China (No. 51408039).
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