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Dr. Yu Ye is an Associate professor in the Department of Architecture, College of Architecture and Urban Planning at Tongji University, China. He gained his PhD degree from The University of Hong Kong and he had worked as a Post-doctoral researcher in Future Cities Lab, ETH Zurich. His research mainly focuses on data-informed urban design utilizing multi-sourced urban data and newly emerged analytical techniques, which has been published on Urban Geography, Landscape and Urban Planning, Environment and Planning B, etc.
This study presents an analytical approach for measuring the degree of balance between urban and tourism development, which has been previously analyzed qualitatively and was difficult to measure. With the help of 1012 million cellular data records generated by 20 million users in two weeks, we tracked the behavior of residents, commuters, and tourists at a set of historical conservation areas in central Shanghai. We calculated the degree of balance and visualized it via ternary graphs. Moreover, the relationships between key urban features derived from multi-sourced urban data and balanced degrees of tourism development were analyzed via multinomial logistic analysis. Insights gained from this analysis will help to achieve a more scientific decision-making process toward balanced urban development for historical conservation area. Achievements in this study contribute to the development of human-centered planning through providing continuous measurements of an “unmeasurable” quality.
Cheng Shi; Mengyang Liu; Yu Ye. Measuring the Degree of Balance between Urban and Tourism Development: An Analytical Approach Using Cellular Data. Sustainability 2021, 13, 9598 .
AMA StyleCheng Shi, Mengyang Liu, Yu Ye. Measuring the Degree of Balance between Urban and Tourism Development: An Analytical Approach Using Cellular Data. Sustainability. 2021; 13 (17):9598.
Chicago/Turabian StyleCheng Shi; Mengyang Liu; Yu Ye. 2021. "Measuring the Degree of Balance between Urban and Tourism Development: An Analytical Approach Using Cellular Data." Sustainability 13, no. 17: 9598.
Precise urban façade color is the foundation of urban color planning. Nevertheless, existing research on urban colors usually relies on manual sampling due to technical limitations, which brings challenges for evaluating urban façade color with the co-existence of city-scale and fine-grained resolution. In this study, we propose a deep learning-based approach for mapping the urban façade color using street-view imagery. The dominant color of the urban façade (DCUF) is adopted as an indicator to describe the urban façade color. A case study in Shenzhen was conducted to measure the urban façade color using Baidu Street View (BSV) panoramas, with city-scale mapping of the urban façade color in both irregular geographical units and regular grids. Shenzhen’s urban façade color has a gray tone with low chroma. The results demonstrate that the proposed method has a high level of accuracy for the extraction of the urban façade color. In short, this study contributes to the development of urban color planning by efficiently analyzing the urban façade color with higher levels of validity across city-scale areas. Insights into the mapping of the urban façade color from the humanistic perspective could facilitate higher quality urban space planning and design.
Teng Zhong; Cheng Ye; Zian Wang; Guoan Tang; Wei Zhang; Yu Ye. City-Scale Mapping of Urban Façade Color Using Street-View Imagery. Remote Sensing 2021, 13, 1591 .
AMA StyleTeng Zhong, Cheng Ye, Zian Wang, Guoan Tang, Wei Zhang, Yu Ye. City-Scale Mapping of Urban Façade Color Using Street-View Imagery. Remote Sensing. 2021; 13 (8):1591.
Chicago/Turabian StyleTeng Zhong; Cheng Ye; Zian Wang; Guoan Tang; Wei Zhang; Yu Ye. 2021. "City-Scale Mapping of Urban Façade Color Using Street-View Imagery." Remote Sensing 13, no. 8: 1591.
Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and natural issues, failing to account for human-scale urban design factors. Accordingly, this study proposes a data-informed approach to planning urban greenway networks using a combination of classical urban design theories, multi-sourced urban data, and machine learning algorithms. Maoming City in China was used as a case study. Per classical urban design theories, specifically, Cervero and Ewing’s 5D variables, density, diversity, design, dimensions of destination accessibility, and distance-to-transit, were selected as key factors. A series of new urban data, including points of interest (PoIs), location-based service (LBS) positioning data, and street view images, were applied in conjunction with machine learning algorithms and geographical information system (GIS) tools to measure these key factors at a human-scale resolution and generate an optimized greenway suitability analysis. This analytical approach is an attempt to take human-scale concerns into account on a city-wide scale regarding greenway network generation. It also pushes the methodological boundaries of greenway planning by combining classical urban design thinking with new urban data and new techniques.
Ziyi Tang; Yu Ye; Zhidian Jiang; Chaowei Fu; Rong Huang; Dong Yao. A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms. Urban Forestry & Urban Greening 2020, 56, 126871 .
AMA StyleZiyi Tang, Yu Ye, Zhidian Jiang, Chaowei Fu, Rong Huang, Dong Yao. A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms. Urban Forestry & Urban Greening. 2020; 56 ():126871.
Chicago/Turabian StyleZiyi Tang; Yu Ye; Zhidian Jiang; Chaowei Fu; Rong Huang; Dong Yao. 2020. "A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms." Urban Forestry & Urban Greening 56, no. : 126871.
Living convenience, as a perceptual quality of life, is gradually playing an increasingly important role in the context of seeking livable cities. A high degree of living convenience positively affects urban vitality, livability, and daily physical activities. However, it is hard to achieve a quantitative measurement of this intangible, subjective issue. This study presents a data-informed analytical approach to measuring the human-scale living convenience using multi-sourced urban data and geodesign techniques. Firstly, according to classical theories, living convenience is translated as the co-presentation of accessed number and diversity of urban facilities. Based on that, this study applies multi-sourced urban data, including points of interest (PoIs), buildings, and street networks, to compute the living convenience of each building in the 15 min community–life circle. Through the geoprocessing tools developed by ArcGIS API for Python (ArcPy), the living convenience of millions of buildings in an entire city can be computed efficiently. Kaifeng City from Henan Province, China, is selected as the case study, and the verification from local experts in urbanism shows high accuracy. The capacity to measure intangible perception exhibits the potential for this analytical approach in urban planning practices. Several explorations have been conducted in this direction, including analyzing the spatial heterogeneity in Kaifeng City and planning decision support for bus station arrangement. In short, this study contributes to the development of human-centered planning by providing continuous measurements of an ‘unmeasurable’ quality across large-scale areas. Insights into the perceptual-based quality and detailed mapping of living conveniences in buildings can assist in efficient planning strategies toward more livable and sustainable urbanism.
Teng Zhong; Guonian Lü; Xiuming Zhong; Haoming Tang; Yu Ye. Measuring Human-Scale Living Convenience through Multi-Sourced Urban Data and a Geodesign Approach: Buildings as Analytical Units. Sustainability 2020, 12, 4712 .
AMA StyleTeng Zhong, Guonian Lü, Xiuming Zhong, Haoming Tang, Yu Ye. Measuring Human-Scale Living Convenience through Multi-Sourced Urban Data and a Geodesign Approach: Buildings as Analytical Units. Sustainability. 2020; 12 (11):4712.
Chicago/Turabian StyleTeng Zhong; Guonian Lü; Xiuming Zhong; Haoming Tang; Yu Ye. 2020. "Measuring Human-Scale Living Convenience through Multi-Sourced Urban Data and a Geodesign Approach: Buildings as Analytical Units." Sustainability 12, no. 11: 4712.
Pedestrian volume is an important indicator of urban walkability and vitality. Hence, information on pedestrian volumes of different streets is indispensable for creating healthy, pedestrian-oriented cities. Pedestrian volume data have traditionally been collected through field observations, which has many methodological limitations, e.g. time-consuming, labor-intensive, and inefficient. Assessing pedestrian volume automatically from Street View images (SVIs) with machine learning techniques can overcome such limitations because this approach offers a wide geographic reach and consistent image acquisition. Nevertheless, this new method has not been rigorously validated, and its accuracy remains unclear. In this study, we conducted a large-scale validation test by comparing pedestrian volume extracted from SVIs with the results from field observations for more than 700 street segments in Tianjin, China. A total of 4507 sampling points along these street segments were used to collect SVIs. The results demonstrated that using SVIs with machine learning techniques is a promising method for estimating pedestrian volumes with a large geographic reach. Automated pedestrian volume detection could achieve reasonable (Cronbach's alpha ≥0.70) or good (Cronbach's alpha ≥0.80) levels of accuracy. It is worth noting that various factors of SVIs and street segments may affect the accuracy. SVIs with higher image quality, larger image size, and collection times closer to the targeted periods produced more accurate results. The automated method also worked better in areas with high pedestrian volume and high street connectivity.
Long Chen; Yi Lu; Qiang Sheng; Yu Ye; Ruoyu Wang; Ye Liu. Estimating pedestrian volume using Street View images: A large-scale validation test. Computers, Environment and Urban Systems 2020, 81, 101481 .
AMA StyleLong Chen, Yi Lu, Qiang Sheng, Yu Ye, Ruoyu Wang, Ye Liu. Estimating pedestrian volume using Street View images: A large-scale validation test. Computers, Environment and Urban Systems. 2020; 81 ():101481.
Chicago/Turabian StyleLong Chen; Yi Lu; Qiang Sheng; Yu Ye; Ruoyu Wang; Ye Liu. 2020. "Estimating pedestrian volume using Street View images: A large-scale validation test." Computers, Environment and Urban Systems 81, no. : 101481.
Many studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people’s holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.
Lingzhu Zhang; Yu Ye; Wenxin Zeng; Alain Chiaradia. A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis. International Journal of Environmental Research and Public Health 2019, 16, 1782 .
AMA StyleLingzhu Zhang, Yu Ye, Wenxin Zeng, Alain Chiaradia. A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis. International Journal of Environmental Research and Public Health. 2019; 16 (10):1782.
Chicago/Turabian StyleLingzhu Zhang; Yu Ye; Wenxin Zeng; Alain Chiaradia. 2019. "A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis." International Journal of Environmental Research and Public Health 16, no. 10: 1782.
The protective effects of street greenery on ecological, psychological, and behavioral phenomena have been well recognized. Nevertheless, the potential economic effect of daily accessed street greenery, i.e., a human-scale and perceptual-oriented quality focusing on exposure to street greenery in people’s daily lives, has not been fully studied because a quantitative measuring of this human-scale indicator is hard to achieve. This study was an attempt in this direction with the help of new urban data and new analytical tools. Shanghai, which has a mature real estate market, was selected for study, and the housing prices of 1395 private neighborhoods in its city center were collected. We selected more than forty variables that were classified under five categories—location features, distances to the closest facilities, density of facilities within a certain radius, housing and neighborhood features, and daily accessed street greenery—in a hedonic pricing model. The distance and density of facilities were computed through a massive number of points-of-interest and a geographical information system. The visible street greenery was collected from Baidu street view images and then measured via a machine-learning algorithm, while accessibility was measured through space syntax. In addition to the well-recognized effects previously discovered, the results show that visible street greenery and street accessibility at global scale hold significant positive coefficients for housing prices. Visible street greenery even obtains the second-highest regression coefficient in the model. Moreover, the combined assessment, the co-presence of local-scale accessibility and eye-level greenery, is significant for housing price as well. This study provides a scientific and quantitative support for the significance of human-scale street greenery, making it an important issue in urban greening policy for urban planners and decision makers.
Yu Ye; Hanting Xie; Jia Fang; Hetao Jiang; De Wang. Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data. Sustainability 2019, 11, 1741 .
AMA StyleYu Ye, Hanting Xie, Jia Fang, Hetao Jiang, De Wang. Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data. Sustainability. 2019; 11 (6):1741.
Chicago/Turabian StyleYu Ye; Hanting Xie; Jia Fang; Hetao Jiang; De Wang. 2019. "Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data." Sustainability 11, no. 6: 1741.
The public benefits of visible street greenery have been well recognised in a growing literature. Nevertheless, this issue was rare to be included into urban greenery and planning practices. As a response to this situation, we proposed an actionable approach for quantifying the daily exposure of urban residents to eye-level street greenery by integrating high resolution measurements on both greenery and accessibility. Google Street View (GSV) images in Singapore were collected and extracted through machine learning algorithms to achieve an accurate measurement on visible greenery. Street networks collected from Open Street Map (OSM) were analysed through spatial design network analysis (sDNA) to quantify the accessibility value of each street. The integration of street greenery and accessibility helps to measure greenery from a human-centred perspective, and it provides a decision-support tool for urban planners to highlight areas with prioritisation for planning interventions. Moreover, the performance between GSV-based street greenery and the urban green cover mapped by remote sensing was compared to justify the contribution of this new measurement. It suggested there was a mismatch between these two measurements, i.e., existing top-down viewpoint through satellites might not be equivalent to the benefits enjoyed by city residents. In short, this analytical approach contributes to a growing trend in integrating large, freely-available datasets with machine learning to inform planners, and it makes a step forward for urban planning practices through focusing on the human-scale measurement of accessed street greenery.
Yu Ye; Daniel Richards; Yi Lu; Xiaoping Song; Yu Zhuang; Wei Zeng; Teng Zhong. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landscape and Urban Planning 2018, 191, 103434 .
AMA StyleYu Ye, Daniel Richards, Yi Lu, Xiaoping Song, Yu Zhuang, Wei Zeng, Teng Zhong. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landscape and Urban Planning. 2018; 191 ():103434.
Chicago/Turabian StyleYu Ye; Daniel Richards; Yi Lu; Xiaoping Song; Yu Zhuang; Wei Zeng; Teng Zhong. 2018. "Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices." Landscape and Urban Planning 191, no. : 103434.
Few studies have examined the role of space in social movements. The existing studies have primarily emphasized the physical nature of space (e.g., space as distance) and overlooked other attributes of space, such as space as the materialization of power relations and space as lived experience. In this article, we explore the role of space in social movements based on a case study of the Occupy Central in Hong Kong in 2014. During the protest, the organizers occupied and reconfigured the campuses and mobilized the participants both through and in space. We find that the campus space helped stimulate the feelings and emotions of the students and increased their enthusiasm to participate in the demonstration. The participants were then sent from the campuses (mobilization spaces) to the demonstration spaces where they occupied and transformed the urban public spaces into private spaces, thus leading to contention over and of space with the state powers. Our findings reveal that the campus space is an important resource that organizers can use for mobilization. We also find that the special features of a campus, including aggregation, networks, isolation, and homogeneity, can facilitate the formation of social movements. We argue that the three attributes of space interact with one another in facilitating the social movement. Thus, our findings suggest that space acts as not only the vessel of struggle but also a useful tool and a target of struggle.
Xu Wang; Yu Ye; Chris King-Chi Chan. Space in a Social Movement: A Case Study of Occupy Central in Hong Kong in 2014. Space and Culture 2018, 22, 434 -448.
AMA StyleXu Wang, Yu Ye, Chris King-Chi Chan. Space in a Social Movement: A Case Study of Occupy Central in Hong Kong in 2014. Space and Culture. 2018; 22 (4):434-448.
Chicago/Turabian StyleXu Wang; Yu Ye; Chris King-Chi Chan. 2018. "Space in a Social Movement: A Case Study of Occupy Central in Hong Kong in 2014." Space and Culture 22, no. 4: 434-448.
Yu Ye; Yu Zhuang. A Hypothesis of Urban Morphogenesis and Urban Vitality in Newly Built-up Areas: Analyses Based on Street Accessibility, Building Density and Functional Mixture. Urban Planning International 2017, 32, 43 -49.
AMA StyleYu Ye, Yu Zhuang. A Hypothesis of Urban Morphogenesis and Urban Vitality in Newly Built-up Areas: Analyses Based on Street Accessibility, Building Density and Functional Mixture. Urban Planning International. 2017; 32 (2):43-49.
Chicago/Turabian StyleYu Ye; Yu Zhuang. 2017. "A Hypothesis of Urban Morphogenesis and Urban Vitality in Newly Built-up Areas: Analyses Based on Street Accessibility, Building Density and Functional Mixture." Urban Planning International 32, no. 2: 43-49.
Yu Ye; Yu Zhuang. Professional Education Programs in Urban Design: A Comparison Among Multiple Internationally Renowned Universities. Urban Planning International 2017, 32, 110 -115.
AMA StyleYu Ye, Yu Zhuang. Professional Education Programs in Urban Design: A Comparison Among Multiple Internationally Renowned Universities. Urban Planning International. 2017; 32 (1):110-115.
Chicago/Turabian StyleYu Ye; Yu Zhuang. 2017. "Professional Education Programs in Urban Design: A Comparison Among Multiple Internationally Renowned Universities." Urban Planning International 32, no. 1: 110-115.
Yu Ye; Anthony Yeh; Yu Zhuang; Akkelies van Nes; Jianzheng Liu. “Form Syntax” as a contribution to geodesign: A morphological tool for urbanity-making in urban design. URBAN DESIGN International 2016, 22, 73 -90.
AMA StyleYu Ye, Anthony Yeh, Yu Zhuang, Akkelies van Nes, Jianzheng Liu. “Form Syntax” as a contribution to geodesign: A morphological tool for urbanity-making in urban design. URBAN DESIGN International. 2016; 22 (1):73-90.
Chicago/Turabian StyleYu Ye; Anthony Yeh; Yu Zhuang; Akkelies van Nes; Jianzheng Liu. 2016. "“Form Syntax” as a contribution to geodesign: A morphological tool for urbanity-making in urban design." URBAN DESIGN International 22, no. 1: 73-90.