Urban Spatial Informatics Lab

Urban Analytics, Spatial Data Science,

Urban Environmental Planning, Remote Sensing.

Treepedia

Mapping urban greenery in cities around the world using Google Street View

Media Coverage:

Wall Street Journal, "New Tool Lets Cities See Where Trees Are Needed".

Forbes, "Where The Streets Are Paved With Green: Counting Urban Tree".

The Guardian, "One day in the life of San Francisco Bay - Mapped".

Associated Press, "Where are trees? Not Paris, new Green View Index" finds".

CBC News, "Toronto beats NYC, Paris, London in new tree ranking, but experts say more work needed".

CityLab, "Mapping the Urban Tree Canopy in Major Cities"

NextCity, "Mapping Urban Trees in 11 Cities"

Spatial Data Science + AI

Mapping urban greenery in cities around the world using Google Street View

Xiaojiang Li, Bill Yang Cai, Carlo Ratti, Using Street-level Images and Deep Learning for Urban Landscape Analysis, Landscape Architecture Frontier, 2018.

Cai, B. Y., Li, X., Seiferling, I., & Ratti, C. (2018, July). Treepedia 2.0: applying deep learning for large-scale quantification of urban tree cover. In 2018 IEEE International Congress on Big Data (BigData Congress) (pp. 49-56). IEEE.

Fangying Gong, Zhaocheng Zeng, Fan Zhang, Xiaojiang Li, Edward Ng, Les Norford. (2018). Mapping sky, tree, and building view factors of street canyons in a high-density urban environment, Building and Environment. 134, 155-167.

PSPNet Convolutional neural network

Digital Environment Humanities

Environmental injustice in terms of urban greenery

Street greenery plays an important role in enhancing the environmental quality of a city. People with higher incomes tend to live in places with more street greenery. In summary, this study makes contribution to literature by providing insights into the living environments of urban residents in terms of street greenery, and it also generates valuable reference data for future urban greening programs. Results showed that people with various social conditions have different amounts of street greenery in their living environments in Hartford. People with higher incomes tend to live in places with more street greenery.

Li, X., Zhang, C., Li, W., Kuzovkina, Y. A., & Weiner, D. (2015). Who lives in greener neighborhoods? The distribution of street greenery and its association with residents’ socioeconomic conditions in Hartford, Connecticut, USA. Urban Forestry & Urban Greening, 14(4), 751-759.

Li, X., Zhang, C., Li, W., & Kuzovkina, Y. A. (2016). Environmental inequities in terms of different types of urban greenery in Hartford, Connecticut. Urban Forestry & Urban Greening, 18, 163-172.

Mapping the spatial distribution of shade

Street trees provide shade and increase human thermal comfort during hot summer. In this study, we investigated the spatial distribution of shade provision of street trees in Boston, Massachusetts. The sky view factor (SVF), which influences the solar radiation to the ground and affects human thermal comfort, was used to indicate the contribution of street trees on shade provision.

Result shows that Hispanics tend to live in neighborhoods with lower shading level. This study can help to provide a reference for future urban greening projects for global climate change adaption.

Li, X., & Ratti, C. (2018). Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas. Urban Forestry & Urban Greening, 31, 109-119.

Figure. The spatial distribution of shade provision by street trees

Urban Environmental Health

Streetscape environment and human body mass index (BMI)

Streets are the basic unit for human activities in cities, it is important to understand how the streetscape environment can influence human health conditions. In this study, we investigated the influence of street greenery and walkability on body mass index in Cleveland, Ohio, USA. Different from the area level and overhead view greenery metrics, we used the green view index calculated from the Google Street View to represent the amount of street greenery. The Walk Score was used to indicate the walkability of neighborhoods also at the street level. Statistical analysis results show that the Walk Score has a more significant association with decreased BMI for males than females and the street greenery has a more significant association with decreased BMI for females than males in Cleveland, Ohio. The results of this study would provide a reference for designing gender-specific healthy cities.

Li, X., & Ghosh, D. (2018). Associations between Body Mass Index and Urban “Green” Streetscape in Cleveland, Ohio, USA. International journal of environmental research and public health, 15(10), 2186. (Link)

Figure. Google Street View (GSV) image collection and classification

Figure. The spatial distributions of GVI and Walk Score at the site level and census tract level in Cleveland, Ohio.

Streetscape dynamics

Streetscape and Human Walking activities

Having an active lifestyle is recognized to positively contribute to public health. Creating more walkable streets and neighborhoods is an important way to promote an active lifestyle for urban residents. It is therefore important to understand how the urban built environment can influence human walking activities. In this study, we investigated the interaction of human walking activities and physical characteristics of streetscapes in Boston. A large number of anonymous pedestrian trajectories collected from a smartphone application were used to estimate human walking activities. Publicly accessible Google Street View images were used to estimate the amount of the street greenery and enclosure of street canyons, both of which were used to indicate the physical characteristics of streetscapes.

Xiaojiang Li, Paolo Santi, ..., Carlo Ratti. Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data. Transactions in GIS, 2018.

Figure. Map-matching of human trajectories to streets: the purple lines represent the raw trajectories of anonymous individuals, and the green lines represent the matched trajectories based on Open Street Map.

Fig.5. The spatial distributions of independent variables (a) the Walk Score, (b) the Green View Index, (c) the enclosure of streetscapes enclosed by buildings, (d) the enclosure of streetscapes enclosed by the street tree canopies.

Sky View Factor

Mapping urban greenery in cities around the world using Google Street View

As a dimensionless parameter of urban geometry, the sky view factor (SVF) indicates how much sky is obstructed. The SVF also represents the ratio between radiation received by a planar ground and that from the entire hemisphere’s input radiation. The sky view factor (SVF) has been widely applied in forestry, urban climate, air pollution, and urban heat island studies. I have developed a method to calculate SVF using the publicly accessible and globally Google Street View data. It is possible to map SVF at large scale for any place with Google Street View service available, which seems impossible before.

Li. X., Ratti. C., Seiferling. I, 2017, Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View, Landscape and Urban Planning, 169, 81-91.

Figure. Sky view factor (SVF) estimation using Google Street View.

Human-based Urban Computing

Mapping human perceived greenery using Google Street View

Street-level greenery has long played a critical role in the visual quality of urban landscapes. This living landscape element can and should be assessed for the quality of visual impact with the GSV information, and the assessed street-level greenery information could be incorporated into urban landscape planning and management.

Li, X., Zhang, C., Li, W., Ricard, R., Meng, Q., & Zhang, W. (2015). Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry & Urban Greening, 14(3), 675-685.

Mapping shade

Mapping shade provision of street trees using Google Street View

Street trees provide shade and increase human thermal comfort during hot summer. This study investigated the spatial distribution of the shade provision of street trees in Boston, Massachusetts using Google Street View and building height model. The result shows that the street trees help to decrease the SVF by 24.61% in street canyons of Boston.

Xiaojiang Li, et al. Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas, Urban Forestry and Urban Greening, (Accepted).

Li, X., Ratti, C., & Seiferling, I. (2018). Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landscape and Urban Planning, 169, 81-91.

Figure. Comparison of fisheye images created by GSV-based method and simulation-based method

Fig. 2. The spatial distributions of the SVF factors in Boston, (a) the spatial distribution of SVF estimated using building height model, (b) the spatial distribution of SVF estimated using Google Street View panorama, (c) The spatial distribution of the SVF difference between two methods, (d) the tree canopy cover in Boston.

Human Thermal Comfort

Modeling solar radiation in street canyons

Li, X., & Ratti, C. (2018). Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model. Landscape and Urban Planning.

Figure. The overlay of sun path on the generated hemispherical images using Google Street View in one day of three sites of Cambridge, MA.

Urban Form Analysis

Urban Form analysis using GSV panoramas

City streets are a focal point of human activity in urban centers. Citizens interact with the urban environment through its streetscape and it is imperative to, not only map city streetscapes but quantify those interactions in terms of human well-being. Google Street View (GSV), which captures the profile view of streetscapes and, thus, shares equivalent viewing angles with those of the citizen. Based on machine learning and image analysis, GSV can be utilized as a high-quality data source for mapping street greenery and openness.

Li, X., Ratti, C., & Seiferling, I. (2017, July). Mapping Urban Landscapes Along Streets Using Google Street View. In International Cartographic Conference (pp. 341-356). Springer, Cham.

Li, X., & Ratti, C. (2019). Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts. In The Mathematics of Urban Morphology (pp. 457-470). Birkhäuser, Cham.

Figure. Hemispherical images generation from Google Street View panoramas

Figure. Modeling solar radiation using building height model

Transportation Research

Sun glare mapping using deep learning + Street view Images

Just to think about that every time you drive from West to East at the sunrise time or drive from East to West during sunset time, do you feel the dazzling sun blind your eyes and make driving super difficult? I guess many people want to know when and where the dazzling sun occurs. I got the idea to predict the occurrence of sun glare when I was in a meeting with Liberty Mutual in Boston. I then think about how to predict it. I have been using Google Street View images in urban applications for many years, my previous project is Treepedia is a good one. I then to realize Google Street View may be a perfect dataset for predicting and mapping the sun glare occurrence because the Google Street View images have similar view angles with drivers and those images are actually collected by cars. Here is the tutorial to show you how to predict and map the sun glare using Google Street View and deep learning.

Li, X., Cai, B. Y., Qiu, W., Zhao, J., & Ratti, C. (2019). A novel method for predicting and mapping the occurrence of sun glare using Google Street View. Transportation Research Part C: Emerging Technologies, 106, 132–144.

Human perception of Environment

Human perceived safety and visibility of greenery

Urban green space provides a series of esthetic, environmental and psychological benefits to urban residents. However, the relationship between the visibility of green vegetation and perceived safety is still in debate. This research investigated whether green vegetation could help to increase the perceived safety based on a crowdsourced dataset: the Place Pulse 1.0 dataset. Place Pulse 1.0 dataset, which was generated from a large number of votes by online participants, includes geo-tagged Google Street View images and the corresponding perceived safety score for each image. Results show that the visibility of green vegetation plays an important role in increasing perceived safety in urban areas. For different land use types, the relationship between vegetation structures and perceived safety varies. In residential, urban public/institutional, commercial and open land areas, the visibility of vegetation higher than 2.5 m has significant positive correlations with perceived safety, but there exists no significant correlation between perceived safety and the percentage of green vegetation in transportation and industrial areas. The visibility of vegetation below 2.5 m has no significant relationship with the perceived safety in almost all land use types, except for multifamily residential land and urban public/institutional land.

Li, X., Zhang, C., & Li, W. (2015). Does the visibility of greenery increase perceived safety in urban areas? Evidence from the place pulse 1.0 dataset. ISPRS International Journal of Geo-Information, 4(3), 1166-1183.

Li et al., 2015

Urban Remote Sensing and Green Space

Metrics fo proximity of buildings to green spaces

Urban areas are major places where intensive interactions between human and the natural system occur. Urban vegetation is a major component of the urban ecosystem, and urban residents benefit substantially from urban green spaces. To measure urban green spaces, remote sensing is an established tool due to its capability of monitoring urban vegetation quickly and continuously.

Since buildings are the major places for residents to live, work and entertain, this index may provide an objective tool for evaluating the proximity of residents to neighbouring green spaces. However, it was suggested that proving correlations between the proposed index and human health or environmental amenity would be important in future research for the index to be useful in urban planning.

Li, X., Li, W., Meng, Q., Zhang, C., Jancso, T., & Wu, K. (2016). Modelling building proximity to greenery in a three-dimensional perspective using multi-source remotely sensed data. Journal of Spatial Science, 61(2), 389-403.

Li, X., Meng, Q., Li, W., Zhang, C., Jancso, T., & Mavromatis, S. (2014). An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery. Annals of GIS, 20(3), 193-203.

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