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Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts

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Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts Senseable City Lab :.:: Massachusetts Institute of Technology This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the definitive publisher-authenticated version, please refer directly to publishing house’s archive system SENSEABLE CITY LAB Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts Xiaojiang Li and Carlo Ratti Abstract City streets are a focal point of human activities in urban areas. As an important element of urban form, streets are also a major interface of the social interaction between urban dwellers and urban built environment. Quantifying the urban built environment is thus important for us to understand the potential impact of the urban built environment on urban dwellers. The publicly accessible Google Street View (GSV), which captures the streetscape appearances of cities around the world, provides a very good tool for urban studies at a fine level. In this study, we illustrated using GSV for describing and mapping urban form at street-level in terms of the enclosure of street canyons in Cambridge, Massachusetts. We further mapped and analyzed the influence of street enclosure on solar radiation reaching the street canyons by estimating the sunlight duration in street canyons. Some other potential applications of GSV data were also introduced in this paper. The results of this study would shed new light on future urban studies using the publicly accessible and globally available GSV data. Other researchers may find the method illustrated in this study is directly deployable for different studies related to urban form analysis. 1 Introduction City streets are a focal point of human activities in urban areas (Li et al. 2017). As an important element of urban form, streets are a major interface of the social interaction between urban dwellers and urban built environment. The openness of street canyons influences human perception of the environment, and enclosed street canyons may give a feeling of oppressiveness to pedestrians (Asgarzadeh et al. 2012, 2014). The urban form would also influence the energy balance in street canyons, which would further affect human thermal comfort and exposure to sunlight (Carrasco-Hernandez X. Li (B) C. Ratti MIT Senseable· City Lab, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Room 9-250, 77 Massachusetts Avenue, Cambridge, MA 02139, USA e-mail: [email protected] ©SpringerNatureSwitzerlandAG2019 457 L. D’Acci (ed.), The Mathematics of Urban Morphology, Modeling and Simulation in Science, Engineering and Technology, https://doi.org/10.1007/978-3-030-12381-9_20 458 X. Li and C. Ratti et al. 2015;Lietal.2017). This effect on human thermal comfort is more obvious during hot summer. Quantifying the urban form is thus important for us to understand the potential impacts of the urban built environment on urban dwellers. Based on digital city models, various metrics can be calculated to describe and quantify different aspects of urban form. With the availability of high-resolution digital city models, it would be also possible to simulate the transmission of solar radiation within street canyons (Gal et al. 2009;RattiandRichens2004). However, digital city models cannot fully represent the streetscapes because most digital city models oversimplify the complex geometries of street canyons (Carrasco-Hernandez et al. 2015). In addition, the urban vegetation, which is a very important part of the urban natural system, is usually not included in those digital city models (Li et al. 2018). What is more, the high-resolution building city models are not always available in many cities. The publicly accessible Google Street View (GSV), which captures the streetscape appearances of cities around the world, provides a very good tool for urban studies at a fine level. GSV was first introduced in 3D city modeling (Torii et al. 2009; Lee 2009;MicusikandKosecka2009)becausethepanoramicsequencesinGSV can be used to reconstruct the 3D model of streetscapes. Since GSV represents the physical appearance of streetscapes and may have more direct connection with human perception of environment, GSV was further used to map human perception of environment using crowdsourcing method (Salesses et al. 2013;Naiketal.2014). Based on time-series GSV images, Naik et al. (2017)measuredthechangesinthe physical appearances of neighborhoods in five U.S. cities. The strong associations between the social characteristics and the streetscape appearance changes show that the GSV-based method can help to predict neighborhood improvement. In this study, we illustrated using GSV for describing and modeling the enclosure of street canyons in Cambridge, Massachusetts. We estimated and mapped the sky view factor (SVF), which is a very important parameter of urban form, at street-level using GSV. We further mapped and analyzed the spatial distributions of sunlight duration in urban street canyons in Cambridge, MA during leaf-on seasons based on the generated hemispherical images from GSV panoramas. 2DataPreparation 2.1 Google Street View (GSV) Panorama Collection GSV panoramas can be collected from Google Server using Google Maps Applica- tion Programming Interfaces (APIs). In this study, in order to collect GSV panora- mas to represent the urban form, we first created sample sites every 100 m along the streets. Figure 1ashowsthegeneratedsamplesitesalongstreetsinCambridge, Massachusetts. Based on the coordinates of these sample sites, we further collected Using Google Street View for Street-Level Urban … 459 Fig. 1 The workflow for GSV panorama collection, a the created sample sites in Cambridge, Massachusetts, b the metadata of GSV panoramas, c aGSVpanoramaofonesamplesite the metadata of GSV panoramas in the study area. Here is an example of collecting the metadata of a GSV panorama located at (42.359048, 71.093574), − URL: http://maps.google.com/cbk?output=xml&ll=42.359048,-71.093574 Metadata of the a GSV panorama { "copyright" : "© 2017 Google", "date" : "2017-9", "location" : { "lat" : 42.358964, "lng" : -71.093537 }, "pano_id" : " 4G5km0yE7QsmzxE7YBPRYw", "pano_yaw_deg": " 341.80734" } Based on the panorama IDs in the metadata, GSV panoramas can also be down- loaded. Figure 1 shows the workflow of collecting GSV panorama metadata and the final GSV panoramas. In this study, we developed a Python script (Appendix A) 460 X. Li and C. Ratti to download tiles of GSV panoramas and mosaic them to a panorama for each site using the panorama ID as input. 2.2 Geometric Transform of Google Street View (GSV) Panoramas The collected GSV panoramas are in the form of equidistant cylindrical projection as shown in Fig. 1c. For urban form studies, the cylindrical projection GSV panoramas need to be transformed to equidistant azimuthal projection. Figure 2 shows the geo- metric model of transforming cylindrical projection to azimuthal projection. A GSV panorama with width of W c and height of Hc can be re-projected to an azimuthal hemispherical image with the width and height of W c/π.Foranypixel(xf , yf )inthe generated hemispherical image, the corresponding pixel in the cylindrical panorama should be (xc, yc), θ xc Wc " 2π r yc Hc (1) " r0 where r and θ are the distance of the pixel (xf , yf )tothecenterofthehemispherical image and the zenith angle, respectively (Fig. 2). Considering the fact that the central column in the cylindrical image represents the driving direction of the GSV vehicle rather than the true north direction. Therefore, the generated hemispherical images need to be further rotated by the yaw angle to make sure the generated hemispherical images represent the north, east, south, and the west direction correctly. The yaw angle can be accessed from the metadata of GSV panorama in Sect. 2.1.Thepixel(xf , yf )inthesynthetichemisphericalimages should be further converted into (xf ′, yf ′)intherotatedhemisphericalimagesas, x′ xf cos ϕ yf sin ϕ f " − y′ xf sin ϕ + yf cos ϕ f " ϕ 360 yaw (2) " − where yaw is the yaw angle from the metadata of GSV panorama. 2.3 Image Classification The sky extraction is a requisite step to derive urban form information from hemi- spherical images. In this study, we applied the object-based image classification Using Google Street View for Street-Level Urban … 461 Fig. 2 Geometrical transform of equidistant cylindrical projection to equidistant azimuthal projec- tion (hemispherical image) method to classify the hemispherical into sky pixels and non-sky pixels the sky (Li et al. 2018). Hemispherical images were first segmented into homogeneous and physically meaningful objects based on the mean-shift algorithm (Comaniciu and Meer 2002;Lietal.2018). Figure 3bshowssegmentationresultsonhemispherical images. Compared with the original hemispherical images (Fig. 3a), the segmented images have enhanced difference between sky pixels and non-sky pixels. Since sky pixels are usually brighter than non-sky pixels and non-sky greenery pixels usually have higher values in the ExG (2 green blue red) image, we used the brightness and ExG to extract the sky pixels× from− the segmented− hemispherical images . The Otsu’s method (Otsu 1979)wasthenusedtofindtheoptimumthresholds 462 X. Li and C. Ratti Fig. 3 The classification of sky pixels in three generated hemispherical images, a the original hemispherical images generated from GSV panoramas, b the segmented images using mean-shift algorithm, c the sky classification results to separate sky pixels and non-sky pixels. Those pixels
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