Sky view factor and thermal comfort analysis using hemispheric images from Google Street View and wavelet in an urban ecosystem of the Brazilian Cerrado

This article aims to analyse the sky view factor (SVF) in one of the hottest cities of the Brazilian Cerrado, and its correlation with thermal comfort in two urban sections with different characteristics, as well as the physiological equivalent temperature (PET) and predicted mean vote (PMV) indices, complemented by a characterisation in the frequency field for a 12-month cut-off in the same year of relative air temperature and humidity. The study area was located in the central region of Cuiabá, Mato Grosso, due to the presence of regions with high urbanisation indices and small parks; one section composed of afforested area and second section composed of varied buildings. To obtain the SVF, the Google Street View image database was used, from which fisheye images were reconstructed and the SVF was determined using RayMan software. The PET and PMV indices were determined for the morning, afternoon, and evening, with comfort in the morning and discomfort in the afternoon and evening. Traditional Morlet wavelets were plotted for time series of relative air temperature and humidity for the year 2015, which qualitatively demonstrated some of the dynamics of these micrometeorological variables for tropical Cerrado climate.

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Application of Method for Calculating Sky View Factor Using Google Street View: Relation Between Sky View Factor and Physical Elements in Urban Space

Proceedings of the ICA ◽

10.5194/ica-proc-2-92-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-8

Author(s):

Shoko Nishio ◽

Fumiko Ito

Keyword(s):

Urban Space ◽

Computation Method ◽

View Factor ◽

Statistical Process ◽

Google Street View ◽

Sky View Factor ◽

Street View ◽

The Difference ◽

Physical Elements

<p><strong>Abstract.</strong> We applied a computation method of calculating the sky view factor (SVF) using Google Street View to Shibuya area, Tokyo, for the purpose of examining the relation between the SVF/SVF change and physical elements. The distribution of the SVF calculated by the above method was visualized, and the statistical process showed the tendency of a high SVF in quasi-residential districts and roadsides of high-graded trunk roads. The difference in the SVF change was small at 10-m intervals. The SVF change tended to be more apparent near an intersection and at different elevations.</p>

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In-situ Measurement of Pedestrian Outdoor Thermal Comfort in Universities Campus of Malaysia

KnE Social Sciences ◽

10.18502/kss.v3i21.4998 ◽

2019 ◽

Cited By ~ 1

Author(s):

Nurnida Elmira Othman ◽

Sheikh Ahmad Zaki ◽

Nurul Huda Ahmad ◽

Azli Razak

Keyword(s):

Thermal Comfort ◽

Field Measurement ◽

Thermal Sensation ◽

Outdoor Thermal Comfort ◽

View Factor ◽

Thermal Preference ◽

Equivalent Temperature ◽

Physiological Equivalent Temperature ◽

Sky View Factor

The present study is intended to evaluate an outdoor thermal comfort at two universities campus in Malaysia. Field measurement and questionnaire survey were conducted simultaneously to assess the microclimatic condition and pedestrian thermal sensation. A total of 3033 samples were collected at seven different sky view factor (SVF) values that range from 0.2 to 0.9. The physiological equivalent temperature (PET) was estimated to evaluate outdoor thermal comfort. It was observed that at a highly shaded area (SVF < 0.35) the respondent’s thermal sensation vote (TSV) are neutral (> 25%), acceptable for thermal acceptance vote (TAV) (> 50%) and no change (> 50%) for thermal preference vote (TPV). For moderate shaded (0.35 ≤ SVF ≤ 0.70) TSV was voted as hot (> 25%), acceptable for TAV (40%), and prefer slightly cooler for TPV (>50%). For less shaded area (0.70 < SVF ≤ 1), TSV was voted as hot and very hot (> 25%), acceptable for TAV (>40%) and prefer slightly cooler for TPV (> 40%). Moreover, the PET value increases simultaneously with the increase of SVF. Results thus suggest that at any given activities such as sitting, walking, and standing also caused effects slightly on the way people thermally perceive it during the on-campus daytime.

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Proposal of System for Calculating Sky View Factor Using Google Street View

New Frontiers in Regional Science: Asian Perspectives - Frontiers of Real Estate Science in Japan ◽

10.1007/978-981-15-8848-8_22 ◽

2021 ◽

pp. 313-324

Author(s):

Shoko Nishio ◽

Fumiko Ito

Keyword(s):

Big Data ◽

View Factor ◽

Panoramic Image ◽

Original Image ◽

Google Street View ◽

Sky View Factor ◽

Street View ◽

Current Point ◽

Extended Area ◽

The Web

AbstractIn recent years, big data entered use in various fields. Google Street View (hereinafter called “GSV”) can be regarded as open big data, and its images can be obtained using API. The streets can be viewed 360° horizontally and 290° vertically from each point on the web. In addition to those, zooming up is available, and the viewpoint can be moved approximately 10 m forward or backward to/from the current point. The original image to provide the view is the panoramic image associated with the latitude and longitude information on the street consecutively at intervals of 10 m, and they exist as massive data on the web. We determine the area of the sky using these images from GSV. In this research, we calculate the sky view factor (hereinafter called “SVF”) in an extended area by defining the area of the sky with the SVF and utilizing the computer.

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Estimation of Mean Radiant Temperature in Urban Canyons Using Google Street View: A Case Study on Seoul

Remote Sensing ◽

10.3390/rs14020260 ◽

2022 ◽

Vol 14 (2) ◽

pp. 260

Author(s):

Eun-Sub Kim ◽

Seok-Hwan Yun ◽

Chae-Yeon Park ◽

Han-Kyul Heo ◽

Dong-Kun Lee

Keyword(s):

Heat Stress ◽

Spatial Patterns ◽

High Density ◽

View Factor ◽

Mean Radiant Temperature ◽

Street Canyons ◽

Google Street View ◽

Street Level ◽

Street View ◽

Radiant Temperature

Extreme heat exposure has severe negative impacts on humans, and the issue is exacerbated by climate change. Estimating spatial heat stress such as mean radiant temperature (MRT) is currently difficult to apply at city scale. This study constructed a method for estimating the MRT of street canyons using Google Street View (GSV) images and investigated its large-scale spatial patterns at street level. We used image segmentation using deep learning to calculate the view factor (VF) and project panorama into fisheye images. We calculated sun paths to estimate MRT using panorama images from Google Street View. This paper shows that regression analysis can be used to validate between estimated short-wave, long-wave radiation and the measurement data at seven field measurements in the clear-sky (0.97 and 0.77, respectively). Additionally, we compared the calculated MRT and land surface temperature (LST) from Landsat 8 on a city scale. As a result of investigating spatial patterns of MRT in Seoul, South Korea, we found that a high MRT of street canyons (>59.4 °C) is mainly distributed in open space areas and compact low-rise density buildings where the sky view factor is 0.6–1.0 and the building view factor (BVF) is 0.35–0.5, or west-east oriented street canyons with an SVF of 0.3–0.55. However, high-density buildings (BVF: 0.4–0.6) or high-density tree areas (Tree View Factor, TVF: 0.6–0.99) showed low MRT (<47.6). The mapped MRT results had a similar spatial distribution to the LST; however, the MRT was lower than the LST in low tree density or low-rise high-density building areas. The method proposed in this study is suitable for a complex urban environment consisting of buildings, trees, and streets. This will help decision makers understand spatial patterns of heat stress at the street level.

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Assessing The Thermal Comfort Conditions In Open Spaces: A Transversal Field Survey On The University Campus In India

International Journal of Built Environment and Sustainability ◽

10.11113/ijbes.v8.n3.786 ◽

2021 ◽

Vol 8 (3) ◽

pp. 77-92

Author(s):

Pardeep Kumar ◽

Amit Sharma

Keyword(s):

Thermal Comfort ◽

Air Temperature ◽

Arid Regions ◽

Thermal Sensation ◽

Probit Analysis ◽

Outdoor Thermal Comfort ◽

Cold Weather ◽

Open Spaces ◽

Physiological Equivalent Temperature ◽

Preferred Temperature

Outdoor thermal comfort (OTC) promotes the usage frequency of public places, recreational activities, and people's wellbeing. Despite the increased interest in OTC research in the past decade, less attention has been paid to OTC research in cold weather, especially in arid regions. The present study investigates the OTC conditions in open spaces at the campus area in the arid region. The study was conducted by using subjective surveys(questionnaire) and onsite monitoring (microclimate parameters). The study was conducted at the Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana-India campus during the cold season of 2019. The timings of surveys were between 9:00 and 17:00 hours. The authors processed the 185 valid questionnaire responses of the respondents to analyze OTC conditions. Only 8.6% of the respondents marked their perceived sensation "Neutral." Regression analysis was applied between respondents' thermal sensations and microclimate parameters to develop the empirical thermal sensation model. The air temperature was the most dominant parameter affecting the sensations of the respondents. The empirical model indicated that by increasing air temperature, relative humidity, and solar radiation, the thermal sensations also increased while wind speed had an opposite effect. Physiological equivalent temperature (PET) was applied for assessing the OTC conditions; the neutral PET range was found to be 18.42-25.37°C with a neutral temperature of 21.89°C. The preferred temperature was 21.99 °C by applying Probit analysis. The study's findings could provide valuable information in designing and planning outdoor spaces for educational institutions in India's arid regions

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Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach

Remote Sensing ◽

10.3390/rs9050411 ◽

2017 ◽

Vol 9 (5) ◽

pp. 411 ◽

Cited By ~ 27

Author(s):

Jianming Liang ◽

Jianhua Gong ◽

Jun Sun ◽

Jieping Zhou ◽

Wenhang Li ◽

...

Keyword(s):

Big Data ◽

View Factor ◽

Sky View Factor ◽

Street View ◽

Factor Estimation

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A Numerical Study on the Correlation between Sky View Factor and Summer Microclimate of Local Climate Zones

Atmosphere ◽

10.3390/atmos10080438 ◽

2019 ◽

Vol 10 (8) ◽

pp. 438 ◽

Cited By ~ 2

Author(s):

Tong Lyu ◽

Riccardo Buccolieri ◽

Zhi Gao

Keyword(s):

Wind Speed ◽

Thermal Comfort ◽

Numerical Study ◽

Local Effect ◽

Urban Morphology ◽

View Factor ◽

Urban Microclimate ◽

Local Climate ◽

Sky View Factor ◽

Local Climate Zones

In the context of urbanization, research on urban microclimate and thermal comfort has become one of the themes of eco-city design. Sky view factor (SVF), one of the parameters of urban spatial form, combines multiple morphological information, such as plane opening, aspect ratio, and building density and has an important impact on the urban microclimate. However, there is still no clear research conclusion on the correlation between SVF and microclimate. In this paper, nine Local Climate Zone (LCZ) models are used and typical summer meteorological conditions of Nanjing are applied as an attempt to partially fill this gap. The calculated microclimate and thermal comfort indices include air temperature (AT), surface temperature (ST), relative humidity (RH), wind speed (WS), mean radiant temperature (MRT), and predicted mean vote (PMV). Results show that the local effect of urban morphology on thermal comfort can be retrieved from the use of comprehensive parameters such as SVF (which takes into account the building height, layout, and density) whose distribution in the investigated models showed to be correlated with MRT, so did PMV under low wind speed conditions.

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