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A good skylight environment in urban residential areas is an important component of a healthy city, and has always been highly valued. With the rapid development of new-type urbanization, the density of buildings continues to increase, and megacities have entered the stage of stock transformation. An effective method for evaluating the skylight environment of large-scale urban residential areas is urgently needed. However, there is still a lack of empirical research methods and cases of large-scale residential skylight environment. In this regard, this article takes the megacity Beijing as the research object, and proposes an efficient analysis method of residential skylight environment that integrates multiple real-world data at city scale. In terms of data, it collects and integrates 3D data of urban-scale building space and residential boundary data; in terms of algorithm, Sky View Factor (SVF) is used as the evaluation index of residential skylight environment, and an efficient analysis method of urban-scale skylight environment based on cloud parallel simulation is realized. Through analysis, it is found that: (1) the average SVF value of Beijing residential area is 61%, which means that its skylight quality is in general level; (2) the skylight environment of Beijing residential area is distributed in a circle, and there are 4 types of skylight environment quality residential areas; (3) The skylight environment of Beijing residential area is relatively weakly related to the distance from the residential area to the city center and the average height of the residential buildings, and is closely related to the plot volume ratio, the residential building density and the shading from surrounding buildings. The highlight of this study lies in the empirical research on the skylight environment of mega-city residential areas that incorporates multiple real data for the first time, which can promote the study of skylight environment on a city scale and provide a reference for the updating of Beijing's residential daylight environment.

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Publication history
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Acknowledgements

Publication history

Received: 06 February 2020
Accepted: 03 August 2020
Published: 26 September 2020
Issue date: August 2021

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020

Acknowledgements

This research is supported by Outstanding Youth Program of the National Natural Science Foundation of China (No. 51825802) and General Program of the National Natural Science Foundation of China (No. 51578302).

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