Abstract
Buildings are fundamental components of cities. Understanding the function of buildings is therefore of great importance for urban development and management. Some studies have identified building functions using spatiotemporal data, which assumes that buildings with the same function have similar temporal activity patterns. However, these methods present difficulties in coping with the situation when buildings with the same function have heterogeneous activity patterns. To solve this problem, this research proposes a new method to identify building functions from the perspective of the spatial distribution and spatial interactions of human activities. First, taxi data were used to acquire the spatiotemporal interaction characteristics among buildings with different functions. Then, the spatiotemporal population density distribution was adopted to depict the building vitality. Finally, an iterative clustering method was introduced to identify the building functions. The proposed scheme was applied in the Haizhu district of Guangzhou and compared with the traditional method. The results prove that the spatial interaction characteristics are more helpful than the temporal variation characteristics and therefore can be used to improve the accuracy of building function identification. A higher accuracy for identifying building functions can be realized by combining the spatiotemporal interactions and building vitality characteristics. The overall accuracy reaches 0.8566, with a Kappa coefficient of 0.8174, which are both better than the results of using a single characteristic only.
Highlights
Buildings are fundamental structural elements of the urban physical space and serve many functions with respect to human living, working, and recreation
We explored the application of the spatial interaction characteristics at the building-level scale and proposed a new method to identify the building functions from the perspective of the spatial distribution and spatial interactions of human activities
The spatiotemporal interaction characteristics among different functional buildings were extracted from taxi trajectory data, while the spatiotemporal distribution characteristics of the population density were extracted from real-time high spatiotemporal resolution Tencent user density data
Summary
Buildings are fundamental structural elements of the urban physical space and serve many functions with respect to human living, working, and recreation. Obtaining the spatial distribution of buildings and identifying their functions can enhance the understanding of various temporal and spatial behavior patterns and assist in analyzing complex urban functional structures as well as their changes. This provides key data to support high spatiotemporal resolution population estimates and risk assessment, and serves as an important basis for urban economic development planning and urban management. Traditional studies use the spectral, textural and shape features derived from medium- or high-resolution remote sensing images and further combine the landscape attributes to obtain the building type information [3,4,5,6]. Some scholars overcame the limitation of using a single remote sensing image and proposed comprehensive building classification schemes based on multisource remote sensing data, including multispectral remote sensing images, LiDAR data, and nighttime lighting data [10,11,12,13,14]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
