Abstract
Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.
Highlights
IntroductionThe relationship between human activity patterns and urban spatial structure has been a key research topic in urban geography and urban planning [1,2]
This study aims to examine the integration of eigendecomposition approach and k-means clustering in inferring building function types
This study presented a comprehensive method that integrated the eigendecomposition approach and k-means clustering for inferring building function types based on location-based social media data, Tencent user density (TUD) data
Summary
The relationship between human activity patterns and urban spatial structure has been a key research topic in urban geography and urban planning [1,2]. As the fundamental structural elements of urban physical space [3], buildings are the basic spatial unit for urban spatial structure and urban form studies [4,5]. Buildings are the important carries of human activities (e.g., living, working, and entertainment) in the urban socioeconomic space [5], which can serve as the basic unit for analyzing human socioeconomic activities and urban functional areas. Identifying building functions is significant for understanding urban spatial structure and urban functional areas, which can assist urban management and future smart city planning
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