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Article

Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

by
Feng Gao
1,2,3,†,
Guanping Huang
1,4,†,
Shaoying Li
1,*,
Ziwei Huang
1 and
Lei Chai
1
1
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
2
Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
3
Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China
4
Guangdong Provincial Institute of Land Surveying & Planning, Guangzhou 510062, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2021, 10(12), 834; https://doi.org/10.3390/ijgi10120834
Submission received: 2 November 2021 / Revised: 6 December 2021 / Accepted: 10 December 2021 / Published: 13 December 2021
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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.
Keywords: social media data; building function; eigendecomposition; k-means clustering; Guangzhou social media data; building function; eigendecomposition; k-means clustering; Guangzhou

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MDPI and ACS Style

Gao, F.; Huang, G.; Li, S.; Huang, Z.; Chai, L. Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data. ISPRS Int. J. Geo-Inf. 2021, 10, 834. https://doi.org/10.3390/ijgi10120834

AMA Style

Gao F, Huang G, Li S, Huang Z, Chai L. Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data. ISPRS International Journal of Geo-Information. 2021; 10(12):834. https://doi.org/10.3390/ijgi10120834

Chicago/Turabian Style

Gao, Feng, Guanping Huang, Shaoying Li, Ziwei Huang, and Lei Chai. 2021. "Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data" ISPRS International Journal of Geo-Information 10, no. 12: 834. https://doi.org/10.3390/ijgi10120834

APA Style

Gao, F., Huang, G., Li, S., Huang, Z., & Chai, L. (2021). Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data. ISPRS International Journal of Geo-Information, 10(12), 834. https://doi.org/10.3390/ijgi10120834

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