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

ISPRS international journal of geo-information, 2021-12, Vol.10 (12), p.834 [Tạp chí có phản biện]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2220-9964 ;EISSN: 2220-9964 ;DOI: 10.3390/ijgi10120834

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Nhan đề:
Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data
Tác giả: Gao, Feng ; Huang, Guanping ; Li, Shaoying ; Huang, Ziwei ; Chai, Lei
Chủ đề: Activity patterns ; Big Data ; building function ; Buildings ; Central business districts ; Cluster analysis ; Clustering ; Datasets ; Digital media ; eigendecomposition ; Guangzhou ; Identification ; k-means clustering ; Location based services ; Methods ; Population density ; Random sampling ; Remote sensing ; Social media ; social media data ; Social networks ; Social research ; Statistical sampling ; Structure-function relationships ; Urban planning ; Vector quantization
Là 1 phần của: ISPRS international journal of geo-information, 2021-12, Vol.10 (12), p.834
Mô tả: 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.
Nơi xuất bản: Basel: MDPI AG
Ngôn ngữ: English
Số nhận dạng: ISSN: 2220-9964
EISSN: 2220-9964
DOI: 10.3390/ijgi10120834
Nguồn: ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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

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