Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k -medoids method

Chen, Yi-Min; Li, Xiaoping; Li, Xia; Liu, Xingjian; Yao, Yao; Hu, Guohua; Xu, Xiaocong; Pei, Fengsong

doi:10.1016/j.landurbplan.2016.12.001

Cited by 205 publications

(103 citation statements)

References 34 publications

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“…Actually, a number of studies has been conducted to analyze urban landscapes or urban functions [17][18][19][20][21][22][23][24], without human activities information, but more and more human sensing data give us an alternative approach to mapping the spatial structure of the city [35,37,41]. Fusing human sensing data with remote sensing imagery in urban studies has seldom been reported or analyzed in the literature.…”

Section: Discussionmentioning

confidence: 99%

Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

et al. 2018

Remote Sensing

119

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Portraying urban functional zones provides useful insights into understanding complex urban systems and establishing rational urban planning. Although several studies have confirmed the efficacy of remote sensing imagery in urban studies, coupling remote sensing and new human sensing data like mobile phone positioning data to identify urban functional zones has still not been investigated. In this study, a new framework integrating remote sensing imagery and mobile phone positioning data was developed to analyze urban functional zones with landscape and human activity metrics. Landscapes metrics were calculated based on land cover from remote sensing images. Human activities were extracted from massive mobile phone positioning data. By integrating them, urban functional zones (urban center, sub-center, suburbs, urban buffer, transit region and ecological area) were identified by a hierarchical clustering. Finally, gradient analysis in three typical transects was conducted to investigate the pattern of landscapes and human activities. Taking Shenzhen, China, as an example, the conducted experiment shows that the pattern of landscapes and human activities in the urban functional zones in Shenzhen does not totally conform to the classical urban theories. It demonstrates that the fusion of remote sensing imagery and human sensing data can characterize the complex urban spatial structure in Shenzhen well. Urban functional zones have the potential to act as bridges between the urban structure, human activity and urban planning policy, providing scientific support for rational urban planning and sustainable urban development policymaking.

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Section: Discussionmentioning

confidence: 99%

Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

et al. 2018

Remote Sensing

119

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“…The closer the index is to 1, the better the clustering effect. In this study, the best clustering results are selected and compared with the enrichment factors of the POIs (Chen et al, ). The enrichment factor of a POI class greater than 1 signified that the proportion of the POI in that cluster was higher than that of the entire study area, and the corresponding land parcel was more functional (Verburg, de Nijs, van Eck, Visser, & de Jong, ).…”

Section: Methodsmentioning

confidence: 99%

“…The RTUD records the locations of smartphone users who are using Tencent applications and are able to reflect the characteristics of urban residents' behavior in megacities, since over 93% of residents in Guangzhou and Shenzhen use Tencent location‐based services (Chen et al, ; Yao et al, ; Yuan, Zheng, & Xie, ). The spatial resolution of the RTUD data is 25 m, and covers a temporal range from May to June 2016, as shown in Figure .…”

Section: Study Area and Datamentioning

confidence: 99%

Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

Yao

Liu

Hong

et al. 2019

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The rapid development of urban retail companies brings new opportunities to the Chinese economy. Due to the spatiotemporal heterogeneity of different cities, selecting a business location in a new area has become a challenge. The application of multi‐source geospatial data makes it possible to describe human activities and urban functional zones at fine scale. We propose a knowledge transfer‐based model named KTSR to support citywide business location selections at the land‐parcel scale. This framework can optimize customer scores and study the pattern of business location selection for chain brands. First, we extract the features of each urban land parcel and study the similarities between them. Then, singular value decomposition was used to build a knowledge‐transfer model of similar urban land parcels between different cities. The results show that: (1) compared with the actual scores, the estimated deviation of the proposed model decreased by more than 50%, and the Pearson correlation coefficient reached 0.84 or higher; (2) the decomposed features were good at quantifying and describing high‐level commercial operation information, which has a strong relationship with urban functional structures. In general, our method can work for selecting business locations and estimating sale volumes and user evaluations.

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“…Social media data has been utilized extensively to delineate human activities in land use analyses. Related applications include land use classification (Frias‐Martinez & Frias‐Martinez, ), urban functional region detection (Chen et al, ; Niu et al, ), population estimation (Yao, Liu, et al, ), and mixed land use evaluation (Liu et al, ; Yue et al, ). Compared with other sources of data [such as points of interest (POI)], social media data can reveal regular patterns of dynamic human activities at the individual scale, instead of simply capturing the static distribution of land use forms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Regarding the measurement of temporal distance, dynamic time warping and edit distance on real sequences have been used widely in the related research (Chen et al, 2017;Chen, Özsu, & Oria, 2005;Tormene, Giorgino, Quaglini, & Stefanelli, 2009). Recent research by Serra and Arcos (2014) revealed the potential of the commonly unconsidered measure, time-warped edit distance (TWED) (Marteau, 2009), which consistently outperformed all the above measures.…”

Section: Dynamic Features Clustering With Temporal Similaritymentioning

confidence: 99%

A dynamic human activity‐driven model for mixed land use evaluation using social media data

Xing

Meng

Shi

2018

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Massive amounts of social media data have enabled the evaluation of mixed land use from the human activities perspective. While most studies have focused on approaches to better measure mixed land use, the dynamic activity patterns used to distinguish land use types have seldom been discussed. In light of this, in this study, a mixed land use evaluation model is constructed, which is driven by dynamic human activities hidden in social media data. Specifically, temporal topics related to human activities are first extracted from Twitter messages. On this basis, these topics are clustered with successive measurements of both temporal and semantic similarities, and then, they are further transformed into dynamic features. In addition, the dynamic feature‐based Shannon entropy is employed for mixed land use evaluation. For verification, the building classes from OpenStreetMap are referenced to investigate their correlations with dynamic features in mixed land uses. Finally, a strong correlation demonstrates the effectiveness and power of the proposed method in the evaluation of mixed land uses.

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Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k -medoids method

Cited by 205 publications

References 34 publications

Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

Fine‐scale intra‐ and inter‐city commercial store site recommendations using knowledge transfer

A dynamic human activity‐driven model for mixed land use evaluation using social media data

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