Delineating urban functional zones using mobile phone data: A case study of cross-boundary integration in Shenzhen-Dongguan-Huizhou area

Zhang, Bowen; Zhong, Chen; Gao, Qi-Li; Shabrina, Zahratu; Tu, Wei

doi:10.1016/j.compenvurbsys.2022.101872

Cited by 8 publications

(2 citation statements)

References 63 publications

(95 reference statements)

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“…The utilization of mobile phone data has proven to be successful in various aspects of understanding urban dynamics. Notably, it has been employed to study city structure based on population density (Xinyi et al, 2015 ), analyze mobility patterns (Yu et al, 2020 ; Zhang et al, 2022 ), investigate their relation to socioeconomic status (Xu et al, 2018 , 2019 ), delineate urban park catchment areas (Guan et al, 2020 ), and examine changes in the average distance between individuals (Louail et al, 2014 ). Despite these achievements, the full potential of mobile phone data in comprehending intraday commute patterns remains largely untapped, highlighting the need for further research in this area.…”

Section: Introductionmentioning

confidence: 99%

Urban delineation through a prism of intraday commute patterns

Bogomolov,

Belyi,

Sobolevsky

2024

Front. Big Data

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IntroductionUrban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies.MethodsWe developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city.ResultsOur analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city.DiscussionThe study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

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Section: Introductionmentioning

confidence: 99%

Urban delineation through a prism of intraday commute patterns

Bogomolov,

Belyi,

Sobolevsky

2024

Front. Big Data

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“…The arrival of the geographic big data era has brought massive online geographic information resources, such as social perception data from social media [13], cab trajectory data [14], cell phone data [15], street-view image data [16], and point of interest (POI) data [17,18]. These record a large amount of information about human activities and have been widely used in the study of urban spatial structure characteristics.…”

Section: Introductionmentioning

confidence: 99%

Identification of Urban Building Functions Based on Points of Interest and Spatial Relationships between Geographic Entities

Wei,

Luo,

et al. 2024

Applied Sciences

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Knowing the functions of buildings is valuable in urban planning and management. For example, it can be used for the assessment of urban planning implementation and the fine-tuning of community governance. At large scales, determining building functions can be time-consuming and laborious. While point of interest (POI) data can be used to identify urban building functions, it is prone to missing values. The present study proposes combining POIs with the spatial relationships between geographic entities and geographic information systems (GIS) to improve the accuracy of urban building function identification. First, the POIs are reclassified according to building functions. Second, the spatial relationships among road networks, buildings and POIs are analysed, and the frequency density ratios of POI types are calculated to identify the functions of buildings that contain POIs. Finally, buildings that do not contain POIs are identified by calculating the spatial similarity between unrecognised buildings and recognised buildings within the same road network mesh. The method can identify buildings with singular residential, commercial, office, and public services functions, as well as seven mixed functions, with an accuracy, recall, and F1 value of 90.28%, 97.52%, and 93.76%, respectively. Public service buildings and residential buildings have the highest identification precision, while the identification precision of mixed commercial and public service buildings and mixed residential and public service buildings are the lowest. An experiment demonstrates the effectiveness of the method. The results indicate that the spatial relationships between entities can compensate for missing POI data.

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