Resolving urban mobility networks from individual travel graphs using massive-scale mobile phone tracking data

Cao, Jinzhou; Li, Qingquan; Tu, Wei; Gao, Qi-Li; Cao, Rui; Zhong, Chen

doi:10.1016/j.cities.2020.103077

Cited by 40 publications

(17 citation statements)

References 94 publications

(90 reference statements)

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“…Moreover, all four counties in this study exhibit similarity of their human visitation networks, demonstrating consistency in the general structure of human visitations, despite the significant differences in socio-demographic dimension and spatial structures. This study provides additional insight to this theory by drawing similarities between cross-sectional comparisons among different counties in United States, similarly to previous studies, such as those on Chicago (C. , Boston (Yin & Chi, 2020), San Francisco (Li et al, 2021), Singapore (Jiang et al, 2017), and Shenzhen (Cao et al, 2021).…”

Section: Discussionsupporting

confidence: 60%

“…Furthermore, lifestyles in urban environments can be measured from both spatial and temporal perspectives. Spatially, many scholars previously used spatial distance to measure a person's visitation pattern (Cao et al, 2021;Yin & Chi, 2020). Accordingly, this study adopted average distance of motifs to measure the variability of lifestyles.…”

Section: Fig 3 Human Visitation Motifs With Attributes Of Two-digit N...mentioning

confidence: 99%

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Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

Ma¹,

Li²,

Mostafavi³

2022

Preprint

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The lifestyles of urban dwellers could reveal important insights regarding the dynamics and complexity of cities. The availability of human movement data, anonymized and aggregated data captured from cell phone movement, enables characterization of distinct and recurrent human daily visitation patterns. Despite a growing body of research on analysis and evaluation of lifestyle patterns in cities, little is known about the characteristics of people's lifestyles patterns at urban scale. This limitation is primarily due to challenges in characterizing lifestyle patterns when human movement data is aggregated to protect the privacy of users. To address this gap, in this study, we model cities based on aggregated human visitation data to construct a network of places. We then examine the subgraph signatures (i.e., motifs) in the networks of places to map and characterize lifestyle patterns at city scale. Location-based data from Harris County, Dallas County, New York County, and Broward County in the United States were examined to reveal lifestyle signatures in cities. For the motif analysis, two-node, three-node, and four-node motifs without location attributes were extracted from human visitation networks. Second, homogenized nodes in motifs were encoded with location categories from NAICS codes. Multiple statistical measures, including network metrics and motif properties, were quantified to characterize lifestyle signatures. The results show that: i) people's lifestyles in urban environments can be well depicted and quantified based on distribution and attributes of motifs in networks of places; ii) motifs in networks of places show stability in quantity and distance as well as periodicity on weekends and weekdays indicating the stability of lifestyle patterns in cities; iii) human visitation networks and lifestyle patterns show similarities across different metropolitan areas implying the universality of lifestyle signatures across cities; iv) lifestyles represented by attributed motifs are spatially heterogeneous suggesting variations of lifestyle patterns within different population groups based on where they live in a city. The findings provide deeper insights into urban lifestyles signatures in urban studies and provide important insights for data-informed urban planning and management.

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Section: Discussionsupporting

confidence: 60%

Section: Fig 3 Human Visitation Motifs With Attributes Of Two-digit N...mentioning

confidence: 99%

Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

Ma¹,

Li²,

Mostafavi³

2022

Preprint

View full text Add to dashboard Cite

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“…Recently, limited studies related to characterizing urban mobility networks based on their motifs have primarily focused on understanding structurally and spatially heterogeneous patterns of urban mobility networks [17], finding differences between telecommuters and commuters [18], modeling of human activity and mobility [19,7], and urban traffic speed prediction [20]. A number of other studies have examined motifs in transportation networks (such as road networks and airline networks).…”

Section: Introductionmentioning

confidence: 99%

Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility Networks during Urban Flooding

Rajput¹,

Mostafavi²

2022

Preprint

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In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilized high-resolution aggregated location-based data to construct temporal human mobility networks in Harris County, Texas (Houston metropolitan area) in the context of the 2017 Hurricane Harvey. Using the constructed temporal network models, we examined characteristics such as motif distribution, motif persistence and temporal stability, and motif attributes to reveal latent substructural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persists in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration is heterogeneous across different network types. Also, perturbation impacts persist at the sub-structure level while global topological network properties might indicate the network has recovered. The patterns of impact and recovery at the global-network scale is influenced by more abundant motifs; however, less abundant motifs (which are shown to have more stability during steady state) experience greater sustained impacts and take longer to recover. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks), it is essential to examine the microstructures and their perturbation impacts and recovery. The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and to monitor recovery in affected communities based on the patterns of impact and recovery in human mobility networks at both sub-structure and global-network levels.

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“…The location data of cell phone users are capable of representing close-to-real population distribution in some cases (Alexander et al, 2015;Shi et al, 2015;Zhang & Thill, 2019). More and more studies have, therefore, begun to use cell phone location data to estimate human flow and identify its patterns (Cao et al, 2021;Tokey, 2021;Zhang & Ng, 2021).…”

Section: Introductionmentioning

confidence: 99%

Deriving intercity human flow pattern and mechanism based on cell phone location data: case study of Guangdong Province, China

Chen

et al. 2022

Comput.Urban Sci.

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The spatial pattern and mechanism of human flow are of great significance for urban planning, economic development, transportation planning and so on. In this study, we used cell phone location data to represent the human flow network in Guangdong Province, China, using the 21 cities in Guangdong as “nodes” and the human flow intensity among them as “edges”. Then we explored macro and micro features of the human flow network, by using the index of degree distribution, alter-based centrality and alter-based power, respectively. Finally, we proposed a human flow estimation model which integrates individual urban characteristics, intercity links, and differences to further analyze the affecting factors of human flow. We found that the human flow network in this region is significantly scale-free, with Guangzhou, Shenzhen, Foshan, and Dongguan being the most important cities. We also found that the newly proposed model can explain the human flow in the study area, with an R2 of 0.914. Analysis results show that the factors of employment in tertiary sector, intercity internet attention, intercity differences in the number of tertiary workers, differences in population size, and distance have significant impacts on the human flow. This study may provide insights into human activity mechanisms that can contribute to urban planning and management.

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Resolving urban mobility networks from individual travel graphs using massive-scale mobile phone tracking data

Cited by 40 publications

References 94 publications

Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

Characterizing Urban Lifestyle Signatures Using Motif Properties in Network of Places

Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility Networks during Urban Flooding

Deriving intercity human flow pattern and mechanism based on cell phone location data: case study of Guangdong Province, China

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