Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved PageRank algorithm

Chen, Jia; Du, Yunyan; Wang, Siying; Bai, Tianyang; Fei, Teng

doi:10.1111/tgis.12515

Cited by 24 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid development of information and communication technologies (ICT) and GPS embedded devices, large-scale mobile phone data provides an unprecedented opportunity in tracking human trajectories, which benefits research about human mobility patterns. Existing studies have used such data to investigate basic laws governing human movements 25,26 , model regional transportation connectedness and economy 27,28 , describe daily commuting flows 29 , compute urban vibrancy 30,31 , inform public health policy 4,17,32 , and understand spatial interaction patterns [33][34][35] . Figure 1 illustrates detailed processing steps for how this human mobility flow dataset is generated.…”

Section: Background and Summarymentioning

confidence: 99%

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

et al. 2020

View full text Add to dashboard Cite

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Spatial interaction has always been an important perspective from which to understand human mobility and urban dynamics [49], though very few studies have implemented interaction indices in urban vitality evaluation [27]. To the best of our knowledge, no study has implemented an index to measure the diversity of spatial interaction as a component of the vitality model.…”

Section: Spatial Interaction Diversitymentioning

confidence: 99%

Nighttime Vitality and Its Relationship to Urban Diversity: An Exploratory Analysis in Shenzhen, China

Zhang

Liu

Tan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Relationship between urban diversity and urban vitality is imperative for guiding better design in urban development, though existing frameworks are not able to efficiently examine the relationship at multiple scales. In this article, we propose a new framework to integrate nighttime light (NTL) imagery and multisource urban data into multiscale geographically weighted regression (MGWR) models to examine the varying relationship between diversity and vitality across space and time. NTL is used as a proxy for urban nighttime vitality. Public transport, taxi transit, and points of interest (POI) data are used to derive three aspects of urban diversity indices: ridership diversity, spatial interaction diversity, and built environment diversity. By comparing the models in holiday and non-holiday weeks in Shenzhen, China, the NTL-based vitality proxy was found to be strongly correlated with the urban diversity indices, given by the satisfactory goodness of fit (r-squared = 0.9) of the MGWR models. The spatially varying relationships between diversity indices and nighttime vitality were observed and patterns discussed. The analysis of the coefficients revealed the importance of stable public transport and fluctuating taxi trips for nighttime vitality. The new index proposed for the diversity of spatial interaction (DSI) is a strong indicator for nighttime vitality, adding to existing vitality indicators. Furthermore, this study found that DSI and density of catering (DOC) have less temporal variation, indicating their robustness in measuring nighttime vitality. This study provided empirical insights into how nighttime vitality is related to urban diversity, demonstrating new applications of NTL for intracity studies.

show abstract

“…Centrality metrics indicate the importance and vibrancy of a location, which may reflect the underlying urban structure that breeds such activities (Jia et al., 2019). We project the centralities of zones into geographical space (Figure 6), which helps obtain a better understanding of the polycentric structure of NYC (Zhong et al., 2014).…”

Section: Resultsmentioning

confidence: 99%

A multilayer mobility network approach to inferring urban structures using shared mobility and taxi data

Zhang

Liu

Senousi

2021

Transactions in GIS

View full text Add to dashboard Cite

Developing data‐driven approaches to understanding urban structures is important for urban planning. However, it is still challenging to combine different transport datasets into a unified framework and reveal the dynamics of urban structures with the emergence of shared mobility. In this study, we propose two empirical multilayer networks to infer and profile urban structures. First, a temporal network is constructed using traditional taxi data over years to reveal the urban structures. Second, a multimodal network is constructed using shared mobility and traditional taxi data over a year to reveal the urban structures. The proposed networks are tested in New York City using a large volume of shared bike, shared vehicle, and traditional taxi data. The multilayer network centralities and community detection enable us to profile the characteristics of the urban flows and urban structure. The analytical results allow us to acquire a better understanding of urban structures from a multilayer perspective, and also provide a geocomputation framework that is useful for urban and geographic researchers.

show abstract

Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved PageRank algorithm

Cited by 24 publications

References 39 publications

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

Nighttime Vitality and Its Relationship to Urban Diversity: An Exploratory Analysis in Shenzhen, China

A multilayer mobility network approach to inferring urban structures using shared mobility and taxi data

Contact Info

Product

Resources

About