Monitoring urban structure and development requires high-quality data at high spatiotemporal resolution. While traditional censuses have provided foundational insights into demographic and socioeconomic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analyzing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.
Mobility energetic balance frames spatial configuration of cities
Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB)（综合物理与复杂系统研究所）；Human Openware Research Lab (HOWLab), Engineering Research Institute of Aragon (I3A), University of Zaragoza (UZ)（萨拉戈萨大学阿拉贡工程研究院）；Arquitecturas Open Source (AOS) Research Group, School of Architecture and Technology, Universidad San Jorge（圣乔治大学建筑与技术学院）
The scales of urban mobility shape socioeconomic mixing
Central European University（中欧大学）；University of Glasgow（格拉斯哥大学）；TU Dortmund University（多特蒙德工业大学）；Universitat Pompeu Fabra（庞培法布拉大学）；Max Planck Institute for Demographic Research（马克斯·普朗克人口研究研究所）；Liverpool University（利物浦大学）
This study identifies the limitations and underlying characteristics of urban mobility networks that influence the performance of the gravity model. The gravity model is a widely-used approach for estimating and predicting population flows in urban mobility networks, assuming the scale-free property. Prior studies have reported good performance results for the gravity model at certain levels of aggregation. However, the characteristics of urban mobility networks might vary depending on the spatial and temporal resolutions of data. Hence, the sensitivity of gravity model performance to variatio…
Mobility cross spatial units represents the embodiment of how people manage activities between locations along temporal sequences. Spatiotemporal pattern nevertheless interacts with the socioeconomic characteristics of respected origin (push factors) and destination (pull factors) which widely discussed in spatial interaction literature. Observing this dynamics at higher spatial resolution allows us to entangle multifaceted nature of city, its complexity as a system or network, and the way it shapes movement of people. This study explore the extent interconnected elements of urban system or ur…
The rising adoption of plug-in electric vehicles (PEVs) leads to the alignment of their electricity and their mobility demands. Therefore, transportation and power infrastructures are becoming increasingly interdependent. In this work, we uncover patterns of PEV mobility by integrating for the first time two unique data sets: (i) mobile phone activity of 1.39 million Bay Area residents and (ii) charging activity of PEVs in 580,000 sessions obtained in the same region. We present a method to estimate individual mobility of PEV drivers at fine temporal and spatial resolution integrating survey d…
Understanding the relationship between spatial structures of cities and environmental hazard exposures (such as urban heat) is essential for urban health and sustainability planning. However, a critical knowledge gap exists in terms of the extent to which socio-spatial networks shaped by human mobility exacerbate or alleviate urban heat exposures of populations in cities. In this study, we utilize location-based data to construct human mobility networks in twenty metropolitan areas in the U.S. The human mobility networks are analyzed in conjunction with the urban heat characteristics of spatia…
Percolation Transition of K-Destinations Reveals Universal Backbone of Urban Mobility Networks
Institute of Remote Sensing and Geographical Information Systems, Peking University（北京大学遥感与地理信息系统研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Urban mobility enables deprivation bubble breaking in Indian and Mexican cities
Chalmers University of Technology（查尔姆斯理工大学）；Technical University of Denmark（丹麦技术大学）；Lund University（隆德大学）；Centro Ricerche Enrico Fermi（恩里科·费米研究中心）；ISI Foundation（ISI基金会）
Revealing the structural properties and understanding the evolutionary mechanisms of the urban heavy truck mobility network (UHTMN) provide insights in assessment of freight policies to manage and regulate the urban freight system, and are of vital importance for improving the livability and sustainability of cities. Although massive urban heavy truck mobility data become available in recent years, in-depth studies on the structure and evolution of UHTMN are still lacking. Here we use massive urban heavy truck GPS data in China to construct the UHTMN and reveal its a wide range of structure pr…
Estimating hourly population distribution change at high spatiotemporal resolution in urban areas using geo-tagged tweets, land use data, and dasymetric maps
Many of our routines and activities are linked to our ability to move; be it commuting to work, shopping for groceries, or meeting friends. Yet, factors that limit the individuals' ability to fully realise their mobility needs will ultimately affect the opportunities they can have access to (e.g., cultural activities, professional interactions). One important aspect frequently overlooked in human mobility studies is how gender-centred issues can amplify other sources of mobility disadvantages (e.g., socioeconomic inequalities), unevenly affecting the pool of opportunities men and women have ac…
