In this paper, we present a three-step methodological framework, including location identification, bias modification, and out-of-sample validation, so as to promote human mobility analysis with social media data. More specifically, we propose ways of identifying personal activity-specific places and commuting patterns in Beijing, China, based on Weibo (China's Twitter) check-in records, as well as modifying sample bias of check-in data with population synthesis technique. An independent citywide travel logistic survey is used as the benchmark for validating the results. Obvious differences are discerned from Weibo users' and survey respondents' activity-mobility patterns, while there is a large variation of population representativeness between data from the two sources. After bias modification, the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%. Synthetic data proves to be a satisfactory cost-effective alternative source of mobility information. The proposed framework can inform many applications related to human mobility, ranging from transportation, through urban planning to transport emission modelling.
The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half million individuals and 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also well reproduces the exponential trip displacement distribution. Howe…
The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has many applications. An important example is given by origin-destination matrices which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method which extracts a coarse-grained signature of mobility networks, under the form of a $2\times 2$ matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in th…
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…
It is very important to understand urban mobility patterns because most trips are concentrated in urban areas. In the paper, a new model is proposed to model collective human mobility in urban areas. The model can be applied to predict individual flows not only in intra-city but also in countries or a larger range. Based on the model, it can be concluded that the exponential law of distance distribution is attributed to decreasing exponentially of average density of human travel demands. Since the distribution of human travel demands only depends on urban planning, population distribution, reg…
The vast majority of travel takes place within cities. Recently, new data has become available which allows for the discovery of urban mobility patterns which differ from established results about long distance travel. Specifically, the latest evidence increasingly points to exponential trip length distributions, contrary to the scaling laws observed on larger scales. In this paper, in order to explore the origin of the exponential law, we propose a new model which can predict individual flows in urban areas better. Based on the model, we explain the exponential law of intra-urban mobility as …
Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-lo…
The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users. Surprisingly, while there are variations in human movement in different cities, our analysis shows that those ar…
Understanding the drivers of urban mobility is vital for epidemiology, urban planning, and communication networks. Human movements have so far been studied by observing people's positions in a given space and time, though most recent models only implicitly account for expected costs and returns for movements. This paper explores the explicit impact of cost and network topology on mobility dynamics, using data from 2 city-wide public bicycle share systems in the USA. User mobility is characterized through the distribution of trip durations, while network topology is characterized through the pa…
Since the industrial revolution, accelerated urban growth has overflown administrative divisions, merged cities into large built extensions, and blurred the boundaries between urban and rural land-uses. These traits, present in most of contemporary metropolis, complicate the definition of cities, a crucial issue considering that objective and comparable metrics are the basic inputs needed for the planning and design of sustainable urban environments. In this context, city definitions that respond to administrative or political criteria usually overlook human dynamics, a key factor that could h…
People's daily activities in the urban environment are complex and vary by individuals. Existing studies using mobile phone data revealed distinct and recurrent transitional activity patterns, known as mobility motifs, in people's daily lives. However, the limitation in using only a few inferred activity types hinders our ability to examine general patterns in detail. We proposed a mobility network approach with geographic context-aware Twitter data to investigate granular daily activity patterns in the urban environment. We first utilized publicly accessible geo-located tweets to track the mo…
Tourism favors more economic activities, employment, revenues and plays a significant role in development; thus, the improvement of this activity is a strategic task. In this work, we show how social sensing can be used to understand the key characteristics of the behavior of tourists and residents. We observe distinct behavioral patterns in those classes, considering the spatial and temporal dimensions, where cultural and regional aspects might play an important role. Besides, we investigate how tourists move and the factors that influence their movements in London, New York, Rio de Janeiro a…
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing…
This study leverages mobile phone data to analyze human mobility patterns in developing countries, especially in comparison to more industrialized countries. Developing regions, such as the Ivory Coast, are marked by a number of factors that may influence mobility, such as less infrastructural coverage and maturity, less economic resources and stability, and in some cases, more cultural and language-based diversity. By comparing mobile phone data collected from the Ivory Coast to similar data collected in Portugal, we are able to highlight both qualitative and quantitative differences in mobil…
We analyse two large-scale intra-city urban networks and traffic flows therein measured by GPS traces of taxis in San Francisco and Shanghai. Our results coincide with previous findings that, based purely on topological means, it is often insufficient to characterise traffic flow. Traditional shortest-path betweenness analysis, where shortest paths are calculated from each pairs of nodes, carries an unrealistic implicit assumption that each node or junction in the urban network generates and attracts an equal amount of traffic. We also argue that weighting edges based only on euclidean distanc…
Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city's multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately …
The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity …
The identification of urban mobility patterns is very important for predicting and controlling spatial events. In this study, we analyzed millions of geographical check-ins crawled from a leading Chinese location-based social networking service (Jiepang.com), which contains demographic information that facilitates group-specific studies. We determined the distinct mobility patterns of natives and non-natives in all five large cities that we considered. We used a mixed method to assign different algorithms to natives and non-natives, which greatly improved the accuracy of location prediction co…
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；University of Helsinki（赫尔辛基大学）；The Hong Kong University of Science and Technology（香港科技大学）
Understanding urban dynamics, i.e., how the types and intensity of urban residents' activities in the city change along with time, is of urgent demand for building an efficient and livable city. Nonetheless, this is challenging due to the expanding urban population and the complicated spatial distribution of residents. In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data. UrbanRhythm addresses three questions: 1) What mobility feature should be used to present residents' high-dimensional activities in the city…
Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico
Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility
Singapore-MIT Alliance for Research and Technology (SMART)（新加坡-麻省理工学院研究与技术创新联盟）；Massachusetts Institute of Technology（麻省理工学院）；Hong Kong University of Science & Technology (Guangzhou)（香港科技大学（广州））；Korea Advanced Institute of Science & Technology（韩国科学技术院）；University of Florida（佛罗里达大学）
As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the "15-minutes city" have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relations…
Potential Landscapes Reveal Spatiotemporal Structure in Urban Mobility: Hodge Decomposition and Principal Component Analysis of Tokyo Before and During COVID-19
Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large amounts of diverse crowdsensed data, many studies have made contributions to this field in various aspects. They need proper review and summary. In this paper, therefore, we first review these recent studies with a proper taxonomy with corresponding examples. Then, based on the experience learnt from the studies, we provide a comprehensive tutorial for future res…
Recent mobility scaling research, using new data sources, often relies on aggregated data alone. Hence, these studies face difficulties characterizing the influence of factors such as transportation mode on mobility patterns. This paper attempts to complement this research by looking at a category-rich mobility data set. In order to shed light on the impact of categories, as a case study, we use conventionally collected German mobility data. In contrast to `check-in'-based data, our results are not biased by Euclidean distance approximations. In our analysis, we show that aggregation can hide …
Many modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore's quadriennial Household Interview Travel Sur…
On-line social networks have grown quickly over the last few years and nowadays many people use them frequently. Furthermore the emergence of smartphones allows to access these networks any time from any physical location. Among the social networks, Twitter offers a particularly large set of data publicly available. Here we discuss the procedure to mine this data and store it in distributed databases using Python scripts. We also illustrate how geolocated tweets can be used to study the mobility of people in urban areas.
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 disaster related data to construct temporal human mobility networks. Using the constructed temporal network models, we examined characteristics such as motif distribution, motif persistence and temporal stability, and motif attributes to reveal …
Urban mobility network centrality predicts social resilience
The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Northeastern University（东北大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Santa Fe Institute（圣塔菲研究所）；University of Chicago（芝加哥大学）
Institute for Clarity in Documentation（文档清晰度研究所）；Brookhaven Laboratories（布鲁克海文实验室）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Palmer Research Laboratories（帕尔默研究实验室）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗克库尔中心）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；University of Pittsburgh（匹兹堡大学）
Which area in NYC is the most similar to Lower East Side? What about the NoHo Arts District in Los Angeles? Traditionally this task utilizes information about the type of places located within the areas and some popularity/quality metric. We take a different approach. In particular, urban dwellers' time-variant mobility is a reflection of how they interact with their city over time. Hence, in this paper, we introduce an approach, namely hood2vec, to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins. We co…
At the turn of the 21st century, urban development has experienced a paradigm shift so that the quest for smarter cities has become a priority agenda, with the direct participation of industry, policymakers, practitioners, and the scientific community alike. The 2008 financial crisis, the exodus from rural areas, and the densification of urban centers coupled with environmental and sustainability concerns have posed enormous challenges to municipalities all over the globe. The United Nations predicts that the world population will reach 9.8 billion by 2050, a growth of 2.1 billion from the 201…
What people buy is an important aspect or view of lifestyles. Studying people's shopping patterns in different urban regions can not only provide valuable information for various commercial opportunities, but also enable a better understanding about urban infrastructure and urban lifestyle. In this paper, we aim to predict city-wide shopping patterns. This is a challenging task due to the sparsity of the available data -- over 60% of the city regions are unknown for their shopping records. To address this problem, we incorporate another important view of human lifestyles, namely mobility patte…
The research objectives are exploring characteristics of human mobility patterns, subsequently modelling them mathematically depending on inter-event time and traveled distances parameters using CDRs (Call Detailed Records). The observations are obtained from Armada festival in France. Understanding, modelling and simulating human mobility among urban regions is excitement approach, due to itsimportance in rescue situations for various events either indoor events like evacuation of buildings or outdoor ones like public assemblies,community evacuation in casesemerged during emergency situations…
Urban population grows constantly. By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions: areas with over- and under-representation of specific population groups, named hot and cold spots respectively. Using aggregated mobile phone data, we apply this methodology to the city of Barcelona to assess the mobility of three groups of people: women, …
Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work,we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for understanding these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comp…
With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge'' extracted from multiple sources in a cohesive manner. Specific…
