![The correlation between recurrent and overall mobility. The scatter plots represent the correlation between total rg and for k=2, 4, 8 in the GSM data set (a–c) and the GPS data set (d–f). Each point is coloured from blue to red, indicating the density of points in the corresponding region. Most of the points gather around the x-axis, the diagonal and the origin. The insets magnify the origin of the plot to [0, 100 km] for GSM and [0, 16 km] for GPS, demonstrating that the split emerges for smaller radii as well. As k increases explorers become returners. This transition is faster in the GPS case, consistent with the fact that the vehicle mobility represents a subset of trips and visited locations.](https://www.researchgate.net/profile/Luca-Pappalardo-3/publication/281590314/figure/fig5/AS:281559400763413@1444140400449/The-correlation-between-recurrent-and-overall-mobility-The-scatter-plots-represent-the_Q320.jpg)
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Returners and explorers dichotomy in human mobility
Abstract and Figures
The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.
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... While the method is exploratory, it illustrates that, unlike traditional census or survey data, geolocated tweets offer the opportunity to capture mobility without a priori definitions of migration. Another promising use of continuous mobility information is the identification of returners, which Pappalardo et al. (2015) identified as people for whom recurrent movement constitutes a large part of their mobility and are clearly distinct from explorers who spread their movement over a larger number of locations. ...
Geolocated social media data hold a hitherto untapped potential for exploring the relationship between user mobility and their interests at a large scale. Using geolocated Twitter data from Nigeria, we provide a feasibility study that demonstrates how the linkage of (1) a trajectory analysis of Twitter users' geolocation and (2) natural language processing of Twitter users' text content can reveal information about the interests of migrants. After identifying migrants via a trajectory analysis, we train a language model to automatically detect the topics of the migrants' tweets. Biases of manual labelling are circumvented by learning community-defined topics from a Nigerian web forum. Results suggest that differences in users' mobility correlate with varying interests in several topics, most notably religion. We find that Twitter data can be a flexible source for exploring the link between users' mobility and interests in large-scale analyses of urban populations. The joint use of spatial techniques and text analysis enables migration researchers to (a) study migrant perspectives in greater detail than is possible with census data and (b) at a larger scale than is feasible with interviews. Thereby, it provides a valuable complement to interviews, surveys and censuses, and holds a large potential for further research.
... Primary and secondary k-means clustering. K-means clustering is an unsupervised learning method for creating clusters and cluster centers in a set of unlabeled data (Lenormand et al., 2015;Pappalardo et al., 2015;Thuillier et al., 2018;Toole et al., 2015). Instead of analyzing the lifestyle patterns of each CBG separately, we used k-means clustering to group CBGs with similar POI visitation patterns (i.e., the lifestyle patterns in the primary clusters) and recovery rates (i.e., differential recovery trajectories in the secondary clusters). ...
Lifestyle recovery captures the collective effects of population activities as well as the restoration of infrastructure and business services. This study uses a novel approach to leverage privacy-enhanced location intelligence data, which is anonymized and aggregated, to characterize distinctive lifestyle patterns and to unveil recovery trajectories after 2017 Hurricane Harvey in Harris County, Texas (USA). The analysis integrates multiple data sources to record the number of visits from home census block groups (CBGs) to different points of interest (POIs) in the county during the baseline and disaster periods. For the methodology, the research utilizes unsupervised machine learning and ANOVA statistical testing to characterize the recovery of lifestyles using privacy-enhanced location intelligence data. First, primary clustering using k-means characterized four distinct essential and non-essential lifestyle patterns. For each primary lifestyle cluster, the secondary clustering characterized the impact of the hurricane into four possible recovery trajectories based on the severity of maximum disruption and duration of recovery. The findings further reveal multiple recovery trajectories and durations within each lifestyle cluster, which imply differential recovery rates among similar lifestyles and different demographic groups. The impact of flooding on lifestyle recovery extends beyond the flooded regions, as 59% of CBGs with extreme recovery durations did not have at least 1% of direct flooding impacts. The findings offer a twofold theoretical significance: (1) lifestyle recovery is a critical milestone that needs to be examined , quantified, and monitored in the aftermath of disasters; (2) spatial structures of cities formed by human mobility and distribution of facilities extend the spatial reach of flood impacts on population lifestyles. These provide novel data-driven insights for public officials and emergency managers to examine, measure, and monitor a critical milestone in community recovery trajectory based on the return of lifestyles to normalcy.
... Many individuals (~10%) had travel days labelled as long-distance or away-from-home, but these clusters were the dominant pattern of activity for only a small number (0.2%) of this subset of individuals. This provides evidence that important behaviours of "exploration", such as travelling long distances and visiting new locations form a component of many individuals' activity, instead of being confined to a small group of unique people (33). ...
We analysed daily travel patterns from in-app GPS data in the United Kingdom to identify characteristic modes of travel behaviour, and the relative importance of different behaviours for the topology of the overall travel network. We clustered the detailed travel trajectories and identified four characteristic modes of travel that we named: regular-travel, and stay-at-home which represented the majority of travel days, and two 'long tailed' travel modes: long-distance, and away-from-home, which represented ~2.6% of travel days. We focused on these second two modes, in which individuals travel a long distance from home and deviate from predictable routines. We demonstrated how this relatively small portion of travel behaviour plays an outsized role in influencing the connectivity of the aggregate travel network. This analysis highlights the need to understand individual behavioural variations that can dictate the collective dynamics recorded by aggregate descriptions of human travel.
... The OD distance variable is calculated as each phone's Cartesian distance between the estimatedcoordinates of origin and destination. The radius of gyration was computed following the procedure common to human mobility research using cell-phone data (Gonzalez et al., 2008;Pappalardo et al., 2015;Xu et al., 2018;Gauvin et al., 2021;Barbosa et al. 2018;Matekenya et al., 2021;Blumenstock, 2012;Hernando et al., 2020;Bachir, 2019;Kang et al., 2012). Conceptually it measures how far the individual "strays" from the centre of gravity of all locations for a specific period -normally a day. ...
Sweden had the most liberal lockdown policies in Europe during the Covid-19 pandemic. Relying on individual responsibility and behavioural nudges, their effectiveness was questioned from the perspective of others who responded with legal restrictions on behaviour. In this study, using mobile phone data, we therefore examine daily spatial mobilities in Stockholm to understand how they changed during the pandemic from their pre-pandemic baseline given this background. The analysis demonstrates: that mobilities did indeed change but with some variations according to (a) the residential social composition of places and (b) their locations within the city; that the changes were long lasting; and that the average fall in spatial mobility across the whole was not caused by everybody moving less but instead by more people joining the group of those who stayed close to home. It showed, furthermore, that there were seasonal differences in spatial behaviour as well as those associated with major religious or national festivals. The analysis indicates the value of mobile phone data for spatially fine-grained mobility research but also shows its weaknesses, namely the lack of personal information on important covariates such as age, gender, and education.
... Initiatives like the Data for Development (D4D) challenge in Senegal and the Data for Refugees (D4R) challenge in Turkey motivated researchers to empirically analyze and theoretically model human mobility by utilizing communication data (Blondel et al., 2012;Salah et al., 2018). Previous research employing social big data has significantly contributed to the understanding of individual and group mobility (Giannotti et al., 2011;González et al., 2009;Lulli et al., 2017;Pappalardo et al., 2015) as well as to the forecasting of mobility intentions (Böhme et al., 2020). Communication data is proven to be instrumental in delineating social interaction patterns among immigrants and discerning geo-spatial patterns of segregation (Gao et al., 2021). ...
Bu çalışma, göç çalışmaları alanında gerçekleştirilen büyük veri araştırmalarında karşılaşılan etik zorluklara odaklanmakta ve verinin sorumlu bir şekilde kullanımı için izlenmesi gereken ilkeler hakkında bir diyaloğa katkı sunmayı amaçlamaktadır. Çalışmada göç araştırmacılarının çalışmalarını tasarlama, yürütme ve bulgularını yayma süreçlerinde karşılaştıkları zorluklara odaklanılmaktadır. Bu bağlamda, çalışmada araştırmacıların birey ve grup gizliliği ile güç dengesizliklerinin arttırımı ve yeni eşitsizliklerin yaratılmasına ilişkin etik sorumlukları olmalıdır. Çalışmada ayrıca göç araştırmacılarının büyük veri araştırmaları için etik standartların belirlenmesindeki kritik rolü vurgulanmaktadır.
Recent studies confirm that air pollution disproportionately affects socially disadvantaged populations¹⁻³. Yet, they have not fully explored the effect of personal mobility on population exposure. Our study bridges the existing gap in quantifying mobility-based air pollution exposure across socio-demographic groups. We combine the granular mobility of over 500,000 unique users daily and air pollution data at 100 m resolution to quantify disparities in particulate matter exposure in the Bronx, New York City, a racially diverse and dense urban area. Using individual daily particulate matter exposure rather than population-weighted concentrations, this approach not only reveals the streets with the highest prevalence of air pollution but also how different demographic groups are exposed. We observe significantly different spatial patterns between personal exposure and exposure disparities, where people from Hispanic-majority and low-income neighborhoods are the most severely and disproportionately exposed to fine particulate matter (PM2.5) pollution. We reveal that race/ethnicity is a much stronger indicator of exposure disparity than income. The study further demonstrates that within-group variation contributes a major portion of exposure disparities, suggesting more granular mitigation plans are needed to target high-exposure individuals of socially disadvantaged groups in addition to generic air quality improvement.
There have been tremendous advancements in technology-based assessments in new modes of data collection and the use of artificial intelligence. Traditional assessment techniques in the fields of psychology, business, education, and health need to be reconsidered. Yet, while technology is pervasive, its spread is not consistent due to national differences in economics and culture. Given these trends, this book offers an integrative consolidation of how technology is changing the face of assessments across different regions of the world. There are three major book sections: in foundations, core issues of computational models, passively sensed data, and privacy concerns are discussed; in global perspectives, the book identifies ways technology has changed how we assess human attributes across the world, and finally, in regional focus, the book surveys how different regions around the world have adopted technology-based assessments for their unique cultural and societal context.
Understanding human mobility patterns is vital for the coordinated development of cities in urban agglomerations. Existing mobility models can capture single-scale travel behavior within or between cities, but the unified modeling of multi-scale human mobility in urban agglomerations is still analytically and computationally intractable. In this study, by simulating people’s mental representations of physical space, we decompose and model the human travel choice process as a cascaded multi-class classification problem. Our multi-scale unified model, built upon cascaded deep neural networks, can predict human mobility in world-class urban agglomerations with thousands of regions. By incorporating individual memory features and population attractiveness features extracted by a graph generative adversarial network, our model can simultaneously predict multi-scale individual and population mobility patterns within urban agglomerations. Our model serves as an exemplar framework for reproducing universal-scale laws of human mobility across various spatial scales, providing vital decision support for urban settings of urban agglomerations.
The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.
This study leverages mobile phone data to analyze human mobility patterns in a developing nation, especially in comparison to those of a more industrialized nation. 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 mobility patterns - such as differences in likelihood to travel, as well as in the time required to travel - that are relevant to consideration on policy, infrastructure, and economic development. Our study illustrates how cultural and linguistic diversity in developing regions (such as Ivory Coast) can present challenges to mobility models that perform well and were conceptualized in less culturally diverse regions. Finally, we address these challenges by proposing novel techniques to assess the strength of borders in a regional partitioning scheme and to quantify the impact of border strength on mobility model accuracy.
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 mobility patterns -
such as differences in likelihood to travel, as well as in the time required to
travel - that are relevant to consideration on policy, infrastructure, and
economic development. Our study illustrates how cultural and linguistic
diversity in developing regions (such as Ivory Coast) can present challenges to
mobility models that perform well and were conceptualized in less culturally
diverse regions. Finally, we address these challenges by proposing novel
techniques to assess the strength of borders in a regional partitioning scheme
and to quantify the impact of border strength on mobility model accuracy.
Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge.
Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining.
The potential of low-frequency bus localization data for the monitoring and control of bus system performance is investigated in this paper. It is shown that data with a sampling rate as low as 1 min, when processed appropriately, can provide ample information. Accurate estimates of stop arrival and departure times are obtained; these estimates in turn allow the analysis of headways and travel times. A three-parameter gamma family of distributions is fitted for headways at the stops along a bus line. The evolution of the parameters demonstrates critical points on the line where bus bunching is significantly increased. Moreover, this analysis allows differentiating problems associated with varying passenger demand from uncertainties associated with traffic conditions. Furthermore it is shown that expected travel time and travel time variability can be calculated from low-frequency localization data. Finally, the way in which the results can be used to calibrate a simulation model that can test bus control strategies is presented. The methods are applied and validated to data obtained from Bus Route Number 1 in Boston, Massachusetts.