Human Mobility Research Articles

Mobility behaviors shift disparity in flood exposure in U.S. population groups

The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enable a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions.

Hazard exposure heterophily in socio-spatial networks contributes to post-disaster recovery in low-income populations

Despite recognition of the influence of socio-spatial networks on disaster recovery, limited data-driven evidence exists regarding the nature of this relationship. To address this knowledge gap, this study investigated the duration of the human mobility recovery process and its relationship with hazard-exposure heterophily (as a measure for the resourcefulness of social ties) and various other socio-demographic characteristics. We applied the concept of hazard-exposure heterophily to the possibility of resource sharing, through social-spatial networks, between communities with different hazard exposures. We used human movement patterns to measure recovery duration. To understand the disparities during recovery, we examined the correlations between recovery duration and the social demographic characteristics of race and income caused by Hurricane Harvey which made landfall in Harris County, Texas, on August 25, 2017. We discerned high/low hazard-exposure heterophily through the use of a threshold-classification method, and we got four clusters according to the flood-inundation percentage and social connectedness values. Then we applied correlation analysis to analyze the relations between hazard-exposure heterophily/income and recovery weeks among different income groups. The results revealed that hazard-exposure heterophily considerably accelerates human mobility recovery in low-income areas, as the hazard-exposure heterophily allows for the increased exchange of knowledge and resources between members of diverse groups, ultimately accelerating the recovery process. The results of this study could benefit local agents to better allocate recovery resources.

Species habitat suitability increased during COVID-19 lockdowns

COVID-19 lockdowns had strong positive and negative effects on the environment and biodiversity. The most evident effects were the occurrence of wildlife in the middle of urban settlements, the reduction of noise, and the improvement in air quality. However, other effects on species are less well known. Our main question is: did species habitat suitability increase during lockdowns? Here, we analysed trends in species habitat suitability over time by modelling the species distributions with a time series of environmental variables obtained from satellite remote sensing. We modelled the habitat suitability of 381 species of vascular plants, amphibians, reptiles, birds, and mammals, before, during, and after the lockdowns in the Iberian Peninsula (Portugal and Spain), from July 2017 to August 2022 with the Maxent presence-background algorithm and five MODIS variables (Albedo, Evapotranspiration, LST, NDVI, and Surface reflectance) with a periodicity of 8 days, and analysed their trends with the Mann-Kendall test. We compared our results with air quality data (PM10, CO, and NO2) from ground stations and Sentinel 5 P and with mobility data from Google mobility reports. Habitat suitability increased during both lockdowns when considering all species together. The peak in species' habitat suitability trends during the first lockdown was coincident with air quality and mobility trends: air pollution decreased when people stayed at home. Reduction in human mobility during COVID-19 lockdowns revealed positive effects on species’ habitat quality, highlighting the importance of reducing human activities as the only solution for nature conservation.

Reconstructing and tracing the evolution of the road networks in the Haidai region of China during the Bronze and Early Iron Ages

Reconstructing ancient transportation networks is critical to studying past human mobility patterns. China’s Haidai region was a thriving political and economic hub during the Bronze and Early Iron Ages. We used GIS spatial analysis techniques to build a “Settlement Interaction Model” based on archaeological data from the Haidai region during the Bronze and Early Iron Age (Shang Dynasty, Western Zhou Dynasty, Spring & Autumn Period, and Warring States period). The eight-level road network maps with traffic attributes were distinguished based on topography and settlement size. The total lengths of the road networks were estimated to be 19,112 km in the Shang Dynasty, 35,269 km in the Western Zhou Dynasty, 51,555 km in the Spring & Autumn Period, and 77,456 km in the Warring States Period, with the average road flows of 6.6, 31.7, 42.8, and 75.5, respectively. The Z score and one-sample t-test (p < 0.01) confirmed the reliability of the reconstructed road networks. The Shang Dynasty saw the sporadic appearance of simple road routes. More complex routes emerged during the Western Zhou Dynasty and Spring & Autumn Period. The road networks were finally built during the Warring States Period. The development of road networks was closely related to population growth and urbanization. Exploring methods for reconstructing road networks may help us uncover ancient road networks and better understand ancient cultural exchanges.

To internalize or not? Addressing key differences between Turkish migrant and native workers in construction employment

PurposeMigration is on the rise due to globalization and human mobility. This has led to increased impacts that have affected many industries, including the construction industry. A large number of migrants are employed in the construction sector, and employers are challenged to make sure all employees are properly integrated to meet the demands needed for construction projects. This article addresses key differences between migrant and native workers to help hiring departments in the construction industry analyse workers' attitudes based on cultural and motivational factors to have the workforce they need to succeed.Design/methodology/approachThe research used both quantitative and qualitative surveys. A two-part questionnaire, designed through a comprehensive literature review, was carried out to identify key differences between native and migrant workers. The data were obtained and then analysed using different statistical approaches, including factor analysis protocol, factor structure model, reliability analysis, relative importance index and nonparametric test analysis. A semi-structured interview was then conducted to discuss all the findings.FindingsThe study indicated that migrant workers, compared to natives, tend to give more importance to their working environment, particularly accommodation, work safety and relations with teammates. Also, migrants typically take a socialistic approach instead of an individual approach while at work and reveal an extensive range of behaviours based on a sense of belonging. It might be more important for migrants to have a place in society, to have a settled life and to be integrated into an established order than to improve their rights and benefits. On the other hand, the study argued that native workers tend to prioritize their benefits at work, such as regular payments for overtime and insurance premiums. Their behaviours might carry a more neutral and individual attitude as well as specific cultural traces.Research limitations/implicationsThe study is limited to a sample of participants in the Turkish construction sector. Further research based on more cultural models and motivational factors with a larger group of respondents from different countries could offer better results. The results of the study might not apply to a broad context due to many other factors that affect worker behaviours, such as geography, cultural structures and working conditions. Despite these drawbacks, the present paper may help employers and other stakeholders understand the best way to incorporate migrants into the construction industry.Originality/valueThis research is very important for the construction industry in various countries that are currently employing thousands of migrants. Being able to address the key differences between migrants and native workers based on cultural and motivational factors might help with engagement and create a level of harmony in the field for greater productivity.

Personalized behavior modeling network for human mobility prediction

Personalized behavior modeling network for human mobility prediction

Regulating mobility through detention: Understanding the new geography of control and containment at the Southern European border

The aim of this article is to describe the evolution of immigration detention policies at the Southern European border. This will be done by presenting original data on the actual functioning of immigration detention in Italy in the wake of the so-called “refugee crisis”. By shedding light on these developments, the article reveals a notable convergence of first reception and return policies, which in turn is driving a transformation of the landscape of immigration detention leading to a proliferation of detention regimes and spaces of containment. Drawing on the literature on carceral geographies, this development is analyzed within the framework of Italy's distinct role in the geopolitics of EU border control policies. The article ultimately suggests that the immigration detention system has gradually been co-opted by the border control infrastructure, becoming part of a broader and intricate control assemblage whose essential function is the regulation of human mobility.

Inference of dynamic hypergraph representations in temporal interaction data.

A range of systems across the social and natural sciences generate data sets consisting of interactions between two distinct categories of items at various instances in time. Online shopping, for example, generates purchasing events of the form (user, product, time of purchase), and mutualistic interactions in plant-pollinator systems generate pollination events of the form (insect, plant, time of pollination). These data sets can be meaningfully modeled as temporal hypergraph snapshots in which multiple items within one category (i.e., online shoppers) share a hyperedge if they interacted with a common item in the other category (i.e., purchased the same product) within a given time window, allowing for the application of hypergraph analysis techniques. However, it is often unclear how to choose the number and duration of these temporal snapshots, which have a strong influence on the final hypergraph representations. Here we propose a principled nonparametric solution to this problem by extracting temporal hypergraph snapshots that optimally capture structural regularities in temporal event data according to the minimum description length principle. We demonstrate our methods on real and synthetic data sets, finding that they can recover planted artificial hypergraph structure in the presence of considerable noise and reveal meaningful activity fluctuations in human mobility data.

Resilience, Migratory Grief, and the Influence of Sociodemographic Factors on Venezuelan Immigrants, Case of Peru

The current study aimed to determine the relationship between resilience and migratory grief among Venezuelan immigrants in Peru. This was a basic, quantitative, correlational, cross-sectional study. The sample consisted of 267 Venezuelan immigrants aged between 18 and 50 years from the human mobility and human trafficking board of a diocese in Peru. To measure the variables, the RS-14 Resilience Scale by Wagnild and Young and the Migratory Grief Questionnaire created by De la Revilla et al. were used. For the data analysis, SPSS v25 software was used, and Spearman's correlation coefficient was calculated to determine the relationship between resilience and migratory grief. Additionally, to understand the influence of sociodemographic factors, analysis of variance (ANOVA) and the Scheffé post hoc test were utilized. The results showed that there was a statistically significant relationship between the level of resilience and migratory grief. Similarly, employment status is an influential factor for both resilience and migratory grief. Received: 6 March 2024 / Accepted: 16 April 2024 / Published: 5 May 2024

The spatial dimensions of cultural consumption: how distance influences consumption levels in a spatial setting

AbstractCultural consumption has increasingly acquired a fundamental role in urban policy frameworks, thanks to its empirically proven positive effects on individuals and on societies. Although several theoretical and empirical contributions have examined the main socio-economic determinants that explain cultural consumption; its spatial dimensions remain significantly under-considered and require further analysis. The aim of this paper is to analyse the factors influencing cultural consumption with a focus on spatial distance and spatial dependence. Specifically, it seeks to inquire into the extent to which spatial distance between consumers and cultural institutions plays a role in neighbourhood’s levels of cultural consumption. To do this, human mobility data towards cultural institutions are used as a proxy for individual cultural consumption levels in six French cities. Results show that spatial proximity with the cultural offer matters in explaining consumption patterns, but that traditional socioeconomic determinants have a higher explanatory value.

Mobility census for monitoring rapid urban development.

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic 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 analysing 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 by-product 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 500 m) 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.

The Role of Human Mobility Flow in the Region-to-Region Spread of Respiratory Syncytial Virus Infection among Infants: An Infographic Analysis.

The region-to-region spread of human infectious diseases is considered to be dependent on the human mobility flow (HMF). However, it has been hard to obtain the evidence for this. Since the onset of the coronavirus disease 2019 (COVID-19) pandemic in Japan 2020, the government has enforced countermeasures against COVID-19 nationwide, namely the restriction of personal travelling, universal masking, and hand hygiene. As a result, the spread of acute respiratory infections had been effectively controlled. However, COVID-19 as well as pediatric respiratory syncytial virus (RSV) infections were not well-controlled. The region-to-region spread of pediatric RSV infections in 2020-2021 was recognizable unlike those in 2018 and 2019. In this study, we investigated the correlation between the trend of regional reports of the pediatric RSV infections and the HMF based on cellular phone signal data. Upon closer examination of both epidemiological trend and HMF data, the spread of pediatric RSV infection from one region to another was logically explained by HMF, which would serve as the evidence of the dependence of regional transmission on HMF. This is the first solid evidence where this correlation has been clearly observed for the common respiratory infections. While social implementation of infection control measures has successfully suppressed the droplet-mediated respiratory infections, such as influenza, but not the airborne infections, it was suggested that the aerosol transmission and adult asymptomatic carrier were involved in the transmission of RSV akin to COVID-19.

Urban mobility analytics amid COVID-19 pandemic: A framework for promoting work resumption based on mobile phone data

The Corona Virus Disease 2019 (COVID-19) pandemic had a pernicious influence on the whole world, so that the international community implemented various travel policies to inhibit the viral spread. With the COVID-19 pandemic under control, the overly lenient measures allowed the new variant to take advantage. Also, it is difficult to determine the geographical scope of transport policy that has the least impact on work resumption. Therefore, it is necessary to find policies to promote work resumption while preventing and controlling the pandemic in a geographical perspective. In this paper, a framework based on mobile phone data is proposed for promoting work resumption during the pandemic. First, human mobility networks are constructed through the origin-destination flow extracted from mobile phone data. Second, the singular value decomposition (SVD) algorithm is utilized to recognize the mobility patterns and explore the urban mobility dynamics of work-resumption-related travel during the pandemic. Third, the nonoverlapping community detection algorithm is used to generate policy zoning for work-resumption-related travel. Finally, the policy zoning is validated by public health data based on the overlay analysis and spatial statistics method, and policy implications are proposed. A case study of Shanghai, China is applied to verify the proposed framework. The results show that the framework can capture the mobility patterns and implement the policy zoning based on mobile phone data to balance the economic activity and public health during the pandemic, and the proposed framework can be applied to other cities with future public health events.

Human Mobility Prediction Based on Trend Iteration of Spectral Clustering

Human mobility prediction is crucial for epidemic control, urban planning, and traffic forecasting systems. We observe urban traffic flow prediction has a hierarchical structure, in which human mobility prediction should consider not only the spatial and the temporal relationships, but also the high-level mobility trend between individuals and regions. In this paper, we propose a human mobility clustering algorithm based on trend iteration of spectral clustering (TISC) to incorporate the high-level human mobility trend between individuals and regions. We integrate our TISC clustering algorithm with two existing urban traffic flow predictive models: namely, deep spatio-temporal residual network (ST-ResNet) and deep spatio-temporal 3D network (ST-3DNet). By adapting our TISC clustering algorithm, the prediction accuracy of both algorithms has been improved significantly (30.96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for ST-ResNet and 24.66 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for ST-3DNet). We also compare the TISC-based predictive framework with 26 state-of-the-art human mobility prediction algorithms. We observe that our TISC algorithm considerably outperforms all 26 methods, reducing the predictive error from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6.93\%$</tex-math></inline-formula> to 69.55 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density.

This article introduces an analytical framework that interprets individual measures of entropy-based mobility derived from mobile phone data. We explore and analyze two widely recognized entropy metrics: random entropy and uncorrelated Shannon entropy. These metrics are estimated through collective variables of human mobility, including movement trends and population density. By employing a collisional model, we establish statistical relationships between entropy measures and mobility variables. Furthermore, our research addresses three primary objectives: firstly, validating the model; secondly, exploring correlations between aggregated mobility and entropy measures in comparison to five economic indicators; and finally, demonstrating the utility of entropy measures. Specifically, we provide an effective population density estimate that offers a more realistic understanding of social interactions. This estimation takes into account both movement regularities and intensity, utilizing real-time data analysis conducted during the peak period of the COVID-19 pandemic.

Epidemicity indices and reproduction numbers from infectious disease data in connected human populations

We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to the maximum instantaneous growth rate of new infections, echoing concepts and tools developed to study the reactivity of ecosystems. Suitable epidemicity indices can showcase the dynamics of infections, together with commonly employed effective reproduction numbers, especially when the latter assume values less than 1. In particular, epidemicity evaluates the short-term reactivity to perturbations of a disease-free equilibrium. Here, we show that sufficient epidemicity thresholds to prevent transient epidemic outbreaks in a spatially connected setting can be estimated by generalizing existing analogues derived when spatial effects are neglected. We specifically account for the discrete nature, in both space and time, of surveillance data of the type typically employed to estimate effective reproduction numbers that formed the bulk of the communication of the state of the COVID-19 pandemic and its controls. After analyzing the effects of spatial heterogeneity on the considered prognostic indicators, we perform a short- and long-term analysis on the COVID-19 pandemic in Italy, showing that endemic conditions were maintained throughout the duration of our simulation despite stringent control measures. Our method provides a portfolio of prognostic indices that are essential to pinpoint the ongoing pandemic in both a qualitative and quantitative manner, as our results demonstrate. We base our conclusions on extended investigations of the effects of spatial fragmentation of communities of different sizes owing to connectivity by human mobility and contact scenarios, within real geographic contexts and synthetic setups designed to test our framework.

Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model

The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.

Physiography, foraging mobility, and the first peopling of Sahul

The route and speed of migration into Sahul by Homo sapiens remain a major research question in archaeology. Here, we introduce an approach which models the impact of the physical environment on human mobility by combining time-evolving landscapes with Lévy walk foraging patterns, this latter accounting for a combination of short-distance steps and occasional longer moves that hunter-gatherers likely utilised for efficient exploration of new environments. Our results suggest a wave of dispersal radiating across Sahul following riverine corridors and coastlines. Estimated migration speeds, based on archaeological sites and predicted travelled distances, fall within previously reported range from Sahul and other regions. From our mechanistic movement simulations, we then analyse the likelihood of archaeological sites and highlight areas in Australia that hold archaeological potential. Our approach complements existing methods and provides interesting perspectives on the Pleistocene archaeology of Sahul that could be applied to other regions around the world.

Developing a statewide active transportation planning dashboard with mobility data

ABSTRACT Existing walking/biking data are often insufficient to support active transportation planning activities. This study used an emerging large-scale human mobility dataset to identify places where there are a higher number of short-distance trips to public places (expressed by a mobility index). Demographic variables were integrated into the mobility index to prioritize access for more people and address equity concerns. In addition, a safety index (reflecting crash injuries/fatalities involving pedestrians/bicyclists) and a connectivity index (reflecting the density of existing active transportation infrastructure) were also considered together with the mobility index in generating an investment potential score. All the indices/scores were mapped at hexagon and segment level on an online dashboard. The developed dashboard is expected to support statewide active transportation planning and project selection/prioritization decision-makings. The proposed methodology is replicable for any places that have few active transportation facilities and where pedestrian/bicyclist count data are not sufficient to model demand directly.

YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals’ human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users’ privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.