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Understanding and predicting the emergence and evolution of cultural tastes manifested in consumption patterns is of central interest to social scientists, analysts of culture, and purveyors of content. Prior research suggests that taste preferences relate to personality traits, values, shifts in mood, and immigration destination. Understanding everyday patterns of listening and the function music plays in life has remained elusive, however, despite speculation that musical nostalgia may compensate for local disruption. Using more than one hundred million streams of four million songs by tens of thousands of international listeners from a global music service, we show that breaches in personal routine are systematically associated with personal musical exploration. As people visited new cities and countries, their preferences diversified, converging toward their travel destinations. As people experienced the very different disruptions associated with COVID-19 lockdowns, their preferences diversified further. Personal explorations did not tend to veer toward the global listening average, but away from it, toward distinctive regional musical content. Exposure to novel music explored during periods of routine disruption showed a persistent influence on listeners' future consumption patterns. Across all of these settings, musical preference reflected rather than compensated for life's surprises, leaving a lasting legacy on tastes. We explore the relationship between these findings and global patterns of behavior and cultural consumption.

Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters. [ABSTRACT FROM AUTHOR]

The rise of data from smart city services and the emergence of advanced algorithms have emphasized the need for a deeper understanding of the underlying patterns of urban mobility and the potential opportunities for more efficient urban planning and policymaking. By identifying communities with similar mobility patterns, researchers can gain better insights into how people move within and between different regions. Traditional community detection methodologies mainly focused on identifying geographic communities defined by shared locations. However, this perspective overlooks the broader definition of communities. Communities can also emerge from shared social interests, collective actions, and activity patterns. This implies that geographically disparate areas might exhibit similar patterns, which is particularly relevant in the study of mobility pattern similarity. Rather than focusing on regions with strong spatial interactions, this study aims to identify regions that show more similarity to each other than to other areas. Such similarities may indicate parallel urban functionalities, which are essential for effective urban planning and policymaking. To bridge this gap, our study introduces a customized community detection algorithm that employs Dynamic Time Warping (DTW) to quantitatively assess the similarity in mobility patterns between different communities. This advanced approach not only improves the identification of comparable mobility patterns but also demonstrates remarkable flexibility, broadening its application to various other social phenomena. The results demonstrate the effectiveness of the proposed model in capturing complex mobility patterns across different locations and days of the week. [ABSTRACT FROM AUTHOR]

As sea level rises, low-lying coastal cites face increasing threat of flood disruption, particularly in terms of human mobility. Commuters are vulnerable to bad weather, as it is difficult to cancel trips even in extreme weather conditions. Using Shanghai's automobile commuting population as an example, we categorized commuters by travel distance and income level to assess disruptions and delays due to floods, considering future sea level rise. The results show that local flooding disrupts commuting patterns by affecting roadways, with disruption decreasing with distance from the flooded area. This offers a mobility perspective on the indirect impacts of floods. During baseline flood events, long-distance commuters and the low-income group are most affected, while short-distance commuters and the high-income group are less impacted. As sea level rises, floods will threaten all commuting groups, especially the high-income group. Using inaccessibility-commuting delay bivariate maps, this study revealed how socioeconomic differences impact mobility recovery after floods under climate change. The research highlights the differential impacts of floods on various socioeconomic groups in the context of climate change, offering insights for future urban planning and disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed several infectious disease outbreaks, including the coronavirus disease (COVID-19) pandemic, which had catastrophic impacts on societies around the globe. At the same time, the twenty-first century has experienced an unprecedented era of technological development and demographic changes: exploding population growth, increased airline flights, and increased rural-to-urban migration, with an estimated 281 million international migrants worldwide in 2020, despite COVID-19 movement restrictions. In this review, we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks. This article covers eight infectious diseases, ranging from respiratory illnesses to sexually transmitted and vector-borne diseases. The review revealed a strong association between human mobility and infectious disease spread, particularly strong for respiratory illnesses like COVID-19 and Influenza. Despite significant research into the relationship between infectious diseases and human mobility, four knowledge gaps were identified based on reviewed literature in this study: 1) although some studies have used big data in investigating infectious diseases, the efforts are limited (with the exception of COVID-19 disease), 2) while some research has explored the use of multiple data sources, there has been limited focus on fully integrating these data into comprehensive analyses, 3) limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks, and 4) lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread. By tackling the recognized knowledge gaps and adopting holistic, interdisciplinary methods, forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases. [ABSTRACT FROM AUTHOR]

The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people's daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic. [ABSTRACT FROM AUTHOR]

Spatial mobility is a distinctive feature of human history and has important repercussions in many aspects of societies. Spatial mobility has always been a subject of interest in many disciplines, even if only mobility observable from traditional sources, namely migration (internal and international) and more recently commuting, is generally studied. However, it is the other forms of mobility, that is, the temporary forms of mobility, that most interest today's societies and, thanks to new data sources, can now be observed and measured. This contribution provides an empirical and data-driven reflection on human mobility during the COVID pandemic crisis. The paper has two main aims: (a) to develop a new index for measuring the attrition in mobility due to the restrictions adopted by governments in order to contain the spread of COVID-19. The robustness of the proposed index is checked by comparing it with the Oxford Stringency Index. The second goal is (b) to test if and how digital footprints (Google data in our case) can be used to measure human mobility. The study considers Italy and all the other European countries. The results show, on the one hand, that the Mobility Restriction Index (MRI) works quite well and, on the other, the sensitivity, in the short term, of human mobility to exogenous shocks and intervention policies; however, the results also show an inner tendency, in the middle term, to return to previous behaviours. [ABSTRACT FROM AUTHOR]

Climate change impacts are gendered. This is also true for climate-induced migration, which affects men and women differently. On account of this difference, legal instruments and policies seeking to address and support climate-induced migration need to be gender-focused to address differentiated needs and outcomes. This paper looks at existing policies and legal instruments for the inclusion of gender aspects of climate-related migration. We focus on Ethiopia, India, and Peru, all of them with developed instruments to address the human mobility-climate change nexus. We investigate the scope of provisions concerning gender in relevant instruments in the three country contexts, their likely impact to tackle gender-specific vulnerabilities arising with climate-induced migration and suggest strategies and priorities for enhancing gender-inclusion in policy development and application broadly. [ABSTRACT FROM AUTHOR]

In recent decades, a strong focus has emerged on exploring the scale-independent nature of our daily spatial motion. Similarly, heavy-tailed distributions have been observed for human locomotor activity, measured by actigraphs in medical fields. We recently proved that the raw acceleration data and also the activity signals calculated from them in diverse ways exhibit a general spectral characteristic; 1/f noise is observable above a certain cutoff frequency, while white noise and peaks corresponding to daily rhythms are visible at lower frequencies. We show that this pattern is strikingly similar to what we found earlier for GPS data and this similarity raises fundamental questions like what is the relation between human mobility and physical activity, what are the benefits of the analysis from this perspective, and how it helps us to better understand and model the scale-independent nature of human dynamics. [ABSTRACT FROM AUTHOR]

ABSTRACT The COVID‐19 pandemic highlighted and worsened social inequalities in the United States. This study uses mobile phone location data at the Census Block Group level and explainable machine learning methods to examine the relationships between various factors and human mobility in ten Metropolitan Statistical Areas to uncover how different factors influenced mobility disparities. Our key findings show significant reductions in all mobility indices during the summer of 2020 compared to 2019, mainly due to stay‐at‐home orders, business closures, and health concerns. Median household income was a consistent positive driver of mobility, while the minority rate negatively impacted movement, exacerbating existing inequalities. Remote work significantly affected full‐time and part‐time job mobility. These findings highlight the need for fair and resilient mobility policies that consider decentralized commuting, flexible work models, and sustainable transportation. These insights can help policymakers and urban planners address inequalities, support economic recovery, and build inclusive urban environments. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Climate change impacts are gendered. This is also true for climate-induced migration, which affects men and women differently. On account of this difference, legal instruments and policies seeking to address and support climate-induced migration need to be gender-focused to address differentiated needs and outcomes. This paper looks at existing policies and legal instruments for the inclusion of gender aspects of climate-related migration. We focus on Ethiopia, India, and Peru, all of them with developed instruments to address the human mobility-climate change nexus. We investigate the scope of provisions concerning gender in relevant instruments in the three country contexts, their likely impact to tackle gender-specific vulnerabilities arising with climate-induced migration and suggest strategies and priorities for enhancing gender-inclusion in policy development and application broadly.

Recent decades have witnessed several infectious disease outbreaks, including the coronavirus disease (COVID-19) pandemic, which had catastrophic impacts on societies around the globe. At the same time, the twenty-first century has experienced an unprecedented era of technological development and demographic changes: exploding population growth, increased airline flights, and increased rural-to-urban migration, with an estimated 281 million international migrants worldwide in 2020, despite COVID-19 movement restrictions. In this review, we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks. This article covers eight infectious diseases, ranging from respiratory illnesses to sexually transmitted and vector-borne diseases. The review revealed a strong association between human mobility and infectious disease spread, particularly strong for respiratory illnesses like COVID-19 and Influenza. Despite significant research into the relationship between infectious diseases and human mobility, four knowledge gaps were identified based on reviewed literature in this study: 1) although some studies have used big data in investigating infectious diseases, the efforts are limited (with the exception of COVID-19 disease), 2) while some research has explored the use of multiple data sources, there has been limited focus on fully integrating these data into comprehensive analyses, 3) limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks, and 4) lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread. By tackling the recognized knowledge gaps and adopting holistic, interdisciplinary methods, forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.

Effective hurricane preparedness and response demand a thorough understanding of the impact on mobility patterns. While existing studies have explored mobility disruptions caused by hurricanes, very few have delved into the impact, considering both mobility vulnerability and recovery, on a state level. Utilising mobile phone location data, this research examines the mobility patterns of Florida residents throughout Hurricane Ian. The findings reveal that the hurricane profoundly disturbed the mobility patterns of Floridians. The state experienced a maximum average daily mobility reduction of 63.41%, with certain neighbourhoods coming to a complete standstill at 100% cessation in mobility. On average, Florida neighbourhoods needed 2.61 days for mobility recovery, though this period stretched to as long as 92 days in the most severely affected areas. The bivariate map highlights a prevailing trend: neighbourhoods with prolonged recovery periods also witnessed more substantial reductions in mobility. This dual disadvantage underscores the critical need for increased focus on these neighbourhoods. Furthermore, our findings highlight the significance of factoring in forecasted hurricane paths when analysing mobility impacts, as we noted more substantial effects on neighbourhoods along the predicted trajectory.

The rise of data from smart city services and the emergence of advanced algorithms have emphasized the need for a deeper understanding of the underlying patterns of urban mobility and the potential opportunities for more efficient urban planning and policymaking. By identifying communities with similar mobility patterns, researchers can gain better insights into how people move within and between different regions. Traditional community detection methodologies mainly focused on identifying geographic communities defined by shared locations. However, this perspective overlooks the broader definition of communities. Communities can also emerge from shared social interests, collective actions, and activity patterns. This implies that geographically disparate areas might exhibit similar patterns, which is particularly relevant in the study of mobility pattern similarity. Rather than focusing on regions with strong spatial interactions, this study aims to identify regions that show more similarity to each other than to other areas. Such similarities may indicate parallel urban functionalities, which are essential for effective urban planning and policymaking. To bridge this gap, our study introduces a customized community detection algorithm that employs Dynamic Time Warping (DTW) to quantitatively assess the similarity in mobility patterns between different communities. This advanced approach not only improves the identification of comparable mobility patterns but also demonstrates remarkable flexibility, broadening its application to various other social phenomena. The results demonstrate the effectiveness of the proposed model in capturing complex mobility patterns across different locations and days of the week.

A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips’ destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips’ origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips’ geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.

Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters.

Despite recognition of the relationship between infrastructure resilience and community recovery, very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure services contribute to the speed of community recovery. To address this gap, this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts, focusing on the recovery dynamics of population activity and power infrastructure restoration. Empirical observational data were utilized to analyze the extent of impact, recovery duration, and recovery types of both systems in the aftermath of Hurricane Ida. The study reveals three key findings. First, power outage duration positively correlates with outage extent until a certain impact threshold is reached. Beyond this threshold, restoration time remains relatively stable regardless of outage magnitude. This finding underscores the need to strengthen power infrastructure, particularly in extreme weather conditions, to minimize outage restoration time. Second, power was fully restored in 70% of affected areas before population activity levels normalized. This finding suggests the role infrastructure functionality plays in post-disaster community recovery. Quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation, housing damage, and business interruptions. Finally, if power outages last beyond two weeks, community activity resumes before complete power restoration, indicating adaptability in prolonged outage scenarios. This implies the capacity of communities to adapt to ongoing power outages and continue daily life activities. These findings offer valuable empirical insights into the interaction between human activities and infrastructure systems, such as power outages, during extreme weather events. They also enhance our empirical understanding of how infrastructure resilience influences community recovery. By identifying the critical thresholds for power outage functionality and duration that affect population activity recovery, this study furthers our understanding of how infrastructure performance intertwines with community functioning in extreme weather conditions. Hence, the findings can inform infrastructure operators, emergency managers, and public officials about the significance of resilient infrastructure in life activity recovery of communities when facing extreme weather hazards.

AbstractUnderstanding changes in mobility patterns during extreme weather is crucial for urban resilience. Existing studies often overlook the transitions between different transportation modes. This study develops a framework that measures the spatiotemporal anomalies of mobilities and builds transition paths across multiple modes to reveal how people adapt to extreme weather. Analyzing four extreme rainfall events in New York City, we find that Citibike riders are most sensitive to rainfall. In the absence of subway disruptions, they tend to switch to the subway. When the subway system is paralyzed, indicating flooding of the system by heavy rainfall, riders shift to For-Hire Vehicles, followed by taxis. Both demonstrate the value of flexible service in urban resilience. The paralysis-prone subway and the uneven distribution of flexible service indicate that the current transit infrastructure lacks coordination and is unprepared for climate change. Recommendations for enhancing urban resilience include upgrading and maintaining the subway system; enhancing inter-transportation-modal coordination; introducing amphibious transportation modes; improving pre-disaster awareness of inland populations; encouraging safety shared ride; and connecting affordable transition paths for underprivileged groups. [ABSTRACT FROM AUTHOR]

Human mobility typically exhibits temporal and spatial predictability. However, during hazardous events, roadways, footpaths and public transport networks can be disrupted by detours, closures and congestion. Urban fires, exemplified by the October 2019 New Zealand International Convention Centre (NZICC) fire in Auckland, New Zealand, are on the rise, posing threats to life, property and mobility. In this study, we employ geospatial analyses to investigate the impact of the NZICC fire on human mobility, encompassing both driving and walking. We generate predicted surfaces of particulate matter (PM2.5 and PM10) from seven local fixed air monitoring stations to estimate network-based exposure and inhalation dosage during travel. High levels of air pollution during the fire exceeded baseline concentrations, surpassing National Environmental Standards for Air Quality (NESAQ) and World Health Organization (WHO) limits for both PM2.5 and PM10. Private car commuters faced delays and congestion due to road closures, prolonging smoke exposure. Pedestrians, including those accessing essential public transportation infrastructure, experienced unavoidable exposure to smoke along the fastest routes from NZICC to places like Britomart train station and the Downtown Ferry. We recommend increasing the availability and dissemination of air pollutant monitoring data to enhance public awareness of the health risks associated with smoke exposure. [ABSTRACT FROM AUTHOR]

AbstractMining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues. [ABSTRACT FROM AUTHOR]

This introductory article explores the rise of travel as an area of research within the field of biblical studies. It first discusses some major trends in the study of travel in the ancient Jewish tradition, including travel as a literary motif in biblical narrative and the evidence for travel in the context of early Judaism. While the significance of travel as a topic of research has been established in biblical studies, more work remains to be done regarding various aspects of the topic, including non-human travellers and the experience and effects of travel. Travel is not just about geographical relocations, as the selected focus on travel and transformation also seeks to emphasize. Drawing on related discussions in literary studies, the article then discusses travel as a practice that requires transitions which take people into liminal spaces and lead to potentially transformative outcomes. Finally, it explains how the articles included in the thematic issue add to this conversation from different yet complementary angles. They primarily focus on travel as a literary motif in various early Jewish corpora but also consider later and contemporary travel of ancient fragments, highlighting how travel may shape and change those on the move in different ways. [ABSTRACT FROM AUTHOR]

Human mobility is not a conjunctural element, it is part of the construction of a society and it will go through different phases throughout its history. Faced with this social reality, the narratives that are constructed and that seem to be imposed tend, ignoring data, to criminalise migrants, supporting restrictive regulations and policies based on security and the externalisation of borders. The challenge lies in dismantling these narratives and favouring discourses and policies based on human rights that emphasise the dignity of the person. [ABSTRACT FROM AUTHOR]

In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there's a need for Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of the data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in the data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in the data-poor scenarios and achieve SOTA performance. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused widespread disruption and prompted the implementation of nonpharmaceutical measures in several countries to contain the spread of the disease until a vaccine became available. Urban mobility reduction has historically been employed to limit the transmission of epidemics. However, only some studies have quantified the effectiveness of such restrictions across different sectors of society. To address this gap, we have adapted an agentbased model that utilizes data from the Google Community Mobility Report to simulate the dynamics of COVID-19 across 14 Brazilian capitals. Each agent in the model has a network of contacts established from mobility data across various categories. We simulated six scenarios for each capital, each with different probabilities of contagion in each category. Our findings show that different scenarios are more effective in describing the curve of infected people and deaths in each city. In particular, our results indicate that the same scenario was optimal for describing the number of cases and deaths in Belo Horizonte. However, the first peak in the number of deaths could not be reproduced in the model, possibly due to issues with the data recording. Our proposed model can be further developed to incorporate additional elements related to the dynamics of an epidemic and can serve as an additional tool in understanding and planning actions to contain the spread of COVID-19 beyond mobility, particularly in urban centers. [ABSTRACT FROM AUTHOR]

Human mobility, encompassing the movement of individuals and/or groups across space and time, significantly impacts various aspects of society, with intra-urban mobility being a major research focus of scholars in diverse disciplines. Bike-sharing systems have become an alternatives in cities for achieving more sustainable transportation. Hence, bike-sharing-related data are considered an important data source to study intra-urban human mobility. To better understand human mobility in cities, it is essential to characterize the typical patterns involved in intra-urban human mobility. This paper mainly focuses on characterizing the temporal patterns of intra-urban human mobility on bike-sharing based on the trip information of the acquired bike-sharing data. To achieve this, on the one hand, we adopted an exploratory data analysis (EDA) method to describe the temporal patterns by performing exploratory analyses of bike-sharing trips. On the other hand, we used the continuous triangular model (CTM) to conduct multi-temporal-scale analysis of bike-sharing trips for further explorations of the temporal patterns where necessary. The data of bike-sharing trips in Shanghai, China, were adopted as the dataset for the case study. Generally, the study was conducted at two different levels: the trip level and the bike level. Specifically, at each level, the explorations were conducted from varying perspectives. According to the analyses, numerous meaningful temporal patterns were discovered, and several distinctive findings were acquired. The results of this study show the effectiveness of the EDA and CTM methods in characterizing temporal patterns of intra-urban human mobility, based on which potentially insightful information and suggestions can be provided to assist related actions. [ABSTRACT FROM AUTHOR]

In reducing greenhouse gas (GHG) emissions, there is a recognition triggered by the pandemic of the role that walking and cycling (active travel) can make to substitute motorized travel, particularly on short trips. However, there is a lack of evidence at the micro level on the realistic, empirically derived, potential of these options. Here, we used reliable tracing data to examine the potential of these mitigation options for reducing GHG emissions in Vietnam. Apart from similar categories of travel purposes as in other studies, we decided to categorize "visit relatives" and "eating out" as two more separate categories of travel purposes in Vietnamese case, which together accounts for nearly 16% of total trips. We discovered that 65% of all motorcycle trips in this case study were less than 3 miles in duration, therefore active travel was able to create a significant impact on GHG emissions from personal travel. Active travel can replace 62% of short motorcycle trips if considering travel patterns and constraints while saving 18% of GHG emissions that would have come from motorized transport. If active travel can further replace all shopping trips normally done by motorcycles, in total being equivalent to 84% of short trips, 22% of GHG emissions from motorcycles can be reduced. It should be noticed that active travels have time cost implications, impacting economy at both household and city levels, but from a comprehensive "co-benefit" standpoint, this transformation could act as a catalyst for addressing traffic congestion, air pollution, and even community health and well-being in urban areas. [ABSTRACT FROM AUTHOR]

(1) Background: The association of COVID-19 infection and mortality with mobility and weather in Indiana and Kentucky was compared for the period from 1 March to 15 May 2020. (2) Methods: The risk of COVID-19 infection and mortality was evaluated using Cox regressions with the following covariates: mobility (retail/recreation, grocery/pharmacy, and workplace), weather (precipitation, minimum and maximum temperature, ultraviolet [UV] index), and metropolitan status. (3) Results: A higher maximum temperature (adjusted hazard ratioinfection (aHRi) = 1.01; adjusted hazard ratiodeath (aHRd) = 1.001), metropolitan status (aHRi = 1.12; aHRd = 2.05), and a higher minimum temperature (aHRi = 1.01) were associated with increased risks of COVID-19 infection and/or mortality. Protection against COVID-19 infection and/or mortality was associated with retail/recreation (aHRi = 0.97; aHRd = 0.937), grocery/pharmacy (aHRi = 0.991; aHRd = 0.992), workplace (aHRi = 0.99; aHRd = 0.965), precipitation (aHRi = 0.999; aHRd = 0.9978), UV index (aHRi = 0.37; aHRd = 0.748), and a higher minimum temperature (aHRd = 0.994). COVID-19 infection (aHRi = 1.18) and mortality (aHRd = 1.59) risks were higher in Indiana compared to Kentucky. (4) Conclusions: COVID-19 infection and mortality were 18% and 59% more likely among Indiana residents compared to Kentucky residents, respectively. This may be attributed to variations in stay-at-home order compliance and enforcement between Indiana and Kentucky. [ABSTRACT FROM AUTHOR]

This study seeks to examine the possible impacts of sociodemographic factors, individual mobility patterns, and daily activities on individual exposure to COVID-19 risk when assessed by different risk measures. Taking Hong Kong as the study area, we first model the risk of COVID-19 using a density-based approach and a network-based approach and reveal the differences in the spatial distributions of COVID-19 transmission risk they obtained. Then, using two-day individual Global Positioning System trajectory data and travel diaries collected from individuals in two communities, we measure individual exposure to COVID-19 transmission risk based on their mobility patterns and reveal the disparities in COVID-19 risk exposure among residents of different demographic groups and neighborhoods when conducting different activities. This study reveals the differences in the spatial patterns of COVID-19 transmission risk when the risk is conceptualized by density and network. It demonstrates the power of a spatial network approach in enhancing our understanding of individual exposure to infectious diseases. This study also advances the existing literature by exploring COVID-19 risk exposure, considering individual daily mobility and addressing the uncertain geographic context problem in analyzing individual COVID-19 risk exposure. [ABSTRACT FROM AUTHOR]

Archaeological studies carried out in recent decades have demonstrated that the Pre‐Pyrenees, a mountain range in north‐east Iberia, were regularly frequented by several human groups during the Late Pleistocene. The Cova del Parco archaeological site is an example of this large‐scale and regular human presence. The site was discovered and first excavated in the 1970s, and since the 1980s, a team from the University of Barcelona has been conducting archaeological work. So far, we have found that the site was at least frequented from the Middle Magdalenian upon historical times. In this paper, we present the results of the archaeopetrological, geochemical and geographic information system (GIS) analyses of chert tools ascribed to the Middle Magdalenian sequence. The textural, micropalaeontological and geochemical analysis of the lithic artefacts has allowed us to identify several chert types from local, regional and long‐distance sources. Some of these cherts had their origin in the northern slopes of the Pyrenean chain, suggesting that this mountain chain was regularly crossed by Magdalenian groups. Next, we performed GIS analyses to determine the paths and connections that may have linked the archaeological site with the different chert outcrops, and to identify the best routes for crossing the Pyrenean Mountain range. Moreover, this study provides a larger vision of the mobility and the complex economic interactions between the different Magdalenian groups that settled Cova del Parco at the end of the Late Pleistocene. [ABSTRACT FROM AUTHOR]

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AbstractUncovering the collective behavior of flows among locations is critical to understanding the structure within an ever-changing spatial network. When a network evolves, there may exist subgraphs within which the internal flows generally follow a rule: the change rates of the flow weight are either collectively high or low. Classic network measures such as degree, clustering, and betweenness can be used to quantify the process of network evolution by profiling the overall characteristics over time. However, it remains challenging to elucidate how a spatial network is evolving without looking at structures where collective changes emerge. To bridge this gap, we introduce the concept of the Collective Flow-Evolutionary Pattern (CFEP) as a mesoscopic description for spatial network evolution. Four types of patterns with distinct features are defined to clarify the collective behaviors of the flow-evolutionary characteristics. We provide an analytical framework that utilizes flow change rates between two snapshots of the spatial network to detect CFEPs as optimized flow evolution (evo-groups). Synthetic experiments are presented to validate the method. A case study of large-scale individual mobile positioning data is conducted in the Twin Cities Metropolitan Area, Minnesota, US to demonstrate how CFEP can effectively understand the evolution of human mobility networks. [ABSTRACT FROM AUTHOR]

Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we aim to solve (e.g., home/work identification). This approach allows to tackle place identification from a supervised perspective, offering an alternative to unsupervised clustering techniques. These clustering techniques rely on data characteristics that may not always be directly related to classification objectives. Our results, using mobility data provided by call detail records (CDRs) from Milan, demonstrate superior performance compared to applying clustering techniques. For all types of CDRs, the best results are obtained with the 20 × 20 subgrid, indicating that the model performs better when supplied with information from neighboring cells with a close spatial relationship, establishing neighborhood relationships that allow the model to clearly learn to identify transitions between cells of different types. Considering that it is common for a place or cell to be labeled in multiple categories at once, this supervised approach opens the door to addressing the identification of meaningful places from a multi-label perspective, which is difficult to achieve using classical unsupervised methods. [ABSTRACT FROM AUTHOR]

Littoral resources have been consumed by humans since at least the Middle Palaeolithic. Examples of the use of molluscs have been documented along the shores of Europe during that period but it was not until many millennia later that European hunter-fisher-gatherer societies exploited those resources intensively—see the case of Nerja cave during the Younger Dryas. This economic activity caused the accumulation of shells at archaeological sites during the Mesolithic, resulting in the formation of the so-called shell middens, a very common type of deposit along the Atlantic seaboard of Europe. Despite the large number of research projects that have studied the exploitation of coastal environments and the way of life of Mesolithic populations, questions such as the relationship between human mobility and mollusc exploitation patterns still remain. The archaeomalacological study of the shell midden in La Chora cave (Cantabria, Spain) confirms that people foraged for shellfish at several places along the coast, mainly in the estuary of the River Asón. The main difference between La Chora and other Mesolithic sites is its longer shellfish collection radius as the inhabitants travelled over 10 km to the open coast to collect shellfish. This study has expanded the available data about the subsistence strategies of Mesolithic groups in a little-studied area and improved our knowledge of mobility patterns among Mesolithic societies in the northern Iberian Peninsula. [ABSTRACT FROM AUTHOR]

Summary: Background: Co-existence of efficient transportation networks and geographic imbalance of medical resources greatly facilitated inter-city migration of patients of infectious diseases in China. Methods: To characterize the migration patterns of major notifiable infectious diseases (NIDs) during 2016–2020 in China, we collected migratory cases, who had illness onset in one city but were diagnosed and reported in another, from the National Notifiable Infectious Disease Reporting System, and conducted a nationwide network analysis of migratory cases of major NIDs at the city (prefecture) level. Findings: In total, 2,674,892 migratory cases of NIDs were reported in China during 2016–2020. The top five diseases with the most migratory cases were hepatitis B, tuberculosis, hand, foot and mouth disease (HFMD), syphilis, and influenza, accounting for 79% of all migratory cases. The top five diseases with the highest proportions of migratory cases were all zoonotic or vector-borne (37.89%‒99.98%). The network analysis on 14 major diseases identified three distinct migration patterns, where provincial capitals acted as key node cities: short distance (e.g., pertussis), long distance (e.g., HIV/AIDS), and mixed (e.g., HFMD). Strong drivers for patient migration include population mobility and labor flow intensities between cities as well as the economic development level of the destination city. Interpretation: Collaborative prevention and control strategies should target cities experiencing frequent patient migration and cater to unique migration patterns of each disease. Addressing disparity in healthcare accessibility can also help alleviate case migration and thereby reduce cross-regional transmission. Funding: National Key Research and Development Program of China.

Confronting the escalating challenge of emergencies, the urban supply network of daily necessity is an important defense line for human well-being. This study introduces a groundbreaking approach that leverages mobile signaling data, departing from static regional data, to model large-scale and high-precision urban supply-demand network. Moreover, a significant stride in assessing network invulnerability is presented by incorporating cascading failure and emphasizing demand-side factors in attack strategy simulations. This approach marks a paradigm shift in network invulnerability simulation: moving from network topology characteristics to a human-centric approach, which helps better identify vulnerable zones. The model’s robustness is corroborated through simulations of major disaster scenarios. The results indicate that: 1) High-precision human mobility data promises large-scale urban supply-demand network modeling with high accuracy. 2) In regions characterized by greater vulnerability, the establishment of local supply networks demonstrates efficacy in mitigating the impacts of minor disasters. 3) During various stages of cascading failure, the leading factors contributing to community supply shortages vary, with population density being the predominant factor. This research propels the methodology forward, incorporating multi-scenario simulations to augment practicality, and offers valuable insights for urban supply system enhancement.

Effective hurricane preparedness and response demand a thorough understanding of the impact on mobility patterns. While existing studies have explored mobility disruptions caused by hurricanes, very few have delved into the impact, considering both mobility vulnerability and recovery, on a state level. Utilising mobile phone location data, this research examines the mobility patterns of Florida residents throughout Hurricane Ian. The findings reveal that the hurricane profoundly disturbed the mobility patterns of Floridians. The state experienced a maximum average daily mobility reduction of 63.41%, with certain neighbourhoods coming to a complete standstill at 100% cessation in mobility. On average, Florida neighbourhoods needed 2.61 days for mobility recovery, though this period stretched to as long as 92 days in the most severely affected areas. The bivariate map highlights a prevailing trend: neighbourhoods with prolonged recovery periods also witnessed more substantial reductions in mobility. This dual disadvantage underscores the critical need for increased focus on these neighbourhoods. Furthermore, our findings highlight the significance of factoring in forecasted hurricane paths when analysing mobility impacts, as we noted more substantial effects on neighbourhoods along the predicted trajectory. [ABSTRACT FROM AUTHOR]

Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

Abstract With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and in a safe location. In this paper, we unveil how transit service to employment hubs, not just housing policies, can prevent individuals from improving their housing conditions. We approach this question in three steps, applying the workflow to 20 cities in the United States of America. First, we propose a comprehensive framework to quantify housing insecurity and assign a housing demographic to each neighbourhood. Second, we use transit-pedestrian networks and public transit timetables (GTFS feeds) to estimate the time it takes to travel between two neighbourhoods using public transportation. Third, we apply geospatial autocorrelation to identify employment hotspots for each housing demographic. Finally, we use stochastic modelling to highlight how commuting to areas associated with better housing conditions results in transit commute times of over an hour in 15 cities. Ultimately, we consider the compounded burdens that come with housing insecurity, by having poor transit access to employment areas. In doing so, we highlight the importance of understanding how negative outcomes of housing insecurity coincide with various urban mechanisms, particularly emphasising the role that public transportation plays in locking vulnerable demographics into a cycle of poverty.

The COVID-19 pandemic, with increased home cooking and decreased restaurant dining, significantly altered seafood consumption patterns. By applying an ordered logit model to identify factors affecting seafood consumption during the pandemic, this study found that the shift in seafood consumption was driven by factors such as changes in meal preparation methods, more time spent at home, and shifts in financial situations. While take-out consumption boosted overall seafood intake, popular varieties saw a rise in home consumption, while high-end seafood suffered from decreased demand as consumers focused more on home dining. This study underscores the importance of supporting suppliers, restaurants, and retailers dealing with high-end seafood, as they face economic challenges due to reduced consumption. In summary, pandemic-induced restrictions on mobility led to a notable transition from restaurant-prepared seafood to home-cooked options, highlighting the need for targeted policies to aid affected sectors.

Abstract Natural, geographical barriers have historically limited the spread of communicable diseases. This is no longer the case in today’s interconnected world, paired with its unprecedented environmental and climate change, emphasising the intersection of evolutionary biology, epidemiology and geography (i.e. biogeography). A total of 14 articles of the special issue entitled “Geography and health: role of human translocation and access to care” document enhanced disease transmission of diseases, such as malaria, leishmaniasis, schistosomiasis, COVID-19 (Severe acute respiratory syndrome corona 2) and Oropouche fever in spite of spatiotemporal surveillance. High-resolution satellite images can be used to understand spatial distributions of transmission risks and disease spread and to highlight the major avenue increasing the incidence and geographic range of zoonoses represented by spill-over transmission of coronaviruses from bats to pigs or civets. Climate change and globalization have increased the spread and establishment of invasive mosquitoes in non-tropical areas leading to emerging outbreaks of infections warranting improved physical, chemical and biological vector control strategies. The translocation of pathogens and their vectors is closely connected with human mobility, migration and the global transport of goods. Other contributing factors are deforestation with urbanization encroaching into wildlife zones. The destruction of natural ecosystems, coupled with low income and socioeconomic status, increase transmission probability of neglected tropical and zoonotic diseases. The articles in this special issue document emerging or re-emerging diseases and surveillance of fever symptoms. Health equity is intricately connected to accessibility to health care and the targeting of healthcare resources, necessitating a spatial approach. Public health comprises successful disease management integrating spatial surveillance systems, including access to sanitation facilities. Antimicrobial resistance caused, e.g. by increased use of antibiotics in health, agriculture and aquaculture, or acquisition of resistance genes, can be spread by horizontal gene transfer. This editorial reviews the key findings of this 14-article special issue, identifies important gaps relevant to our interconnected world and makes a number of specific recommendations to mitigate the transmission risks of infectious diseases in the post-COVID-19 pandemic era.

Abstract Given the worldwide increase of forcibly displaced populations, particularly internally displaced persons (IDPs), it’s crucial to have an up-to-date and precise tracking framework for population movements. Here, we study how the spatial and temporal pattern of a large-scale internal population movement can be monitored using human mobility datasets by exploring the case of IDPs in Ukraine at the beginning of the Russian invasion of 2022. Specifically, this study examines the sizes and travel distances of internal displacements based on GPS human mobility data, using the combinations of mobility pattern estimation methods such as truncated power law fitting and visualizing the results for humanitarian operations. Our analysis reveals that, although the city of Kyiv started to lose its population around 5 weeks before the invasion, a significant drop happened in the second week of the invasion (4.3 times larger than the size of the population lost in 5 weeks before the invasion), and the population coming to the city increased again from the third week of the invasion, indicating that displaced people started to back to their homes. Meanwhile, adjacent southern areas of Kyiv and the areas close to the western borders experienced many migrants from the first week of the invasion and from the second to third weeks of the invasion, respectively. In addition, people from relatively higher-wealth areas tended to relocate their home locations far away from their original locations compared to those from other areas. For example, 19 % of people who originally lived in higher wealth areas in the North region, including the city of Kyiv, moved their home location more than 500 km, while only 9 % of those who originally lived in lower wealth areas in the North region moved their home location more than 500 km.