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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.

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]

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]

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. [ABSTRACT FROM AUTHOR]

Fishing behavior is an important link between management decisions and bioeconomic outcomes. The diversity in fishers' behavior, motivations, and abilities should inform incentive structures if management aims to consider heterogeneous impacts on and by communities. Modeling human behavior in resource management is important for successful fisheries, especially for fisheries undergoing rapid transformation, such as the Alaskan snow crab fishery. This study modeled how snow crabbers choose where to fish in the eastern Bering Sea and examined the diversity of strategies among individuals by fitting a random utility model to data on fishing locations using a variety of potential drivers of behavior as covariates. The overall fishing strategy of the snow crab fleet prioritizes revenue and shared information, while avoiding risk, poor weather, and cost. Diversity of fishing strategies was driven by differences in spatial footprint, vessel size, and ports of landing. Larger vessels ventured farther north, where weather conditions are more extreme. Despite differences in vessel size, crabbers were spatially adaptive and switched fishing regions depending on the abundance and distribution of crab. These findings on the patterns of fishing strategies can inform better management of the Alaskan snow crab fishery as it undergoes rebuilding. [ABSTRACT FROM AUTHOR]

Objectives This study aims to evaluate the changes in human mobility during a public health emergency.Methods Employing detailed spatiotemporal big data on human mobility from China and the USA, we constructed three mobility indices: Mobility change index, distance change index, and volatility change index. We investigated the mobility characteristics within China's Guangdong-Hong Kong-Macau Greater Bay Area (GBA) and the United States' San Francisco Bay Area (SBA) during the epidemic. The singular value decomposition (SVD) algorithm was applied to identified underlying structures and patterns of mobility in these regions.Results The results show that: (1) The GBA outperformed the SBA in terms of human mobility control with a greater decline in movement volumes, smoother volatility in daily movement and shorter average travel distances during the pandemic. (2) Human mobility patterns in the GBA were influenced by both the Chinese Spring Festival holiday and public health policies, from which the daily travel patterns, returning home travel characteristics and returning-to-work characteristics were identified. Human mobility patterns in the SBA show strong regularity (including daily travel characteristics, weekday travel characteristics and weekend travel characteristics), and public health policies do not have profound impacts.Conclusions We quantify the changes in human mobility patterns under different epidemic control measures in two Bay Areas of China and the United States, which is essential to assess and identify intervention effectiveness. It also provides important evidences and references for various infectious disease control in the future. [ABSTRACT FROM AUTHOR]

Human mobility data are crucial for transportation planning and congestion management. However, challenges persist in accessing and using raw mobility data due to privacy concerns and data quality issues such as redundancy, missing values, and noise. This research introduces an innovative GIS-based framework for creating individual-level long-term spatio-temporal mobility data at a city scale. The methodology decomposes and represents individual mobility by identifying key locations where activities take place and life patterns that describe transitions between these locations. Then, we present methods for extracting, representing, and generating key locations and life patterns from large-scale human mobility data. Using long-term mobility data from Shanghai, we extract life patterns and key locations and successfully generate the mobility of 30,000 virtual users over seven days in Shanghai. The high correlation (R² = 0.905) indicates a strong similarity between the generated data and ground-truth data. By testing the combination of key locations and life patterns from different areas, the model demonstrates strong transferability within and across cities, with relatively low RMSE values across all scenarios, the highest being around 0.04. By testing the representativeness of the generated mobility data, we find that using only about 0.25% of the generated individuals' mobility is sufficient to represent the dynamic changes of the entire urban population on a daily and hourly resolution. The proposed methodology offers a novel tool for generating long-term spatiotemporal mobility patterns at the individual level, thereby avoiding the privacy concerns associated with releasing real data. This approach supports the broad application of individual mobility data in urban planning, traffic management, and other related fields. [ABSTRACT FROM AUTHOR]

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning. [ABSTRACT FROM AUTHOR]

The article presents MCN4Rec, a novel Multi-Level Collaborative Neural Network for next location recommendation, addressing the complexity of considering various factors such as user preferences, spatial locations, and temporal contexts, utilizing multi-level representation learning and contrastive learning to capture complex relationships. Topics include the design of a causal encoder-decoder for recommending next locations, and extensive experiments demonstrating superior performance.

By tapping into the human mobility of the urban rail transit (URT) network to understand the travel demands and characteristics of passengers in the urban space, URT managers are able to obtain more support for decision-making to improve the effectiveness of operation and management, the travel experience of passengers, as well as public safety. However, not all URT networks have sufficient human mobility data (e.g., newly-operated URT networks). It is necessary to provide data support for mining human mobility in data-poor URT networks. Therefore, we propose a method called Meta Long Short-Term Memory Network (Meta-LSTM) for passenger flow prediction at URT stations to provide data support for networks that lack data. The Meta-LSTM is to construct a framework that increases the generalization ability of a long short-term memory network (LSTM) to various passenger flow characteristics by learning passenger flow characteristics from multiple data-rich stations and then applying the learned parameter to data-scarce stations by parameter initialization. The Meta-LSTM is applied to the URT network of Nanning, Hangzhou, and Beijing, China. The experiments on three real-world URT networks demonstrate the effectiveness of our proposed Meta-LSTM over several competitive baseline models. Results also show that our proposed Meta-LSTM has a good generalization ability to various passenger flow characteristics, which can provide a reference for passenger flow prediction in the stations with limited data. [ABSTRACT FROM AUTHOR]

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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.

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.

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]

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.

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.

ABSTRACTHurricane 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.

ABSTRACTThe rise in human mobility data in recent years provides a new frontier to understanding the spatiotemporal dynamics of mobility to urban parks. These human mobility data offer profound merit in the study of how accessible urban parks are through real-time analytics of individual travel patterns and locations. This study systematically reviewed 46 peer-reviewed publications from the Web of Science and Academic Search Complete databases relevant to human mobility data and urban park accessibility measures. The study aims to provide a comprehensive and quantitative evaluation of the utilization of human mobility data in the field of urban park accessibility research. This objective is pursued through a systematic examination of existing literature within this research domain. We summarize the types of mobility datasets and the modelling tasks adopted. Meanwhile, we grouped the methodological/thematic frameworks from empirical use of human mobility data in park accessibility into seven categories: 1) inequality and inequity level, 2) users’ perception and exposure level, 3) frequency and variations in park visitations, 4) park service area or effective service radius, 5) happiness and sentiment level, 6) travel mode choice and trip assignment, and 7) park characteristics. Additionally, we highlighted the obstacles in integrating human mobility data into park accessibility research and engaged in a discussion surrounding prominent ethical dilemmas related to the utilization of big data.

AbstractUnderstanding the response of human mobility to disruptive weather events is beneficial for the development of urban risk mitigation and emergency response policies, thus enhancing urban resilience. Most human mobility studies relying on aggregate flow data inevitably neglect the heterogeneity of disaggregate travel patterns with distinctive spatiotemporal characteristics, causing the uncertainty problem for identifying meaningful travel behaviors. Moreover, there is a lack of robust methodological approaches to extracting stable and genuine travel patterns under normal or disruptive situations. To address these issues, this study proposes a data-driven approach to spatiotemporal flow decomposition based on non-negative matrix factorization. With sparseness factored in the decomposition, stable disaggregate travel patterns can be extracted from origin-destination mobility flows. By combining temporal, spatial, and urban functional perspectives, heterogeneous travel behaviors can be analyzed and inferred. With a case study of the Zhengzhou ‘7.20’ heavy rainfall in 2021, the most extreme rainfall ever recorded in China, this study validated the effectiveness of the proposed approach and managed to identify representative and interesting travel patterns and behaviors, facilitating a better understanding of human travel behaviors under external impacts. In practice, this study can provide valuable insights for coping strategies in the face of increasingly frequent disruptive events. [ABSTRACT FROM AUTHOR]

The rise of socio-technical systems in which humans interact with various forms of Artificial Intelligence, including assistants and recommenders, multiplies the possibility for the emergence of large-scale social behavior, possibly with unintended negative consequences. In this work, we discuss a particularly interesting case, i.e., navigation services' impact on urban emissions, showing through simulations that the sum of many individually "optimal" choices may have unintended negative outcomes because such choices influence and interfere with each other on top of shared resources. To prove this point, we demonstrate how the introduction of a random component in the path suggestion phase may help to relieve the effect of collective and individual choices on the urban environment in terms of urban emissions. [ABSTRACT FROM AUTHOR]

Transportation research has shown that socio‐demographic factors impact people's mobility patterns. During the COVID‐19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in‐person gatherings, closure of in‐person businesses, and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the associations between socio‐demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We used aggregate deidentified cell tower location data to measure two mobility metrics—movement time and radius of gyration—and socio‐demographic data from the 2016 Canadian Census to model these associations across Ontario, Canada in 2020 using a linear model and a geographically weighted regression model. We find that certain associations between socio‐demographics and mobility have changed from what we previously observed before the pandemic, and we can see the variation of these associations across space. These findings will improve our understanding of how socio‐demographic factors affect mobility patterns in different communities and demonstrate the importance of measuring these associations at a more fine‐grained level using models that consider spatial variation to best reflect the nature of these associations. Key messages: Geographically weighted regression was used to study associations between mobility and five socio‐demographic factors in Ontario, Canada during the COVID‐19 pandemic.All five variables exhibited positive and negative associations with mobility change for at least one period of analysis, highlighting the complexity of these relationships.Our results provide new information on the geographical relationships between mobility change during COVID‐19 and neighbourhood‐level socio‐demographic variables. [ABSTRACT FROM AUTHOR]

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a foundational understanding of MSA mobility patterns, paving the way for more detailed studies on the nuances of these patterns. [ABSTRACT FROM AUTHOR]

The rise in human mobility data in recent years provides a new frontier to understanding the spatiotemporal dynamics of mobility to urban parks. These human mobility data offer profound merit in the study of how accessible urban parks are through real-time analytics of individual travel patterns and locations. This study systematically reviewed 46 peer-reviewed publications from the Web of Science and Academic Search Complete databases relevant to human mobility data and urban park accessibility measures. The study aims to provide a comprehensive and quantitative evaluation of the utilization of human mobility data in the field of urban park accessibility research. This objective is pursued through a systematic examination of existing literature within this research domain. We summarize the types of mobility datasets and the modelling tasks adopted. Meanwhile, we grouped the methodological/thematic frameworks from empirical use of human mobility data in park accessibility into seven categories: 1) inequality and inequity level, 2) users' perception and exposure level, 3) frequency and variations in park visitations, 4) park service area or effective service radius, 5) happiness and sentiment level, 6) travel mode choice and trip assignment, and 7) park characteristics. Additionally, we highlighted the obstacles in integrating human mobility data into park accessibility research and engaged in a discussion surrounding prominent ethical dilemmas related to the utilization of big data. [ABSTRACT FROM AUTHOR]

This study advances the measurement of community social context by introducing the daily dynamic perspective to promote a better understanding of the relationship between community social context and community attachment. It measured the social context averaging or polarization (SCAP) effect of communities every 3 h using census and cell phone data and investigated residents' community attachment in 71 communities in Guangzhou, China. There are three findings. First, the social contexts of many communities varied during the day, either moving toward the mean of the whole city or away from the mean. Distinct patterns exist during work hours, evening hours, and night hours. Second, considering variations in social contexts during the evening hours significantly enhances the explanation of the heterogeneity in residents' community attachment. Third, variations in social contexts are more likely to influence residents' attachment in old blocks and migrant communities, and communities that may have gated sub-units are less likely to be influenced. The study advocates that context dynamics be taken as a new dimension of community indicators in place perception studies. The study is also instructive in targeting space and time that deserve special attention in community governance practice. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as "sensors", which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. [ABSTRACT FROM AUTHOR]

Background The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics. Methods We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected by outbreaks of Middle East respiratory syndrome (MERS), Zika and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over the past decade. We then used a stochastic discrete time susceptible-exposed-infected-recovered (SEIR) metapopulation model to simulate the global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data. Results We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruptions occurred during the SARS-CoV-2 pandemic. For outbreak events that were accompanied by local, small and short-term changes in air travel, epidemic models using historical flight data gave similar projections of the timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2, in which there were durable and extensive levels of global travel disruption. Conclusion The use of historical flight data as a proxy in epidemic models is an acceptable practice, except in rare, large epidemics that lead to substantial disruptions to international travel. [ABSTRACT FROM AUTHOR]

The article focuses on developing a privacy-preserving framework for individual-level COVID-19 infection prediction using federated learning and graph neural networks. Topics include proposing a novel method, Falcon, that utilizes a hypergraph structure for contagion process representation, incorporating differential privacy mechanisms and region-level models to protect user privacy while improving prediction accuracy.

Most mobility modeling methods are designed to solve specific tasks, leading to questions regarding their deficiency in generalizability. Inspired by the bloom of foundation models, we proposed a Trajectory Generation framework based on the Diffusion Model (TrajGDM) to capture the universal mobility pattern in a trajectory dataset by learning the trajectory generation process. The process is modeled as a step-by-step uncertainty-reducing process, in which a deep learning network with a novel training method is proposed to learn from the process. We compared the proposed trajectory generation method with six baselines on two public trajectory datasets. The results showed that the similarity between the generated and real trajectory movements measured by the Jensen-Shannon Divergence improved significantly on both datasets. Moreover, we applied zero-shot inferences on two basic trajectory tasks: trajectory prediction and trajectory reconstruction. The accuracy improved by a maximum of 25.6% on two tasks. The universal mobility pattern that is suitable for solving multiple trajectory tasks is verified, inferring the strong generalizability of our model. Finally, the study provides insights into artificial intelligence's understanding of human mobility by exploring the way the model maps the trajectory in the latent space into reality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods: We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results: Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)–third quartile (Q3) = 94–108%) of typical usage to 10% (Q1–Q3 = 6–15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859–0.896]) and a decrease in the effective reproduction number (β = −0.058; 95% CI: [−0.068 to −0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion: Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. [ABSTRACT FROM AUTHOR]

This paper examines the impact of China's "Zero‐Covid" policy on subnational cross‐region export performance during 2019–2021. Using monthly export data at the product‐destination level for China's 31 provinces and municipality cities, we find that new infections in a region have a significantly negative effect on the region's export growth, particularly for non‐processing exports and industries with low dependence on remote working and products that are more substitutable and used mostly in the downstream of supply chains. However, restrictions on people inflows have no significant effect. The severity of the pandemic in export destinations is negatively correlated with China's export growth, suggesting an important demand‐side reason for China's export downturn during the pandemic. [ABSTRACT FROM AUTHOR]

AbstractThe gravity model is a classical spatial interaction model for estimating geographical flows between regions, such as trade and migration. A key to enhancing the model performance is addressing the fixed effects (FE) to capture unobserved group-based characteristics. Traditionally, this is done by grouping flows sharing the same origin or destination. However, it ignores the fact that FE of flows are directional and spatially heterogeneous, e.g., for the same origin (destination), the FE vary among destinations (origins). In this study, we propose a new method that addresses FE of flows based on higher-order origin-destination (OD) pairs. Specifically, we estimate FE by grouping flows that share the same OD dyad at an upper geographic level, e.g., all county-level migration flows from California to Florida are grouped to estimate their FE. We conducted two case studies using U.S. migration flows and for-hire vehicle trips in New York City. The results prove that our method can enhance the model performance. More importantly, our results demonstrate that the FE based on higher-order OD pairs can reveal the directionality, reciprocity, regionality, and temporal regularity of geographical flows. This insight helps us achieve a deeper understanding of the space-time mechanisms underlying spatial interaction phenomena. [ABSTRACT FROM AUTHOR]

In this study, we construct a network-based SAIS model that accounts for the interplay of human mobility and asymptomatic infected individuals. The stability analysis of equilibrium points is performed by constructing suitable Lyapunov functions. Based on the next generation matrix method, the basic reproduction number R 0 is attained in a form similar to the results derived in the two-strain epidemic model. Numerical results demonstrate the impact of R 0 on the final steady state of disease transmission and inspire us that the role of asymptomatic cases cannot be overlooked in the course of a disease. Further simulations reveal the influence mechanism of human mobility on disease spreading in population with heterogeneous contact modes, which will undergo four different stages as the degree of nodes increases. It is concluded that the optimal movement rate should be determined on the basis of human contact patterns, and that controlling population flow with this rate could significantly reduce the peak density of infected individuals. [ABSTRACT FROM AUTHOR]

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]

Although the fear of Yhwh has been presented as an intrinsic feature of the ancient Israelite religion, the fear of God(s) is not limited to the people of Israel in the Hebrew Bible, and similar ideas of fearing deities occur in various texts produced by neighbouring cultures in the ancient eastern Mediterranean. This article investigates the prosocial role of this virtue in situations of human mobility and intercultural contact in the light of the Hebrew Bible and the Odyssey. First, I analyse those Hebrew Bible texts in which the fear of God(s) characterizes or is presented as being intelligible to non-Israelite people in situations involving movement and cultural encounter (Gen 20:11; 42:18; Exod 1:17, 21; Deut 25:18; Jon 1:9). Second, I explore the fear motif in other texts from the ancient eastern Mediterranean region and argue that biblical scholars have overlooked illuminating intertexts found in ancient Greek literature. I especially highlight the interpretative importance of the Odyssey, which frequently stresses the prosocial role of the virtue of fearing deities in the context of travel and contact with outsiders. In so doing, the Odyssey helps us see how the Hebrew Bible texts portraying the fear of God(s) as a universalistic virtue are rooted in and belong to a broader ancient Mediterranean milieu. [ABSTRACT FROM AUTHOR]

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The research aims to explore the complex and interrelated connections of climate change, conflict and migration in the Sahel region. Furthermore, it explores the patterns of human mobility, both internal displacement and cross-border migration, driven by climate-related environmental stressors, food insecurity, and conflict-induced displacement. The Sahel region of Africa stands at the crossroads of intersecting environmental stressors and human mobility, with climate change acting as a catalyst for conflict and migration. Using an interdisciplinary approach, this research synthesizes existing literature and incorporates quantitative analysis, qualitative insights, and geospatial mapping. It highlights case studies to explore the complex relationships between climate change, conflict, and migration in the Sahel. The study identified notable patterns in which environmental stressors, such as droughts, are closely associated with a rise in violence and displacement. The results explain how these stressors increase rivalry for limited resources, therefore intensifying disputes and stimulating migration. To address the challenges the region is facing, implement sustainable resource management and diversified economic opportunities, alongside robust governance to manage conflicts and promote social unity. Enhance community resilience through infrastructure investment and advance research, analytical tools, and multi-level stakeholder engagement for comprehensive problem-solving. [ABSTRACT FROM AUTHOR]

Human travelling behaviours are markedly regular, to a large extent predictable, and mostly driven by biological necessities and social constructs. Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual preferences and choices, through social groups and households, all the way to the global scale, such as mobility restrictions in response to external shocks such as pandemics. In this work, we explore how temporal, activity and location variations in individual-level mobility—referred to as predictability states—carry a large degree of information regarding the nature of mobility regularities at the population level. Our findings indicate the existence of contextual and activity signatures in predictability states, suggesting the potential for a more nuanced approach to estimating both short-term and higher-order mobility predictions. The existence of location contexts, in particular, serves as a parsimonious estimator for predictability patterns even in the case of low resolution and missing data.

Human mobility is mostly dominated by the use of private cars, leading to disproportionate carbon emissions, resource consumption, traffic jams, and pollution. Public transport, with buses, trains, etc., can mitigate these issues via its higher pooling potential. However, often times, public transport is considered less convenient and is therefore avoided. Here, we study a bi-modal public transport system consisting of a rail bound line service and a fleet of on-demand shuttles providing connections to the line service stops, aiming at fast transit at low energy and resource consumption. By means of agent-based simulations and analytical theory, we demonstrate that bi-modal transit indeed has the potential to significantly reduce energy consumption of human mobility at reasonable service quality. We further investigate the influence of the stop density along the rails upon the performance of the bi-modal system. We find that within a range of realistic technical parameters, additional stops tend to impede train speed without significantly enhancing the overall performance of bi-modal transit in terms of service quality and energy consumption. Hence, it can be beneficial to reduce the number of stops within an existing railway system and to implement bi-modal transit as a complement.

IntroductionCommunity-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions.MethodsHere, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission.Results and discussionThrough this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging 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.