Search

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.

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]

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]

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]

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]

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]

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]

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]

(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]

Copyright of Estudios Demográficos y Urbanos is the property of El Colegio de Mexico AC 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.)

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.

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.

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]

Copyright of Estado & Comunes, revista de politicas y problemas publicos is the property of Instituto de Altos Estudios Nacionales (IAEN) 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.)

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]

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.

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.

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 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.

Abstract Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the “when→what→where”, a.k.a. “time→activity→location” decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers’ destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over baselines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL .

Abstract Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.

IntroductionHuman mobility is a critical aspect of existence and survival, but may compromise care engagement among people living with HIV (PLHIV). We examined the association between various forms of human mobility with retention in HIV care and antiretroviral treatment (ART) interruptions.MethodsIn a cohort of adult PLHIV in Kenya and Uganda, we collected surveys in 2016 about past 6-month travel and lifetime migration histories, including reasons and locations, and engagement in HIV care defined as (1) discontinuation of care, and (2) history of a treatment interruption among those who remained in care. We estimated associations between mobility and these care engagement outcomes via logistic regression, adjusted for sex, prior mobility, age, region, marital status, household wealth, and education.ResultsAmong 1081 participants, 56 (5%) reported having discontinued care; among those in care, 104 (10%) reported treatment interruption. Past-year migration was associated with a higher risk of discontinuation of care (adjusted odds ratio [aOR] 1.98, 95% CI 1.08-3.63). In sex-stratified models, the association was somewhat attenuated in women, but remained robust among men. Past-year migration was associated with reduced odds of having a treatment interruption among men (aOR 0.51, 95% CI 0.34-0.77) but not among women (aOR 2.67, 95% CI 0.78, 9.16). Travel in the past 6 months was not associated with discontinuation of care or treatment interruptions.ConclusionsWe observed both negative and protective effects of recent migration on care engagement and ART use that were most pronounced among men in this cohort. Migration can break ties to ongoing care, but for men, who have more agency in the decision to migrate, may foster new care and treatment strategies. Strategies that enable health facilities to support individuals throughout the process of transferring care could alleviate the risk of care disengagement.