Human Mobility Patterns Research Articles

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant - Shanghai Municipality, China, 2022.

Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale. This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions. Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.

The Indoor Predictability of Human Mobility: Estimating Mobility with Smart Home Sensors.

Analyzing human mobility patterns is valuable for understanding human behavior and providing location-anticipating services. In this work, we theoretically estimate the predictability of human movement for indoor settings, a problem that has not yet been tackled by the community. To validate the model, we utilize location data collected by ambient sensors in residential settings. The data support the model and allow us to contrast the predictability of various groups, including single-resident homes, homes with multiple residents, and homes with pets.

Comprehensive Analysis of Key Spatial Parameters for Characterizing Human Mobility Patterns

Comprehensive Analysis of Key Spatial Parameters for Characterizing Human Mobility Patterns

A Context- and Trajectory-Based Destination Prediction of Public Transportation Users

Location prediction based on contextual information is the core of a plethora of personalized location-based services (LBSs). Several applications require the use of techniques for predicting travel destinations based on human movement. Network analyses of human behavioral data show how the spatial-temporal regularity of human movement can be harnessed for inferring human mobility patterns. However, techniques are often based on a limited number of contextual features, which may limit prediction accuracy, especially if only historical location data are used. Using movement data obtained from public transportation users, we investigate the utility of contextual features derived via the installation of Bluetooth beacons in transportation vehicles and software tools in end-users’ travel applications. Using a multiclass random forest classifier, we show that contextual information of a user’s past travel history and at journey onset goes beyond spatial information and boosts destination prediction accuracy. The likely destination and travel-path length obtained at journey onset can then serve as the input for a stochastic-based model to predict a destination based on acquired trajectory information. Here we show that previously predicted destinations boost the performance of a Markov chain network. Thus, various contextual information at the start of a journey provides information beyond the location information acquired during a journey’s progression that can be employed for destination prediction. These findings have strong implications for LBSs as they require accurate destination prediction at early stages of a journey while at the same time mitigating the privacy concerns associated with collection of location data.

An inventory tool to assess displacement data in the context of weather and climate-related events

Weather and climate-related events have already altered human mobility patterns and led to displacement. While the evidence base is low due to data gaps and scarcity, climate change is expected to reinforce disaster-related displacement. Here we systematically analyze how components of internal displacement data are related to disaster outcomes in Somalia, Afghanistan, the Marshall Islands and Bangladesh using a data inventory tool. We contribute the first programmatic approach to evaluate the evidence of extreme weather impacts on internal displacement in four distinct case studies. We find that the data evidence differs considerably from the disaster displacement process recorded qualitatively in reports. We provide a straightforward, bottom-up application guide that ultimately improves the understanding of changing disaster displacement risks for researchers and practitioners alike. We thereby show nuances in data availability and granularity for potential causal statements between climate change and internal displacement. We conclude that the implementation of data-focused climate policy and action could address obstacles to averting and minimizing disaster displacement.

Correlation of Road Network Structure and Urban Mobility Intensity: An Exploratory Study Using Geo-Tagged Tweets

Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ≈0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns.

Dynamic community detection considering daily rhythms of human mobility

Detecting human mobility community structures is an important means to reveal urban spatial structures. Due to the circadian clock, human mobility has a daily rhythm, representing dynamic interactions among urban functional areas at different times of the day. However, few community detection methods have been developed by explicitly considering such daily rhythms of human mobility. To fill the gap, this study proposes a novel method for detecting dynamic community structures derived from the daily rhythms of human mobility. The proposed method employs a consensus clustering technique to detect consensus structures of dynamic communities throughout the day. The time series characteristics of the detected dynamic communities are further introduced. The hierarchical clustering technique is used to uncover evolving patterns of detected dynamic communities. A case study was carried out using taxi trajectories in New York City to verify the proposed method. Results indicated that the proposed method could well detect a robust structure of dynamic communities even when human mobility patterns changed dramatically at certain time periods. Results also suggested that the proposed method could identify underlying evolving patterns of detected dynamic communities.

Analysis of digital contact tracing technologies employed during COVID-19 Pandemic

Corona virus (COVID-19) is a worldwide pandemic that was first reported in Wuhan, China 2019.The virus claimed lives and negatively changed the mobility patterns of humans. As a measure to control the spread of the virus, Digital Contact Tracing Technologies (DCTTs) were employed to contain the virus. The (DCTTs) are used to track and notify users digitally about their interactions with infected individuals and help strengthen health service systems. The most widely used Digital Contact Tracing Technologies include those that use Global Positioning, Bluetooth, Quick Response (QR) Codes and Wireless Fidelity (Wi-Fi). These technologies depend on monitoring symptoms, mobility of a person, location and proximity of individuals. This paper reviews literature by analyzing the Digital Contact Tracing Technologies (DCTTs) employed for COVID-19 through a desk study. The review highlights the advantages and disadvantages of using these technologies, with an aim of recommending ways in which the technologies can be used to attain maximum benefit.

Remaining popular: power-law regularities in network dynamics

The structure of networks has been a focal research topic over the past few decades. These research efforts have enabled the discovery of numerous structural patterns and regularities, bringing forth advancements in many fields. In particular, the ubiquitous power-law patterns evident in degree distributions, graph eigenvalues and human mobility patterns have provided the opportunity to model many different complex systems. However, regularities in the dynamical patterns of networks remain a considerably less explored terrain. In this study we examine the dynamics of networks, focusing on stability characteristics of node popularity, and present our results using various empirical datasets. Specifically, we address several intriguing questions – for how long are popular nodes expected to remain so? How much time is expected to pass between two consecutive popularity periods? What characterizes nodes which manage to maintain their popularity for long periods of time? Surprisingly, we find that such temporal aspects are governed by a power-law regime, and that these power-law regularities are equally likely across all node ages.

Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix

The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users' mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access control) address, providing an in-depth analysis of similarity behaviors on a daily basis. We analyzed the similarity distributions of users on different days of the week for different locations based on their real movements. The results show similar characteristics of users with common mobility behaviors based on location and time to showcase the efficacy. The results show that the proposed SAA approach is 33% more accurate in terms of recognizing the user's similarity as compared to the existing similarity approach.

The role of neighbourhood design in cycling activity during COVID-19: An exploration of the Melbourne experience

COVID-19 restrictions imposed significant changes on human mobility patterns, with some studies finding significant increases or decreases in cycling. However, to date there is little understanding on how the neighbourhood-level built environment influenced cycling behaviour during the COVID-19 restrictions. As different neighbourhood have different built environment characteristics, it is possible that cycling trends varied across different built environment settings. We aimed to answer this question by examining recreational cycling during different stages of lockdown in Melbourne, Australia. We compared self-reported recreational cycling frequency (weekly) data from 1344 respondents between pre-COVID and two different stages in lockdown. We tested whether the built environment of their residential neighbourhood and different sociodemographic characteristics influenced leisure cycling rates and whether the effect of these factors varied between different stages of COVID-19 restriction. We found that cycling declined significantly during the two stages of COVID-19 lockdown. Cycling infrastructure density and connectivity are two built environment factors that had a significant effect on limiting the decline in leisure cycling during the pandemic. Furthermore, men and younger people had higher cycling rates in comparison to other groups, suggesting that restrictions on indoor activities and travel limits were not enough to encourage women or older people to cycle more during the pandemic.

Sensitivity of coastal southern African climate to changes in coastline position and associated land extent over the last glacial

Paleoclimatic changes in South Africa, especially around the southern Cape region, are of intense interdisciplinary interest; as this is an important area in the context of human evolution, hosting several prominent archaeological sites such as Blombos Cave (located near today's shoreline). In this study, we investigate the sensitivity of climate of coastal southern Africa to changes in glacial variations in sea level and associated land extent, by performing a model sensitivity experiment. Employing a high-resolution (12 km grid spacing) regional climate model (WRF), output from a global model simulation at 70 thousand years ago (ka) is downscaled twice: First using the present-day coastline, then with an estimated coastline position at 70 ka; keeping all other forcing factors identical. Thus we focus on the response of the local climate to an expanded land surface area equivalent to a glacial sea level low stand scenario. Our results reveal that the climate of previously coastal localities shows strong continental characteristics when sea level drops, as the coastline moves away, and a coastal plain is exposed. This is most evident from the year-round warmer days and cooler nights, with up to 6 °C increases (decreases) in daily maximum (minimum) temperatures. This result also extends to extremes. For instance, at the archaeological site of Blombos Cave, temperature extremes (1st and 99th percentiles) of the modelled marine climate become 25- to 75-fold more probable as the coastline shifts. We also find year-round reductions (5–40%) in the amount of precipitation within the region, owing to local modifications in near-surface atmospheric circulation caused by the exposed land. The reductions in precipitation are accompanied by a significant drop in the annual number of rainy days (31 days locally at Blombos Cave). Simulated changes also vary seasonally, with more pronounced and widespread changes in temperature and precipitation occurring during summer. Through isolating and quantifying the effects of land extent variation, our approach demonstrates, for the first time, the role of coastline position in shaping local climate around near-coastal environs. Our results have significant implications for future studies exploring the influence of local coastline changes on early human settlement and mobility patterns.

Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean

We present a spatiotemporal picture of human genetic diversity in Anatolia, Iran, Levant, South Caucasus, and the Aegean, a broad region that experienced the earliest Neolithic transition and the emergence of complex hierarchical societies. Combining 35 new ancient shotgun genomes with 382 ancient and 23 present-day published genomes, we found that genetic diversity within each region steadily increased through the Holocene. We further observed that the inferred sources of gene flow shifted in time. In the first half of the Holocene, Southwest Asian and the East Mediterranean populations homogenized among themselves. Starting with the Bronze Age, however, regional populations diverged from each other, most likely driven by gene flow from external sources, which we term "the expanding mobility model." Interestingly, this increase in inter-regional divergence can be captured by outgroup-f3-based genetic distances, but not by the commonly used FST statistic, due to the sensitivity of FST, but not outgroup-f3, to within-population diversity. Finally, we report a temporal trend of increasing male bias in admixture events through the Holocene.

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis.

The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

Characterization of human mobility based on Information Theory quantifiers

Location-aware services provide valuable information for capturing human mobility patterns. In this context, analyzing the mobility dynamics, such as the means of transportation and their speeds, leads to better solutions by understanding the underlying data generating process and identifying different patterns. Strategies based on extracting Information Theory measures associated with ordinal patterns methods, for example, Complex-Entropy Causality Plane and Fisher–Shannon Causality Plane, have reached relevant advancements in distinguishing different time series dynamics. Thus, they are promising tools to explain those complex behaviors to improve human mobility-based services. In this work, we aim to characterize the users’ means of transportation based on their speed time series derived from the Geolife dataset. Therefore, for each type of transportation, we observe the speed dynamics over time and correlate their associated Information Theory quantifiers with colored noises mapped onto the causal planes. Evaluation results show the potential of our study, allowing us to distinguish motorized and non-motorized means of transportation. Also, based on that mapping, we can estimate the transportation switching.

Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion

Trip recommendation is a popular and significant location-aware service that can help visitors make more accurate travel plans. Its principal purpose is to provide a sequence of points of interest (POIs) for a visitor who has a specific demand, such as the number of attractions, start location, end location, etc. Previous studies either regard the trip recommendation task as a simplistic orienteering problem or endeavor to maximize the users’ visiting preferences and transitional regularities in a multi-round iteration manner. However, significant contexts such as long-term transitional dependencies and spatial–temporal correlations are under-explored. Although the emerging deep recursive models (e.g., recurrent neural networks) enable us to relieve the above limitations, there still exist three major challenges: (1) most conventional offline-homogeneous knowledge distillation approaches for key entities (e.g, POI and visiting time) could lead to the inadequacy of capturing the inherent heterogeneous interactions among the different entities and even result in the deviation of understanding human real individual preferences; (2) data sparsity in transitional regularity learning impedes the ability to comprehend human diverse mobility patterns; and (3) the lack of considering contextual facts such as attractive bias would degrade the generalization ability of the model. To remedy the above issues, this work presents a novel framework based on spatial–temporal graph representation learning, namely GraphTrip. Specifically, we first introduce a location-aware information fusion after building the spatial–temporal graph (ST-Graph). Then, a dual-grained human mobility learning module is proposed to address the sparsity of periodic regularity. In addition, we fuse the explicit information (e.g., POI popularity) behind the trip data as prior knowledge to facilitate the performance of trip inference. In the end, the experimental results conducted on five real-world trip datasets demonstrate our proposed GraphTrip achieves promising gains against several cutting-edge baselines, e.g., up to 5.75% and 4.45% improvements on Toronto regarding F1 and pairs-F1.

Perception of Spatial Legibility and Its Association with Human Mobility Patterns: An Empirical Assessment of the Historical Districts in Rasht, Iran.

Achieving legibility within the context of historical districts has become a controversial problem due to their widespread growth and unconventional constructions within, which has led to inconsistencies in the urban context system, and a decrease in the level of urban sociability. This paper aims to provide an empirical assessment towards facilitating the perception of spatial legibility and its association with human mobility patterns. To this end, a novel mix method was developed in order to comprehend the association between spatial legibility and human mobility patterns using Space Syntax, cognitive sketch maps, and time-lapse photography. The results revealed that there is a significant association between spatial legibility and human mobility patterns, such that the incorporation of objective and subjective factors affecting legibility, including highly integrated morphological characteristics along with the saliency of landmarks featuring historical values, can lead to increased human mobility patterns in terms of use frequency. Accordingly, this research aids urban planners and designers in recognizing how to deal with historical districts in order to foster the sociability of these areas and create a lively and socially sustainable urban environment.

Mobile phone handover data for measuring and analysing human population mobility in Western Ethiopia: implication for malaria disease epidemiology and elimination efforts

BackgroundHuman mobility behaviour modelling plays an essential role in the understanding and control of the spread of contagious diseases by limiting the contact among individuals, predicting the spatio-temporal evolution of an epidemic and inferring migration patterns. It informs programmatic and policy decisions for effective and efficient intervention. The objective of this research is to assess the human mobility pattern and analyse its implication for malaria disease epidemiology.MethodsIn this study, human mobility patterns in Benishangul-Gumuz and Gambella regions in Western Ethiopia were explored based on a cellular network mobility parameter (e.g., handover rate) via real world data. Anonymized data were retrieved for mobile active users with mobility related information. The data came from anonymous traffic records collected from all the study areas. For each cell, the necessary mobility parameter data per hour, week and month were collected. A scale factor was computed to change the mobility parameter value to the human mobility pattern. Finally, the relative human mobility probability for each scenario was estimated. MapInfo and Matlab softwares were used for visualization and analysis purposes. Hourly travel patterns in the study settings were compared with hourly malaria mosquito vector feeding behaviour.ResultsHeterogeneous human movement patterns were observed in the two regions with some areas showing typically high human mobility. Furthermore, the number of people entering into the two study regions was high during the highest malaria transmission season. Two peaks of hourly human movement, 8:00 to 9:00 and 16:00 to 18:00, emerged in Benishangul-Gumuz region while 8:00 to 10:00 and 16:00 to 18:00 were the peak hourly human mobility time periods in Gambella region. The high human movement in the night especially before midnight in the two regions may increase the risk of getting mosquito bite particularly by early biters depending on malaria linked human behaviour of the population.ConclusionsHigh human mobility was observed both within and outside the two regions. The population influx and efflux in these two regions is considerably high. This may specifically challenge the transition from malaria control to elimination. The daily mobility pattern is worth considering in the context of malaria transmission. In line with this malaria related behavioural patterns of humans need to be properly addressed.

The effect of human mobility restriction during the Covid-19 pandemic on the level of environmental damage

Environmental damage that occurs is generally caused by human activities. The activities carried out lead to mobilities with a tendency to a certain pattern. During the Covid-19 Pandemic, the Indonesian government issued a policy of restricting the human mobility to reduce the spread of the Covid-19 virus. The reduced human mobility in mining activities may lessen the level of environmental damage that occurs. Therefore, this study aims to determine the effect of human mobility restrictions during the Covid-19 pandemic on the level of environmental damage that has occurred. Pangkalpinang City was taken as the location for data collection in this study. Data collection will be undertaken by distributing questionnaires online to people who live in the research location. From the results of the questionnaire, human mobility patterns will be mapped to the activities conducted and the level of environmental damage that has occurred. The results of this study can show the distribution of human mobility patterns to environmental damage that occurred due to policies to restrict human movement during Covid-19 Pandemic.

Connecting the dots: understanding how human mobility shapes TB epidemics.

Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.