The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

scholarly journals An Empirical Study of Human Mobility Patterns

2018 ◽  
Author(s):  
Douglas do Couto Teixeira ◽  
Jussara M. Almeida
Keyword(s):  
Social Network ◽  
Empirical Study ◽  
Factorial Design ◽  
Prediction Models ◽  
Human Mobility ◽  
Contextual Information ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Work Location ◽  
Two Phases

This paper documents our efforts towards understanding which factors are more relevant in human mobility prediction. Our work is divided into two phases. First, we characterize a dataset consisting of more than 200,000 user check-ins in the Foursquare social network, inferring important patterns in human mobility. Second, we use factorial design to quantify the importance of several types of contextual information in human mobility prediction. Our results show that the proximity of the users possible next check-in to his or her home and work location are the most important factors (among the ones we analyzed) to be used by mobility prediction models.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals The impact of dengue illness on social distancing and caregiving behavior

2021 ◽  
Vol 15 (7) ◽  
pp. e0009614
Author(s):  
Kathryn L. Schaber ◽  
Amy C. Morrison ◽  
William H. Elson ◽  
Helvio Astete-Vega ◽  
Jhonny J. Córdova-López ◽  
...  
Keyword(s):  
Human Mobility ◽  
Social Contact ◽  
Febrile Illness ◽  
Well Being ◽  
Denv Infection ◽  
Transmission Dynamics ◽  
Mobility Patterns ◽  
Social Contacts ◽  
Infectious Individual ◽  
The Impact

Background Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility. Methodology and principal findings Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low “health-related quality of well-being” during illness (Fisher’s Exact, p = 0.01). Conclusions/Significance Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual’s exposure to virus or a presymptomatic/clinically inapparent individual’s contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

Modeling geographical spread of COVID-19 in India using network-based approach (Preprint)

2020 ◽  
Author(s):  
Ankush Kumar
Keyword(s):  
Human Mobility ◽  
Uttar Pradesh ◽  
Decisive Role ◽  
Northeast India ◽  
North India ◽  
Mobility Patterns ◽  
Large Country ◽  
Geographical Spread ◽  
Internal Mobility ◽  
The Impact

BACKGROUND COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. OBJECTIVE This study aims to propose a framework for predicting the geographical spread of COVID-19 based on human mobility, by incorporating migration and transport statistics. The motivation of the research is to identify the locations, which can be at higher level COVID -19 spread risk, during migrants transfer and transportation activities. METHODS We use reported COVID-19 cases, census migration data, and monthly airline data of passengers. RESULTS We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration towards the native places can risk Uttar Pradesh and Bihar. We anticipate that the state Kerala, with one of the highest cases of COVID-19, may not receive significant incoming cases, while Karnataka and Haryana may receive the challenge of high incoming cases, with medium cases so far. Using airline passenger's data, we also estimate the number of potential incoming cases at various airports. The study predicts that the airports located in the region of north India are vulnerable, whereas in the northeast India and in some south India are relatively safe. CONCLUSIONS A model is developed for systematically understanding the effect of migration and transport on the spreading of COVID-19, and predetermining the hotspots on real time basis. Through the model, we identified the airports and states that are at higher level of COVID-19 risk. The study can guide policymakers in prior planning of transport and estimate the required medical and quarantine facilities to minimize the impact of COVID-19.

scholarly journals Analysis of human mobility patterns from GPS trajectories and contextual information

2015 ◽  
Vol 30 (5) ◽  
pp. 881-906 ◽  
Author(s):  
Katarzyna Siła-Nowicka ◽  
Jan Vandrol ◽  
Taylor Oshan ◽  
Jed A. Long ◽  
Urška Demšar ◽  
...  
Keyword(s):  
Human Mobility ◽  
Contextual Information ◽  
Mobility Patterns ◽  
Gps Trajectories

scholarly journals Coordinating Measurements in Uncertain Participatory Sensing Settings

2018 ◽  
Vol 61 ◽  
pp. 433-474 ◽  
Author(s):  
Alexandros Zenonos ◽  
Sebastian Stein ◽  
Nicholas R. Jennings
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Participatory Sensing ◽  
Sufficient Information ◽  
Mobility Patterns ◽  
Computationally Efficient ◽  
The Impact

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

scholarly journals The duration of travel impacts the spatial dynamics of infectious diseases

2020 ◽  
Vol 117 (36) ◽  
pp. 22572-22579 ◽  
Author(s):  
John R. Giles ◽  
Elisabeth zu Erbach-Schoenberg ◽  
Andrew J. Tatem ◽  
Lauren Gardner ◽  
Ottar N. Bjørnstad ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Human Mobility ◽  
Trip Duration ◽  
Disease Dynamics ◽  
Mobility Patterns ◽  
Spatial And Temporal Scales ◽  
The Impact ◽  
Spatial Transmission

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

scholarly journals Mobility Patterns and Mode Choice Preferences during the COVID-19 Situation

2022 ◽  
Vol 14 (2) ◽  
pp. 768
Author(s):  
Hector Monterde-i-Bort ◽  
Matus Sucha ◽  
Ralf Risser ◽  
Tatiana Kochetova
Keyword(s):  
Public Transport ◽  
Human Mobility ◽  
Mode Choice ◽  
Blind Spot ◽  
Significant Shift ◽  
Shared Services ◽  
Mobility Patterns ◽  
Two Phases ◽  
Reduced Mobility ◽  
The Impact

The empirical research on the COVID-19 epidemic’s consequences suggests a major drop in human mobility and a significant shift in travel patterns across all forms of transportation. We can observe a shift from public transport and an increase in car use, and in some cases also increase of cycling and (less often) walking. Furthermore, it seems that micromobility and, more generally, environmentally friendly and comanaged mobility (including shared services), are gaining ground. In previous research, much attention was paid to the mode choice preferences during lockdown, or early stages of the SARS-CoV-2 situation. The blind spot, and aim of this work, is how long observed changes in mode choice last and when or if we can expect the mode choice to shift back to the situation before the SARS-CoV-2 episodes. The research sample consisted of 636 cases; in total, 10 countries contributed to the sample examined in this study. The data were collected in two phases: the first in the spring of 2020 and the second in the fall of the same year. Results showed that respondents reduced mobility by car, local public transport and walking, but not bicycling during the lockdown, compared to the time before the pandemic started. When the easing came, respondents assessed their own use of the car and walking as almost back to normal. They also reported an increase in the use of public transport, but not reaching the level prior the pandemic by far. It seems that cycling was affected least by the pandemic; use of a bicycle hardly changed at all. As for the implication of our study, it is evident that special attention and actions will be needed to bring citizens back to public transport, as it seems that the impact of the pandemic on public transport use will last much longer than the pandemic itself.

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