Geolocation Data Research Articles

An evaluation of geo-located Twitter data for measuring human migration

This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.

Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies

While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties. Understanding the transmission process and quantifying the costs of local policies are essential to the task of tackling this challenge. Here, we investigate the dynamic contact patterns of the populations from anonymized, geo-localized mobility data and census and demographic data to create data-driven, agent-based contact networks. We then simulate the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States and evaluate a combination of mobility reduction, mask use, and reopening policies. We find that our model captures the spatial-temporal and heterogeneous case trajectory within various counties based on dynamic population behaviors. Our results show that a decision-making tool that considers both economic cost and infection outcomes of policies can be informative in making decisions of local containment strategies for optimal balancing of economic slowdown and virus spread.

The Impact of Mobile Social App Usage on Offline Shopping Store Visits

This research is a first empirical investigation into the effect of mobile social app usage on consumers’ decisions to visit offline stores and how such an effect varies across consumers and carries over time. The analysis combines data on mobile app usage at the individual consumer level with a fine-grained geolocation data set. The authors find a positive effect of social app usage on consumers’ visits to brick-and-mortar stores. This effect is amplified by the consumer's mobility level and the offline store density in the consumer's neighborhood. The positive impact of social app usage carries over for up to nine days, indicating a short-term effect. Additional analyses indicate that such an effect is likely due to consumers’ social discovery of product- and store-related information via word of mouth on strong-tie social apps (i.e., instant messaging apps). These results point to new opportunities for offline retailers seeking to acquire customers via the mobile channel.

The potential of disaster digital archives in disaster education: The case of the Japan disasters digital archive (JDA) and its geo-location functions

Using the Japan Disasters Digital Archive (JDA) as an example, the present study examines how geo-location functions in disaster digital archives may contribute to disaster risk awareness. It focuses on the sites affected by or representing lessons learned from the 2011 Great East Japan Earthquake (GEJE), such as the tsunami inundated coastal areas or museums. The study tests the thesis that geo-located data in digital archives can enhance disaster risk awareness by allowing its users to discover novel location-specific information. The Covid-19 pandemic drastically influenced the research design and the results. The research methods had to be changed from group-based field trips and in-person interviews to virtual research methods that participants could follow on their own. Although there is room for improvements in the JDA design and function, the research results suggest that options for visitors to retrieve information through digital archives on their own while visiting sites of their interest can contribute to their understanding of the history of these specific places and local risks. Promoting self-guided tours by giving access to disaster digital archives with a geolocation-function could also be used as a cost saving option for museums and visitors alike to explore disaster-affected areas, especially in times of a pandemic when visitors are discouraged from joining crowded tours. As a whole, this research aims to not only improve (disaster) digital archives but also to contribute to disaster education in general and offer a widened learning experience for visitors of disaster affected areas in particular.

Spatial Interaction and Hybrid Spaces

The notion of spaces constantly is growing with the advancement of technology. The spatial interactions using digital media provide new possibilities to bridge between physical and digital spaces. Location is not all about longitude and latitude, but all the added digital layers may change the idea of positioning. Over the last decades, studying space faced many conceptual perceptions. The approach of having/imagining the different worlds and spaces, such as cyberspace and physical space, has been mostly formed by distinguishing them. Adriana de Souza e Silva (2006) drew a chronology for these imaginary spaces from cyberspace to virtual and hybrid space. Hybridizing of space with the advancement of nomadic technology like mobiles become more popular and indicates how the interfaces are transparent in our everyday life. So, hybridity occurs when the interconnection of the spaces is reachable for the people who can interact with, through the devices, wearables, headsets, etc. In this respect, the physical space needed to be filled with data, as Manovich (2006, p.226) defined this as augmentation of the space through “overlay[ing] physical space with layers of data.” The question of reality may not be valid anymore since both are affecting each other. And, it is time to think about the interlinked worlds and their effects rather than separating and labelling them as real and unreal. Technology can play a crucial role in changing our interaction with space. The paper will investigate the concept of hybrid spaces in relation to the production of dis-locative space. The dislocation environment reveals the interconnected spaces and their realities through new ways of interactions and narratives. The realities are created under the influence of each other. The paper will use the art project Shadows from Another Place as a case study. It will explore the idea of dis-location through spatial interaction and digital technology. The paper will profit from the theories including Production of Space by Henry Lefebvre, Supermodernity and Non-places by Marc Augé, and Relational Spaces by Scott McQuire. Paula Levine is a scholar and media artist who experiments with maps, geolocation data, and the dynamic of spaces. Shadows from Another Place is a web-based project using transposed maps, including three artworks San Francisco <-> Baghdad, The Wall, and TheWall-TheWorld. In this series, she tries to challenge the reality of geographical distances by hybridization of the spaces. Shadows from Another Place is a series of artwork that examines the discussion around dis-locative space, and spatial interaction using digital technology. The paper investigates three questions of locations and dislocation, real and unreal, and intervening in the physical space by the virtual world and vice versa. Question of Location ‘where am I’? What is un/real? How virtual world affects the physical world and vice versa?

Data Quality Influences the Predicted Distribution and Habitat of Four Southern-Hemisphere Albatross Species

Few studies have assessed the influence of data quality on the predicted probability of occurrence and preferred habitat of marine predators. We compared results from four species distribution models (SDMs) for four southern-hemisphere albatross species, Buller’s (Thalassarche bulleri), Campbell (T. impavida), grey-headed (T. chrysostoma), and white-capped (T. steadi), based on datasets of differing quality, ranging from no location data to twice-daily locations of individual birds collected by geolocation devices. Two relative environmental suitability (RES) models were fit using minimum and maximum preferred and absolute values for each environmental variable based on (1) monthly 50% kernel density contours and background environmental data, and (2) primary literature or expert opinion. Additionally, two boosted regression tree (BRT) models were fit using (1) opportunistic sightings data, and (2) geolocation data from bird-borne electronic tags. Using model-specific threshold values, habitat was quantified for each species and model. Model variables included distance from land, bathymetry, sea surface temperature, and chlorophyll-a concentration. Results from both RES models and the BRT model fit with opportunistic sightings were compared to those from the BRT model fit using geolocation data to assess the influence of data quality on predicted occupancy and habitat. For all species, BRT models outperformed RES models. BRT models offer a predictive advantage over RES models by being able to identify relevant variables, incorporate environmental interactions, and provide spatially explicit estimates of model uncertainty. RES models resulted in larger, less refined areas of predicted habitat for all species. Our study highlights the importance of data quality in predicting the distribution and habitat of albatrosses and emphasises the need to consider the pros and cons associated with different levels of data quality when using SDMs to inform management decisions. Furthermore, we examine the overlap in preferred habitat predicted by each SDM with fishing effort. We discuss the influence of data quality on predicting the wide-scale distributions of pelagic seabirds and how these impacts could result in different protection measures.

Non‐breeding distribution and at‐sea activity patterns of the smallest European seabird, the European Storm Petrel (Hydrobates pelagicus)

Determining the non‐breeding distribution and activity patterns of migratory animals is essential to understand the trade‐offs across breeding, moulting and migratory periods and to evaluate the differential levels of exposure of these animals to threats throughout the year. By taking advantage of the current miniaturization of geolocators, during the breeding season of 2019, we deployed geolocators on 10 European Storm PetrelsHydrobates pelagicusbreeding on Benidorm Island, Western Mediterranean, to determine the distribution and activity patterns of this small seabird species. Of the eight individuals that were recaptured with usable geolocation data, all individuals migrated to the North Atlantic Ocean, ranging between the Canary Islands and south of Iceland. They possibly take advantage of the more productive waters in the North Atlantic areas explored during the non‐breeding period compared with the Western Mediterranean areas explored during the breeding period. This migratory pattern contrasted with the partial migration described for individuals breeding in the Central Mediterranean. Tracked individuals were more synchronous in their prenuptial than in their postnuptial migratory phenology, probably due to a higher probability of breeding success related to earlier arrival to the breeding area. At‐sea activity data indicated that individuals spent more time on the water at night (and especially on those nights around the new moon) during the first half of the non‐breeding period (matching the dates of the moulting period of the species). Although birds may begin to moult their feathers while breeding, some seemed to overlap the moulting period with the postnuptial migration. Our results show that multi‐colony studies are needed to understand the drivers of distinct migratory patterns at intra‐ and inter‐population levels and how small migratory species trade‐off the overlap of moult with other energetically demanding activities such as breeding or migrating.

Location Based Recommendation System with machine learning.

Machine learning allows us to give vast volumes of data to computer algorithms and have them assess and make data-driven judgments and recommendations entirely based on the incoming data. This project will employ machine learning to analyze geolocation data and user preferences in order to provide the user with intelligent recommendations. In today's fast-paced and hectic world, it's common to be too exhausted to prepare a home-cooked supper. Even if you eat home-cooked meals every day, it's not uncommon to desire to treat yourself to a decent dinner now and then for social or recreational reasons. Consider the instance of someone who has recently relocated to a new location. They already have particular tastes and interests. . If a person lives near his favorite outlet, it will save him and his food providers a lot of time and effort. This project involves categorizing popular snack destinations for incoming students based on their mood and preferences for amenities, affordability, and proximity to the place using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choice) by using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choice) by using K-Means Clustering to find the easiest food location for students in Greater Noida.

Resisting Lockdown: The Influence of COVID-19 Restrictions on Social Unrest

Abstract Responding to the COVID-19 pandemic has created unprecedented social and political challenges. Mitigation strategies often disrupt the daily lives of citizens and constrain rights and privileges. Policies intended to contain disease spread have provoked resentment, resistance, and backlash. We examine the extent to which specific COVID-19 policy responses influence the frequency of civil unrest. Combining insights from both grievance and opportunity models of dissent, we contend that pandemic-response policies are most likely to lead to unrest when the grievances and opportunities created by disease-mitigation strategies reinforce each other. We test our arguments with nuanced information on specific pandemic-mitigation policies, combined with geolocated events data on COVID-19-related social unrest activities. We find that policies such as workplace and school closures, which induce intense grievances and reduce the opportunity cost of engaging in collective mobilization, are associated with increases in dissent activities. Policies that restrict opportunities for mobilization, such as restrictions on public transportation, reduce the number of dissent activities. Notably, economic support policies attenuate the effects of workplace closures on dissent. Our results illustrate the varying effects of pandemic-mitigation policies on unrest depending on how the grievances they inspire relate to the opportunity they create.

Whole genome characterization, and geographical distribution of M. tuberculosis in central region of Veracruz, Mexico

The purpose of this work was to perform by Whole Genomic Sequencing (WGS) a characterization of tuberculosis isolates circulating in the central region of Veracruz, Mexico, and to determine its geographical distribution. The genome of 25 clinical isolates of tuberculosis patients, recovered from central zone of Veracruz, Mexico, were sequenced and the information obtained was used to characterize lineage, prediction of drug resistance, identification of clonal complexes, and finally correlated with the geolocalization data. Isolates analyzed were included into seven L4 sublineages, most frequent was X3; X1 (4.1.1.3) in 35%. rpoBSer450Leu polymorphism was the most frequently found variant. Sublineage Haarlem (4.1.2) had the widest distribution, found in five municipalities. Of the of two clonal complexes found, the most abundant included eight isolates, with X3/X1 lineage, placed in two municipalities. Combination of WGS and geographic information system was very useful for the identification of sublineages, clonal complexes, and their geographical dispersion with important implications in the epidemiological surveillance and clinical control of TB.

Geolocation features differentiate healthy from remitted depressed adults.

Depression recurrence is debilitating, and there is a pressing need to develop clinical tools that detect the reemergence of symptoms with the aim of bridging patients to treatment before recurrences. At baseline, remitted depressed adults (n = 22) and healthy controls (n = 24) were administered clinical interviews and completed self-report symptom measures. Then, smartphone apps were installed on personal smartphones to acquire geolocation data over 21 days and ecological momentary assessment of positive and negative affect during the initial 14-day period. Compared with healthy controls, remitted depressed adults exhibited reduced circadian routine (regularity of one's daily routine) and lower average daily distance traveled. Further, reduced distance traveled associated with greater daily negative affect after controlling for depression severity; however, this effect was not more pronounced among remitted adults. A least absolute shrinkage and selection operator (LASSO) regression indicated that a linear combination of circadian routine, average distance traveled, and baseline depression severity classified remitted depressed individuals with 72% accuracy; outperforming models restricted to either geolocation or clinical measures alone. Mobile sensing approaches hold enormous promise to improve clinical care for depressive disorders. Although barriers remain, leveraging technological advancements related to real-time monitoring can improve treatment for depressed patients and potentially, reduce high rates of recurrence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

METHODS FOR ENSURING DATA SECURITY IN MOBILE STANDARDS

The analysis of mobile communication standards is carried out, the functional structure and interfaces of interaction between the structural elements of the cellular network are considered. To understand the principle of communication according to the GSM standard, a block diagram of a mobile switching center (MSC), base station equipment (BSS), control and service center (MCC), mobile stations (MS) is presented. The main algorithms for ensuring the confidentiality and security of mobile subscribers' data, in different types of standards, as well as the vulnerabilities of information flows are considered. In particular, the following dangerous types of attacks have been identified, to which mobile network subscribers are sensitive: sniffing; leakage of personal data; leakage of geolocation data; spoofing; remote capture of SIM-card, execution of arbitrary code (RCE); denial of service (DoS). It is established that the necessary function of the mobile network is the identification of subscribers, which is performed by IMSI, which is recorded in the SIM card of the subscriber and the HLR of the operator. To protect against spoofing, the network authenticates the subscriber before starting its service. In the case of subscriber identification, the subscriber and the network operator are protected from the effects of fraudulent access. In addition, the user must be protected from eavesdropping. This is achieved by encrypting the data transmitted over the radio interface. Thus, user authentication in UMTS, as well as in the GSM network, is carried out using encryption with a common key using the "hack-response" protocol (the authenticating party sends a random number to the authenticated party, which encrypts it according to a certain algorithm using a common key and returns the result back).

Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models

BackgroundSevere acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas.MethodsA secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.ResultsIn Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.ConclusionsEradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones.

BackgroundMost smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms.ObjectiveThe objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD.MethodsWe used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse–Major Depressive Disorder study. The participants were recruited from three study sites: King’s College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables.ResultsParticipant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI −0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day).ConclusionsOur findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD.

Fine-scale estimation of effective reproduction numbers for dengue surveillance.

The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010–2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2EpiEstim = 0.95, R2EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.

A Method for Cheating Indication in Unproctored On-Line Exams.

COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students’ dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student’s on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student’s cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19.

The geography of sobriety checkpoints and alcohol-impaired driving.

Sobriety checkpoints are an effective strategy to reduce alcohol-impaired driving, motor vehicle crashes, injuries and fatalities. The aim of this study was to identify the geographic extent over which individual sobriety checkpoints affect alcohol-impaired driving. Spatial ecological panel analysis using geolocated breath test data from the Queensland Police Service, Australia, for January 2012 to June 2018. Data were aggregated over 338 weeks within 528 Statistical Area level 2 (SA2) units (n = 178 464 SA2-weeks) and 84 Statistical Area level 3 (SA3) units (n = 28 392 SA3-weeks). SA2 units in Queensland contain a mean population of 8883.5 (SD = 55 018.3) and encompass 468.9 roadway kilometers (SD = 1490.0); SA3 units contain a mean population of 57 201.6 (SD = 29521.6) and encompass 2936.0 roadway kilometers (SD = 7025.0). Independent measures were the density of sobriety checkpoints conducted per 500 roadway kilometers within local and spatially adjacent space-time units. The dependent measure was the rate of tests that detected breath alcohol concentration (a proxy for blood alcohol concentration [BAC]) greater than the legal maximum value of 0.05% for fully licensed drivers in Queensland. Bayesian hierarchical spatial negative binomial models-related sobriety checkpoints to the rate of breath tests with BAC ≥ 0.05% within and between space-time units. One additional sobriety checkpoint conducted per 500 roadway kilometers was associated with 2.5% reduction in the rate of breath tests with BAC ≥ 0.05% within local SA2 units (incidence rate ratio [IRR] = 0.975; 95% credibility interval (CrI): 0.973-0.978), and with 5.5% reduction in the rate of breath tests with BAC ≥ 0.05% within local SA3 units (IRR = 0.945; 95%CrI: 0.937-0.953). Associations were attenuated towards null in spatially adjacent units and in temporally lagged units (e.g. SA3-weeks; adjacent lagged 1week: IRR = 0.969; 95%CrI: 0.937-1.003). Individual sobriety checkpoints appear to be associated with reductions in nearby alcohol-impaired driving. Relationships decay after approximately 1week and beyond local areas containing approximately 60 000 residents and 3000 kilometers of roadway.

Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data.

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.

Detection of vehicle-based operations from geolocation data

Geolocation data identifies the geographic location of people or objects, which may unveil the performance of some activity or operation. A good example is, if a vehicle is in a gas station then one may assume that the vehicle is being refuelled. This work aims to obtain vehicle-based operations from geolocation data by analysing the stationary states of vehicles, which may identify some motionless event (e.g. bus line stops and traffic incidents). Ultimately, these operations may be analysed with Process Mining techniques in order to discover the most significant ones and extract process related information. In this work, we studied the application of diverse approaches for detecting vehicle-based operations and identified different operations related to the bus services. The operations were also characterized according the distribution of their events, allowing to identify specific operations characteristics. The public transport network of Rio de Janeiro is used as a case study, which is supported by a real-time data stream of buses geolocations.

The Arctic tern Sterna paradisaea: consistency and variability in spatial use at a global oceanographic scale

Elucidating the ecological factors underpinning migratory strategies of seabirds is necessary for understanding resilience to environmental change. Arctic terns Sterna paradisaea breed in the Northern Hemisphere and are unique for the global scale of their migration. Geolocator data from 37 Arctic terns breeding in a low-latitude colony, 10 of which were re-tagged in successive years, were analysed to characterise their migratory behaviour and to test the hypothesis that individuals have repeatable migration strategies. Seawater immersion data suggested a fly-forage strategy, with birds remaining on the wing at night and only foraging during daylight. Southward movement was focused initially along Atlantic eastern-boundary upwelling systems. Most terns then reoriented eastwards, crossing the southern Indian Ocean before moving south to the Antarctic. Foraging intensity differed between migration phases. Indian Ocean foraging locations were diverse, and less frequent over deep ocean basins. Foraging intensity was highest in the later stages of return migration, particularly in and around the Azores Confluence Zone. High movement speeds and foraging intensity on return migration may be adaptations to optimise reproductive success. Some aspects of migration phenology were repeatable between years, but trajectories were displaced by wind. Repeat birds did not use the same foraging areas in different years, and their trajectories across the Indian Ocean also differed. The results of this study suggest that the Indian Ocean crossing is a behaviour pattern, surviving since the last ice age, enabling Arctic terns breeding at low-latitude northwest European colonies to arrive at fragmenting Antarctic sea ice when foraging conditions are suitable.