Proxy Mobile Research Articles

Geochemical Dynamics and Evolutionary Implications of Sediments at the Xingu–Amazon Rivers’ Confluence: Proxies for Mixing, Mobility and Weathering

This study investigates the geochemical characteristics and evolutionary implications of sediments at the confluence of the Xingu and Amazon Rivers. The main objective is to understand sediment mixing, mobility, and weathering processes through geochemical proxies. Samples were collected from various sections of the lower Xingu River, focusing on its interaction with the Amazon River. Analytical techniques such as X-ray diffraction (XRD), X-ray fluorescence (XRF), and inductively coupled plasma mass spectrometry (ICP-MS) were employed to analyze major and trace elements. The results reveal significant spatial variations in mineralogical and textural patterns, with sediments forming distinct groupings based on their location. The data suggest that the lower Xingu River is strongly influenced by sediment inputs from the Amazon River, particularly affecting sediment composition and chemical weathering processes. This research highlights the critical interactions between river systems and their implications for the evolution of the Amazon basin, especially regarding sediment contributions from various geological sources. Even though the Xingu River drains cratonic regions at higher elevations, the geochemistry of the bottom sediments confirms that the bedload is derived from heterogeneous sources with primarily intermediate igneous compositions and has undergone substantial recycling during river transport.

Climate deterioration and subsistence economy in prehistoric Southern Iberia: an evaluation of potential links based on regional trajectories

The potential impact of climatic deterioration on societal breakdowns in prehistory is often based on the mere coincidence of archaeological and palaeoclimatological proxies. For a more profound discussion, climate-sensitive archaeological parameters need to be identified. As agriculture and livestock are significantly affected by the recent climate crisis, the analysis of archaeobotanical and archaeozoological remains can deepen our understanding of this topic. Here, we analyze regional trajectories in subsistence and seasonal precipitation variability across southern Iberia focusing on well-known prehistoric breakdowns around 2200 and 1600 BCE. Results suggest that the ratios of the importance of sheep/goat versus swine and sheep/goat versus cattle, respectively, may serve as a proxy for prehistoric mobility. The importance of hunting deer represents a proxy for societal turnover. While no direct link is evident between climate deterioration and the archaeozoological data, archaeobotanical data reveals a potential relation to precipitation variability. In particular, a close connection to reductions in cold-season precipitation in south-east Iberia appears likely for the ratio between barley and naked wheat. The increased importance of drought-tolerant barley correlates with a trend to reduced cold-season precipitation levels after ca. 2700 BCE. We hypothesize that prehistoric people in south-east Iberia cultivated more barley in order to adapt to periods of drier cold-season climate.

100 ancient genomes show repeated population turnovers in Neolithic Denmark

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales1–4. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution5–7. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (13C and 15N content), mobility (87Sr/86Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.

Evaluation of bicycle sharing scheme data as a proxy for cycling mobility – How COVID-19 measures influenced cycling in Paris

The use of bicycles can provide myriad benefits to society, especially in crowded urban centres where other modes of transport are at or near capacity. However, integrating cycling into policy and planning requires more comprehensive data about their use in space and time. Current approaches, using sparse networks of counters provide one possible route to more comprehensive data. In this paper we investigate another, using data collected from Paris’ bicycle sharing system to explore use during 2020. We chose 2020 as a test year because the use of bicycles was strongly influenced by the COVID-19 pandemic, allowing us to explore how bicycle use adapted to both legal and environmental influences. We used interactive visualization to allow hypothesis generation and data exploration, before analysing bicycle use as a function of weather and COVID-19 restrictions. Our results show that bicycle sharing system data and Paris’ counters both capture very similar behaviour patterns, and therefore bicycle sharing system data are a reliable proxy for overall cycling behaviour, providing finer spatial granularity than existing sparse counter networks. Seasonally, precipitation influenced bicycle use more strongly in 2020 than COVID-19 measures.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study.

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue. Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends

BackgroundNon-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time.MethodsIn this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022.ResultsWe found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well.ConclusionIn a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time.

Automatic Mapping of Physical Urban Problems Using Remotely Sensed Imagery

While big cities are expected to exercise cost-effective, evidence-based planning, many are under reactive management, facing simultaneous problems and limited resources. This project develops a proof-of-concept workflow for the automatic monitoring of physical urban problems by combining remote sensing for detection and cartography for visualization. The example problem treated was the obstructive parking of vehicles on pavements as proxy for restricted urban mobility. Nine aerial images of UK urban areas were processed by a deep learning object detector of standard cars, achieving an F-score of 70.72%. Two large scale map reports of 200m wide areas were produced, featuring car detections and overlaps with topographic mapping features. Complementary analysis included the calculation of total detection window overlap per roadside pavement and its change with time. The proposed method combines uniform city-wide coverage with fast interpretation and can inspire the development of professional urban planning tools.

Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis.

While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.

The undetectability of global biodiversity trends using local species richness

Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e. time between original survey and re‐survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility) and spatial sampling biases (i.e. site‐selection biases). We use PREDICTS model‐based estimates as a proxy for the real‐world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30‐year period, the number of sites for detecting trends doubled for a decade, increased 10‐fold within three years and yearly trends were undetectable. Measurement errors had a non‐linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.

Estimating mobility of tourists. New Twitter-based procedure

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.

Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

The Impact of Long–Term Orientation Traits on Pandemic Fatigue Behavior: Evidence from the Columbian Exchange

Leveraging exogenous variation in time preferences, we measure the causal effects of culturally embodied long-term orientation traits on voluntary social distancing behavior, COVID-19 cases, hospitalizations, and mortality outcomes in 2020 in the United States. We establish that long-term orientation traits with bio-geographical origins causally reduce measures of COVID-19 cases, deaths and hospitalization, inpatient bed utilization, and age-specific excess deaths. Mobility indicators measuring voluntary decisions to socially distance, comprising measures of visitors/visits to recreational locations, and mobility proxy measuring duration of hours away from home show that a lower prevalence of long-term orientation traits explains persistent resistance to social distancing.

Experimental Improvements to the Volume Ratio and Quantifying Movement Using Stone Artefact Analysis

Many conceptual frameworks of archaeological assemblages have assumed that stone artefact assemblages include all products of manufacture, use, and discard, although recent studies have indicated this is not always the case. The volume ratio is a method that examines the potential for the removal or addition of stone artefacts to an assemblage after manufacture. As humans transport material, and the movement of material effects the composition of assemblages, the alteration of an assemblage through the addition or removal of material can act as a proxy for mobility. This research uses three experimental assemblages to test the effect that different methods of volume quantification and reconstruction have on the calculation of the volume ratio. Results suggest observed assemblage volume is accurately and efficiently calculated using a standardised density of 2.46 g/cm³, while the modelled assemblage volume is relatively accurately calculated using either the Volumetric Reconstruction Method (VRM) or the Flake Volumetric Reconstruction Method (FVRM) with the potential for future research to further improve this methodology.

A Multi-User Shared Mobile Payment Protocol in the Context of Smart Homes

A multi-user shared mobile payment protocol based on group signature is proposed. In order to solve the problem of limited resources such as computing power and storage space of mobile devices, a group signature algorithm with a shorter signature length is adopted. And smart phone is used as mobile payment proxy for smart devices. For user privacy protection, anonymous mobile payment and device use among family members are realized. This protocol uses group signatures and challenge-response based secure mutual identity authentication to resist parallel sessions, replay, man-in-the-middle, denial of service and other attacks in the network. Moreover, the complexity of protocol interactions of mobile payment and device use is reduced from O(n) to O(1). Finally, the indistinguishability game proof and analysis show that the proposed protocol in this paper can realize the anonymity of family members and meet the security requirements of the system, such as mutual identity authentication and traceability, and has certain application prospects in the smart home market.

Comparing the Impact of Road Networks on COVID-19 Severity between Delta and Omicron Variants: A Study Based on Greater Sydney (Australia) Suburbs.

The Omicron and Delta variants of COVID-19 have recently become the most dominant virus strains worldwide. A recent study on the Delta variant found that a suburban road network provides a reliable proxy for human mobility to explore COVID-19 severity. This study first examines the impact of road networks on COVID-19 severity for the Omicron variant using the infection and road connections data from Greater Sydney, Australia. We then compare the findings of this study with a recent study that used the infection data of the Delta variant for the same region. In analysing the road network, we used four centrality measures (degree, closeness, betweenness and eigenvector) and the coreness measure. We developed two multiple linear regression models for Delta and Omicron variants using the same set of independent and dependent variables. Only eigenvector is a statistically significant predictor for COVID-19 severity for the Omicron variant. On the other hand, both degree and eigenvector are statistically significant predictors for the Delta variant, as found in a recent study considered for comparison. We further found a statistical difference (p < 0.05) between the R-squared values for these two multiple linear regression models. Our findings point to an important difference in the transmission nature of Delta and Omicron variants, which could provide practical insights into understanding their infectious nature and developing appropriate control strategies accordingly.

Amenity counts significantly improve water consumption predictions

Anticipating the increase in water demand in an urban area requires us to properly understand daily human movement driven by population size, land use, and amenity types among others. Mobility data from phones can capture human movement, but not only is this hard to obtain, but it also does not tell where the population is going. Previous studies have shown that amenity types can be used to predict people's movement patterns; thus, we propose using crowd-sourced amenity data and other open data sources as reasonable proxies for human mobility. Here we present a framework for predicting water consumption in areas with established service water connections and generalize it to underserved areas. Our work used features such as geography, population, and domestic consumption ratio and compared the prediction performance of various machine learning algorithms. We used 44 months of monthly water consumption data from January 2018 to July 2021, aggregated across 1790 district metering areas (DMAs) in the east service zone of Metro Manila. Results show that amenity counts reduce the mean absolute error (MAE) of predictions by 1,440 m3/month or as much as 5.73% compared to just using population and topology features. Predicted consumption during the pandemic also improved by as much as 1,447 m3/month or nearly 16% compared to just using population and topology features. We find that Gradient Boosting Trees are the best models to handle the data and feature set used in this work. Finally, the developed model is robust to disruptions in human mobility, such as lockdowns, indicating that amenities are sufficient to predict water consumption.

An analysis of twitter as a relevant human mobility proxy

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

Big Data-Driven Approach to Analyzing Spatio-Temporal Mobility Pattern

It is imperative to understand human movement and behavior, from epidemic monitoring to complex communications. So far, most research and studies on investigating and interpreting human movements have traditionally depended on private and accumulated data such as mobile records. In this work, social network data is suggested as a proxy for human mobility, as it relies on a large amount of publicly accessible data. A mechanism for urban mobility mining and extraction scheme is proposed in this research to shed light on the importance and benefits of the publicly available social network data. Given the potential value of the Big Data obtained from social network platforms, we sought to demonstrate the process of analyzing and understanding human mobility patterns and activity behavior in urban areas through the social network data. Human mobility is far from spontaneous, follows well-defined statistical patterns. This research provides evidence of spatial and temporal regularity in human mobility patterns by examining daily individual trajectories of users covering an average time span of three years (2018 to 2020). Despite the diversity of individual movements history, we concluded that humans follow simple, reproducible patterns. Additionally, we studied and evaluated the effect of COVID-19 on human mobility and activity behavior in urban areas and established a strong association between human mobility and COVID-19 spread. Numerous years of mobility data analysis can reveal well-established trends, such as social or cultural activities, which serve as a baseline for detecting anomalies and changes in human mobility and activity behavior.

Does life satisfaction vary with income inequality and social mobility?

This article provides a novel analysis using the Global Social Mobility Index (GSMI) in order to explore whether the relationship between income inequality and life satisfaction differs depending on the level of social mobility. Unlike typical social mobility proxies, such as intergenerational persistence in education, the GSMI (by the World Economic Forum) views mobility as a broad concept which captures both quantity and quality which results in a more precise measure. Using a cross-country panel dataset (143 countries, 2005–2020), the main results indicate that, overall, a higher level of income inequality is related with a lower national average life satisfaction. Furthermore, the findings support the tunnel effect hypothesis showing that the negative relationship between income inequality and life satisfaction is more prevalent in groups with lower social mobility. The contributions of this study are devoted to the area of wellbeing policy development. Policies aimed at promoting income equality are recommended to improve the overall life satisfaction of citizens, especially for countries with low social mobility.

Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study

BackgroundIn early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA.MethodsIn this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (Rt) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of Rt values with mobility proxies.FindingsWe show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [−0·492 to −0·392]), consistent with the emergence of more complex local policies and behaviours, including masking.InterpretationOur study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and Rt. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health.FundingThere was no funding source for this study.