Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

To investigate the complex phenomenon of bird migration, researchers rely on sophisticated methods for tracking long‐distant migrants. While large birds can be equipped with satellite tags, these are too heavy for many species. Instead, researchers often use light‐level geolocation for tracking individual small migratory birds. Unfortunately, light‐level geolocation is often coarse and unreliable, with positioning errors of anything up to hundreds of kilometres. Recent Bayesian models try to constrain the route to plausible corridors: they couple light‐level measurements with information about the bird's likely movement. While these models improve inference, they still lack information on weather conditions, specifically the impact of wind. For example, birds might encounter tailwinds—considerably increasing their (ground) speed and making longer routes more likely, or headwinds—having the opposite effect. Miniaturised multi‐sensor tags allow monitoring not only light but also acceleration and air pressure. These measurements provide essential additional information about the exact timing of flight activity and the corresponding flight altitudes. This article proposes a Bayesian model for inferring bird migration. The model integrates air pressure to estimate flight altitudes and considers wind data to calculate the most likely flight trajectory. The model constrains the migratory routes to those likely given by the winds en route and the observed timing of flight activity. We apply the model to infer the migration of European Hoopoes Upupa epops. Adding wind data for route inference excludes flight trajectories with unrealistic high airspeeds, decreases the uncertainty of the position estimates and returns more plausible migratory routes. Faithful reconstruction of migratory routes helps unravel the influence of physiological and environmental factors on bird migration. This is crucial for habitat protection where limited resources need to be allocated to relevant areas

Why GPS makes distances bigger than they are

Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are among the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation error. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS: the distance between two points recorded with a GPS is -- on average -- bigger than the true distance between these points. This systematic `overestimation of distance' becomes relevant if the influence of interpolation error can be neglected, which is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and we illustrate that it functionally depends on the autocorrelation of GPS measurement error ($C$). We argue that $C$ can be interpreted as a quality measure for movement data recorded with a GPS. If there is strong autocorrelation any two consecutive position estimates have very similar error. This error cancels out when average speed, distance or direction are calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine $C$ in real-world GPS movement data sampled at high frequencies. We apply our approach to a set of pedestrian and a set of car trajectories. We find that the measurement error in the data is strongly spatially and temporally autocorrelated and give a quality estimate of the data. Finally, we want to emphasize that all our findings are not limited to GPS alone. The systematic bias and all its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.Comment: 17 pages, 8 figures, submitted to IJGI

Inferring the history of spatial diffusion processes (Short Paper)

When studying the spatial diffusion of a phenomenon, we often know its geographic distribution at one or more snapshots in time, while the complete history of the diffusion process is unknown. For example, we know when and where the first Indo-European languages arrived in South America and their current distribution. However, we do not know the history of how these languages spread, displacing the indigenous languages from their original habitat. We present a Bayesian model to interpolate the history of a diffusion process between two points in time with known geographical distributions. We apply the model to recover the spread of the Indo-European languages in South America and infer a posterior distribution of possible evolutionary histories of how they expanded their areas since the time of the first invasion by Europeans. Our model is more generally applicable to infer the evolutionary history of geographic diffusion phenomena from incomplete data

What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?

Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car’s dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD

Hidden spatial clusters – and how to find them

Spatial clustering finds groups of neighbouring objects with similar attributes, revealing patterns of spatial interaction and influence. However, not all similarities in spatial data are due to areal effects. Confounders can mask similarities and hide the spatial signal in the data. We see this, for example, in cultural evolution where language similarities due to shared ancestry mask similarities due to contact and interaction. In this article, we present sBayes a Bayesian mixture model for spatial clustering in the presence of confounders. sBayes learns which similarities in a set of spatial point objects are explained by confounding effects and assigns objects to clusters based on the remaining similarities in the data. We introduce the algorithm to a geographic audience on the example of a fictional mobility analysis. We discuss how sBayes can be applied to ecology, health, and economy problems, revealing hidden geographic structures and patterns

Evidence for Britain and Ireland as a linguistic area

Approaches to linguistic areas have largely focused either on purely qualitative investigation of area formation processes, on quantitative and qualitative exploration of synchronic distributions of linguistic features without considering time, or on theoretical issues related to the definition of the notion "linguistic area". What is still missing are approaches that supplement qualitative research on area formation processes with quantitative methods. Taking a bottom-up approach, we bypass notional issues and propose to quantify area formation processes by a) measuring the change in linguistic similarity given a geographical space, a socio-cultural setting, a time span, a language sample, and a set of linguistic data, and b) testing the tendency and magnitude of the process using Bayesian inference. Applying this approach to the expression of reflexivity in a dense sample of languages in northwestern Europe from the early Middle Ages to the present, we show that the method yields robust quantitative evidence for a substantial gain in linguistic similarity that sets the languages of Britain and Ireland apart from languages spoken outside Britain and Ireland and cross-cuts lines of linguistic ancestry

Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth

As users of mobile devices make phone calls, browse the web, or use an app, large volumes of data are routinely generated that are a potentially useful source for investigating human behavior in space. However, as such data are usually collected only as a by-product, they often lack stringent experimental design and ground truth, which makes interpretation and derivation of valid behavioral conclusions challenging. Here, we propose an unsupervised, data-driven approach to identify different user types based on high-resolution human movement data collected from a smartphone navigation app, in the absence of ground truth. We capture spatio-temporal footprints of users, characterized by meaningful summary statistics, which are then used in an unsupervised step to identify user types. Based on an extensive dataset of users of the mobile navigation app Sygic in Australia, we show how the proposed methodology allows to identify two distinct groups of users: ‘travelers’, visiting different areas with distinct, salient characteristics, and ‘locals’, covering shorter distances and revisiting many of their locations. We verify our approach by relating user types to space use: we find that travelers and locals prefer to visit distinct, different locations in the Australian cities Sydney and Melbourne, as suggested independently by other studies. Although we use high-resolution GPS data, the proposed methodology is potentially transferable to low-resolution movement data (e.g. Call Detail Records), since we rely only on summary statistics