Global navigation satellite systems such as the Global Positioning System (GPS) is one of 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 errors. 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 in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and 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 a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is 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 pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.

In geographic information science, a plethora of different approaches and methods is used to assess the similarity of movement. Some of these approaches term two moving objects similar if they share akin paths. Others require objects to move at similar speed and yet others consider movement similar if it occurs at the same time. We believe that a structured and comprehensive classification of movement comparison measures is missing. We argue that such a classification not only depicts the status quo of qualitative and quantitative movement analysis, but also allows for identifying those aspects of movement for which similarity measures are scarce or entirely missing.In this review paper we, first, decompose movement into its spatial, temporal, and spatiotemporal movement parameters. A movement parameter is a physical quantity of movement, such as speed, spatial path, or temporal duration. For each of these parameters we then review qualitative and quantitative methods of how to compare movement. Thus, we provide a systematic and comprehensive classification of different movement similarity measures used in geographic information science. This classification is a valuable first step toward a GIS toolbox comprising all relevant movement comparison methods.

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.

Culture evolves in ways that are analogous to, but distinct from, genomes. Previous studies examined similarities between cultural variation and genetic variation (population history) at small scales within language families, but few studies have empirically investigated these parallels across language families using diverse cultural data. We report an analysis comparing culture and genomes from in and around northeast Asia spanning 11 language families. We extract and summarize the variation in language (grammar, phonology, lexicon), music (song structure, performance style), and genomes (genome-wide SNPs) and test for correlations. We find that grammatical structure correlates with population history (genetic history). Recent contact and shared descent fail to explain the signal, suggesting relationships that arose before the formation of current families. Our results suggest that grammar might be a cultural indicator of population history while also demonstrating differences among cultural and genetic relationships that highlight the complex nature of human history.

Culture evolves in ways that are analogous to, but distinct from, genetic evolution. Previous studies have demonstrated correlations between genetic and cultural diversity at small scales within language families, but few studies have empirically investigated parallels between genetic and cultural evolution across multiple language families using a diverse range of cultural data. Here we report an analysis comparing cultural and genetic data from 13 populations from in and around Northeast Asia spanning 10 different language families/isolates. We construct distance matrices for language (grammar, phonology, lexicon), music (song structure, performance style), and genomes (genome-wide SNPs) and test for correlations among them. After controlling for spatial autocorrelation and recent contact, robust correlations emerge between genetic and grammatical distances. Our results suggest that grammatical structure might be one of the strongest cultural indicators of human population history, while also demonstrating differences among cultural and genetic relationships that highlight the complex nature of human cultural and genetic evolution. Significance StatementComparing cultural traits to the genetic relationships of populations can reveal the extent to which cultural diversification reflects population history. To date, this approach has been mostly used to compare genetic relationships with the linguistic relationships that hold within language families, thereby limiting time depth to considerably less than 10,000 years. Here, we compare the genetic relationships of 13 populations in and around Northeast Asia to linguistic and musical relationships spanning different language families, thereby probing potential effects of population history at deeper time depths. We find that after controlling for geography, similarities in grammatical relationships reflect genetic relationships, suggesting that grammatical structure captures deep-time population history.

Abstract: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.

When speakers of different languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographical areas of past human interaction and migration. However, other factors may contribute to similarities between languages. Inheritance from a shared ancestral language and universal preference for a linguistic property may both overshadow contact signals. How can we find geographical contact areas in language data, while accounting for the confounding effects of inheritance and universal preference? We present sBayes , an algorithm for Bayesian clustering in the presence of confounding effects. The algorithm learns which similarities are better explained by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographical prior ensures their spatial coherence. We test sBayes on simulated data and apply it in two case studies to reveal language contact in South America and the Balkans. Our results are supported by findings from previous studies. While we focus on detecting language contact, the method can also be used to uncover other traces of shared history in cultural evolution, and more generally, to reveal latent spatial clusters in the presence of confounders.

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 (i) measuring the change in linguistic similarity given a geographical space, a sociocultural setting, a time span, a language sample, and a set of linguistic data, and (ii) 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 of Britain and Ireland and cross-cuts lines of linguistic ancestry.*