Big data analyses reveal patterns and drivers of the movements of southern elephant seals

The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with big data, that require no a priori assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for big data techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.Comment: 18 pages, 5 figures, 6 supplementary figure

The Effect of Recency to Human Mobility

In recent years, we have seen scientists attempt to model and explain human dynamics and, in particular, human movement. Many aspects of our complex life are affected by human movements such as disease spread and epidemics modeling, city planning, wireless network development, and disaster relief, to name a few. Given the myriad of applications it is clear that a complete understanding of how people move in space can lead to huge benefits to our society. In most of the recent works, scientists have focused on the idea that people movements are biased towards frequently-visited locations. According to them, human movement is based on an exploration/exploitation dichotomy in which individuals choose new locations (exploration) or return to frequently-visited locations (exploitation). In this work, we focus on the concept of recency. We propose a model in which exploitation in human movement also considers recently-visited locations and not solely frequently-visited locations. We test our hypothesis against different empirical data of human mobility and show that our proposed model is able to better explain the human trajectories in these datasets

On the Inability of Markov Models to Capture Criticality in Human Mobility

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish a theoretical upper bound on the predictability of human mobility (expressed as a minimum error probability limit), based on temporally correlated entropy. Since its inception, this bound has been widely used and empirically validated using Markov chains. We show that recurrent-neural architectures can achieve significantly higher predictability, surpassing this widely used upper bound. In order to explain this anomaly, we shed light on several underlying assumptions in previous research works that has resulted in this bias. By evaluating the mobility predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-range correlations, bearing similarity to a power-law decay. This is in contrast to the initial assumption that human mobility follows an exponential decay. This assumption of exponential decay coupled with Lempel-Ziv compression in computing Fano's inequality has led to an inaccurate estimation of the predictability upper bound. We show that this approach inflates the entropy, consequently lowering the upper bound on human mobility predictability. We finally highlight that this approach tends to overlook long-range correlations in human mobility. This explains why recurrent-neural architectures that are designed to handle long-range structural correlations surpass the previously computed upper bound on mobility predictability

Destination branding and visitor loyalty: The case of agrotourism

It has been established that strong destination brands are important in the agrotourism industry. Agrotourism brands provide the link between visitors and the agrotourism firms and destination, and tourists may or may not develop a degree of loyalty to relevant brands. The present study suggests that confidence in an agrotourism brand has high influence in development of brand loyalty. Based on hypotheses developed, confidence in an agrotourism brand is influenced by brand characteristics, agrotourism company characteristics and visitor characteristics. The present survey took place in Greece and examined the attitudes of visitors in agrotourism firms at the island of Lesvos. Survey results demonstrate that agrotourism firm brand characteristics appear more important in their impact on a visitor’s confidence in a brand. It was also established that confidence in a brand is positively influencing loyalty. Recommendations are developed for agrotourism marketers in relation to building and maintaining visitor confidence in a brand

Confidence and loyalty for agrotourism brands: the Lesvos paradigm

It has been established that strong brands are important in the agrotourism industry. Agrotourism brands provide the link between visitors and the agrotourism firms and tourists may or may not develop a degree of loyalty to relevant brands. The present study suggests that confidence in an agrotourism brand has high influence in development of brand loyalty. Based on hypotheses developed, confidence in an agrotourism brand is influenced by brand characteristics, agrotourism company characteristics and visitor characteristics. The present survey took place in Greece and examined the attitudes of visitors in agrotourism firms at the island of Lesvos. Survey results demonstrate that agrotourism firm brand characteristics appear more important in their impact on a visitor’s confidence in a brand. It was also established that confidence in a brand is positively influencing loyalty. Recommendations are developed for agrotourism marketers in relation to building and maintaining visitor confidence in a brand

Tracking Human Mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking

Economic Contributions of Winter Sports in a Changing Climate

In mountain towns across the United States that rely on winter tourism, snow is currency. For snow lovers and the winter sports industry, predictions of a future with warmer winters, reduced snowfall, and shorter snow seasons is inspiring them to innovate, increase their own efforts to address emissions, and speak publicly on the urgent need for action. This report examines the economic contribution of winter snow sports tourism to U.S. national and state-level economies. In a 2012 analysis, Protect Our Winters and the Natural Resources Defense Council found that the winter sports tourism industry generates $12.2 billion and 23 million Americans participate in winter sports annually. That study found that changes in the winter season driven by climate change were costing the downhill ski resort industry approximately$1.07 billion in aggregated revenue over high and low snow years over the last decade

On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density

This article introduces an analytical framework that interprets individual measures of entropy-based mobility derived from mobile phone data. We explore and analyze two widely recognized entropy metrics: random entropy and uncorrelated Shannon entropy. These metrics are estimated through collective variables of human mobility, including movement trends and population density. By employing a collisional model, we establish statistical relationships between entropy measures and mobility variables. Furthermore, our research addresses three primary objectives: firstly, validating the model; secondly, exploring correlations between aggregated mobility and entropy measures in comparison to five economic indicators; and finally, demonstrating the utility of entropy measures. Specifically, we provide an effective population density estimate that offers a more realistic understanding of social interactions. This estimation takes into account both movement regularities and intensity, utilizing real-time data analysis conducted during the peak period of the COVID-19 pandemic

Sequences of purchases in credit card data reveal life styles in urban populations

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Comment: 30 pages, 26 figure

Measuring Loyalty in Tourism Events: Evidence from the World Travel Market

The literature extensively focuses on loyalty formulation, but event loyalty is scarcely researched despite the substantial contribution of events in tourism. The paper examines the formulation of event loyalty at the World Travel Market (WTM), London. Using a sample of 274 visitors, it evaluates the impact of performance, the social, educational and emotional value, and the marketing activities of WTM on event loyalty formulation and development. Moreover, it examines event loyalty in terms of employment orientation of an audience characterised in direct relevance with tourism domain. The findings reveal that the most important factor affecting event loyalty is marketing activities followed by event performance, whilst all constructs appear to impact the visitor loyalty of the examined event. In addition, the employment orientation of visitors substantially influences all the examined constructs. Finally, the study discusses several managerial implications, concerning the formulation and development of loyalty in travel and tourism related events