10491 Abstracts Collection -- Representation, Analysis and Visualization of Moving Objects

From December 5 to December 10, 2010, the Dagstuhl Seminar 10491 ``Representation, Analysis and Visualization of Moving Objects\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. The major goal of this seminar has been to bring together the diverse and fast growing, research community that is involved in developing better computational techniques for spatio-temporal object representation, data mining, and visualization massive amounts of moving object data. The participants included experts from fields such as computational geometry, data mining, visual analytics, GIS science, transportation science, urban planning and movement ecology. Most of the participants came from academic institutions, some from government agencies and industry. The seminar has led to a fruitful exchange of ideas between different disciplines, to the creation of new interdisciplinary collaborations, concrete plans for a data challenge in an upcoming conference, and to recommendations for future research directions. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper

Capability of movementfeatures extracted fromGPS trajectoriesforthe classification of fine‐grained behaviors

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Recent advances in tracking technologies provide an unprecedented opportunity for a better understanding of animal movement. Data from multiple sensors can be used to capture crucial factors deriving the behaviors of the animal. Typically, accelerometer data is used to describe and classify fine-grained behaviors, while GPS data are rather used to identify more large-scale mobility patterns. In this study, however, the main research question was to what extent fine-grained foraging behaviors of wading birds can be classified from GPS tracking data alone. The species used in this study was the Eurasian Oystercatcher, Haematopus ostralegus. First, a supervised classification approach is employed based on parameters extracted from accelerometer data to identify and label different behavioral categories. Then, we seek to establish how movement parameters, computed from GPS trajectories, can identify the previously labeled behaviors. A decision tree was developed to see which movement features specifically contribute to predicting foraging. The methods used in this study suggest that it is possible to extract, with high accuracy, fine-grained behaviors based on high-resolution GPS data, providing an opportunity to build a prediction model in cases where no additional sensor or observational data on behavior is available. The key to success, however, is a careful selection of the movement features used in the classification process, including cross-scale analysis

Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems

Probabilistic inference in high-dimensional state-space models is computationally challenging. For many spatiotemporal systems, however, prior knowledge about the dependency structure of state variables is available. We leverage this structure to develop a computationally efficient approach to state estimation and learning in graph-structured state-space models with (partially) unknown dynamics and limited historical data. Building on recent methods that combine ideas from deep learning with principled inference in Gaussian Markov random fields (GMRF), we reformulate graph-structured state-space models as Deep GMRFs defined by simple spatial and temporal graph layers. This results in a flexible spatiotemporal prior that can be learned efficiently from a single time sequence via variational inference. Under linear Gaussian assumptions, we retain a closed-form posterior, which can be sampled efficiently using the conjugate gradient method, scaling favourably compared to classical Kalman filter based approachesComment: NeurIPS 2023; camera-ready versio

Physics-informed inference of aerial animal movements from weather radar data

Studying animal movements is essential for effective wildlife conservation and conflict mitigation. For aerial movements, operational weather radars have become an indispensable data source in this respect. However, partial measurements, incomplete spatial coverage, and poor understanding of animal behaviours make it difficult to reconstruct complete spatio-temporal movement patterns from available radar data. We tackle this inverse problem by learning a mapping from high-dimensional radar measurements to low-dimensional latent representations using a convolutional encoder. Under the assumption that the latent system dynamics are well approximated by a locally linear Gaussian transition model, we perform efficient posterior estimation using the classical Kalman smoother. A convolutional decoder maps the inferred latent system states back to the physical space in which the known radar observation model can be applied, enabling fully unsupervised training. To encourage physical consistency, we additionally introduce a physics-informed loss term that leverages known mass conservation constraints. Our experiments on synthetic radar data show promising results in terms of reconstruction quality and data-efficiency.Comment: NeurIPS 2022, AI4Science worksho

A multidisciplinary study of an exceptional prehistoric waste dump in the mountainous inland of Calabria (Italy) : implications for reconstructions of prehistoric land use and vegetation in Southern Italy

The mountainous inland of northern Calabria (Southern Italy) is known for its sparse prehistoric human occupation. Nevertheless, a thorough multidisciplinary approach of field walking, geophysical survey and invasive research led to the discovery of a major archaeological archive. This archive concerns a rich multi-phased dump, spanning about 3000 years (Late Neolithic to Late Imperial Roman Age) and holding two Somma-Vesuvius tephra. Of these, the younger is a distinct layer of juvenile tephra from the Pompeii eruption, while the older concerns reworked tephra from the Bronze Age AP2 eruption (ca. 1700 cal. yr BP). The large dump contains abundant ceramics, faunal remains and charcoal, and most probably originated through long-continued deposition of waste in a former gully like system of depressions. This resulted in an inversed, mound-like relief, whose anthropogenic origin had not been recognized in earlier research. The tephras were found to be important markers that support the reconstruction of the occupational history of the site. The sequence of occupational phases is very similar to that observed in a recent palaeoecological study from nearby situated former lakes (Lago Forano/Fontana Manca). This suggests that this sequence reflects the more regional occupational history of Calabria, which goes back to ca. 3000 BC. Attention is paid to the potential link between this history and Holocene climatic phases, for which no indication was found. The history deviates strongly from histories deduced from the few, but major palaeorecords elsewhere in the inlands of Southern Italy (Lago Grande di Monticchio and Lago Trifoglietti). We conclude that major regional variation occurred in prehistoric land use and its impacts on the vegetation cover of Southern Italy, and studies of additional palaeoarchives are needed to unravel this complex history. Finally, shortcomings of archaeological predictive models are discussed and the advantages of truly integrated multidisciplinary research

Second supplement to the Federated Malay States Government Gazette of Friday, the 27th of February, 1920 (No. 1, Vol. XII).

The customs duties enactment, 1897 and 1898. The quarantine and prevention of disease enactment, 1903. The food control regulations, 1918

Balancing food and density-dependence in the spatial distribution of an interference-prone forager

Foraging distributions are thought to be density-dependent, because animals not only select for a high availability and quality of resources, but also avoid conspecific interference. Since these processes are confounded, their relative importance in shaping foraging distributions remains poorly understood. Here we aimed to rank the contribution of density-dependent and density-independent effects on the spatio-temporal foraging patterns of eurasian oystercatchers. In our intertidal study area, tides caused continuous variation in oystercatcher density, providing an opportunity to disentangle conspecific interference and density-independent interactions with the food landscape. Spatial distributions were quantified using high-resolution individual tracking of foraging activity and location. In a model environment that included a realistic reconstruction of both the tides and the benthic food, we tested a family of behaviour-based optimality models against these tracking data. Density-independent interactions affected spatial distributions much more strongly than conspecific interference, even in an interference-prone species like oystercatchers. Spatial distributions were governed by avoidance of bill injury costs, selection for high interference-free intake rates and a decreasing availability of benthic bivalve prey after their exposure. These density-independent interactions outweighed interference competition in terms of effect size. We suggest that the bottleneck in our mechanistic understanding of foraging distributions may be primarily the role of density-independent prey attributes unrelated to intake rates, like damage costs in the case of oystercatchers foraging on perilous prey. At a landscape scale, above the finest inter-individual distances, effects of conspecific interaction on spatial distributions may have been overemphasised

Comparison of paleobotanical and biomarker records of mountain peatland and forest ecosystem dynamics over the last 2600 years in central Germany

As peatlands are a major terrestrial sink in the global carbon cycle, gaining an understanding of their development and changes throughout time is essential in order to predict their future carbon budget and potentially mitigate the adverse outcomes of climate change. With this aim to understand peat development, many studies have investigated the paleoecological dynamics by analyzing various proxies, including pollen, macrofossil, elemental, and biomarker analyses. However, as each of these proxies is known to have its own benefits and limitations, examining them in parallel allows for a deeper understanding of these paleoecological dynamics at the peatland and a systematic comparison of the power of these individual proxies. In this study, we therefore analyzed peat cores from a peatland in Germany (Beerberg, Thuringia) to (a) characterize the vegetation dynamics over the course of the peatland development during the late Holocene and (b) evaluate to what extent the inclusion of multiple proxies, specifically pollen, plant macrofossils, and biomarkers, contributes to a deeper understanding of those dynamics and interaction among factors. We found that, despite a major shift in the regional forest composition from primarily beech to spruce as well as many indicators of human impact in the region, the local plant population in the Beerberg area remained stable over time following the initial phase of peatland development up until the last couple of centuries. Therefore, little variation could be derived from the paleobotanical data alone. The combination of pollen and macrofossil analyses with the elemental and biomarker analyses enabled further understanding of the site development as these proxies added valuable additional information, including the occurrence of climatic variations, such as the Little Ice Age, and more recent disturbances, such as drainage