TY - JOUR A1 - Discher, Sören A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - Interactive and View-Dependent See-Through Lenses for Massive 3D Point Clouds JF - Advances in 3D Geoinformation N2 - 3D point clouds are a digital representation of our world and used in a variety of applications. They are captured with LiDAR or derived by image-matching approaches to get surface information of objects, e.g., indoor scenes, buildings, infrastructures, cities, and landscapes. We present novel interaction and visualization techniques for heterogeneous, time variant, and semantically rich 3D point clouds. Interactive and view-dependent see-through lenses are introduced as exploration tools to enhance recognition of objects, semantics, and temporal changes within 3D point cloud depictions. We also develop filtering and highlighting techniques that are used to dissolve occlusion to give context-specific insights. All techniques can be combined with an out-of-core real-time rendering system for massive 3D point clouds. We have evaluated the presented approach with 3D point clouds from different application domains. The results show the usability and how different visualization and exploration tasks can be improved for a variety of domain-specific applications. KW - 3D point clouds KW - LIDAR KW - Visualization KW - Point-based rendering Y1 - 2016 SN - 978-3-319-25691-7 SN - 978-3-319-25689-4 U6 - https://doi.org/10.1007/978-3-319-25691-7_3 SN - 1863-2246 SP - 49 EP - 62 PB - Springer CY - Cham ER - TY - JOUR A1 - Gonschorek, Julia A1 - Langer, Anja A1 - Bernhardt, Benjamin A1 - Raebiger, Caroline T1 - Big Data in the Field of Civil Security Research: Approaches for the Visual Preprocessing of Fire Brigade Operations JF - Science N2 - This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed. KW - Big Data KW - Civil Security KW - Explorative (Data-) Analysis KW - Geovisual Analytics KW - Visualization Y1 - 2016 U6 - https://doi.org/10.4018/IJAEIS.2016010104 SN - 1947-3192 SN - 1947-3206 VL - 7 SP - 54 EP - 64 PB - IGI Global CY - Hershey ER - TY - JOUR A1 - Lischeid, Gunnar A1 - Kalettka, Thomas A1 - Merz, Christoph A1 - Steidl, Jörg T1 - Monitoring the phase space of ecosystems: Concept and examples from the Quillow catchment, Uckermark JF - Ecological indicators : integrating monitoring, assessment and management N2 - Ecosystem research benefits enormously from the fact that comprehensive data sets of high quality, and covering long time periods are now increasingly more available. However, facing apparently complex interdependencies between numerous ecosystem components, there is urgent need rethinking our approaches in ecosystem research and applying new tools of data analysis. The concept presented in this paper is based on two pillars. Firstly, it postulates that ecosystems are multiple feedback systems and thus are highly constrained. Consequently, the effective dimensionality of multivariate ecosystem data sets is expected to be rather low compared to the number of observables. Secondly, it assumes that ecosystems are characterized by continuity in time and space as well as between entities which are often treated as distinct units. Implementing this concept in ecosystem research requires new tools for analysing large multivariate data sets. This study presents some of them, which were applied to a comprehensive water quality data set from a long-term monitoring program in Northeast Germany in the Uckermark region, one of the LTER-D (Long Term Ecological Research network, Germany) sites. Short-term variability of the kettle hole water samples differed substantially from that of the stream water samples, suggesting different processes generating the dynamics in these two types of water bodies. However, again, this seemed to be due to differing intensities of single processes rather than to completely different processes. We feel that research aiming at elucidating apparently complex interactions in ecosystems could make much more efficient use from now available large monitoring data sets by implementing the suggested concept and using corresponding innovative tools of system analysis. (C) 2015 Elsevier Ltd. All rights reserved. KW - Ecosystem research KW - Monitoring KW - Concept KW - Effective dimensionality KW - Continuity KW - Visualization Y1 - 2016 U6 - https://doi.org/10.1016/j.ecolind.2015.10.067 SN - 1470-160X SN - 1872-7034 VL - 65 SP - 55 EP - 65 PB - Elsevier CY - Amsterdam ER -