From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Multimodal and multidimensional geodata interaction and visualization

This PhD proposes the development of a Science Data Visualization System, SdVS, that analyzes and presents different kinds of visualizing and interacting techniques with Geo-data, in order to deal with knowledge about Geo-data using GoogleEarth. After that, we apply the archaeological data as a case study, and, as a result, we develop the Archaeological Visualization System, ArVS, using new visualization paradigms and Human-Computer-Interaction techniques based on SdVS. Furthermore, SdVS provides guidelines for developing any other visualization and interacting applications in the future, and how the users can use SdVS system to enhance the understanding and dissemination of knowledge

Dynamic Data Citation Service-Subset Tool for Operational Data Management

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of "dynamic data citation". The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

3D Cadastres Best Practices, Chapter 5: Visualization and New Opportunities

This paper proposes a discussion on opportunities offered by 3D visualization to improve the understanding and the analysis of cadastre data. It first introduce the rationale of having 3D visualization functionalities in the context of cadastre applications. Second the publication outline some basic concepts in 3D visualization. This section specially addresses the visualization pipeline as a driven classification schema to understand the steps leading to 3D visualization. In this section is also presented a brief review of current 3D standards and technologies. Next is proposed a summary of progress made in the last years in 3D cadastral visualization. For instance, user’s requirement, data and semiotics, and platforms are highlighted as main actions performed in the development of 3D cadastre visualization. This review could be perceived as an attempt to structure and emphasise the best practices in the domain of 3D cadastre visualization and as an inventory of issues that still need to be tackled. Finally, by providing a review on advances and trends in 3D visualization, the paper initiates a discussion and a critical analysis on the benefit of applying these new developments to cadastre domain. This final section discusses about enhancing 3D techniques as dynamic transparency and cutaway, 3D generalization, 3D visibility model, 3D annotation, 3D data and web platform, augmented reality, immersive virtual environment, 3D gaming, interaction techniques and time

3D-Stereoscopic Immersive Analytics Projects at Monash University and University of Konstanz

Immersive Analytics investigates how novel interaction and display technologies may support analytical reasoning and decision making. The Immersive Analytics initiative of Monash University started early 2014. Over the last few years, a number of projects have been developed or extended in this context to meet the requirements of semi- or full-immersive stereoscopic environments. Different technologies are used for this purpose: CAVE2™ (a 330 degree large-scale visualization environment which can be used for educative and scientific group presentations, analyses and discussions), stereoscopic Powerwalls (miniCAVEs, representing a segment of the CAVE2 and used for development and communication), Fishtanks, and/or HMDs (such as Oculus, VIVE, and mobile HMD approaches). Apart from CAVE2™ all systems are or will be employed on both the Monash University and the University of Konstanz side, especially to investigate collaborative Immersive Analytics. In addition, sensiLab extends most of the previous approaches by involving all senses, 3D visualization is combined with multi-sensory feedback, 3D printing, robotics in a scientific-artistic-creative environment

Automated web-based analysis and visualization of spatiotemporal data

Most data are associated with a place, and many are also associated with a moment in time, a time interval, or another linked temporal component. Spatiotemporal data (i.e., data with elements of both space and time) can be used to assess movement or change over time in a particular location, an approach that is useful across many disciplines. However, spatiotemporal data structures can be quite complex, and the datasets very large. Although GIS software programs are capable of processing and analyzing spatial information, most contain no (or minimal) features for handling temporal information and have limited capability to deal with large, complex multidimensional spatiotemporal data. A related problem is how to best represent spatiotemporal data to support efficient processing, analysis, and visualization. In the era of "big data," efficient methods for analyzing and visualizing large quantities of spatiotemporal data have become increasingly necessary. Automated processing approaches, when made scalable and generalizable, can result in much greater efficiency in spatiotemporal data analysis. The growing popularity of web services and server-side processing methods can be leveraged to create systems for processing spatiotemporal data on the server, with delivery of output products to the client. In many cases, the client can be a standard web browser, providing a common platform from which users can interact with complex server-side processing systems to produce specific output data and visualizations. The rise of complex JavaScript libraries for creating interactive client-side tools has enabled the development of rich internet applications (RIA) that provide interactive data exploration capabilities and an enhanced user experience within the web browser. Three projects involving time-series tsunami simulation data, potential human response in a tsunami evacuation scenario, and large sets of modeled time-series climate grids were conducted to explore automated web-based analysis, processing, and visualization of spatiotemporal data. Methods were developed for efficient handling of spatiotemporal data on the server side, as well as for interactive animation and visualization tools on the client side. The common web browser, particularly when combined with specialized server side code and client side RIA libraries, was found to be an effective platform for analysis and visualization tools that quickly interact with complex spatiotemporal data. Although specialized methods were developed to for each project, in most cases those methods can be generalized to other disciplines or computational domains where similar problem sets exist