EcoGIS – GIS tools for ecosystem approaches to fisheries management

Executive Summary: The EcoGIS project was launched in September 2004 to investigate how Geographic Information Systems (GIS), marine data, and custom analysis tools can better enable fisheries scientists and managers to adopt Ecosystem Approaches to Fisheries Management (EAFM). EcoGIS is a collaborative effort between NOAA’s National Ocean Service (NOS) and National Marine Fisheries Service (NMFS), and four regional Fishery Management Councils. The project has focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization, Bycatch Analysis, and Habitat Interactions. Of these four functional areas, the project team first focused on developing a working prototype for catch and effort analysis: the Fishery Mapper Tool. This ArcGIS extension creates time-and-area summarized maps of fishing catch and effort from logbook, observer, or fishery-independent survey data sets. Source data may come from Oracle, Microsoft Access, or other file formats. Feedback from beta-testers of the Fishery Mapper was used to debug the prototype, enhance performance, and add features. This report describes the four priority functional areas, the development of the Fishery Mapper tool, and several themes that emerged through the parallel evolution of the EcoGIS project, the concept and implementation of the broader field of Ecosystem Approaches to Management (EAM), data management practices, and other EAM toolsets. In addition, a set of six succinct recommendations are proposed on page 29. One major conclusion from this work is that there is no single “super-tool” to enable Ecosystem Approaches to Management; as such, tools should be developed for specific purposes with attention given to interoperability and automation. Future work should be coordinated with other GIS development projects in order to provide “value added” and minimize duplication of efforts. In addition to custom tools, the development of cross-cutting Regional Ecosystem Spatial Databases will enable access to quality data to support the analyses required by EAM. GIS tools will be useful in developing Integrated Ecosystem Assessments (IEAs) and providing pre- and post-processing capabilities for spatially-explicit ecosystem models. Continued funding will enable the EcoGIS project to develop GIS tools that are immediately applicable to today’s needs. These tools will enable simplified and efficient data query, the ability to visualize data over time, and ways to synthesize multidimensional data from diverse sources. These capabilities will provide new information for analyzing issues from an ecosystem perspective, which will ultimately result in better understanding of fisheries and better support for decision-making. (PDF file contains 45 pages.

AstroGrid-D: Grid Technology for Astronomical Science

We present status and results of AstroGrid-D, a joint effort of astrophysicists and computer scientists to employ grid technology for scientific applications. AstroGrid-D provides access to a network of distributed machines with a set of commands as well as software interfaces. It allows simple use of computer and storage facilities and to schedule or monitor compute tasks and data management. It is based on the Globus Toolkit middleware (GT4). Chapter 1 describes the context which led to the demand for advanced software solutions in Astrophysics, and we state the goals of the project. We then present characteristic astrophysical applications that have been implemented on AstroGrid-D in chapter 2. We describe simulations of different complexity, compute-intensive calculations running on multiple sites, and advanced applications for specific scientific purposes, such as a connection to robotic telescopes. We can show from these examples how grid execution improves e.g. the scientific workflow. Chapter 3 explains the software tools and services that we adapted or newly developed. Section 3.1 is focused on the administrative aspects of the infrastructure, to manage users and monitor activity. Section 3.2 characterises the central components of our architecture: The AstroGrid-D information service to collect and store metadata, a file management system, the data management system, and a job manager for automatic submission of compute tasks. We summarise the successfully established infrastructure in chapter 4, concluding with our future plans to establish AstroGrid-D as a platform of modern e-Astronomy.Comment: 14 pages, 12 figures Subjects: data analysis, image processing, robotic telescopes, simulations, grid. Accepted for publication in New Astronom

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures

Multiscale visualization approaches for Volunteered Geographic Information and Location-based Social Media

Today, “zoomable” maps are a state-of-the-art way to explore the world, available to anyone with Internet access. However, the process of creating this visualization has been rather loosely investigated and documented. Nevertheless, with an increasing amount of available data, interactive maps have become a more integral approach to visualizing and exploring big datasets and user-generated data. OpenStreetMap and online platforms such as Twitter and Flickr offer application programming interfaces (APIs) with geographic information. They are well-known examples of this visualization challenge and are often used as examples. In addition, an increasing number of public administrations collect open data and publish their data sets, which makes the task of visualization even more relevant. This dissertation deals with the visualization of user-generated geodata as a multiscale map. The basics of today’s multiscale maps—their history, technologies, and possibilities—are explored and abstracted. This work introduces two new multiscale-focused visualization approaches for point data from volunteered geographic information (VGI) and location-based social media (LBSM). One contribution of this effort is a visualization methodology for spatially referenced information in the form of point geometries, using nominally scaled data from social media such as Twitter or Flickr. Typical for this data is a high number of social media posts in different categories—a post on social media corresponds to a point in a specific category. Due to the sheer quantity and similar characteristics, the posts appear generic rather than unique. This type of dataset can be explored using the new method of micro diagrams to visualize the dataset on multiple scales and resolutions. The data is aggregated into small grid cells, and the numerical proportion is shown with small diagrams, which can visually merge into heterogenous areas through colors depicting a specific category. The diagram sizes allow the user to estimate the overall number of aggregated points in a grid cell. A different visualization approach is proposed for more unique points, considered points of interest (POI), based on the selection method. The goal is to identify more locally relevant points from the data set, considered more important compared to other points in the neighborhood, which are then compared by numerical attribute. The method, derived from topographic isolation and called discrete isolation, is the distance from one point to the next with a higher attribute value. By using this measure, the most essential points can be easily selected by choosing a minimum distance and producing a homogenous spatial of the selected points within the chosen dataset. The two newly developed approaches are applied to multiscale mapping by constructing example workflows that produce multiscale maps. The publicly available multiscale mapping workflows OpenMapTiles and OpenStreetMap Carto, using OpenStreetMap data, are systematically explored and analyzed. The result is a general workflow for multiscale map production and a short overview of the toolchain software. In particular, the generalization approaches in the example projects are discussed and these are classified into cartographic theories on the basis of literature. The workflow is demonstrated by building a raster tile service for the micro diagrams and a vector tile service for the discrete isolation, able to be used with just a web browser. In conclusion, these new approaches for point data using VGI and LBSM allow better qualitative visualization of geodata. While analyzing vast global datasets is challenging, exploring and analyzing hidden data patterns is fruitful. Creating this degree of visualization and producing maps on multiple scales is a complicated task. The workflows and tools provided in this thesis will make map production on a worldwide scale easier.:1 Introduction 1 1.1 Motivation .................................................................................................. 3 1.2 Visualization of crowdsourced geodata on multiple scales ............ 5 1.2.1 Research objective 1: Visualization of point collections ......... 6 1.2.2 Research objective 2: Visualization of points of interest ......... 7 1.2.3 Research objective 3: Production of multiscale maps ............. 7 1.3 Reader’s guide ......................................................................................... 9 1.3.1 Structure ........................................................................................... 9 1.3.2 Related Publications ....................................................................... 9 1.3.3 Formatting and layout ................................................................. 10 1.3.4 Online examples ........................................................................... 10 2 Foundations of crowdsourced mapping on multiple scales 11 2.1 Types and properties of crowdsourced data .................................. 11 2.2 Currents trends in cartography ......................................................... 11 2.3 Definitions .............................................................................................. 12 2.3.1 VGI .................................................................................................. 12 2.3.2 LBSM .............................................................................................. 13 2.3.3 Space, place, and location......................................................... 13 2.4 Visualization approaches for crowdsourced geodata ................... 14 2.4.1 Review of publications and visualization approaches ........... 14 2.4.2 Conclusions from the review ...................................................... 15 2.4.3 Challenges mapping crowdsourced data ................................ 17 2.5 Technologies for serving multiscale maps ...................................... 17 2.5.1 Research about multiscale maps .............................................. 17 2.5.2 Web Mercator projection ............................................................ 18 2.5.3 Tiles and zoom levels .................................................................. 19 2.5.4 Raster tiles ..................................................................................... 21 2.5.5 Vector tiles .................................................................................... 23 2.5.6 Tiling as a principle ..................................................................... 25 3 Point collection visualization with categorized attributes 26 3.1 Target users and possible tasks ....................................................... 26 3.2 Example data ......................................................................................... 27 3.3 Visualization approaches .................................................................... 28 3.3.1 Common techniques .................................................................... 28 3.3.2 The micro diagram approach .................................................... 30 3.4 The micro diagram and its parameters ............................................ 33 3.4.1 Aggregating points into a regular structure ............................ 33 3.4.2 Visualizing the number of data points ...................................... 35 3.4.3 Grid and micro diagrams ............................................................ 36 3.4.4 Visualizing numerical proportions with diagrams .................. 37 3.4.5 Influence of color and color brightness ................................... 38 3.4.6 Interaction options with micro diagrams .................................. 39 3.5 Application and user-based evaluation ............................................ 39 3.5.1 Micro diagrams in a multiscale environment ........................... 39 3.5.2 The micro diagram user study ................................................... 41 3.5.3 Point collection visualization discussion .................................. 47 4 Selection of POIs for visualization 50 4.1 Approaches for point selection .......................................................... 50 4.2 Methods for point selection ................................................................ 51 4.2.1 Label grid approach .................................................................... 52 4.2.2 Functional importance approach .............................................. 53 4.2.3 Discrete isolation approach ....................................................... 54 4.3 Functional evaluation of selection methods .................................... 56 4.3.1 Runtime comparison .................................................................... 56 4.3.2 Use cases for discrete isolation ................................................ 57 4.4 Discussion of the selection approaches .......................................... 61 4.4.1 A critical view of the use cases ................................................. 61 4.4.2 Comparing the approaches ........................................................ 62 4.4.3 Conclusion ..................................................................................... 64 5 Creating multiscale maps 65 5.1 Examples of multiscale map production .......................................... 65 5.1.1 OpenStreetMap Infrastructure ................................................... 66 5.1.2 OpenStreetMap Carto ................................................................. 67 5.1.3 OpenMapTiles ............................................................................... 73 5.2 Methods of multiscale map production ............................................ 80 5.2.1 OpenStreetMap tools ................................................................... 80 5.2.2 Geoprocessing .............................................................................. 80 5.2.3 Database ........................................................................................ 80 5.2.4 Creating tiles ................................................................................. 82 5.2.5 Caching .......................................................................................... 82 5.2.6 Styling tiles .................................................................................... 82 5.2.7 Viewing tiles ................................................................................... 83 5.2.8 The stackless approach to tile creation ................................... 83 5.3 Example workflows for creating multiscale maps ........................... 84 5.3.1 Raster tiles: OGC services and micro diagrams .................... 84 5.3.2 Vector tiles: Slippy map and vector tiles ................................. 87 5.4 Discussion of approaches and workflows ....................................... 90 5.4.1 Map production as a rendering pipeline .................................. 90 5.4.2 Comparison of OpenStreetMap Carto and OpenMapTiles .. 92 5.4.3 Discussion of the implementations ........................................... 93 5.4.4 Generalization in map production workflows .......................... 95 5.4.5 Conclusions ................................................................................. 101 6 Discussion 103 6.1 Development for web mapping ........................................................ 103 6.1.1 The role of standards in map production .............................. 103 6.1.2 Technological development ..................................................... 103 6.2 New data, new mapping techniques? ............................................. 104 7 Conclusion 106 7.1 Visualization of point collections ..................................................... 106 7.2 Visualization of points of interest ................................................... 107 7.3 Production of multiscale maps ........................................................ 107 7.4 Synthesis of the research questions .............................................. 108 7.5 Contributions ....................................................................................... 109 7.6 Limitations ............................................................................................ 110 7.7 Outlook ................................................................................................. 111 8 References 113 9 Appendix 130 9.1 Zoom levels and Scale ...................................................................... 130 9.3 Full information about selected UGC papers ................................ 131 9.4 Timeline of mapping technologies .................................................. 133 9.5 Timeline of map providers ................................................................ 133 9.6 Code snippets from own map production workflows .................. 134 9.6.1 Vector tiles workflow ................................................................. 134 9.6.2 Raster tiles workflow.................................................................. 137Heute sind zoombare Karten Alltag für jeden Internetznutzer. Die Erstellung interaktiv zoombarer Karten ist allerdings wenig erforscht, was einen deutlichen Gegensatz zu ihrer aktuellen Bedeutung und Nutzungshäufigkeit darstellt. Die Forschung in diesem Bereich ist also umso notwendiger. Steigende Datenmengen und größere Regionen, die von Karten abgedeckt werden sollen, unterstreichen den Forschungsbedarf umso mehr. Beispiele für stetig wachsende Datenmengen sind Geodatenquellen wie OpenStreetMap aber auch freie amtliche Geodatensätze (OpenData), aber auch die zunehmende Zahl georeferenzierter Inhalte auf Internetplatformen wie Twitter oder Flickr zu nennen. Das Thema dieser Arbeit ist die Visualisierung eben dieser nutzergenerierten Geodaten mittels zoombarer Karten. Dafür wird die Entwicklung der zugrundeliegenden Technologien über die letzten zwei Jahr-zehnte und die damit verbundene Möglichkeiten vorgestellt. Weitere Beiträge sind zwei neue Visualisierungsmethoden, die sich besonders für die Darstellung von Punktdaten aus raumbezogenen nutzergenerierten Daten und georeferenzierte Daten aus Sozialen Netzwerken eignen. Ein Beitrag dieser Arbeit ist eine neue Visualisierungsmethode für raumbezogene Informationen in Form von Punktgeometrien mit nominal skalierten Daten aus Sozialen Medien, wie beispielsweise Twitter oder Flickr. Typisch für diese Daten ist eine hohe Anzahl von Beiträgen mit unterschiedlichen Kategorien. Wobei die Beiträge, bedingt durch ihre schiere Menge und ähnlicher Ei-genschaften, eher generisch als einzigartig sind. Ein Beitrag in den So-zia len Medien entspricht dabei einem Punkt mit einer bestimmten Katego-rie. Ein solcher Datensatz kann mit der neuen Methode der „micro diagrams“ in verschiedenen Maßstäben und Auflösungen visualisiert und analysiert werden. Dazu werden die Daten in kleine Gitterzellen aggregiert. Die Menge und Verteilung der über die Kategorien aggregierten Punkte wird durch kleine Diagramme dargestellt, wobei die Farben die verschiedenen Kategorien visualisieren. Durch die geringere Größe der einzelnen Diagramme verschmelzen die kleinen Diagramme visuell, je nach der Verteilung der Farben für die Kategorien. Bei genauerem Hinsehen ist die Schätzung der Menge der aggregierten Punkte über die Größe der Diagramme die Menge und die Verteilung über die Kategorien möglich. Für einzigartigere Punkte, die als Points of Interest (POI) angesehen werden, wird ein anderer Visualisierungsansatz vorgeschlagen, der auf einer Auswahlmethode basiert. Ziel ist es dabei lokal relevantere Punkte aus dem Datensatz zu identifizieren, die im Vergleich zu anderen Punkten in der Nachbarschaft des Punktes verglichen nach einem numerischen Attribut wichtiger sind. Die Methode ist von dem geographischen Prinzip der Dominanz von Bergen abgeleitet und wird „discrete isolation“ genannt. Es handelt sich dabei um die Distanz von einem Punkt zum nächsten mit einem höheren Attributwert. Durch die Verwendung dieses Maßes können lokal bedeutende Punkte leicht ausgewählt werden, indem ein minimaler Abstand gewählt und so räumlich gleichmäßig verteilte Punkte aus dem Datensatz ausgewählt werden. Die beiden neu vorgestellten Methoden werden in den Kontext der zoombaren Karten gestellt, indem exemplarische Arbeitsabläufe erstellt werden, die als Er-gebnis eine zoombare Karte liefern. Dazu werden die frei verfügbaren Beispiele zur Herstellung von weltweiten zoombaren Karten mit nutzergenerierten Geo-daten von OpenStreetMap, anhand der Kartenprojekte OpenMapTiles und O-penStreetMap Carto analysiert und in Arbeitsschritte gegliedert. Das Ergebnis ist ein wiederverwendbarer Arbeitsablauf zur Herstellung zoombarer Karten, ergänzt durch eine Auswahl von passender Software für die einzelnen Arbeits-schritte. Dabei wird insbesondere auf die Generalisierungsansätze in den Beispielprojekten eingegangen und diese anhand von Literatur in die kartographische Theorie eingeordnet. Zur Demonstration des Workflows wird je ein Raster Tiles Dienst für die „micro diagrams“ und ein Vektor Tiles Dienst für die „discrete isolation“ erstellt. Beide Dienste lassen sich mit einem aktuellen Webbrowser nutzen. Zusammenfassend ermöglichen diese neuen Visualisierungsansätze für Punkt-daten aus VGI und LBSM eine bessere qualitative Visualisierung der neuen Geodaten. Die Analyse riesiger globaler Datensätze ist immer noch eine Herausforderung, aber die Erforschung und Analyse verborgener Muster in den Daten ist lohnend. Die Erstellung solcher Visualisierungen und die Produktion von Karten in verschiedenen Maßstäben ist eine komplexe Aufgabe. Die in dieser Arbeit vorgestellten Arbeitsabläufe und Werkzeuge erleichtern die Erstellung von Karten in globalem Maßstab.:1 Introduction 1 1.1 Motivation .................................................................................................. 3 1.2 Visualization of crowdsourced geodata on multiple scales ............ 5 1.2.1 Research objective 1: Visualization of point collections ......... 6 1.2.2 Research objective 2: Visualization of points of interest ......... 7 1.2.3 Research objective 3: Production of multiscale maps ............. 7 1.3 Reader’s guide ......................................................................................... 9 1.3.1 Structure ........................................................................................... 9 1.3.2 Related Publications ....................................................................... 9 1.3.3 Formatting and layout ................................................................. 10 1.3.4 Online examples ........................................................................... 10 2 Foundations of crowdsourced mapping on multiple scales 11 2.1 Types and properties of crowdsourced data .................................. 11 2.2 Currents trends in cartography ......................................................... 11 2.3 Definitions .............................................................................................. 12 2.3.1 VGI .................................................................................................. 12 2.3.2 LBSM .............................................................................................. 13 2.3.3 Space, place, and location......................................................... 13 2.4 Visualization approaches for crowdsourced geodata ................... 14 2.4.1 Review of publications and visualization approaches ........... 14 2.4.2 Conclusions from the review ...................................................... 15 2.4.3 Challenges mapping crowdsourced data ................................ 17 2.5 Technologies for serving multiscale maps ...................................... 17 2.5.1 Research about multiscale maps .............................................. 17 2.5.2 Web Mercator projection ............................................................ 18 2.5.3 Tiles and zoom levels .................................................................. 19 2.5.4 Raster tiles ..................................................................................... 21 2.5.5 Vector tiles .................................................................................... 23 2.5.6 Tiling as a principle ..................................................................... 25 3 Point collection visualization with categorized attributes 26 3.1 Target users and possible tasks ....................................................... 26 3.2 Example data ......................................................................................... 27 3.3 Visualization approaches .................................................................... 28 3.3.1 Common techniques .................................................................... 28 3.3.2 The micro diagram approach .................................................... 30 3.4 The micro diagram and its parameters ............................................ 33 3.4.1 Aggregating points into a regular structure ............................ 33 3.4.2 Visualizing the number of data points ...................................... 35 3.4.3 Grid and micro diagrams ............................................................ 36 3.4.4 Visualizing numerical proportions with diagrams .................. 37 3.4.5 Influence of color and color brightness ................................... 38 3.4.6 Interaction options with micro diagrams .................................. 39 3.5 Application and user-based evaluation ............................................ 39 3.5.1 Micro diagrams in a multiscale environment ........................... 39 3.5.2 The micro diagram user study ................................................... 41 3.5.3 Point collection vis

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft