Connected 2D and 3D Visualizations for the Interactive Exploration of Spatial Information
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Catalogue Description
INF 454: Data Visualization and User Interface Design Spring 2016 Syllabus Day/Times: TBD (4 Units) Location: TBD Instructor: Dr. Luciano Nocera Email: [email protected] Phone: (213) 740-9819 Office: PHE 412 Course TA: TBD Email: TBD Office Hours: TBD IT Support: TBD Email: TBD Office Hours: TBD Instructor’s Office Hours: TBD; other hours by appointment only. Students are advised to make appointments ahead of time in any event and be specific with the subject matter to be discussed. Students should also be prepared for their appointment by bringing all applicable materials and information. Catalogue Description One of the cornerstones of analytics is presenting the data to customers in a usable fashion. When considering the design of systems that will perform data analytic functions, both the interface for the user and the graphical depictions of data are of utmost importance, as it allows for more efficient and effective processing, leading to faster and more accurate results. To foster the best tools possible, it is important for designers to understand the principles of user interfaces and data visualization as the tools they build are used by many people - with technical and non-technical background - to perform their work. In this course, students will apply the fundamentals and techniques in a semester-long group project where they design, build and test a responsive application that runs on mobile devices and desktops and that includes graphical depictions of data for communication, analysis, and decision support. Short description: Foundational course focusing on the design, creation, understanding, application, and evaluation of data visualization and user interface design for communicating, interacting and exploring data. -
Volume Rendering
Volume Rendering 1.1. Introduction Rapid advances in hardware have been transforming revolutionary approaches in computer graphics into reality. One typical example is the raster graphics that took place in the seventies, when hardware innovations enabled the transition from vector graphics to raster graphics. Another example which has a similar potential is currently shaping up in the field of volume graphics. This trend is rooted in the extensive research and development effort in scientific visualization in general and in volume visualization in particular. Visualization is the usage of computer-supported, interactive, visual representations of data to amplify cognition. Scientific visualization is the visualization of physically based data. Volume visualization is a method of extracting meaningful information from volumetric datasets through the use of interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of volumetric datasets. Its objective is to provide mechanisms for peering inside volumetric datasets and to enhance the visual understanding. Traditional 3D graphics is based on surface representation. Most common form is polygon-based surfaces for which affordable special-purpose rendering hardware have been developed in the recent years. Volume graphics has the potential to greatly advance the field of 3D graphics by offering a comprehensive alternative to conventional surface representation methods. The object of this thesis is to examine the existing methods for volume visualization and to find a way of efficiently rendering scientific data with commercially available hardware, like PC’s, without requiring dedicated systems. 1.2. Volume Rendering Our display screens are composed of a two-dimensional array of pixels each representing a unit area. -
Visual Highlighting Methods for Geovisualization
VISUAL HIGHLIGHTING METHODS FOR GEOVISUALIZATION Anthony C. Robinson GeoVISTA Center, Department of Geography 302 Walker Building The Pennsylvania State University University Park, PA 16802, USA [email protected] ABSTRACT A key advantage of interactive geovisualization tools is that users are able to quickly view data observations across multiple views. This is usually supported with a transient visual effect applied around the edges of an object during a mouse selection or rollover. This effect, commonly called highlighting, has not received much attention in geovisualization research. Today, most geovisualization tools rely on simple color-based highlighting. This paper proposes additional visual highlighting methods that could be used in geovisualizations. It also presents a typology of interactive highlighting behaviors that specify multiple ways in which highlighting techniques can be blended together or modified based on data values to enhance visual recognition in multiple coordinated view applications. INTRODUCTION The key analytical affordances of geovisualization tools include dynamic interactive behaviors and coordinated multiple views of geospatial data. Much attention in geovisualization research is devoted to developing new types of views, integrating new types of data, and designing customized systems for specific analytical tasks and domains (MacEachren and Kraak, 2001). This paper focuses on refining and extending the interactively-driven visual indication, known as highlighting, that allows users to make connections between data objects in multiple views in a geovisualization. As data and the number of views we use to explore those data become increasingly complicated (Andrienko et al., 2007), it is timely to consider new highlighting methods to ensure that visual discovery is efficient and effective. -
Introduction to Geospatial Data Visualization
Introduction to Geospatial Data Visualization Lecturers: Viktor Lagutov, Katalin Szende, Joszef Laszlovsky. Ruben Mnatsakanian Duration: Fall term (September – December) Credits: 2 Course level: PhD / MA Maximum number of students: 15 Pre-requisites: none Software: GoogleEarthPro, qGIS, online mapping tools (e.g. GoogleMaps, ArcGISonline) Rapidly growing cross-disciplinary recognition and availability made Geospatial Methods in general, and Mapping, in particular, a popular approach in many research areas. Till recently, maps development had been a prerogative of cartographers and, later, experts in specialized mapping packages. Latest advances in hardware and software have opened this area to researchers in other disciplines and allowed them to enhance traditional research methods. The wide spectrum of such technologies and approaches is often referred as Geographic Information Systems (GIS) and includes, among others, mapping packages, geospatial analysis, crowdsourcing with mobile technologies, drones, online interactive data publishing. The geospatial literacy is becoming not an optional advantage for researchers and policy officers, but a basic requirement for many employers. The aim of the course is to develop basic understanding of spatially referenced data use and to explore potential applications of GIS in various research areas. The sessions provide both theoretical understanding and practical use of geospatial data and technologies for mapping societal and environmental phenomena. Students will learn basic features of GIS packages and the ways to utilize them for own research. The course is focused on practical skills in geospatial data visualization (mapping) and consists of • Theoretical sessions on principles of geospatial data visualization, cartography and GIS basics; • Practicals on learning GIS methods and getting mapping skills using free open source packages; • Supervised and independent students’ work on individual course projects. -
Tree-Map: a Visualization Tool for Large Data
TREE-MAP: A VISUALIZATION TOOL FOR LARGE DATA Mahipal Jadeja Kesha Shah DA-IICT DA-IICT Gandhinagar,Gujarat Gandhinagar,Gujarat India India Tel:+91-9173535506 Tel:+91-7405217629 [email protected] [email protected] ABSTRACT 1. INTRODUCTION Traditional approach to represent hierarchical data is to use Tree-Maps are used to present hierarchical information on directed tree. But it is impractical to display large (in terms 2-D[1] (or 3-D [2]) displays. Tree-maps offer many features: of size as well complexity) trees in limited amount of space. based upon attribute values users can specify various cate- In order to render large trees consisting of millions of nodes gories, users can visualize as well as manipulate categorized efficiently, the Tree-Map algorithm was developed. Even file information and saving of more than one hierarchy is also system of UNIX can be represented using Tree-Map. Defi- supported [3]. nition of Tree-Maps is recursive: allocate one box for par- Various tiling algorithms are known for tree-maps namely: ent node and children of node are drawn as boxes within Binary tree, mixed treemaps, ordered, slice and dice, squar- it. Practically, it is possible to render any tree within prede- ified and strip. Transition from traditional representation fined space using this technique. It has applications in many methods to Tree-Maps are shown below. In figure 1 given fields including bio-informatics, visualization of stock port- hierarchical data and equivalent tree representation of given folio etc. This paper supports Tree-Map method for data data are shown. One can consider nodes as sets, children integration aspect of knowledge graph. -
(Geo-)Visualization
Geographic Data Science - Lecture III (Geo-)Visualization Dani Arribas-Bel Today Visualization What and why History Examples Geovisualization What "A map for everyone" Dangers of geovisualization Visualization "Data graphics visually display measured quantities by means of the combined use of points, lines, a coordinate system, numbers, symbols, words, shading, and color." The Visual Display of Quantitative Information. Edward R. Tufte. [Source] [Source] A bit of history Maps A bit of history Maps --> Data Maps (XVIIth.C.) A bit of history Maps --> Data Maps (XVIIth.C.) --> Time series (1786) A bit of history Maps --> Data Maps (XVIIth.C.) --> Time series (1786) --> Scatter plots A bit of history Maps --> Data Maps (XVIIth.C.) --> Time series (1786) --> Scatter plots Surprisingly recent: 1750-1800 approx. (much later than many other advances in math and stats!) William Playfair's "linear arithmetic": encode/replace numbers in tables into visual representations. Other relevant names throughout history: Lambert, Minard, Marey. Visualization The Visual Display of Quantitative Information. Edward R. Tufte. By encoding information visually, they allow to present large amounts of numbers in a meaninful way. If well made, visualizations provide leads into the processes underlying the graphic. Learning by seeing time... Time series [Source] XVIIIth. Cent. - Pytometrie by J. H Lambert Bar chart [Source] Playfair's bar chart in The Commercial and Political Atlas (1786) Abstract line plot [Source] Lambert - Evaporation rate against temperature, 1769 Minard - Napoleon army map (XIXth. Cent.) [Source] "It may well be the best statistical graphic ever drawn" (E. R. Tufte) Geovisualization Tufte (1983) "The most extensive data maps [...] place millions of bits of information on a single page before our eyes. -
Geovisualization: Integration and Visualization of Multiple Datasets Using Mapbox Cecilia Cadenas California Polytechnic State University, San Luis Obispo
Geovisualization: Integration and Visualization of Multiple Datasets Using Mapbox Cecilia Cadenas California Polytechnic State University, San Luis Obispo Abstract—The use of geovisualizations to different from information visualization dealing analyze multiple datasets can ease the with abstract data spaces. [3] research and investigation to find B. Mapbox & TileMill correlations between these datasets. However, making these visualizations with The creation of these visualizations can be multiple datasets can be challenging due to challenging due to the complexity and the lack of standardization within file formats differences between the geographical data types, and the software needed to render the and how easily they can be accessed and used. visualizations. In order to help with the creation of these visualizations, many cartographers and software This project investigates the making of engineers have created many different tools that these visualizations, maps in this case, using can make the creation of these visualizations an open source platforms like Mapbox. Using easier and accessible to anyone with a computer. Python scripts for the data parsing, For the creation of the maps in this project, CartoCSS for the designing of the maps, one of these tools was used. After some research JavaScript to make the maps interactive, and and testing different tools and platforms, HTML to combine all the layers together a Mapbox was selected as the best option for the set of maps were created. These maps customization level needed for the maps wanted. contained data regarding tons of wine grape Mapbox is an online platform that makes the data crushed and yearly heat accumulation viewing of these maps simple across multiple during the growing season that demonstrate platforms since it requires no extra software to how combining multiple data sets can lead to be downloaded. -
What Is Geovisualization? to the Growing Field of Geovisualization
This issue of GeoMatters is devoted What is Geovisualization? to the growing field of geovisualization. Brian McGregor uses geovisualiztion to by Joni Storie produce animated maps showing settle- ment patterns of Hutterite colonies. Dr. Marc Vachon’s students use it to produce From a cartography perspective, dynamic presentation options to com- videos about urban visualization (City geovisualization represents a change in municate knowledge. For example, at- Hall and Assiniboine Park), while Dr. how knowledge is formed and repre- lases require extra planning compared Chris Storie shows geovisualiztion for sented. Traditional cartography is usu- to individual maps, structurally they retail mapping in Winnipeg. Also in this issue Honours students describe their ally seen a visualization (a.k.a. map) could include hundreds of maps, and thesis projects for the upcoming collo- that is presented after the conclusion all the maps relate to each other. Dr. quium next March, Adrienne Ducharme is reached to emphasize or compliment Danny Blair and Dr. Ian Mauro, in the tells us about her graduate research at the research conclusions. Geovisual- Department of Geography, provide an ELA, we have a report about Cultivate ization changes this format by incor- excellent example of this integration UWinnipeg and our alumni profile fea- porating spatial data into the analysis with the Prairie Climate Atlas (http:// tures Michelle Méthot (Smith). (O’Sullivan and Unwin, 2010). Spatial www.climateatlas.ca/). The combina- Please feel free to pass this newsletter data, statistics and analysis are used to tion of maps with multimedia provides to anyone with an interest in geography. answer questions which contribute to for better understanding as well as en- Individuals can also see GeoMatters at the Geography website, or keep up with the conclusion that is reached within riched and informative experiences of us on Facebook (Department of Geog- the research. -
Visual Bandwidth Selection for Kernel Density Maps
Photogrammetrie • Fernerkundung • Geoinformation 5/2009, S. 445–454 Visual Bandwidth Selection for Kernel Density Maps Jukka M. krisp, stefan peters,Christian e. Murphy & hongChao fan, München Keywords: Kernel Density, Visualization, Visual Analytics, Geostatistics, Kernel Bandwidth, Cartography Summary: Within this paper we investigate the Zusammenfassung: Visuelle Radius Festlegung challenge to find an appropriate bandwidth in ker- für Kernel Dichte Abschätzungen. In dieser Arbeit nel density estimation. Kernel density estimation untersuchen wir die Möglichkeiten, um einen ange- methods can be used in visualizing and analyzing messenen Radius in der Kernel-Dichte Schätzung spatial data, with the objective of understanding zu erhalten. Kernel-Dichte Schätzung Methoden and potentially predicting event patterns. Our aim können in der Visualisierung und Analyse räumli- is to provide a computational tool to visually select cher Daten zum Verständnis und zur Vorhersage the bandwidth parameter and to visually investi- von räumlichen Mustern dienen. Unser Ziel ist es, gate the effect of different parameters within the ein Computer-Werkzeug zur visuellen Parameter kernel density calculations. With this tool the user Bestimmung zu entwickeln. Die Radien verschie- is able to visually find the most appropriate band- deneR Kernel werden visuell geprüft und können width foR the particular dataset and scale. The slid- experimentell untersucht werden. Mit diesem er-tool includes a graphical user interface and can Werkzeug kann der Anwender den am besten ge- read point datasets. A specific number of kernel eigneten Radius für den jeweiligen Datensatz be- density maps are generated by using a range of stimmen. Das Schieberegler-Werkzeug beinhaltet bandwidths. In the graphical interface these kernel eine grafische Benutzeroberfläche und kann Punkt- density maps can be drawn near real-time while the datensätze einladen. -
From Surface Rendering to Volume
What is Computer Graphics? • Computational process of generating images from models and/or datasets using computers • This is called rendering (computer graphics was traditionally considered as a rendering method) • A rendering algorithm converts a geometric model and/or dataset into a picture Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK What is Computer Graphics? This process is also called scan conversion or rasterization How does Visualization fit in here? Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Computer Graphics • Computer graphics consists of : 1. Modeling (representations) 2. Rendering (display) 3. Interaction (user interfaces) 4. Animation (combination of 1-3) • Usually “computer graphics” refers to rendering Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Computer Graphics Components Department of Computer Science CSE364 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Surface Rendering • Surface representations are good and sufficient for objects that have homogeneous material distributions and/or are not translucent or transparent • Such representations are good only when object boundaries are important (in fact, only boundary geometric information is available) • Examples: furniture, mechanical objects, plant life • Applications: video games, virtual reality, computer- aided design Department of -
Infovis and Statistical Graphics: Different Goals, Different Looks1
Infovis and Statistical Graphics: Different Goals, Different Looks1 Andrew Gelman2 and Antony Unwin3 20 Jan 2012 Abstract. The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey’s Exploratory Data Analysis (1977) and Tufte’s books in the succeeding decades. But statistical graphics still occupies an awkward in-between position: Within statistics, exploratory and graphical methods represent a minor subfield and are not well- integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles. We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphics designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization. Recent decades have seen huge progress in statistical modeling and computing, with statisticians in friendly competition with researchers in applied fields such as psychometrics, econometrics, and more recently machine learning and “data science.” But the field of statistical graphics has suffered relative neglect. -
Recent Publications 1984 — 2017 Issues 1 — 100
RECENT PUBLICATIONS 1984 — 2017 ISSUES 1 — 100 Recent Publications is a compendium of books and articles on cartography and cartographic subjects that is included in almost every issue of The Portolan. It was compiled by the dedi- cated work of Eric Wolf from 1984-2007 and Joel Kovarsky from 2007-2017. The worldwide cartographic community thanks them greatly. Recent Publications is a resource for anyone interested in the subject matter. Given the dates of original publication, some of the materi- als cited may or may not be currently available. The information provided in this document starts with Portolan issue number 100 and pro- gresses to issue number 1 (in backwards order of publication, i.e. most recent first). To search for a name or a topic or a specific issue, type Ctrl-F for a Windows based device (Command-F for an Apple based device) which will open a small window. Then type in your search query. For a specific issue, type in the symbol # before the number, and for issues 1— 9, insert a zero before the digit. For a specific year, instead of typing in that year, type in a Portolan issue in that year (a more efficient approach). The next page provides a listing of the Portolan issues and their dates of publication. PORTOLAN ISSUE NUMBERS AND PUBLICATIONS DATES Issue # Publication Date Issue # Publication Date 100 Winter 2017 050 Spring 2001 099 Fall 2017 049 Winter 2000-2001 098 Spring 2017 048 Fall 2000 097 Winter 2016 047 Srping 2000 096 Fall 2016 046 Winter 1999-2000 095 Spring 2016 045 Fall 1999 094 Winter 2015 044 Spring