Advisor:
Dr. Sergiu Dascalu
Committee:
Dr. Valerie Fridland
Dr. Fred Harris
Dr. Yaakov Varol
Dr. Yantao Shen

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6.

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Background
Requirements
Architecture
Research Plan
Conclusions

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Cyberinfrastructure (CI) developments are part of an NSF EPSCoR project (2008-2013, cca $21.7 million)
Focused on climate change (CC) research, education, and policy making in Nevada
Six project components:
◦ climate modeling (air)
◦ water resources (water)
◦ ecological change (land)
◦ education
◦ cyber infrastructure
◦ policy, decision making and outreach

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◦ Create research capabilities to add value to the existing R&D resources
◦ Establish unique positions in focused research fields
◦ Increase inter-institutional and interdisciplinary collaborations

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◦ The effects of regional climate change on ecosystem resources
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◦ How climate changes affect water resources and linked ecosystem services and human systems?
◦ How will climate changes affect disturbance regimes (e.g., wildland fires, insect outbreaks, droughts) and linked systems?

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◦ Facilitate interdisciplinary climate change research, education, policy, decision-making, and outreach by using CI to develop and make available integrated data repositories and intelligent, userfriendly software solutions



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◦ Nevada Climate Change Portal ( NCCP )
◦ Software tools for climate change research, outreach and education: software frameworks
◦ Integration and interaction across project and among CI groups within the 3-State Western
Consortium: facilitator of collaboration

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◦ Although most of the climate related data is easily available on the World Wide Web, it is a complex and demanding task to analyze very large datasets without the help of visualization.

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◦ Presenting the results in a comprehensible manner for decision makers, stakeholders and general public.
◦ Evolution of climate models.
◦ Verification of hypotheses.
◦ Data exploration in order to find the trends and patterns.

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

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◦ What specific visualization techniques and displays can increase the efficiency of the environmental scientists?
◦ What mechanism for integrating data extraction, conversion and visualization are most beneficial for the environmental scientists work?
◦ What are the challenges facing researchers in the field of data visualization?

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Significant features of VISTED
◦
Data Visualization
◦
Data Download
◦
Data Extraction
◦
Data Conversion
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Capabilities of VISTED
◦
Handling several input data formats such as Network Common Data
Form (NetCDF), Comma-Separated Values (CSV), American
Standard Code for Information Exchange (ASCII) and Hierarchal
Data Format (HDF5).
◦
Providing different kinds of visualizations such as line chart, bar chart, bubble chart, and many more.

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◦ A web based tool for climate researchers, students, educators and general public.
◦ Uploading datasets from users machine.
◦ Reading input from several data formats such as
NetCDF, CSV, ASCII and HDF5.
◦ Extracting NetCDF, CSV, ASCII and HDF5 datasets.
◦ Converting into different data format.
◦ Introducing new visualization techniques to the climate researchers.

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Introduction
Requirements
Architecture
Research Plan
Conclusions

Table 1: Matrix representing the features of visualization tools
# Tool Name
1
3
ArcGIS
2 AVS/Expres s
Ferret
Operating system support
Microsoft
Windows,
Linux,Sun
Solaris
Windows, Mac
OS X, Linux,
Solaris, and
HP-UX, IRIX and Alph
Tru64
Unix systems, and
Windows on
XP/NT/9x
Visualization Techniques
Map (MXD), Globe, Geoprocessing,
Geocoding, Network Analysis,Geodata ,
Mobile
2D line field plots, Gamma plot, 3D shaded,contour, and arrow field plots,
Animations, particle tracing using stream lines and streak lines, isosurfaces, Volume Visualization
Geophysical formatting, symmetrical processing.
Programming/
Scripting languages
# of variables
VBA , VB, .NET, Java,
C++, COM, Python,
VBScript, JavaScript,
ASP, JSP, ColdFusion,
Java, .NET, JavaScript,
XML, FLASH, PHP
C, C++, and
FORTRAN.
Multidimensio nal data
2D, 3D, univariate,mult ivariate data
Ferret Scripts 3D, 4D,
Multidimensio nal data
4
5
GGobi
Google
Visualizatio n API
Windows,
Mac, Unix
Windows,
Mac, Unix
Histogram, textured dot plot, barchart, spineplot, Scatterplot, parallel coordinates, time series plot pie chart , Scatterplot, Guage, geo chart, bar chart, tree map, bubble chart, line graph, stack graph, , combo chart, column chart, area chart, candlestick chart, word cloud generator, and maps.
Ggobi scripting
Javascript
3D,
Multivariate data
2D

AVS/Express
Terrain and Weather
Source: http://www.avs.com/products/avs-express/gallery.html
Wind Modeling

Impacts of Sea Level Rise
Source: http://www.esri.com/library/ebooks/climate-change.pdf
Climate change

Table 1: Matrix representing the features of visualization tools
#
6
Tool
Name
GrADS
7 Integrated
Data
Viewer
(IDV)
8 Mathemati ca
Operating system support
Visualization Techniques
Linux, Mac OS X,
Windows,
Solaris, IBM AIX,
DEC Alpha, IRIX
Windows, Linux,
Solaris (SPARC and x86), Mac
OS-X
Windows, Mac,
Unix line and bar graphs, scatter plots, smoothed contours, shaded contours, streamlines, wind vectors, grid boxes, shaded grid boxes, and station model plots
Charts, maps, radar displays, gridded data displays, isosurfaces, volume rendering, globe display, plan view, profiler winds polar and spherical plots, contour and density plots, parametric line and surface plots, and vector, stream plots, candlestick charts, quantile plots, box whisker charts, Bode plots, histograms,
2D and 3D bar charts, pie charts, bubble charts, B-spline curves in 2D or 3D
Programming/
Scripting languages
FORTRAN, GrADS scripts
Java
C++, Java, .Net,
FORTRAN, CUDA,
OpenCL
# of variables
5-dimensional
3D, multidimensional data
2D, 3D
9 Matlab Linux, Microsoft
Windows
Line, area, bar, pie charts, Histograms,
Scatter/bubble plots, Animations,
Direction and velocity plots, isosurfaces,
Volume Visualization
C, C++, and
Fortran.
1D,2D, 3D visualizations
10 OpenDX Windows, Mac
OS X, Linux,
Solaris, and Unix
Animations, Direction and velocity plots, isosurfaces, Volume Visualization
C, FORTRAN and
Visual Basic
2D, 3D, univariate,multiva riate data

Grads Temperature Forecast
Source: http://wxmaps.org/pix/temp5.html
IDV view of Hurricane Charlie
Source: http://www.unidata.ucar.edu/software/idv/docs/userguide/index.html

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11
Table 1: Matrix representing the features of visualization tools
# of variables Tool
Name
Operating system support
Prefuse Windows,
Mac, Unix
Visualization Techniques
Area chart, Bar chart, Pie chart, scatter chart, line graph, Tree map, network diagram and animations
Programming/
Scripting languages
Java 2D
12
13
14
15
R Windows,
Mac OS X,
Linux and
Unix
Graphs, traditional statistical tests, time series analysis, linear & nonlinear modeling, classification, clustering
C, Python, Perl 3D
S-PLUS Windows,
Linux,
UNIX,
Solaris
SPSS Windows,
Mac, and
Linux
Graphs, linear & nonlinear modeling, classification, clustering
Tables, graphs, linear regression, cluster analysis, and non-parametric tests
FORTRAN,C, S
Java, Python,
SaxBasic
3D
2D
Tableau Windows Scatterplot, matrix chart, bar chart, area chart, bubble chart, stack graph, pie chart, link map and spatial maps
No programming or scripting required
2D, univariate, multivariate data

R-Statistical Package
Source: http://www.r-project.org/
Tableau Gallery
Source: http://www.tableausoftware.com/learn/gallery

Table 1: Matrix representing the features of visualization tools
#
16
Tool Name Operating system support
UV-CDAT Mac, Linux
Visualization Techniques multi-view visualization,
Direction and velocity plots, isosurfaces, Volume
Visualization, and parameter space exploration
Programming
/ Scripting languages
Python,
C/C++,Java,
FORTRAN
# of variables
3D, multidimensional data
17 Python 3D, multidimensional data
18
19
VisTrails Windows,
Mac, Linux multi-view visualization,
Direction and velocity plots, isosurfaces, Volume
Visualization, and parameter space exploration
VisIt
Visualizati on toolkit
(VTK)
Windows,
Mac,
Linux,
Unix, AIZ,
Solaris,
Tru64,
IRIZ
Windows,
Mac,
Unix
Contour 3D, Pseudo color plot,
Contour 3D, volume plot, vector plot, subset plot, molecule plot, parallel axis plot scalar, vector, tensor, texture, volumetric methods, implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation
Python
C++
3D, multidimensional data
3D

Vis Trails Gallery
Source: http://www.vistrails.org/index.php/File:Screen_Shot_2012-01-
12_at_2.50.19_PM.png
VisIt Gallery
Source: https://wci.llnl.gov/codes/visit/gallery.html

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NASA (National Aeronautics and Space Administration)
◦ * Provides data extraction.
◦ * Data can be downloaded in several formats.
◦ - No data interaction.
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NOAA ( National Oceanic and Atmospheric Administration)
◦ * Supports data interaction.
◦ * Provides data extraction.
◦ - Data can be downloaded only in ASCII format.
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Cal-adapt
◦ * Supports data interaction.
◦ - Cannot change visualization technique
◦ - Does not support data conversion.
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Many eyes
◦ * Supports several visualization techniques.
◦ * Allows users to upload data
◦ -Supports only CSV and ASCII file formats.

NASA
Source: http://mynasadata.larc.nasa.gov/

NOAA
Source: http://www.climate.gov/#climateWatch

CAL- Adapt
Source: http://cal-adapt.org/temperature/decadal/

Many Eyes
Source: http://www-958.ibm.com/software/analytics/manyeyes/page/create_visualization.html

• Less learning time
• No programming knowledge required
• ArcGIS, Tableau, Graphpad, Many eyes
• Programming/Scripting knowledge required
• AVS/Express, VisTrails, VisIt, VTK, Ferret, UV-CDAT, GrADS, IDV, R, SPSS, Jquery visualize,
D3
• Open Source
• Ferret, GrADS, IDV, R, UV-CDAT, VisTrails, VisIt
• Supporting several input formats
• ArcGIS, GrADS, VisIt, Ferret, NCL
• Supporting several visualization techniques
• VisTrails, UV-CDAT, VTK, IDV, Many eyes
• Supporting large and complex datasets
• AVS/Express, IDV, VisIt, VTK, Ferret

• Degrading performance while working with large datasets
• VisTrails, VisIt, XmdvTool, IDV
• Poor data modeling capabilities
• VTK, Tableau,
• Not supporting data interaction
• ArcGIS, VTK
• Supporting limited operating systems/ browsers/ hardware
• UV-CDAT, OpenDX, Many eyes, Ferret

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None of the tools fulfill the needs of climate researchers completely.
Switching among the tools could be easier if there is a standard input data format.
Support of interactive 3D/4D visualizations.
Support of several devices such as touch pads, display walls, mobile devices, and desktops.
Handling erroneous data and missing data values.

◦ One-Dimensional
 histograms, normal distributions
◦ Two-Dimensional
 line graphs, bar charts, area charts, pie charts, maps, scatterplots, and stream line and arrow visualizations.
◦ Three-Dimensional
 Isosurface techniques , direct volume rendering, slicing techniques , 3D bar charts and realistic renderings.
◦ Multi-Dimensional
 scatterplot matrices, parallel coordinates, star coordinates, maps, and autoglyphs

Source: http://www-958.ibm.com/software/analytics/manyeyes/page/Visualization_Options.html

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6.
Introduction
Background
Architecture
Research Plan
Conclusions

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VISTED shall allow user to select a climate variable.
VISTED shall allow user to select a combination of climate variables.
VISTED shall allow user to select a time period.
VISTED shall allow user to select a particular location.
VISTED shall accept input data in netCDF format.
VISTED shall allow user to download data in netCDF format.
VISTED shall accept input data in CSV format.
VISTED shall allow user to download data in CSV format.
VISTED shall accept input data in binary format.
VISTED shall allow visualization of datasets that are loaded from users system.

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VISTED shall allow user to download data in binary format.
VISTED shall allow user to view the selected data.
VISTED shall provide the links for the navigation across the website.
VISTED shall provide some sample visualizations to the users.
VISTED shall allow user to choose a visualization technique.
VISTED shall allow user to view data as time series graphs.
VISTED shall allow user to pick a location from the map.
VISTED shall provide users with frequently asked questions and answers.

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VISTED shall be platform independent.
VISTED shall support many browsers
VISTED shall be developed using competitive technologies like HTML5, jQuery, and CSS3.
VISTED shall be extensible and reusable.
VISTED shall be fault tolerant.
VISTED shall have high performance.
VISTED shall have high reliability.
VISTED shall support devices like tablets and mobile phones.

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◦ HTML5
◦ D3 JavaScript Library
◦ C#
◦ Visual studio 2012

D3 is the winner!
* Provides several visualization techniques.
* Provides data interactivity.
Source: https://github.com/mbostock/d3/wiki/Gallery

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4.
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6.
Introduction
Background
Requirements
Research Plan
Conclusions

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3.
4.
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6.
Introduction
Background
Requirements
Architecture
Conclusions

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Exploration of the current state-of-the art methods and technologies/tools used for the presentation and visualization of environmental data.
Research and design of a new web-based software toolset for processing and visualizing transect data (these activities will lead to
“advanced data processing capabilities for the
NCCP”).
Development, experimentation, and integration of the new processing and visualization software into the Nevada Climate Change Portal.

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Task 1: Survey existing methods and supporting tools used for the presentation and visualization of environmental data. Identify strengths and limitations.
◦ Outputs: survey report.
Task 2: Elaborate conceptual design and operational approach (method) for a new web-based software toolset dedicated to presenting and visualizing NCCP environmental data.
◦ Outputs: conceptual design document; documented method.
Task 3: Create software specification and architectural design of the new software toolset.
◦ Outputs: Software requirements specification document; design document (high-level sign, detail-level design, data design, user interface design, interface design).
Task 4: Implement web-based software solution.
◦ Outputs: Implemented software; documented code.

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Task 5: Integrate web-based software toolset into the Nevada Climate Change Portal and prepare user manual.
◦ Outputs: Integrated software, executable through the NCCP; tutorial and user manual.
Task 6: Perform usability tests on the data portal and process results.
◦ Output: usability test report.
Task 7: Based on user feedback, revise and improve web-based software toolset for data presentation and visualization.
◦ Output: improved web-based, NCCP-integrated software toolset for environmental data presentation and visualization.
Task 8: Disseminate research and development results.
◦ Outputs: Journal or conference paper; one or two poster presentations.


◦ Performed survey on existing data visualization tools and techniques for environmental data. (Task 1)
◦ Gathered the requirements and created the concept and specification document. (Task 2)
◦ Created the detail design of the software toolset. (Task 3)
◦ Designed the initial prototype of the toolset. (Task 4)


In addition to GRA tasks
◦ Wrote chapters 2 and 5 of the dissertation.
◦ Presented a paper at CATA-2013 in March 2013.
 Likhitha R., Qiping Y., Dascalu M. S., Harris F. C. Jr., “A Survey of Visualization Techniques and Tools for Environmental Data”, CATA, March 2013.
◦ Presented a poster in NSF EPSCOR Annual Climate Change
Conference in March 2013.
 Likhitha R. “An overview of visualization approaches for environmental data”, Tri-State EPSCoR Climate Change Workshop, March 2013.
◦ Coauthor on another paper and poster.
 Qiping Y., Michael M. Jr., Dascalu S., Harris F. C. Jr., Likhitha R., “Community Metadata ISO 19115 Adaptor”, CATA,
March 2013.
 Richard k., Michael M. Jr., Eric F., Sohei O., Likhitha R., Ivan G., Jigarkumar P., Adrew D., Ershad S., Shahram.,
Dascalu ., Harris F. C. Jr., “Communicating Climate Change on the Web: The Nevada Climate Change Portal”, Tri-
State EPSCoR Climate Change Workshop, March 2013.

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◦ Get additional input from scientists.
◦ Finalize proposed approach and web-based solution. (Task 4)
◦ Integrate with NCCP. (Task 5)
◦ Perform user tests. (Task 6)
◦ Revise VISTED and compare with related toolsets.
(Task 7)
◦ Disseminate research. (Task 8)
◦ Finalize and defend dissertation. (Task 9)

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Introduction
Background
Requirements
Architecture
Research Plan

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

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◦ New approach that integrates data extraction, conversion, and visualization (with possible extensions for data analysis).
◦ Associated web-based toolset for data manipulation and visualization.
◦ Support provided for several data formats.
◦ Flexible data extraction capabilities.
◦ Mechanisms for efficient visualization of climate data.

◦ I would like to thank all my committee members.
 Dr. Sergiu Dascalu
 Dr. Valerie Fridland
 Dr. Fred Harris
 Dr. Yaakov Varol
 Dr. Yantao Shen
◦ I am also thankful to CSE R&D faculty
 Mr. Eric Fritzinger
 Dr. Richard Kelley

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Nevada Climate Change Portal, available at http://www.sensor.nevada.edu/NCCP/.
“Graphical Forecasts,” Nation Oceanic and Atmospheric Administration, available at: http://graphical.weather.gov/.
“UNR Valley Road Weather Station,” Western RegionalClimate Center, , available at: http://www.wrcc.dri.edu/weather/unr.html.
“Snow Pack: Decadal Averages Map,” Cal-adapt ExploringCalifornia‘s Climate
Change Research, available at: http://caladapt.org/snowpack/decadal/.
Pavlopoulos G. A., Wegener A., and Schneider R., "A survey of visualization tools for biological network analysis", BioDataMining, November 2008.
Aigner W., Bertone A., and Miksch S., "Comparing Information Visualization Tools
Focusing on the Temporal Dimensions," 12th International Conference on
Information Visualization, pp. 69 - 74, July 2008
Mozzafari E. and Seffah A., "From Visualization to Visual Mining: Application to
Environmental Data", IEEE Confererence on Advances in Computer-Human
Interaction, pp.143-148, February 2008.
Aigner W., Miksch S., Schumann H., and Tominski C.,Visualization of Time-
Oriented Data, Springer, May 2011.

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“ArcGIS - Mapping and Spatial Analysis for Understanding Our World”, ESRI, available at:<http://www.esri.com/software/arcgis>.
“ArcGIS QGIS Faceoff”, blog.donmeltz.com, available at:
<http://donmeltz.com/blog/index.php/2011/06/10/arcgisqgis-faceoff/>.
“AVS/Express Data Visualization Software”, AVS/Express,
<http://www.avs.com/products/avsexpress/index.html>.
“GrADS Home Page”, Grid Analysis and Display System,
<http://www.iges.org/grads/grads.html>.
“Unidata | IDV”, Unidata, <http://www.unidata.ucar.edu/software/idv/>.
“UV-CDAT”, UV-CDAT, available at: <http://uv-cdat.llnl.gov/>.
“VisTrailsWiki”, VisTrailsWiki, available at:
<http://www.vistrails.org/index.php/Main_Page#VisTrails_Overview>.
“VisIt Visualization Tool”, visIt, available at
<https://wci.llnl.gov/codes/visit/home.html>.
“VTK - The Visualization Toolkit”, Visualization Toolkit, available at
<http://www.vtk.org/.
Wenzel S. B. J. and Jessen U., "A taxonomy of visualizationtechniques for simulation in production and logistics,"Proceedings of the 2003 Winter Simulation
Conference, pp. 729- 736, December 2003.