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Survey Report on Data Presentation and Visualization for NCCP
by
Likhitha Ravi
Advisor: Dr. Sergiu Dascalu
July 19, 2012
Department of Computer Science and Engineering
College of Engineering
University of Nevada, Reno
Survey Report
1
Table of Contents
1. Introduction…………………………………………………………… 2
2. Existing Methods……………………………………………………… 2
3. Visualization Tools……………………………………………………. 3
4. Matrix……………………………………………………………….....18
5. Discussion……………………………………………………...………28
6. Future Work ……………………………………………………………29
7. References………………….…………………………………………..30
Survey Report
2
1. Introduction
The purpose of this document is to provide the summary of survey results on visualization tools used for
data presentation and visualization of environmental data. Section 2 discusses the existing visualization
methods for environmental data. Section 3 lists the existing visualization tools of different domains and
states their usability in research for large datasets. Section 4 provides a matrix with the features of
visualization tools. Section 5 presents a general discussion about the state of art in data visualization. In
Section 6 the future research directions in field of data visualization are discussed. Section 7 has the list
of references.
2. Existing Methods
The existing visualization methods for environmental data can be classified based on several factors.
Many researchers have introduced taxonomy for visualization techniques [1]. Shneiderman [2] classified
visualization techniques based on data types and user tasks. The data types include 1D, 2D, 3D,
multidimensional, temporal, tree, and network. Some of the user tasks on which Schneiderman
classified the methods were overview, zoom, filter, details-on-demand, relate, history, and extract.
Keim [3] classified them based on data types and interaction/distortion techniques. His data types were
similar to those of Schneiderman expect for algorithms/software. The interaction methods such as
standard, projection, filtering, zoom, distortion, link brush was considered for classification. Silva et al.
[23] classified them based on visualization and interaction features, Muller et al. [24] classifies them as
static and dynamic, Chi [25] classified them based on visualization process and Tory [26] classified them
based on the characteristics of models of data. These classifications more generic, in this paper we have
introduced the taxonomy for visualization methods based on the data types of environmental data.
The data types of the environmental data are one-dimensional such as atmospheric pressure, wind
velocity; two-dimensional variables is a result of combination of two variables for example temperature
and humidity; three-dimensional variables are a combination of three variables; multidimensional is a
combination of more than three variables; and finally climate related text can be found in the
documents or news.
2.1 One-Dimensional
For a one dimensional data set, the data values correspond to one variable and there is only value per
data item. Some of the data visualizations of one-dimensional data are histogram as in figure 4.2 in [4],
and normal distribution as in figure 4.10 in [4].
2.2 Two-Dimensional
Two-Dimensional data corresponds to two variables. The relationship between two variables can be
easily found through visualization. The 2D visualizations of climate data are line graph as in figure 3. (d)
in [5], comparison of variables using plotting as in figure 3 in [6], bar chart as in 7.13 in [7], area chart,
pie chart, maps, scatterplot as in 7.3 as in [8] , stream line and arrow visualization as in figure 1 in [9].
Survey Report
3
2.3 Three-Dimensional
Data values in three-dimensional space have three attributes. The graphical representation of the three
attributes shows the depth and rotation in addition to the two dimensional data. The methods for
representing three-dimensional data are Isosurface techniques figure 9 in [10] and fig [2] in [11], direct
volume rendering figure 2.5 in [12], slicing techniques as in figure 3 in [13], 3D bar charts and realistic
rendering figure 5 in [14].
2.4 Multi-Dimensional
Data attributes in Multi-Dimensional space ranges from four to hundreds. To understand the relations
between multiple variables several techniques are available. The methods for the visualization of
multivariate data are scatterplot matrix as in figure 1 in [15], parallel coordinates figure 2 in [16], star
coordinates as in figure 1 in [17], maps as in figure 9 in [18], and autoglyph as in 9 in [19].
2.5 Text
The structured and unstructured text in climate change related documents can be visualized using
phrase net as in [20], wordle as in [27], and word tree\word net as in [21].
3. Visualization Tools
In this section, several visualization tools, their applicability, references, strengths, limitations and
authors’ comments have been stated. These tools are designed to meet the needs of different user
groups. The goal of the survey was to find several visualizations options for the environmental data by
considering tools that performs diverse tasks such as statistical analysis, numerical analysis, 1D graphs,
2D graphs, 3D graphs, multivariate visualizations and textual visualizations.
Tool Name ArcGIS
Overview It is Geographical Information System (GIS). It is used in data analysis by using
simple maps.
Applicability Geology, hydrology, meteorology, environmental sciences.
References ArcGIS online
http://www.arcgis.com/about/
ArcGIS – Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/ArcGIS
Strengths It reduces cost by efficiently using the hardware and software.
It supports several data types.
It is available in many versions such as desktop, mobile, and web map
version.
Easy to use interface.
Survey Report
4
Limitations ArcGIS occasionally hangs during the installation of registrations.
Comments It is used by many researchers around the world because of its high quality
graphics, scalability and flexibility.
Tool Name AVS/Express
Overview It supports object oriented development and is mainly used for visualization
purposes by programmers and non-programmers.
Applicability Engineering, Fluid Dynamics, earth sciences, business, medicine, manufacturing
telecommunications and environmental research.
References AVS/Express-Visualization Edition-Data Visualization
http://www.avs.com/products/avs-express/visualization.html
Strengths Easily integrates modules from programming languages such as C, C++,
and FORTRAN.
Handles complex and large datasets.
Limitations It depends on virtual memory for sending results to user which gives low
performance at times.
No zooming facility.
Comments It provides powerful visualizations for researchers but zooming in and get more
details of the data is difficult.
Tool Name DataScape
Overview It is a data modeling and visualization tool for complex systems.
Applicability Soft Sensors, Business Intelligence, Process Modeling, Embedded Applications
References Datascape - Overview
http://www.tmpinc.com/datascape_overview.html
Strengths Good graphics.
Identifies the unusual data.
Limitations Not intuitive.
Comments It does not have a very big user group. It is mostly convenient for professionals.
Survey Report
5
Tool Name Ferret
Overview It is an interactive data analysis and visualization tool for large grid data sets.
Applicability Oceanography, climatology.
References Data Visualization and Analysis - Ferret http://ferret.wrc.noaa.gov/Ferret/
Strengths Supports input data from several data sources.
Good quality graphics.
Good memory management for large datasets.
Limitations UNIX, Linux machines need X windows software to run Ferret.
Comments It can be used by researchers working mainly in oceanography and climatology.
Tool Name GGobi
Overview It is used for the visualizations of high dimensional data.
Applicability Engineering, fluid mechanic, meteorology, electromagnetism, and dynamical
systems.
References GGobi data visualization system
http://www.ggobi.org/
GGobi - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/GGobi
Strengths Good data visualization capabilities.
Extensibility through plugins.
It is open source.
Limitations Not very intuitive.
Comments A good choice for professionals in the visualization field.
Tool Name Google Visualization API
Overview One stop for visualizations over the web. The API provides visualizations for
structured data which can be integrated into a website or gadget.
Applicability Engineering, social sciences, and environmental.
References Google Visualization API Reference - Google Chart Tools - Google Developers
https://developers.google.com/chart/interactive/docs/reference
Survey Report
6
Strengths Connects to several data sources.
Easy to embed visualization to an existing website.
Several visualization options.
Saves lots of programming time.
Easy to set up.
Limitations Limited to web- based applications.
Limited styling options.
Comments Good choice for beginners who have less knowledge in programming.
Tool Name GrADS
Overview Grid Analysis and Display System (GrADS) a visualization tool is used for data
manipulation, visualization in 5-dimensional space.
Applicability Earth sciences.
References GrADS Home Page
http://www.iges.org/grads/grads.html
Strengths Open source.
Supports several input data formats.
Limitations Not very intuitive.
Comments Need programming background. But a good tool for researchers of earth
sciences.
Tool Name Graphpad
Overview It is used to analyze, organize and plot data.
Applicability Biostatistics, scientific graphing, Education, Medicine, and pharmaceutical.
References GraphPad Software. Scientific graphing, curve fitting (nonlinear regression)
statistics.
http://www.graphpad.com/welcome.htm
Strengths It is able to import/ export data from excel.
Many products with different graphing packages are available.
Limitations It is expensive for the features it provides.
Comments It is not recommended for huge databases and exploratory tasks.
Survey Report
7
Tool Name Integrated Data Viewer (IDV)
Overview It is a java based software framework used to analyze and visualize the
geoscience data.
Applicability Environmental sciences, climatology.
References Unidata
http://www.unidata.ucar.edu/software/idv/
Strengths It is free.
Provides high quality 3D visualizations.
It can plot data from remote servers.
Supports several data types.
Limitations It requires a lot of RAM which makes it slow for large databases.
Comments It is a good tool for researchers working on supercomputers with lot of RAM and
need high quality graphics.
Tool Name Jquery visualize
Overview It is a plugin which uses JavaScript to generate simple charts in HTML5.
Applicability Web applications, multidomain.
References Update to jQuery Visualize: Accessible Charts with HTML5 from Designing with
Progressive Enhancement | Filament Group, Inc., Boston, MA
http://www.filamentgroup.com/lab/update_to_jquery_visualize_accessible_char
ts_with_html5_from_designing_with/
Strengths Flash is not required.
Draws chart from HTML table.
Default styling can be altered using CSS.
Limitations Limited visualizations.
Need HTML5 which is not supported by older versions of IE. It need IE6 +.
No animations.
Comments Not a very good choice for creating rich and sophisticated visualizations but
perfect for creating easy and simple visualizations.
Survey Report
8
Tool Name Many eyes
Overview It is a web based tool. Users can upload datasets, analyze those using
visualizations, and share them with others.
Applicability Climatology, social sciences, networking, politics, multidomain.
References Many Eyes
http://www-958.ibm.com/software/data/cognos/manyeyes/
Strengths Big active user community.
No software is required.
No installation required
Free visualizations of the user datasets that can be shared.
Limitations Application is not available for mobile devices and tablets like
iphone/ipad as java and flash is required.
Comments The site is still under development. Many visualizations relating to climate
change are available. It allows viewers to add comments on visualizations, so it
good place to post the work and get reviewers from other users. Also, users get
to know the most popular and highly rated visualizations.
Tool Name Map objects
Overview It is a set of software components that embeds maps into applications.
Applicability address/intersection search, census point-in-polygon processing, parks GIS,
traffic counts GIS
References Using MapObjects for Enterprise GIS at The City of Calgary
http://proceedings.esri.com/library/userconf/proc01/professional/papers/pap8
42/p842.htm#mapobjectsapplications
Strengths Very simple and easy to use.
Limitations It supports only windows operating system.
Limited features.
Comments It is a good choice for programmers working with Visual Basic and trying to
develop mapping applications.
Tool Name Mathematica
Overview It is a software package which does several numerical computations and
symbolic computations for data analysis and visualization.
Survey Report
9
Applicability Mathematics, engineering, fluid mechanic, meteorology, electromagnetism, and
dynamical systems.
References Wolfram Mathematica: Technical Computing Software- to
Solution
http://www.wolfram.com/mathematica/
Strengths Good quality graphics.
Good naming conventions.
Limitations Interpreter is very slow.
Extensions are expensive.
Comments This tool is very useful in research areas which need lot of computation and less
programming.
Tool Name Matlab
Overview It is developed by MathWorks. It is a programming language that allows users to
perform data analysis, visualization, algorithm development and numerical
computation. It supports 1D, 2D, and 3D visualizations.
Applicability Signal and image processing, data analysis and exploration, visualization,
programming and application development, communications, control design,
test and measurement, financial modeling and analysis, and computational
biology.
References MATLAB –The language of Technical Computing.
http://www.mathworks.com/products/matlab/
MATLAB – Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/MATLAB
Strengths It cuts development time as no programming is needed for computing
complex calculations, and visualizing the results.
Generates very powerful graphics.
Many ready to use functions
Excellent online help.
Ease of use.
Operating system independent.
Limitations It is an interpreted language, so it could be slow.
Expensive.
Comments Although it allows us to perform visualizations, it is not devoted to visualization.
It is mostly used for complex numerical computations. Working with n-
dimensional data could be very challenging.
Survey Report
10
Tool Name Minitab
Overview It is used for statistical analysis and graphing.
Applicability Statistical analysis, quality improvement, plotting graphs, social sciences and
educational.
References Software for Statistics, Process Improvement, Six Sigma, Quality - Minitab http://www.minitab.com/en-US/default.aspx
Minitab - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Minitab
Strengths Easy to use and learn.
Analysis can be performed using macros.
Requires less disk space.
Limitations Reading input data from other packages is not easy.
Not a good choice to perform complex numerical analysis.
Comments It is mainly used by beginners who would like to learn statistics. It cannot be used
for intensive research. The analysis requires manual programming using macro
which could be time consuming.
Tool Name NCAR Command Language (NCL)
Overview It is an interpreted language used for scientific data analysis and visualization.
Applicability Scientific Visualizations.
References CISL's NCAR Command Language (NCL)
http://www.ncl.ucar.edu/
Strengths Open source
Several data input and output formats.
Limitations Not very intuitive.
Comments It needs knowledge in programming. Not a very good tool for beginners.
Scientists need to learn a new language to explore the data.
Tool Name OpenDX
Overview It is open source software developed by IBM. It supports the data analysis and
visualization of complex applications.
Survey Report
11
Applicability Petroleum Modeling, Demographic Modeling, Environmental Modeling,
Molecular Graphics, Medical Imaging, Weather Modeling.
References Open Visualization Data Explorer
http://www.opendx.org/
Strengths It is a very powerful data visualization tool.
Built in macros are provided to facilitate the functionalities required by
the users.
Suitable for programmers with different skill sets, as it supports
languages like C, FORTRAN and Visual Basic.
It has a client –server architecture.
It is free.
Limitations It is not suitable for all of MAC machines.
It requires more memory.
Comments Researchers can analyze the complex datasets by creating their own macros. The
techniques provided by the tool helps the researchers gain new insights of the
data.
Tool Name Prefuse
Overview It is a toolkit for data interaction, modeling and visualization. It is a java based
library.
Applicability Biology, social sciences, geography.
References prefuse | interactive information visualization toolkit
http://prefuse.org/
Strengths More flexibility as the java modules can be changed as needed.
Data manipulation can be done easily.
Limitations Visualizations do not work on mobile devices.
Integration with other components is not easy.
Comments It is not suitable for time varying continuous data analysis.
Tool Name Processing.js
Overview It is a java based open source programming language used for programming
images, animations, and interaction.
Applicability Arts, animations, simulations, 3D graphics and designing.
Survey Report
12
References Processing.js
http://processingjs.org/
Strengths No need of Flash for interactions.
Limitations It doesn’t support all browsers.
It doesn’t support object level events
Comments It needs knowledge some scripting programming language. It is mostly used by
artists and designers.
Tool Name Qlikview
Overview It is a data analysis and visualization tool used mainly in the field of business
intelligence for decision making.
Applicability Business intelligence.
References Business Discovery: Business Intelligence For Everyone | QlikView
http://www.qlikview.com/
Strengths Good dashboard support.
Good data interactivity.
Links to data from excel.
Limitations The feedback for developers while manipulating controls is not quick.
Comments It works best for someone connecting to several data sources and doesn’t
modify the data analysis often.
Tool Name R
Overview It is GNU software. It is a semi object oriented tool. It is used for statistical
computing and graphics.
Applicability Environmental sciences, Finance, Genetics, Machine Learning, Social Sciences,
Spatial.
References Introduction to Splus and R
http://faculty.nps.edu/sebuttre/home/S/intro.html
Strengths Excellent Graphics.
S-PLUS and R are similar tools but R is free.
It is extensible.
Documentation is freely available.
Survey Report
13
Limitations Computation is slow compared to other tools like Matlab.
It does not have a GUI.
Comments This tool should be a perfect choice for professionals in data analysis field and
someone who loves programming.
Tool Name S-PLUS
Overview It has a GUI which can be used for data analysis and building graphs. It is written
in C++ and is based on S programming language. S is an object oriented and
interpreted language.
Applicability Biology, bioinformatics, medicine, genetics, environmental statistics and life
sciences.
References Introduction to S-PLUS (and R)
https://home.comcast.net/~lthompson221/SPLUS_Manual.pdf
Strengths Excellent Graphics.
Menus and dialogs are available to create graphs.
Easy C and FORTRAN interface.
Documentation is freely available.
Limitations It is very expensive compared to R which has similar features and free.
Some programming skills are needed.
Comments It can be used if cost doesn’t matter and when GUI is essential.
Tool Name SPSS
Overview It has a GUI which is written in java. It is used for data analysis and generating
graphics using simple menu options. It is an acronym of Statistical Package of
Social Sciences. PSPP is a free replacement of SPSS.
Applicability Social sciences, marketing, medicine, surveys, government, education, and
marketing.
References SPSS - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/SPSS
IBM SPSS software
http://www-01.ibm.com/software/analytics/spss/
Strengths Very intuitive. Basic computing knowledge is enough to start with.
Data entry can be done using simple spreadsheet.
Data variables can be added easily based on the existing ones.
Survey Report
14
It is suitable for large datasets.
It can find unusual data easily before analysis.
Limitations It is not useful for complex surveys and research.
Few regression analysis techniques are missing.
Does not involve cluster analysis.
It is Windows based.
Comments It is mostly used in social sciences. Although it can generate interesting graphs
such as histogram, scatterplots, boxplots, it can create complex 3D graphics and
maps.
Tool Name SQL Server Reporting Services
Overview SSRS helps in decision making by generating tabular, interactive and graphical
reports of multidimensional data.
Applicability Business Intelligence.
References SQL Server Reporting Services - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/SQL_Server_Reporting_Services
Reporting Services (SSRS) http://msdn.microsoft.com/en-us/library/ms159106.aspx
Strengths Allows connections with several data sources like SQL, Oracle, and SSAS.
Can be integrated with visual studio.
Limitations The predefined set of visualizations does not allow the users to explore
new ways in visualization.
Comments It is a good tool to learn the basics of data analysis and visualization using
Microsoft technologies.
Tool Name Tableau
Overview It is tool for interactive data visualization and analysis.
Applicability Banking, government, medicine, education, and telecommunications.
References Fast Analytics and Rapid-fire Business Intelligence from Tableau Software |
Tableau Software
http://www.tableausoftware.com/
Tableau Software - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Tableau_Software
Survey Report
15
Strengths It takes less time to implement.
Very good data interactivity.
Excellent data integration with other tools.
Limitations Very poor data modeling.
Comments It is good for beginners to learn different kinds of visualization, not a good
choice for experienced developers.
Tool Name UV-CDAT
Overview Ultrascale Visualization Climate Data Analysis Tools. It provides data analysis and
visualization for large climate datasets.
Applicability Climatology.
References UV-CDAT http://uv-cdat.llnl.gov/
Strengths A good of climate related data visualizations.
Supports provenance functionality.
Open source.
Limitations Supports very limited operating systems.
As it is a new product, it is prone to bugs.
Comments Very good tool for researchers working with climate related data. Different data
visualizations can be explored.
Tool Name VisTrails
Overview It is an open source system that provides support for scientific data workflow and
visualization.
Applicability NASA, environmental sciences, astrophysics, biomedicine, neuro imaging, and
climate modeling.
References VisTrailsWiki
http://www.vistrails.org/index.php/Main_Page#VisTrails_Overview
VisTrails: visualization meets data management
http://dl.acm.org/citation.cfm?id=1142574
Strengths Simple and easy user interface.
Broad user community.
Good comparative visualization.
Survey Report
16
Limitations Sometimes it may hang up while updating large amounts of data from a
remote site.
Limited parallel computing capabilities.
Comments A good tool for researchers who wants to explore different workflows and
compare the results of variables of large datasets.
Tool Name VisIt
Overview It is an open source tool which provides data visualization for complex scientific
data.
Applicability Astrophysics, environmental sciences, fluid dynamics, and molecular science.
References VisIt - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/VisIt
VisIt: An End-User Tool For Visualizing and Analyzing Very Large Data http://vis.lbl.gov/Publications/2011/Childs-SciDAC2011.pdf
Strengths It provides framework for customization.
It provides interactive parallel visualization.
Reads data of different formats.
Limitations Data movement could be challenging in future machines.
Comments Visit can handle datasets ranging from billions to trillions, so it is good tool for
researchers working on super computers.
Tool Name Visualization toolkit (VTK)
Overview It is open source software. It is supports object oriented environment and
consist of libraries written in C++. It is mainly used for data processing and
visualization.
Applicability Scientific computing, Acoustic field visualization, virtual reality, medicine,
computational geometry, rendering and Image processing.
References VTK - The Visualization Toolkit
http://www.vtk.org/
Strengths Manages and represent complex scientific data.
Support many visualization techniques.
Big user community.
Limitations VTK has limited modeling techniques.
Survey Report
17
Data interaction is limited.
User interface is not very intuitive.
Comments It is a good tool for researchers working with complex and large datasets but it is
not a choice for the nonprogrammers.
Tool Name Weave
Overview It is Web-based Analysis and Visualization Environment. Users can visualize large
datasets of any kind.
Applicability Business, social sciences, environmental sciences, mulitdomain.
References Weave (Web-based Analysis and Visualization Environment)
http://oicweave.org/
Strengths Many kinds of visualizations are available.
It is open source and free.
Limitations It is still under development. Understanding the needs of different user
groups, and providing tutorials is a challenging task.
Comments Researchers can look into the demos and screenshots to understand the latest
visualization techniques.
Tool Name XmdvTool
Overview It is a software package for multivariate visual exploration.
Applicability Remote sensing, financial, geochemical, census, and simulation data.
References Xmdv Home page: Overview
http://davis.wpi.edu/xmdv/index.html
Strengths Very powerful, high quality visualizations.
Limitations Handling large datasets requires lot of memory.
There is a limit on user number of dimensions.
Comments A good tool for exploring the multivariate visualization techniques.
4. Matrix
A matrix is designed to represent the features of the visualization tools discussed in Section 3. The tool name, organization or people responsible for the
development of the tool, supporting operating systems, approximate price, supporting visualization techniques, tool type, required or supporting
programming languages, type of user Interface, and number of variables and dimensional supported by the tool are discussed in Table 1.
Table 1: Matrix representing the features of visualization tools [28].
# Tool
Name
Developer Operating
system
support
Open
source
/Propriet
ary
Price Visualization
Techniques
Application
Type
Programmi
ng/
Scripting
languages
Interf
ace
# of variables
1 ArcGIS Esri Microsoft
Windows,
Linux,
Sun
Solaris
Proprieta
ry
$2,500 -
$17,500
Map (MXD), Globe,
Geoprocessing,
Geocoding, Network
Analysis,Geodata ,
Mobile
Visualization
tool
VBA , VB,
.NET, Java,
C++, COM,
Python,
VBScript,
JavaScript,
ASP, JSP,
ColdFusio
n, Java,
.NET,
JavaScript,
XML,
FLASH,
PHP
GUI Multidimens
ional data
2 AVS/Expr
ess
AVS Windows,
Mac OS X,
Linux,
Proprieta
ry
Starts at
$2,995
2D line field plots,
Gamma plot, 3D
shaded,contour, and
Toolkit C, C++,
and
FORTRAN.
GUI/
CGI
2D, 3D,
univariate,m
ultivariate
Survey Report
- 19 -
Solaris,
and HP-
UX, IRIX
and Alph
Tru64
arrow field plots,
Animations, particle
tracing using stream
lines and streak
lines, isosurfaces,
Volume
Visualization
data
3 DataScap
e
Third
Millennium
Production
s
Windows,
Linux
Open
source
Free Visualization data
points, Surface
visualizations
Visualization
tool
C++ GUI 2D, 3D,
multivariate
data
4 Ferret Thermal
Modeling
and
Analysis
Project
(TMAP) at
National
Oceanic
and
Atmospheri
c
Administra
tion
(NOAA)/
Pacific
Marine
Environme
ntal
Unix
systems,
and on
Windows
XP/NT/9
x
Open
Source
Free Geophysical
formatting,
symmetrical
processing.
scripting
language
Ferret
Scripts
CLI 3D, 4D,
Multidimens
ional data
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- 20 -
Laboratory
(PMEL)
5 GGobi Deborah .S,
Michael .L,
Hadley. W,
Duncan .T
.L, Di Cook,
Heike .H
and
Andreas .B
Windows,
Mac, Unix
Open
source
Free Histogram, textured
dot plot, barchart,
spineplot,
Scatterplot, parallel
coordinates, time
series plot
Visualization
tool
Ggobi
scripting
GUI/
CGI
3D,
Multivariate
data
6 Google
Visualizat
ion API
Google Windows,
Mac, Unix
Open
source
Free 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.
Toolkit Javascript GUI 2D
7 GrADS COLA Linux,
Mac OS X,
Windows,
Solaris,
Open
source/
GNU
General
Free line and bar graphs,
scatter plots,
smoothed contours,
shaded contours,
scripting
language
FORTRAN,
GrADS
scripts
CLI 5-
dimensional
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- 21 -
IBM AIX,
DEC
Alpha,
IRIX
Public Lic
ense
streamlines, wind
vectors, grid boxes,
shaded grid boxes,
and station model
plots
8 Graphpad GraphPad
Software,
Inc.
Mac,
Windows
Proprieta
ry
$595 Line graphs, column
graphs, symbol
graphs, bar graphs,
Linear and
nonlinear regression
Analysis
Graphics,
Curve fitting,
and
Statistics
tool
No
programm
ing or
scripting
required
GUI 2D
9 Integrate
d Data
Viewer
(IDV)
Unidata Windows,
Linux,
Solaris
(SPARC
and x86),
Mac OS-X
Open
source
Free Charts, maps, radar
displays, gridded
data displays,
isosurfaces, volume
rendering, globe
display, plan view,
profiler winds
Software
library
Java GUI 3D, multi-
dimensional
data
10 Jquery
visualize
jQuery
Team
Windows,
Mac, Unix
Open
source
Free pie charts, line
charts, bar charts
and area charts
JavaScript
library
Javascript No
GUI
2D
11 Many
eyes
IBM Windows,
Mac OS X,
Linux and
Unix
Open
source
Free Scatterplot, matrix
chart, network
diagram, bar chart,
block histogram,
bubble chart, line
graph, stack graph,
Visualization
tool
No
programm
ing or
scripting
required
GUI 2D
Survey Report
- 22 -
pie chart, tree map,
word tree, tag cloud,
phrase net, word
cloud generator, and
maps.
12 Mapobjec
ts
ESRI Windows Proprieta
ry
Free Maps Visualization
tool
Visual
Basic, C++
and
Delphi
GUI 2D
13 Mathemat
ica
Wolfram
Research
Windows,
Mac,
Unix
Proprieta
ry
$2,495
(Professio
nal),
$1095
(Educatio
n), $140
(Student),
$69.95
(Student
annual
license)
$295
(Personal
)
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
Software
Package
C++, Java,
.Net,
FORTRAN,
CUDA,
OpenCL
GUI/
CGI
2D, 3D
14 Matlab The
MathWorks
Linux,
Microsoft
Windows
Proprieta
ry
Starts at
$99.99 for
students
Line, area, bar, pie
charts, Histograms,
Scatter/bubble
plots, Animations,
Statistical
tool
C, C++,
and
Fortran.
GUI/
CLI
1D,2D, 3D
visualization
s
Survey Report
- 23 -
Direction and
velocity plots,
isosurfaces, Volume
Visualization
15 Minitab Minitab Inc. Windows Proprieta
ry
$895–
$1395[2]
perpetual,
$542 or
less
concurren
t annual,
$29.99/$
49.99/$9
9.99
academic
Tables, Graphs,
Regression Analysis,
factor analysis,
cluster analysis,
correspondence
analysis, Time series
plots
Statistics
tool
C,
FORTRAN
GUI/
CLI
Multivariate
data
16 NCL
National
Center for
Atmospheri
c
Research (
NCAR)
Linux,
MacOSX,
AIX, and
Cygwin/X
running
on
Windows.
Open
source
Free Histograms, Line
graphs, bar charts,
box plots,
scatterplots, area
charts, markers,
wind barbs, maps,
isosurfaces, and
other graphical
objects.
scripting
language
NCL
scripts, C,
FORTRAN
CLI 1D,2D, 3D
17 OpenDX IBM Windows,
Mac OS X,
Linux,
Open
source
Free Animations,
Direction and
velocity plots,
Visualization
tool
C,
FORTRAN
and Visual
GUI 2D, 3D,
univariate,m
ultivariate
Survey Report
- 24 -
Solaris,
and Unix
isosurfaces, Volume
Visualization
Basic data
18 Prefuse Research
team at
University
of
Maryland
Windows,
Mac, Unix
Open
source
Free Area chart, Bar
chart, Pie chart,
scatter chart, line
graph, Tree map,
network diagram
and animations
Toolkit Java CGI 2D
19 Processin
g.js
Ben Fry
and Casey
Reas,
Linux,
Mac OSX,
Windows
Open
source
Free Animations, Graphs,
Charts, digital art,
video games
Toolkit Processing
,
JavaScript,
No
GUI
2D, 3D
20 qlikview QlikTech
team
Windows Proprieta
ry
$1350/
per user,
$15,000 /
concurren
t license,
Scatterplot, matrix
chart, bar chart,
area chart, bubble
chart, stack graph,
pie chart, link map
and spatial maps
Visualization
tool
Java GUI 2D,
univariate,
multivariate
data
21 R R
Foundation
Windows,
Mac OS X,
Linux and
Unix
Open
source
Free Graphs, traditional
statistical tests, time
series analysis,
linear & nonlinear
modeling,
classification,
clustering
Statistics
tool
C, Python,
Perl
GUI/
CLI
3D
Survey Report
- 25 -
22 S-PLUS Insightful
Inc.
Windows,
Linux,
UNIX,
Solaris
Proprieta
ry
$2399/ye
ar
Graphs, linear &
nonlinear modeling,
classification,
clustering
Statistics
tool
FORTRAN,
C, S
GUI 3D
23 SPSS IBM Windows,
Mac, and
Linux
Proprieta
ry
$4975 Tables, graphs,
linear regression,
cluster analysis, and
non-parametric
tests
Statistics
tool
Java,
Python,
SaxBasic
GUI/
CLI
2D
24 SQL
Server
Reporting
Services
Microsoft Windows Proprieta
ry
$1095-
Academic
$2434-
Commerci
al use
Area charts, bar
charts, column
charts, maps, line
charts, polar charts,
range charts, shape
charts, sparklines,
data bars
scatterplots, stock
charts
Visualization
tool
SQL GUI 2D,3D
univariate,
multivariate
data
25 Tableau Research
team lead
by
Professor
Pat
Hanrahan
at Stanford
Uiniversity
Windows Proprieta
ry
$999
(Desktop)
, $1999
(Professio
nal), Free
version
(Tableau
Public)
Scatterplot, matrix
chart, bar chart,
area chart, bubble
chart, stack graph,
pie chart, link map
and spatial maps
Toolkit No
programm
ing or
scripting
required
GUI 2D,
univariate,
multivariate
data
26 UV-CDAT Team Mac, Open Free multi-view Toolkit Python, No 3D, multi-
Survey Report
- 26 -
supported
by Office of
biological
and
environme
ntaresearc
h (BER)
Linux source visualization,
Direction and
velocity plots,
isosurfaces, Volume
Visualization, and
parameter space
exploration
C/C++,Jav
a,
FORTRAN
GUI dimensional
data
27 VisTrails Team at
University
of Utah
Windows,
Mac,
Linux
Open
source
Free multi-view
visualization,
Direction and
velocity plots,
isosurfaces, Volume
Visualization, and
parameter space
exploration
Toolkit Python GUI 3D, multi-
dimensional
data
28 VisIt Lawrence
Livermore
National
Laboratory
Windows,
Mac,
Linux,
Unix, AIZ,
Solaris,
Tru64,
IRIZ
Open
source
Free Contour 3D, Pseudo
color plot, Contour
3D, volume plot,
vector plot, subset
plot, molecule plot,
parallel axis plot
Toolkit Python CLI 3D, multi-
dimensional
data
29 Visualizat
ion toolkit
(VTK)
Kitware
Research
group
Windows,
Mac,
Unix
Open
source
Free scalar, vector,
tensor, texture,
volumetric methods,
implicit modeling,
polygon reduction,
mesh smoothing,
Toolkit C++ No
GUI
3D
Survey Report
- 27 -
cutting, contouring,
and Delaunay
triangulation
30 Weave Research
team at
Institute
for
visualizatio
n and
perception
research of
UMASS
Lowell
Windows,
Mac OS X,
Linux and
Unix
Open
source
Free Scatterplot, matrix
chart, network
diagram, bar chart,
block histogram,
bubble chart, line
graph, stack graph,
pie chart, tree map,
word tree, tag cloud,
phrase net, word
cloud generator, and
maps.
Visualization
system
Flex, Java GUI 2D
31 XmdvTool Team supp
orted by
NSF
UNIX,
LINUX,
MAC and
Windows
Open
source/
GNU
General
Public Lic
ense
Free parallel coordinates,
scatterplots,
dimensional
stacking and star
glyphs
Visualization
tool
Eclipse
using Qt
GUI 2D,
univariate,
multivariate
data
5. Discussion
In this section we will discuss about the state of the art in data visualization. An effective data
visualization system should be able to assist users with the data analysis of large data sets by using the
latest available techniques. From the matrix in Table 1 we it is clear that most of the tools which are
being used for research purposes provides the visualization techniques such as charts, graphs,
volumetric modeling, flow visualization, spatial visualization, temporal visualization, parallel coordinates
plot, Contour 3D, 3D maps, 3D/2D scatterplots, isosurfaces, and star glyphs.
Apart from the visualizations techniques that provided by the tools discussed in the table 1, there are
few techniques which are not available in these tools. Volume splitting is one of the techniques in which
a volume is divided into several semantic components [29]. Volume rendering algorithm is then applied
on each component. This will save enormous time and complexity mainly in the field of medicine. This
technique is not yet available in many existing tools. Slice based volume rendering is also used in cloud
modeling [30]. Another 3D modeling technique used to represent complex organic shapes and structural
relationship in biology and chemistry is Metaballs [31]. It is mostly used in DNA structure, organic forms
visualization, and molecular images. A meatball is a defined by 3D field variable which varies its value
with its distance from the center and influences its surrounding particles. Graph, tree visualizations are
used in defining the taxonomies of large-scale species [32]. 3D volumetric interactive information
visualization is used for representing information in several documents visually so that it helps in
understanding them easily without reading the entire document [33]. It presents stereoscopic viewing
with glyph based rendering.
Most of the data especially in the field of meteorology, environmental sciences and climatology is kept
open in the World Wide Web (WWW) for the researchers, policy makers and general public [34,35,36] .
This data can be of several formats such as simple csv, xls, and xml. The source code of many
visualization tools is also available in their website which encourages the researchers in the field of
visualization to implement their ideas quickly by not having to start everything from scratch.
IBM is providing a web-based visualization tool called Many Eyes [37] which lets users to visualize the
datasets without installing any software on their machines. Users need to upload their dataset and
choose a visualization technique. It does not provide high quality 3D graphics which is often need in
research areas. In future, there might be similar web sites in each field which helps researchers and
public in visualizing their datasets over internet with high quality graphics needed in their domain. Also,
these could be available on mobile devices and tablets in coming days.
Now-a-days, with the existence of high quality computer display devices, visualization is mainly focused
on 3D/4D techniques. The benefits of these techniques replacing the 1D/2D techniques which have
been in use since many years need to be investigated. User interaction with the visualizations is also
increasing. Users are able to discover many details available in the visualization by rolling the mouse
over the visualization, and are able to change the visualization by using functions like zoom in, zoom out,
moving left, right or by selecting several views for example Google maps provides all of these features in
its visualizations [38].
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29
6. Future Work
The demand for data analysis and visualization is increasing day by day in fields like business,
engineering, education, physical science, biology, social science, meteorology, finance, genetics, and
hydrology. After surveying thirty one data visualization tools of different domains it is clear that there
are many issues facing the researchers/designers in the field of data visualization. Each tool is mainly
addressing the needs of a particular application domain which makes it not very useful in other
application domains. But, it is very important for the designers of the visualization tool to get familiar
with several visualization techniques that have been developed for other domains, this will help them in
understanding the difference between a good user interface from a bad one and also gives new options
for visualization in his/her domain which others might not have thought about. To identify the possibility
of new technique, the designer should have insights of the data and user needs thoroughly and then
find new techniques. If there is a possibility of new technique he/she should be able to find the
advantages of it over the existing visualization techniques and see if the existing computation method is
more informative than the new visualization technique. Also, the new technique/tool should look similar
to the existing tools of the domain, and use the most widely used latest technologies to appeal the
users. Although it mainly addresses the needs of a particular domain, it is a grand success if it is
applicable in several other domains.
Other challenges facing the designers of the data visualization include creating applications that can run
on several devices such as desktop, mobile phones, display walls, and touch pads; support diverse
operating systems such as Windows, Mac, and UNIX; provide several visualization options so that users
does not need another tool; provide various interaction techniques; provide high quality graphics with
no loss of useful information; support most of the existing input data formats; support large datasets
with no performance issues; and easy integration with other tools. Apart from including these features
designers should also consider the characteristics of data. Data can be static, dynamic, structured,
unstructured, spatial, nonspatial, scalar, and vector. Understanding the different characteristics of data
and providing a linkage between these types is a very challenging task. As the developed visualizations
play a very crucial role in decision making it is important to check if there are any missing data values
from defective equipment, and if the received data is accurate. The cognitive and perceptual levels of
the users should be considered while designing the tool. Interaction techniques should be given a new
look with the changes and improvements in hardware, skillset of potential users, and decision making
needs.
To conclude, the future work in the field of data visualization can be focused on either finding new ways
of visualization or searching for problems in the existing visualization techniques or solving the existing
problems in the data visualization field. Although, it is hard to guess but it is also not impossible to find
an area with large volumes of ever increasing time varying data that needs to be analyzed and
visualization techniques have not been in that field applied so far and build the a new tool for that
application domain. The designer should have apt knowledge in that domain and data visualization to
build an effective and useful tool.
Survey Report
30
7. References
1. Wenzel .S .B .J, Jessen .U, "A taxonomy of visualization techniques for simulation in production
and logistics," Proceedings of the 2003 Winter Simulation Conference, pp. 729 - 736, Dec. 2003.
2. Shneiderman .B, "The eyes have it: a task by data type taxonomy for information visualizations,"
In the proceedings of IEEE Symposium on Visual Languages, pp. 336-343, 1996.
3. Keim .D.A, "Information visualization and visual data mining," IEEE Transactions on Visualization
and Computer Graphics, pp. 1- 8, Jan/Mar 2002.
4. Edward Linacre, Climate Data and Resources: A Reference and Guide, New York: Routledge,
1992.
5. Stolte .C, Tang .D, Hanrahan. P, "Polaris: a system for query, analysis, and visualization of
multidimensional relational databases," IEEE Transactions on Visualization and Computer
Graphics, pp. 52- 65, Jan/Mar 2002.
6. Daniel .K .R, Dmitry .B, Joseph .V, David .J .S, Donald .S, "A simple 1-dimensional, climate based
dissolved oxygen model for the central basin of Lake Erie," Journal of Great Lakes Research, pp.
465-476, March 2010.
7. Matthew .W, Georges .G, Daniel .K, Interactive data visualization: foundations, techniques, and
applications, Mass.: A K Peters, c2010.
8. “Climate Modeling”, OSU Website, Accessed: June 27, 2012,
<http://mgg.coas.oregonstate.edu/~andreas/OC599/climate_modeling_11/Script.pdf>.
9. Nocke .T, Flechsig .M, Bohm .U, "Visual Exploration and Evaluation of climate-related simulation
data," Simulation conference, pp.703 - 711, December 2007.
10. Lloyd .A .T, “Case study: severe rainfall events in northwestern Peru (visualization of scattered
meteorological data),” Proceedings of the conference on Visualization '94, pp. 350 - 354,
October 1994.
11. Riley .K, Ebert .D, Hansen .C, Levit .J, "Visually accurate multi-field weather visualization," IEEE
Visualization, pp. 279- 286, October 2003.
12. Roni Yagel, “Efficient Techniques for Volume Rendering of Scalar Fields,” 1998.
13. Chaoli .W, Kwan-Liu .M, Wittenberg A.T, "Correlation study of time-varying multivariate climate
data sets," IEEE Pacific Visualization Symposium, pp. 161 - 168, April 2009.
14. Baker .M.P, "After the storm: considerations for information visualization," IEEE Conferences on
Computer Graphics and Applications, pp. 12- 15, May 1995.
15. Richard A. B, William S. C, “Brushing scatterplots,” Technometrics, pp.127–142, 1987.
Survey Report
31
16. Robert .M .E, "The parallel coordinate plot in action: design and use for geographic
visualization," Computational Statistics & Data Analysis, pp. 605–659, November 2002.
17. Kandogan .E, “Star Coordinates: A Multi-dimensional Visualization Technique with Uniform
Treatment of Dimensions,” Proc. of IEEE Information Visualization, Hot Topics, pp. 4-8, 2000.
18. Bottinger .M, Scheuermann .G, "Brushing of Attribute Clouds for the Visualization of
Multivariate Data," IEEE Transactions on Visualization and Computer Graphics, pp. 1459 - 1466, -
December 2008.
19. Wong .P.C, Bergeron .R.D, “30 Years of Multidimensional Multivariate Visualization,” Scientific
Visualization -Overviews, Methodologies, and Techniques, IEEE Computer Society Press, pp. 3-
33, 1997.
20. “Many Eyes: Climate Change Phrase Net”, IBM website, Accessed: July 10 2012, <http://www-
958.ibm.com/software/data/cognos/manyeyes/visualizations/climate-change-phrase-net>.
21. “Many Eyes: Climate Change Word Net”, IBM website, Accessed: July 10 2012, <http://www-
958.ibm.com/software/data/cognos/manyeyes/visualizations/climate-change-word-net>.
22. Aigner .W, Bertone .A, Miksch .S, "Comparing Information Visualization Tools Focusing on the
Temporal Dimensions," 12th International Conference on Information Visualisation, pp. 69 - 74,
July 2008.
23. Silva S.F, Catarci .T, “Visualization of Linear Time-Oriented Data: a Survey,” First International
Conference on Web Information Systems Engineering (WISE), pp. 310-319, Hong Kong, Chine,
IEEE Computer Society, 2000.
24. Muller .W, Schumann .H, “Visualization methods for Time-Dependent Data - An Overview,”
Proceedings of the Winter Simulation Conference WSC, pp. 737-745, New Orleans, USA, ACM
Press, 2003.
25. Chi .E.H, “A Taxonomy of Visualization Techniques using the Data State Reference Model,”
Proceedings of the IEEE Symposium on Information Visualization InfoVis,” pp. 69-76, Salt Lake
City, USA, IEE E Computer Society, 2000.
26. Tory .M, “Rethinking Visualization: A High-Level Taxonomy, IEEE Symposium on Information
Visualization,” pp. 151- 158, 2004.
27. “Wordle – Wiki”, Wordle, Accessed: July 14, 2012, <http://www.wordle.net/show/Wiki>.
28. “Comparison of statistical packages”, Wikipedia, the free encyclopedia, Accessed: July 15, 2012,
<http://en.wikipedia.org/wiki/Comparison_of_statistical_packages>.
29. Islam .S, Silver .D, Min .C, "Volume Splitting and Its Applications," IEEE Transactions on
Visualization and Computer Graphics, pp. 193 - 203, April 2007.
Survey Report
32
30. Joshua .S, Joseph .S, David S. E, Charles .H, “A real-time cloud modeling, rendering, and
animation system,” Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on
Computer animation, pp. 160 - 166, 2003.
31. Juergen .R, Mudur .S.P, “3D visualization techniques to support slicing-based program
comprehension,” Department of Computer Science and Software Engineering, Concordia,
University, April 2005.
32. Jianting .Z, Le .G, "Embedding and extending GIS for exploratory analysis of large-scale species
distribution data," Proceedings of the 16th ACM SIGSPATIAL international conference on
Advances in geographic information system, 2008.
33. Ebert .D.S, Zwa. A, Miller .E.L, Shaw .C.D, Roberts .D.A, "Two-handed volumetric document
corpus management,” IEEE conference on Computer Graphics and Applications", pp. 60 - 62,
Aug 1997.
34. “S.E.N.S.O.R”, NCCP Website, Accessed: July 19 2012,
<http://sensor.nevada.edu/NCCP/Data%20Search/Silverlight%20Data%20Client.aspx>.
35. “Environmental Reports| EPA Response to BP Spill in the Gulf of Mexico | US EPA”, EPA website,
Accessed: July 19 2012, <http://www.epa.gov/bpspill/download.html>.
36. “Environment | Data”, The World Bank Website, Accessed: July 19 2012,
<http://data.worldbank.org/topic/environment>.
37. “Many Eyes: Browsing visualizations”, IBM website, Accessed: July 19 2012, <http://www-
958.ibm.com/software/data/cognos/manyeyes/visualizations>.
38. “Google Maps,” Google Website, Accessed: July 19 2012, < https://maps.google.com/maps>.