Graphics Reference
In-Depth Information
Summary and Overview; Part IV
1.2.3
he final section contains seven chapters on specific applications of data visualiza-
tion. here are, of course, individual applications discussed in earlier chapters, but
here the emphasis is on the application rather than principles or methodology.
Genetic networks are obviously apromising area forinformative graphic displays.
GraceShiehandChin-YuanGuodescribesomeoftheprogressmadesofarandmake
clear the potential for further research.
Modern medical imaging systems have made significant contributions to diag-
noses and treatments. Henry Lu discusses the visualization of data from positron
emission tomography, ultrasound and magnetic resonance.
Twochaptersexamine company bankruptcy datasets. Inthe firstone,Antony Un-
win, Martin heus and Wolfgang Härdle use a broad range of visualization tools to
carry out an extensive exploratory data analysis. No large dataset can be analysed
cold, and this chapter shows how effective data visualization can be in assessing data
quality and revealing features of a dataset. he other bankruptcy chapter employs
graphics tovisualize SVMmodelling.WolfgangHärdle,Rouslan MoroandDorothea
Schäfer use graphics to display results that cannot be presented in a closed analytic
form.
he astonishing growth of eBay has been one of the big success stories of recent
years. Wolfgang Jank, Galit Shmueli, Catherine Plaisant and Ben Shneiderman have
studied data from eBay auctions and describe the role graphics played in their anal-
yses.
Krzysztof Burnecki and Rafal Weron consider the application of visualization in
insurance. his is another example of how the value of graphics lies in providing
insight into the output of complex models.
The Authors
1.2.4
he editors would like to thank the authors of the chapters for their contributions. It
is important for a collective work of this kind to cover a broad range and to gather
many experts with different interests together. We have been fortunate in receiving
the assistance of so many excellent contributors.
he mixture at the end remains, of course, a mixture. Different authors take dif-
ferent approaches and have different styles. It early became apparent that even the
term data visualization means different things to different people! We hope that the
Handbook gains rather than loses by this eclecticism.
Figures
.
and
.
earlier in the chapter showed that the chapter form varied be-
tweenauthorsinvariousways.Figure
.
revealsanotheraspect.hescatterplotshows
an outlier with a very large number of references (the historical survey of Michael
Friendly) and that some papers referenced the work of their own authors more than
others. he histogram is for the rate of self-referencing.
