Kaleidomaps: a new technique for the visualization of multivariate time-series data

Kim Bale, Paul Chapman, Nick Barraclough, Jon Purdy, Nizamettin Aydin, Paul Dark

Research output: Contribution to journalArticlepeer-review


In this paper, we describe a new visualization technique that can facilitate our
understanding and interpretation of large complex multivariate time-series
data sets. `Kaleidomaps' have been carefully developed taking into account
research into how we perceive form and structure within Glass patterns. We
have enhanced the classic cascade plot using the curvature of a line to alter
the detection of possible periodic patterns within multivariate dual periodicity
data sets. Similar to Glass patterns, the concentric nature of the Kaleidomap
may induce a motion signal within the brain of the observer facilitating the
perception of patterns within the data. Kaleidomaps and our associated visualization
tools alter the rapid identification of periodic patterns not only within
their own variants but also across many different sets of variants. By linking
this technique with traditional line graphs and signal processing techniques,
we are able to provide the user with a set of visualization tools that permit the
combination of multivariate time-series data sets in their raw form and also
with the results of mathematical analysis. In this paper, we provide two case
study examples of how Kaleidomaps can be used to improve our understanding
of large complex multivariate time dependent data
Original languageEnglish
Pages (from-to)155-167
Number of pages14
JournalInformation Visualization
Early online date31 May 2007
Publication statusPublished - 2007

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© 2007 Palgrave Macmillan Ltd.

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