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determine the correlation between them. We believe it would be useful if
time-varying data visualization simultaneously displays such associated
information to assist understanding of the correlation.
This chapter presents a new time-varying data visualization technique
which uses tags assigned to the values of each time step. These tags
consist of a set of predefined terms: for example, sunny, cloudy, and rainy
for weather data; or exercising, eating, and sleeping for health care data.
As a preprocessing step, this technique clusters polylines based on their
shapes and tags, and then selects representative polylines from the clusters.
It realizes smooth level-of-detail control by interactive control of the
number of polylines to be displayed. Also, the technique features click and
sketch interfaces so that users can interactively select particular polylines
which are tagged with the user-interested terms.
This chapter presents the effectiveness of the presented technique with
Japanese weather data recorded by AMeDAS (Automated Meteorological
Data Acquisition System). The dataset consists of time-varying
temperature values with weather tags including “Clear,” “Sunny,”
“Cloudy,” “Rainy,” and “Snowy” at 376 observation points. We discovered
visually different patterns of temperature variations from the visualization
result.
Level-of-detail control for time-varying data visualisation
This section introduces a level-of-detail control and sketch interface [7] for
time-varying data visualization that we have presented previously.
The technique assumes the following time series data, consisting of a
set of values P = (p
1
, p
2
, ..., p
n
) represented as n polylines. We define the
values of a polyline as p
i
= (p
i1
, p
i2
, ..., p
im
); p
ij
denotes the value at the j-th
time of the i-th polyline. We draw the set of values as a polyline chart. The
horizontal axis denotes the 1st to the m-th time, and the vertical axis
denotes the magnitude of the values.
As a preprocessing step, the technique temporarily quantizes polylines,
generates clusters of them, and selects representative polylines from the
clusters. During the quantization step, the technique generates a grid
surrounding all polylines, and calculates intersections between polylines
and grid-lines. It then generates rough polylines by connecting the
intersections, and uses them for the clustering. Number of clusters can be
controlled by the resolution of the grid as well as similarity threshold
values, and our implementation prepares several clustering results so that
the number of representative polylines varies smoothly.
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