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regarding the main data types and user tasks [2] to be supported are highly de-
sirable for shaping visual analytics research. A common understanding of data
and problem dimensions and structure, and acceptance of evaluation standards
will make research results better comparable, optimizing research productivity.
Also, there is an obvious need to build repositories of available analysis and vi-
sualization algorithms, which researchers can build upon in their work, without
having to re-implement already proven solutions.
How to assess the value of visualization is a topic of lively debate [42,33]. A
common ground that can be used to position and compare future developments
in the field of data analysis is needed. The current diversification and dispersion
of visual analytics research and development resulted from its focus onto specific
application areas. While this approach may suit the requirements of each of
these applications, a more rigorous and overall scientific perspective will lead to
a better understanding of the field and a more effective and ecient development
of innovative methods and techniques.
3.7 Sub-communities
Spatio-Temporal Data:
While many different data types exist, one of the
most prominent and ubiquitous data types is data with references to time and
space. The importance of this data type has been recognized by a research
community which formed around spatio-temporal data management and anal-
ysis [14]. In geospatial data research, data with references in the real world
coming from e.g., geographic measurements, GPS position data, remote sensing
applications, and so on is considered. Finding spatial relationships and patterns
among this data is of special interest, requiring the development of appropriate
management, representation and analysis functions. E.g., developing ecient
data structures or defining distance and similarity functions is in the focus of re-
search. Visualization often plays a key role in the successful analysis of geospatial
data [6,26].
In temporal data, the data elements can be regarded as a function of time.
Important analysis tasks here include the identification of patterns (either lin-
ear or periodical), trends and correlations of the data elements over time, and
application-dependent analysis functions and similarity metrics have been pro-
posed in fields such as finance, science, engineering, etc. Again, visualization of
time-related data is important to arrive at good analysis results [1].
The analysis of data with references both in space and in time is a chal-
lenging research topic. Major research challenges include [4]: scale, as it is often
necessary to consider spatio-temporal data at different spatio-temporal scales;
the uncertainty of the data as data are often incomplete, interpolated, collected
at different times, or based upon different assumptions; complexity of geograph-
ical space and time, since in addition to metric properties of space and time
and topological/temporal relations between objects, it is necessary to take into
account the heterogeneity of the space and structure of time; and complexity of
spatial decision making processes, because a decision process may involve hetero-
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