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Exploratory Analysis of Spatial and Temporal Data

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Abstract

In this chapter, we make an inventory of the tools suitable for supporting exploratory data analysis. Our major point is that the primary tool for analysis is the human imaginative mind, and that all other tools are supplementary. Only the human mind actually does the analysis; the other tools supply it with the necessary material, appropriately prepared and presented. The most appropriate form for the presentation of such material is visual, since the mind, as most scientists tend to agree, operates predominantly with images.

The techniques and software tools usable in exploratory data analysis are currently very numerous, and new tools continue to appear. It would be completely unfeasible to survey all of them. Therefore, we have tried instead to set out the major tool categories and describe the key functions and properties of each category. The resulting classification looks as follows:

  • Visualisation. The primary function of this tool category is representation of data in a visual form, i.e. creating various pictures from data: graphs, plots, diagrams, maps, etc. For this purpose, elements of data are translated into graphical features, such as positions within a display, colours, sizes, or shapes. It is important, however, that these graphical features coalesce into a single image rather than being perceived separately.

We divide the visual expressive means into display dimensions and visual, or retinal, variables. Display dimensions provide a set of positions within a display at which graphical elements, or marks, can be placed. Retinal variables represent various properties of the marks: shape, size, colour, texture, orientation, etc. In addition to the visual dimensions of a display, such as width, height, or depth, we consider also the display time, which can be used, for example, in animated presentations.

  • Display manipulation. This class consists of interactive tools that support dynamic modification of the appearance of visual displays. The general purpose of such modification is to enhance the image produced: to make it clearer and easier to perceive, to accentuate the distinctive features of the data represented, to focus on a particular item or subset of interest, etc. The manipulation is done through modifying the formula or algorithm used for the translation of data elements into visual features (we call this formula or algorithm the “visual encoding function”).

  • Data manipulation, i.e. derivation of new references and characteristics from existing ones. There are two major purposes in doing this: to simplify the data and make it easier to analyse, and, conversely, to enrich the data and consider various aspects of it. Thus, data aggregation reduces the amount of data and hence simplifies the analysis. Data interpolation, in contrast, produces additional data.

  • Querying, i.e. the automated search for answers to user-specified questions. Most typically, this is to search for references with specified characteristics or to search for the characteristics of specified references. Dynamic query tools, which allow the user to easily modify query conditions and quickly provide the required answer, are especially important for EDA.

  • Computation. In this category, we briefly consider the computational methods of statistics and data mining. Unlike the computations involved in data manipulation, which prepare data for further analysis, for example by transforming the data into a more suitable form, the function of computational tools is a kind of data distillation, or extraction of the essential features of data. Some examples of the outputs produced by computational tools are statistical characteristics of a dataset as a whole, indicators of relatedness between attributes, and models that predict some characteristics on the basis of other characteristics, in particular, future developments on the basis of the current state and of the history.

In exploratory data analysis, it is usually not enough to use a single tool. Various tools need to be combined. We consider two basic modes of tool combination, sequential and concurrent, and discuss the various mechanisms used for tool combination. Visualisation is an essential component of any tool ensemble. Initial data visualisation is used in order to understand what tools should be used for further work. Results produced by any non-visual tool need to be visualised so that the analyst can see and interpret them

Throughout this chapter, we provide many examples of various tools. Even when discussing non-visual tools such as data manipulation or computational methods, we use visualisation intensively to illustrate the examples. Readers can easily note that we have taken every opportunity to stress the great role of visualisation in exploratory data analysis. At the beginning of the chapter, we make an attempt to substantiate the importance of visualisation.

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(2006). Tools. In: Exploratory Analysis of Spatial and Temporal Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31190-4_4

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  • DOI: https://doi.org/10.1007/3-540-31190-4_4

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