Abstract
Data represent results of the observation or measurement of phenomena. By means of data analysis, people can study these phenomena. Data analysis can be regarded as seeking answers to various questions regarding the phenomena. These questions, or, in other words, data analysis tasks, are the focus of our attention. In this chapter, we attempt to develop a general view of data, which will help us to understand what data analysis tasks are potentially possible.
We distinguish two types of components of data, referrers and attributes, which can also be called independent and dependent variables. A dataset can be viewed on an abstract level as a correspondence between references, i.e. values of the referrers, and characteristics, i.e. values of the attributes. Here are a few examples:
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In a dataset containing daily prices of a stock on a stock market, the referrer is time and the attribute is the stock price. The moments of time (i.e. days) are references, and the price on each day is the characteristic corresponding to this reference.
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In a dataset containing census data of a country, the set of enumeration districts is the referrer, and various counts (e.g. the total population or the numbers of females and males in the population) are the attributes. Each district is a reference, and the corresponding counts are its characteristics.
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In a dataset containing marks received by schoolchildren in tests in various subjects (mathematics, physics, history, etc.), the set of pupils and the set of school subjects are the referrers, and the test result is the attribute. References in this case are pairs consisting of a pupil and a subject, and the respective mark is the characteristic of this reference.
As may be seen from the last example, a dataset may contain several referrers. The second example shows that a dataset may contain any number of attributes.
The examples demonstrate the three most important types of referrers:
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time (e.g. days);
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space (e.g. enumeration districts);
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population (e.g. pupils or school subjects).
The term “population” is used in an abstract sense to mean a group of any items, irrespective of their nature.
We introduce a general view of a dataset structure as a function (in the mathematical sense) defining the correspondence between the references and the characteristics.
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(2006). Data. In: Exploratory Analysis of Spatial and Temporal Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31190-4_2
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DOI: https://doi.org/10.1007/3-540-31190-4_2
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