Linked Data Visualization. Laura Po
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systems such as RDF and Graph databases can be used for visualizing the network, and graph data and inverted indexes can be used to support text search capabilities.
Data Visualization. The final task in this process is the actual visualization of the data. This involves the provision of the different types of charts, maps, and graphs that present the data and the different visual means (e.g., searching, browsing, filtering) for performing data analysis. Different types of charts can be provided according to the type of information: numerical and tabular data can be presented through typical charts, such as bar and line diagrams, pies, stacked and scatter diagrams, areas, etc.; temporal (dates and time periods) information can be visualized with timeline diagrams; hierarchical information (e.g., hierarchical coded lists) can be explored with hierarchical diagrams such as tree maps and nested diagrams; network data are visualized as graph diagrams which provide an explicit representation of the interrelationships between the visualized objects; and, finally, geographic information is usually visualized on maps—choropleth, heatmaps, and bubble maps capture the density of an observation over a region, while point and clusters can be used for presenting the location of individual entities on maps. Multiple charts can also be combined to provide the user with more sophisticated visualizations.
Allowing the user to choose from different kinds of visualization is crucial, since no single visualization configuration suits every data analysis context. For example, map-related visualizations such as choropleth maps and heatmaps are suitable for geographical data, while network data are usually represented using graph-related visualizations, and statistical data and indices may be better visualized via traditional charts such as line and area charts, timelines, pies, stacked diagrams and scatter plots. In the next subsections, an overview of the most common techniques used for the visualization of different types of data are presented.
Figure 2.2: A bar chart visualization of the number of people voted per constituency in Greek Elections of January 2015.
2.2 DATA VISUALIZATION TYPES
In most visualization scenarios we are interested in graphically representing numerical values, i.e., amounts corresponding to a real-world observation (e.g., the population) which is measured along a list of categorical data points (e.g, the population of countries in EU). The categories can exhibit a flat, such as a list of colors or a hierarchical structure, such as the organization of regional information in cities, countries, and continents).
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