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# Data visualization, FMIIT, STD

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### Data visualization, FMIIT, STD

1. 1. Data Visualization Authors: Peter Yochev, Danitsa Andreeva, Plamena Radneva, Ivan Chervenkov Software Technology and Design The Paisii Hilendarski University of Plovdiv Plovdiv, January 30, 2014
2. 2. Content ● Patterns Over Time ● Proportions ● Relationships ● Differences
3. 3. Patterns Over Time
4. 4. Patterns Over Time One of the first known graphics: Planetary movement chart, 10th century
5. 5. Patterns Over Time The first bar chart, 1786, William Playfair
6. 6. Patterns Over Time John Snow’s map of Cholera outbreaks in the London epidemic of 1854
7. 7. Patterns Over Time Charles Minard’s 1869 chart showing the number of men in Napoleon’s 1812 infamous Russian campaign army
8. 8. Proportions There are many options to visualize data out there, but it may be hard simply to find the perfect chart or the perfect graph that can suit your data best. To make your decision clearer and easier there are lots of proportions available.
9. 9. Using Different ways to visualize data with proportions Graphs and charts: ● Pie chart ● Donut chart ● Stacked area chart ● Treemap ● Voronoi diagram
10. 10. Pie chart Pie chart: ● Circular chart divided into sectors ● Illustrates numerical proportions ● Most effective when limited components are used ● Most effective when limited components are used
11. 11. Donut chart Donut chart: ● Functionally similar to pie charts ● Display data in rigs, where each ring represents data series ● Can support multiple statistics at once with its blank center ● Total 100% when percentage is used
12. 12. Stacked area chart Stacked area chart: ● Graphical display of quantitive data based on a line chart ● Uses axis and lines ● The area between axis and lines are colored and textured to emphasize changes ● Commonly used to represent trends over time
13. 13. Treemap Treemap: ● Hierarchical displayed data as a set of nested rectangles ● Uses branches-rectangles which tile with smaller subbranches ● Can legibly represent lots of items because of their simple construction
14. 14. Voronoi diagram Voronoi diagram: ● Visualize magnitude using convex polygons ● Uses robust algorithms to sidestep problems when restricted to rectangles ● Can be found also in technology, science and even in art
15. 15. Relationships … Or how to visualize data relationships effectively.
16. 16. The ‘Foundation’: Entity-Relationship (ER) diagram > Can be considered as the ‘base’ of all the charts which display data relationships. Figure 1: ER Diagram legend Figure 2: Elaborated (classic) ER diagram (i.e, contains both entities and properties)
17. 17. The ‘Foundation’: Entity-Relationship (ER) diagram Figure 3: Simplified ER Diagram (i.e, displays relationships only between entities)
18. 18. The ‘Classic’: Scatter Plot chart Figure 4: Simple Scatter Plot chart showing how two variables/entities correlate Figure 4: Bubble chart - can be considered as a variation of the Scatter Plot chart.
19. 19. Hierarchy chart Figure 5: Example of a Hierarchy chart Figure 6: Same idea, different look
20. 20. Circle/Radial chart Figure 7: Radial Cycle chart: Shows the relationship to a central idea and also how the information in the outer ring contributes to the main idea. Figure 8: Stacked Venn chart: Best used for visualizing overlapping relationships.
21. 21. Differences In this lesson I will compare a few charts, which will show you the main difference between them.
22. 22. Differences The first chart is called“apt” chart, which is an addition of the column charts.
23. 23. Differences The apt chart: ● has small number of categories ● can use shades of a single color ● easiest way to show all bars belong to same data type
24. 24. Differences The next one is the bar chart.
25. 25. Differences The bar chart: ● more than 8 but less than 15 categories ● more space for large names ● arranged from lowest to highest ● makes the understanding of the data easier ● connects shape of value faster
26. 26. Differences The Line chart: ● when you have a lot of numbers of data points ● doesn’t show minimum and maximum ● shows the rate of change of a population ● best fits for showing trend-based visualization ● has only 1 value axis ● shows the population with negative spikes,because of famines or some infections.