LET S GO BACK TO THE VERY FIRST HISTORICAL KNOWN EXAMPLES OF INFORMATION VISUALIZATIONS

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1 Introduction to InfoVis and Geovisual Analytics Prof Mikael Jern NCVA, Linköping University Prof Mikael Jern 2014 Discovery consists of seeing what everybody has seen and thinking what nobody has thought Albert von Szent-Gyorgyi ( ) Visualization is one of the oldest communication media LET S GO BACK TO THE VERY FIRST HISTORICAL KNOWN EXAMPLES OF INFORMATION VISUALIZATIONS 1

2 Interest of the National Debt Minard 1858 vs. EU NUTS Cattle sent to Paris Age groups 0-15 and 65+ 2

3 Information Visualization - Cholera outbreak London 1854 Information Visualization - Cholera outbreak London 1854 death locations Dr. John Snow: Investigation of deaths from cholera London, September 1854 spatial cluster infected water pump? 3

4 Information Visualization - Today Industry number of employees Point colour = förändring Rörelse Shape Colour = Företagsklimat A good data representation is the key to solving the problem The most famous example of an early Information Visualization!! Minard s graph from 1861 of Napoleon's march through Russia

5 The most famous example of an early Information Visualization!! Napoleon's march through Russia 1812 from Poland to Moscow 100,000 60,000 Poland 422,000 4,000 Berezina River temperature Geographic location in (X,Y) Flow map - Direction of movements Size of Army in flow (weighted arrows) Temperature TIME! Minard s Graphics produced today with InfoVis!! Napoleon's march through Russia 1812 from Poland to Moscow Geographic location in (X,Y) Flow map - Direction of movements Size of Army in flow (weighted arrows) Temperature TIME! temperature 5

6 AnotherMinard s Graphics Flow Map Visualization of French wine exports around 1864 Flow Map Visualization in InfoVis Today World Trading - Collaboration between OECD and NCVA Trading with focus Japan

7 Flow Map Visualization in InfoVis Today Using Bar Chart AND Flow Map gives more Information (Knowledge) Flow Map Visualization in InfoVis Today Migration to and from Norrköping Kommun 7

8 Sankey energy chart From Computer Graphics to Information Visualization... and Geovisual Analytics...to InfoGraphics 1970: Computer Graphics (vector drawings) 1978: Raster Graphics (pixel oriented) 1980: 1985: Data Visualization Scientific Visualization 1995: 2005: 2008: Information Visualization Visual Analytics Geovisual Analytics Interaction Perception Storytelling Data Transformation Analytics Reasoning 2011: InfoGraphics ( visual representation of information, data and knowledge) 8

9 Example of InfoGraphics This is NOT InfoVis! Example of InfoGraphics This is NOT InfoVis! 741&articleURL=http%3A%2F%2Fwww%2Eguardian%2Eco%2Euk%2Fworld%2Finteractive%2F2011 %2Fmar%2F22%2Fmiddle-east-protest-interactivetimeline&urlhash=uwzt&goback=%2Egde_80552_member_

10 Example of Information Visualization (Visual Analytics) Table Lens Scatter Matrix Volume Rendering Isosurfaces SciVis Physical data Streamlines Glyphs InfoVis Abstract data Parallel Coordinates GeoVis Treemap 10

11 SciVis = Physical Data (human body, earth, molecules, physical space InfoVis = Abstract Data (statistical, financial, business information, text documents 11

12 3D InfoVis vs. 2D InfoVis from one 3D view to multiple linked views 3D InfoVis vs. 2D InfoVis from one 3D view to multiple linked views 12

13 Country Immigrants TIME Multiple Time Shaded 3D Curves 13

14 Energy Consumption 3D Scatter Plots were popular in the late 80s Scatter Plot simple 14

15 Why Information Visualization? Massive statistical and business information and is growing Which information is important? Gain insight and knowledge Why Information Visualization? a picture is worth a thousand words What these numbers could not communicate when presented as text in a table, which our brains interpret through the use of verbal processing, becomes visible and understandable when communicated visually. This is the power of statistics data visualization." Cyclical sales Domestic sales larger than International and growing; Flat international sales and decreases sharply in August; Cyclical sales pattern in Domestic sales repeated on a quarterly basis reaching a peak in last month of quarter; 15

16 Why Information Visualization? Search + Examine + Explain = SEE to search for meaningful patterns Discovery then examine them and once they re found. Gain Understanding to communicate meaningful findings to others.. Explain aha, I see! Why Information Visualization? Instead of One 3D View used in SciVis InfoVis apply multiple linked Views - Dashboards 16

17 Why Information Visualization? InfoVis now also on mobile devices using dashboards (requires HTML5/JS) Why Information Visualization? InfoVis now also on mobile devices using dashboards (requires HTML5/JS) 17

18 World Statistical Data Information Visualization in 4 easy steps 1. ASK A SPECIFIC QUESTION Where do we have high Fertility Rate? 2. GATHER YOUR INFORMATION Get data from World Databank. 3. VISUALLY REPRESENT THE MENTAL MODEL Select most suitable Visualization Method 4. DISCOVER THE RESULT 18

19 Fertility Information Visualization in 4 easy steps 4. RESULT IN A SCATTER PLOT (X,Y): Age 0-14 vs. Fertility Rate Size: Population Time: 1960, 1961, 1962,.. Colour: Fertility Rate Age 0-14 WORLD STATISTICAL DATA 19

20 Information Visualization Definition: Two Mantras Overview, zoom & filter, details-on-demand (Shneiderman ) Analyze first, show the important, zoom, filter and analyze further and details-on-demand (Keim ) 1. OVERVIEW - SCAN THE BIG PICTURE 2. ZOOM & FILTER - SEARCH SPECIFICS 3. DETAILS ON DEMAND - LINK TO FURTHER DETAILS 20

21 1. OVERVIEW - SCAN THE BIG PICTURE 2. ZOOM & FILTER - SEARCH SPECIFICS 3. DETAILS ON DEMAND - LINK TO FURTHER DETAILS 1. OVERVIEW - SCAN THE BIG PICTURE 2. ZOOM & FILTER - SEARCH SPECIFICS 3. DETAILS ON DEMAND - LINK TO FURTHER DETAILS 21

22 1. OVERVIEW - SCAN THE BIG PICTURE 2. ZOOM & FILTER - SEARCH SPECIFICS 3. DETAILS ON DEMAND - LINK TO FURTHER DETAILS Population in the World.xml&layout=[map,(scatterplot,barchart)] Hans Rosling s world 22

23 age 0-14 Scatter Plot how many (attributes)? World Countries Population ages 0-14 vs. Life expectancy at birth; Colour: Population ages 65+ ; Circle Size: Total population; Trails: Time; Size: Population Time: 1960, 1961,.. Colour: age group 65+ age 65+ Life expectancy Scatter Matrix InfoVis method 23

24 An Interactive visualization is worth a thousand pictures Spatial Time - Variable Population in the World.xml&layout=[map,scatterplot] fertility rate fertility age 0-14 time 24

25 Information Visualization Definition Information visualization is the use of interactive, visual representations of abstract data (but as you have seen often with a spatial dimension) and to use perception to amplify cognition. It is the process of forming a mental model of data, thereby supporting insight into that data..forming a mental model of data, thereby supporting insight into that data

26 forming a mental model of data, thereby supporting insight into that data Sweden municipalities age group 0-14 ( ) use COLOUR time.forming a mental model of data, thereby supporting insight into that data. use lines for time movements 26

27 Information visualization is the use of interactive, visual representations... and to use perception Importance of Human User Interface Information Visualization helps users Find patterns, outliers and trends Find the red square? 27

28 Information Visualization helps users Find patterns, outliers and trends Find the blue circle? Information Visualization helps users Find patterns, outliers and trends "The holy grail of information visualization is to make the insights stand out from otherwise chaotic and noisy data." Net Migration 28

29 Net Migration in divided colour vs. grey scale Net Migration Net Migration Information Visualization Definition Information visualization is the use of interactive, visual representations of abstract data (but as you have seen often with a spatial dimension) and to use perception to amplify cognition. It is the process of forming a mental model of data, thereby supporting insight into that data. Information visualization helps users: Find patterns, outliers and trends Explore data to build intuition, understanding and knowledge Communicate understandings and knowledge to others 29

30 The Perfect Car Data Set applied to InfoVis Visualization of multivariate abstract data Abstract Data in Information Visualization Multivariate - Quantitative data and Categorical data Data Items Data types Quantitative (Numerical) Categorical Categorical Quantitative Categorical Quantitative 30

31 Acceleration 0-60 Acceleration 0-60 Scatter Plot with high correlation Color: Horsepower Size: Weight Miles Per Gallon Scatter Plot with high correlation Colour: Miles per gallon Size: Price Weight 31

32 Scatter Plot how many (attributes)? 1D: Weight vs. 2D: Acceleration Circle Size: Price; Colour: Miles Per Gallon; 32

33 Acceleration 0-60 Multivariate - Quantitative data and Categorical data Data Items Categorical Quantitative Categorical (Ordinal) Quantitative Country Miles per gallon 33

34 The Perfect Car Data Set applied to InfoVis Visualization of multivariate abstract data Information Visualization Definition Multidimensional Visualization of Multivariate Data Dependent variables: indicator1, indicator2, indicator3,... Dimensions in Information Visualization Spatial, Time and Variables 34

35 Spatio-Temporal and Multivariate Visualization using a Data Cube Fertility;[data item]; [time] Country 2010 Indicators Time 1960 Population GDP Fertility Italy; Fertility; Italy; [indicators]; [time] Spatio-Temporal and Multivariate Visualization using a Data Cube 35

36 Introducing Treemap using hierarchical data Rectangle Size = Population Colour = Fertility rate Hierarchy = Continent-Country Göteborg Stad tidig med Publicera Öppna Data Statistik visas på ett sätt som engagerar många i stadens utveckling; Trender över tid ökar förståelsen; Statiska rapporter ersätts med interaktiva webb sidor 2008; ngen.se/407/utbildningsniva n-varierar-mellanstadsdelarna/ 36

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38 SCB Statistik Atlas Forskningssamarbete sedan 2004; Statistikatlas 2010; Storytelling; Visuella Nyhetsbulletiner från 2012; Många efterföljare; SCB Statistik Atlas Nya Data 38

39 SCB Statistik Atlas Ett urval av SCB s egna indikatorer Över tid Integrerad Storytelling l Västra Götalandsregionen 39

40 Arbetslöshet Män Västra Götaland explorer Arbetslöshet Arbetslöshet Kvinnor Arbetslöshet Totalt Arbetslöshet Totalt Story Arbetslöshet

41 Graphical Excellence requires Good Perception Graphics excellence.is the well-designed presentation of interesting data, a matter of substance, statistics, and design consists of complex ideas communicated with: clarity, precision, and efficiency is that what gives the viewer: the greatest number of ideas, in the shortest time, with the least ink, in the smallest space is nearly always multivariate..requires telling the truth about the data Tufte Introducing GeoVisual Analytics an extension to InfoVis...in

42 Information Visualization Geographic Visualization Communicate Storytelling Publish Geovisual Analytics Data Multiple Sources Dynamic Filter Cognitive Perceptual Science Time Animation Visualization Challenge and Motivation for Geovisual Analytics.. Interactive Visualization is nice to play with but Difficult to collect and report the results knowledge gained in an Explorative Analytics session ; Use technologies that enable analysts to communicate what they know through use of appropriate visual metaphor and principles of reasoning and graphics representation; Visualize BIG Data! 42

43 Big Data ,000 geographical regions Introducing GeoVisual Analytics an extension to InfoVis... The science of analytical reasoning facilitated by interactive visual interfaces e.g. dynamic linked multiple views; Exploring and analyzing spatial-temporal and multivariate data; Discern trends or patterns - derive insight and draw conclusions; Communicate discovery and knowledge effectively for action with 100% Web compliant; Moving Research into Practice; 43

44 Visual Analytics Reasoning Process Sense Making Loop Gather Data and Information Tasks? Visual representation Choose layout and visual forms that aid analysis Develop insight Interactive session Through exploration What is important?- Filtering Tell the Story Produce results (knowledge) Storytelling, Presentation, Collaboration and Publishing Sense-making is an effort to understanding, to recognize particular characteristics of the data and understand what they mean.. it make sense Motala River Concentration of Nitrogen many cows along the river 44

45 Ericsson Research Visualization of Self-Organizing Networks Operated by Automatic Neighbour Relations Growth of cellular radio networks Need of automatic algorithms Automatic Neighbour Relations (ANR) developed by Ericsson Should we trust automatic algorithms? ANR must be proven to gain network operators trust VoSON (Visualization of Self-Organizing Network) 45

46 The ANR Procedure CGI 657 PCI 481 CGI 671 PCI 11 If the PCI is unknown, the CGI is requested. Ericsson Research VINNOVA Search for mobile cell anomalies 46

47 Ericsson Research VINNOVA Search for mobile cell anomalies Visual Analytics at Ericsson - Mobile accessibility

48 Dashboard multiple views application now also on mobile devices using dashboard 48

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