Spatial storytelling
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Very basic key lessons for teams working on the Editor's Lab hackaton in Tel Aviv. (30 mins woekshop) http://www.globaleditorsnetwork.org/programmes/editors-lab/editors-lab-in-tel-aviv/
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Spatial storytelling
- 1. Spatial Storytelling @mushon shual.com // Shenkar.ac.il // obudget.org
- 2. 1972
- 3. Data Visualization “The representation and presentation of data that exploits our visual perception abilities in order to amplify cognition.” KIRK, ANDY ( 2 0 1 2 -12-2 6) . DATA VISUAL IZATION: A SUCCESSFUL DESIGN PROCESS (KINDLE LOCATIONS 451- 452 ) . PACKT PUBL ISHING. KINDLE EDITION
- 8. דרגות ניתוח גרפי 3
- 9. 1)הדרגה השטחית ביותר )
- 10. ?האם ניתן להבחין בנתונים
- 12. 2)הדרגה הבאה )
- 13. האם ניתן לזהות יחסים/היררכיה/ סדר ?בין הנתונים
- 16. 3)הדרגה העמוקה ביותר )
- 17. להבין את הערכים עצמם/לקרוא
- 19. X X X
- 22. t 1869
- 24. 1854
- 27. בחירת הטון Determining Tone
- 28. רגשי מופשט פרגמטי אנליטי
- 40. כלים זמינים למיפוי נתונים
- 50. Thanks! & Good Luck! @mushon shual.com // Shenkar.ac.il // obudget.org
Editor's Notes
- Apollo 17 The image is one of the few to show a fully illuminated Earth, as the astronauts had the Sun behind them when they took the image. NASA rotated the original picture 180 degrees before publishing it.
- "On Exactitude in Science” by Jorge Luis Borges High to Low Level of graphic interpretation
- Jacques Bertin
- The highest level of Bertin's interpretive acts concerned whether we are able to visually discriminate between different data marks or data series: can we actually see and read the data being presented. We must make sure that the way we visually distinguish different categorical and quantitative values is legible and is in no way hidden by way of unnecessary clutter, noise, or distraction.
- The highest level of Bertin's interpretive acts concerned whether we are able to visually discriminate between different data marks or data series: can we actually see and read the data being presented. We must make sure that the way we visually distinguish different categorical and quantitative values is legible and is in no way hidden by way of unnecessary clutter, noise, or distraction.
- האם ניתן להבחין בנתונים? The highest level of Bertin's interpretive acts concerned whether we are able to visually discriminate between different data marks or data series: can we actually see and read the data being presented. We must make sure that the way we visually distinguish different categorical and quantitative values is legible and is in no way hidden by way of unnecessary clutter, noise, or distraction.
- The second act refers to being able to satisfactorily judge the relative order or ranking of values in terms of their magnitude. This is basic pattern matching where we seek to determine the general hierarchy of the values being displayed: where is the most and where is the least, which is the biggest and which is the smallest.
- The second act refers to being able to satisfactorily judge the relative order or ranking of values in terms of their magnitude. This is basic pattern matching where we seek to determine the general hierarchy of the values being displayed: where is the most and where is the least, which is the biggest and which is the smallest.
- The second act refers to being able to satisfactorily judge the relative order or ranking of values in terms of their magnitude. This is basic pattern matching where we seek to determine the general hierarchy of the values being displayed: where is the most and where is the least, which is the biggest and which is the smallest.
- The second act refers to being able to satisfactorily judge the relative order or ranking of values in terms of their magnitude. This is basic pattern matching where we seek to determine the general hierarchy of the values being displayed: where is the most and where is the least, which is the biggest and which is the smallest.
- The lowest-level act relates to judging values. Studies have shown how the effectiveness of different visual variables can be ranked based on which most accurately support comparison and pattern perception.
- The lowest-level act relates to judging values. Studies have shown how the effectiveness of different visual variables can be ranked based on which most accurately support comparison and pattern perception.
- The lowest-level act relates to judging values. Studies have shown how the effectiveness of different visual variables can be ranked based on which most accurately support comparison and pattern perception.
- Form / Function
- Form / Function
- 1869 - Of all of the visualizations in this post, Charles Minard’s map of Napoleon’s March is probably the most famous. Edward Tufte singled it out as “the greatest statistical graphic ever“, pushing it into the public consciousness. Whether it really is the greatest ever or not, this image does a great job of showing the miserable failure of the march, and the correlation with really cold weather.
- Form / Function
- Today we know that cholera is spread through water, but in the early 1800s people weren’t sure. John Snow’s cholera map helped to show that contaminated wells were at the center of outbreaks. His research helped save countless lives and set the foundation for the field of epidemiology.
- Form / Function
- Form / Function
- - Delivery Tone - Color Type
- Map of downtown Chicago
- Map of downtown Chicago (Waze)
- Mapping geo-spatial data To plot and present datasets with geo-spatial properties via the many different mapping frameworks. A popular approach would be the choropleth map.
- Choropleth map Data variables: 2 x quantitative-interval, 1 x quantitative-ratio. Visual variables: Position, color-saturation/lightness.
- Dot plot map Data variables: 2 x quantitative-interval. Visual variables: Position.
- Bubble plot map Data variables: 2 x quantitative-interval, 1 x quantitative-ratio, 1 x categorical-nominal. Visual variables: Position, area, color-hue.
- Isarithmic map (or contour map or topological map) Data variables: Multiple x quantitative, multiple x categorical. Visual variables: Position, color-hue, color-saturation, color-darkness.
- Particle flow map Data variables: Multiple x quantitative. Visual variables: Position, direction, thickness, speed.
- Cartogram Data variables: 2 x quantitative-interval, 1 x quantitative-ratio. Visual variables: Position, size.
- Dorling cartogram Data variables: 2 x categorical, 1 x quantitative-ratio. Visual variables: Position, size, color-hue.
- Network connection map Data variables: 2 x quantitative-interval, 1 x categorical-nominal. Visual variables: Position, link, color-hue.
- OpenStreetMap.org
- Google fusion tables https://support.google.com/fusiontables/answer/2527132?hl=en
- cartodb.com
- http://cartodb.github.io/odyssey.js/
- mapbox.com
- GeoJSON.io
- leafletjs.com
- d3js.org
- d3js.org