GEOG 400 Week 2 Lecture 2, Fall 21

Transcript

1. GEOG 400: Advanced GIS - Raster Week 2 – Lecture 2 Raster (and vector) Symbology
3. A Quick Exercise to Review…
4. In week 1 lab, we made a map. That map had a colorized raster dataset (the DEM); this week we’ll talk about the many ways to colorize that raster
5. Today’s Outline 1.Vector Symbology (might be review for some!) 1.Choropleth maps 2.Isopleth maps 3.Dot maps 4.Proportional symbols, graduated colors 5.Cartograms 2. Raster Symbology 1. Continuous Data a. Color Ramps b. Stretch Types c. Display Statistics 2. Discrete Data a. Color Ramps
6. Vector Symbology 1. Choropleth Maps [from the Greek choro (color) and pleth (value)] Maps in which areas are distinctly colored to illustrate the average values of things within those areas
7. Vector Symbology 1. Choropleth Maps [from the Greek choro (color) and pleth (value)] Maps in which areas are distinctly colored to illustrate the average values of things within those areas NYTimes – 2016 Election Results
8. Vector Symbology 1. Choropleth Maps [from the Greek choro (color) and pleth (value)] Maps in which areas are distinctly colored to illustrate the average values of things within those areas NYTimes – 2016 Election Results
9. Vector Symbology 1. Choropleth Maps [from the Greek choro (color) and pleth (value)] Maps in which areas are distinctly colored to illustrate the average values of things within those areas NYTimes – 2016 Election Results Pros Cons - Can be used to report values at nearly any scale, nearly anywhere in the world - Very useful for finding hot spots - Particularly useful for finding anomalies when data are normalized - Areas aren’t uniform; gives larger areas more importance, when this may not be the case (e.g., sparse populations) - Can’t drill down any more than the given boundaries
10. Vector Symbology 2. Isopleth Maps [from the Greek iso (equal) and pleth (value)] Maps that use lines joining points of an equal value to show variations in that value
11. Vector Symbology 2. Isopleth Maps [from the Greek iso (equal) and pleth (value)] Maps that use lines joining points of an equal value to show variations in that value
12. Pros Cons - Allows you to have an extended view of what’s going on from limited data Vector Symbology 2. Isopleth Maps [from the Greek iso (equal) and pleth (value)] Maps that use lines joining points of an equal value to show variations in that value - Dependent on a good interpolation between limited observations - You don’t know how the transitions between intervals occur (sharp or gradual)
13. Vector Symbology 3. Dot Maps Maps that use small dots or symbols to show spatial distribution of a geographic phenomenon within a boundary of interest Dots don’t reflect the actual location of an event Rather, ArcGIS equally distributes dots across an area of interest
14. Vector Symbology 3. Dot Maps – Don’t Confuse these with Proportional Symbols! Maps that use small dots or symbols to show spatial distribution of a geographic phenomenon within a boundary of interest
15. Vector Symbology 3. Dot Maps - Don’t Confuse these with Graduated Colors! Maps that use small dots or symbols to show spatial distribution of a geographic phenomenon within a boundary of interest
16. Vector Symbology 3. Dot Maps Maps that use small dots or symbols to show spatial distribution of a geographic phenomenon within a boundary of interest Dots don’t reflect the actual location of an event Rather, ArcGIS equally distributes dots across an area of interest
17. Vector Symbology 3. Dot Maps Maps that use small dots or symbols to show spatial distribution of a geographic phenomenon within a boundary of interest Pros Cons - Spatial distribution of data can be misleading (and subjective) - Overcrowding of dots can be busy - Can show many observations on a single map without being confusing - Can have secondary symbols sizes/colors - Can use in combination with choropleth maps to present multiple variables
18. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent University of Michigan
19. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent
20. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent NYTimes – 2012 Election Results
21. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent NYTimes – 2012 Election Results Steph Abegg
22. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent NYTimes – 2012 Election Results Metrocosm
23. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent NYTimes – 2012 Election Results Metrocosm
24. Vector Symbology 4. Cartograms Maps with areas drawn proportional to the data they represent NYTimes – 2012 Election Results Pros Cons - Give impression of being inaccurate - Logic of mapping is not well understood - Spatial locations are difficult to identify because of distortions - Shocks the reader with unexpected spatial peculiarities - No data lost through classification/simplification
25. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: STRETCHED
26. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: STRETCHED?? REPEATING COLOR RAMP
27. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: STRETCHED
28. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: CLASSIFIED
29. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: CLASSIFIED You can choose (a) the number of bins, (b) bin boundaries, and (c) the color ramp to use!
30. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: CLASSIFIED
31. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: STRETCHED There are many different ways of stretching the color ramp across your range of values! Here are three common ones: “Minimum-Maximum”: colors are evenly split between the low and high values FAITHFULLY DISPLAYS THE WHOLE RANGE HISTOGRAM STRETCHES
32. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: STRETCHED There are many different ways of stretching the color ramp across your range of values! Here are three common ones: “Minimum-Maximum”: colors are evenly split between the low and high values FAITHFULLY DISPLAYS THE WHOLE RANGE “Percent Clip”: highest 5% and lowest 5% of values are smushed into one color SUPRESSES OUTLIER VALUES HISTOGRAM STRETCHES
33. Raster Symbology 1. Continuous Data [example: elevation] Grid cells with gradually changing data Color Ramps for Continuous Data: STRETCHED There are many different ways of stretching the color ramp across your range of values! Here are three common ones: “Minimum-Maximum”: colors are evenly split between the low and high values FAITHFULLY DISPLAYS THE WHOLE RANGE “Percent Clip”: highest 5% and lowest 5% of values are smushed into one color SUPRESSES OUTLIER VALUES “Histogram Equalize”: stretches out the most common values in the raster CREATES MORE CONTRAST IN THE DATA HISTOGRAM STRETCHES
34. Raster Symbology 1. Continuous Data [example: elevation] CREATING MORE CONTRAST WHEN ZOOMED IN Static (colors are the same no matter the scale) vs Dynamic Range Adjustment (whole range shown no matter the scale)
35. Raster Symbology 1. Continuous Data [example: elevation] CREATING MORE CONTRAST WHEN ZOOMED IN Static (colors are the same no matter the scale) vs Dynamic Range Adjustment (whole range shown no matter the scale)
36. Raster Symbology 1. Continuous Data [example: elevation] CREATING MORE CONTRAST WHEN ZOOMED IN Static (colors are the same no matter the scale) vs Dynamic Range Adjustment (whole range shown no matter the scale) But note that Arc doesn’t adjust the legend values!
37. Raster Symbology 2. Discrete Data [example: land cover] Grid cells that represent categories Color Ramps for Discrete Data: UNIQUE VALUES
38. Raster Symbology 2. Discrete Data [example: land cover] Grid cells that represent categories Color Ramps for Discrete Data: UNIQUE VALUES
39. Raster Symbology 2. Discrete Data [example: land cover] Grid cells that represent categories Color Ramps for Discrete Data: UNIQUE VALUES
40. A Quick Exercise to Review…