1. CS 128/ES 228 - Lecture 2b1 Attribute Data and Map Types

2. CS 128/ES 228 - Lecture 2b2 What kinds of data are in a GIS? Spatial data Non-spatial data (also known as attribute data) “A GIS with no attribute data is a mapmaking system, not a GIS!”

3. CS 128/ES 228 - Lecture 2b3 What is Attribute Data? Attribute data is data about objects stored in a GIS that refers to non- spatial properties of the object.

4. CS 128/ES 228 - Lecture 2b4 Examples of Attribute Data Date of construction of a building Purpose of a building Name of a stream Population of a city Breed of dog that lives at a house Photo of a fire hydrant

5. CS 128/ES 228 - Lecture 2b5 How is Attribute Data kept in a GIS? Attribute data is generally stored in database tables. CampusIDNameTypeFloorsFootprint 6MurphyAcademic22001 9HopkinsSupport2946 12MaintenanceSupport11848 15HickeySupport22367 17Shay-LoughlenDorm31298

6. CS 128/ES 228 - Lecture 2b6 How is Attribute Data extracted from a GIS? GIS’s have two main types of output Reports Maps As always, these can be combined

7. CS 128/ES 228 - Lecture 2b7 Reports A Geographic Information System is, at its core, a database. Good database software always has a report generator. (We have something called Crystal Reports.) Ergo, one can produce reports from a GIS.

8. CS 128/ES 228 - Lecture 2b8 Maps A picture is worth 1000 words What attribute data is being shown?

9. CS 128/ES 228 - Lecture 2b9 How is the Data Shown? Symbolization Issues: Realistic vs. abstract symbols Size, texture, and density Use of color

10. CS 128/ES 228 - Lecture 2b10 Visual Display of Attribute Data Easy for discrete features There are many ways to represent continuous features on a map Beware of the boundaries between classifications – they’re not usually very meaningful

11. CS 128/ES 228 - Lecture 2b11 How do we distinguish among our data values? Dichotomous scale (i.e. two classes) Each class quite heterogeneous Placement of boundaries is extremely sensitive to data density & quality

12. CS 128/ES 228 - Lecture 2b12 How many classes to use? Multiple classes: Classes more homogeneous Large number of classes hard to interpret Note: density of symbols should match the quantitative order of the classes, i.e. greater density => greater value

13. CS 128/ES 228 - Lecture 2b13 How shall we determine the class limits? 1. Intervals of constant size

14. CS 128/ES 228 - Lecture 2b14 How to set class limits? 2. Intervals that have equal numbers of cells (equal class size)

15. CS 128/ES 228 - Lecture 2b15 How to set class limits? 3. Natural breaks in distribution

16. CS 128/ES 228 - Lecture 2b16 A GIS Riddle Q: When is a map not a map? A: When we call is something else. Q: Why would we do this? A: Because we do…

17. CS 128/ES 228 - Lecture 2b17 Thinking about the data in a map How “processed” is the data? Not at all Some Lots Image Maps Line MapsCartograms (Choropleths)

18. CS 128/ES 228 - Lecture 2b18 Image maps (unprocessed data) Composed of images of the area under study (usually aerial photos) Often pieced together to make “mosaics”

19. CS 128/ES 228 - Lecture 2b19 Advantages of image maps What you see is what is there (assuming the photo is current)

20. CS 128/ES 228 - Lecture 2b20 Problems with Image Maps Interpretation Details can be tricky – perspective is unusual (see math at right) Distortion Especially near edges and seams No annotation

21. CS 128/ES 228 - Lecture 2b21 Line Maps (Somewhat processed data?) Reality is replaced by “reality-based” renderings “Raw” data is replaced by a representation of that data

22. CS 128/ES 228 - Lecture 2b22 Advantages of Line Maps Can concentrate on information “of interest” “Easy” to understand

23. CS 128/ES 228 - Lecture 2b23 Disadvantages of Line Maps Data is not as accurate due to: Incompleteness Representation (especially scaling) Deliberate “editorial” changes (see Exaggeration from previous lecture)

24. CS 128/ES 228 - Lecture 2b24 Cartograms (Choropleths) Similar to line maps, but geographic data is deliberately distorted to make some other point

25. CS 128/ES 228 - Lecture 2b25 Utility of Cartograms Strengths Highlight exactly what is desired Strong visual imagery Weaknesses Not useful outside initially intended domain Relatively difficult to produce Lots of information is lost

26. CS 128/ES 228 - Lecture 2b26 Question??? Is a cartogram a map?

27. CS 128/ES 228 - Lecture 2b27 Other types It is not uncommon to combine some of these types Cartographically Enhanced Image Maps are particularly common For example, the map we use in lab

28. CS 128/ES 228 - Lecture 2b28 Map Forms Historically, maps have been static, e.g. on sheets of paper Computer technology has rendered maps dynamic and/or interactive

29. CS 128/ES 228 - Lecture 2b29 Two “dynamic” maps

30. CS 128/ES 228 - Lecture 2b30 One last issue When do we compute using the attribute data?

31. CS 128/ES 228 - Lecture 2b31 Early Processing… Compute your answers early and then reveal them when asked. Commonly done for systems such as search engines

32. CS 128/ES 228 - Lecture 2b32 Late Processing… Store only your data; compute answers as needed MapQuest does this, as requests can’t be known in advance

33. CS 128/ES 228 - Lecture 2b33 Hybrid Do some processing early, do some late It is usually hard to detect that this is happening “Caching” is one (not so good) example of this approach. (Not so good because caching isn’t really processing, per se)

34. CS 128/ES 228 - Lecture 2b34 Conclusions A map is interesting, but a map that highlights attribute data is useful There are tradeoffs between completeness of information and ease of user processing Caveat user!