Attribute Data and Map Types CS 128ES 228

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

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

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

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

How is Attribute Data kept in a GIS? Campus. ID Name Type Floors Footprint 6 Murphy Academic 2 2001 9 Hopkins Support 2 946 12 Maintenance Support 1 1848 15 Hickey Support 2 2367 17 Shay-Loughlen Dorm 3 1298 Attribute data is generally stored in database tables. CS 128/ES 228 - Lecture 2 b 5

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

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

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

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

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

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

How many classes to use? Multiple classes: n Classes more homogeneous n 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 CS 128/ES 228 - Lecture 2 b 12

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

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

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

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

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

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

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

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

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

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

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

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

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

Question? ? ? n Is a cartogram a map? CS 128/ES 228 - Lecture 2 b 26

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

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

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

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

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

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

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

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