1. Mapping GIS Data

2. How maps portray the world
Point features
Line features
Polygon features
Annotation features

3. Talking about map scale
A large denominator gives a small fraction  a small scale map. It shows a large area.
A small denominator gives a larger fraction  a large scale map. It shows a small area. 1
50,000,000 1
500,000 1
5,000

4. Generalization and Scale
Small scale map
Intermediate scale map
Large scale map

5. Scale and precision S: main way (60m) P: local road (20m)
1:100,000 scale map
S: main way (60m)
P: local road (20m)
Let S be the size of the main way (with 60m width) represented by a 2 mm line symbol:
2 mm = 1 S
S = 200,000 mm = 200 m ≠ 60 m
Let P be the size of the local way (with 20 m width) represented by a 1 mm line symbol:
1 mm = 1 P
P = 100,000 mm = 100 m ≠ 20 m

6. Estimating precision from scale
Denominator of scale / 1000 gives approximate precision for a 1-mm thick line:
1:5, m
1:24, m
1:100, m
1: 1 million m

7. Source scale and display scale
Most GIS data have an intrinsic scale inherited from the source

8. Display vs source scale
Once in GIS data may be displayed at any scale.
BUT
Original scale of the map does impact the precision and accuracy of the data.
Original scale
1:5 million
Original scale
1:25 million
You should not display or analyze data at scales very different from the original source data.

9. Scale and resolution
Resolution is the sampling distance of the stored x-y values.
A larger scale map generally has a finer sampling distance and better spatial resolution. It can represent features with better precision.
1:5M scale
1:25M scale

10. Map Types and Data Types
Single symbol map
Unique values map
Quantities maps:
Graduated color map
Graduated symbol map
Dot density map
Chart map
Nominal data
Categorical data
Numeric data

11. Nominal data Names or ID Number State names Owner of parcel
Tax ID number
Parcel ID Number
Usually portrayed on a map as labels

12. Single symbol maps Display all features with the same symbol
Combine with labels to portray nominal data

13. Categorical data
Places features into defined number of distinct categories
Portrayed by different symbol for each category

14. Unique values maps Different symbol for each category or value
Volcano types
Road types
Geologic units

15. Numeric data Interval data : Temperature, Elevation Ratio data :
Population, Rainfall

16. Numeric data
Numeric data that divided into classes Mapped using variations in symbol size, thickness, or color

17. Quantities Classed maps
Graduated color map
Graduated symbol map

18. Colors for choropleth maps
Generally use change in saturation or close hues to indicate increase
Avoid using too many colors which tend to mask increase

19. Dot density map
Numeric data that did not divide into classes, mapped using Dot density map.

20. Chart Maps
Proportional chart map