1. Return to Outline Copyright © 2009 by Maribeth H. Price 2-1 Chapter 2 Mapping GIS Data

2. Return to Outline Copyright © 2009 by Maribeth H. Price 2-2 Outline GIS Concepts –Map scaleMap scale –Ways to map dataWays to map data –Classifying numeric dataClassifying numeric data –Displaying rastersDisplaying rasters About ArcGIS –Map documents and data framesMap documents and data frames –Using ArcMapUsing ArcMap –Data frame coordinate systemsData frame coordinate systems –Symbolizing featuresSymbolizing features –Symbolizing rastersSymbolizing rasters

3. Return to Outline Copyright © 2009 by Maribeth H. Price 2-3 Map scale

4. Return to Outline Copyright © 2009 by Maribeth H. Price 2-4 How maps portray the world Point features Line features Polygon features Annotation features

5. Return to Outline Copyright © 2009 by Maribeth H. Price 2-5 Map scale Ratio of distance on the map to distance on the ground Dimensionless: cm or inches or mm… 1 cm on map = 100,000 cm on ground

6. Return to Outline Copyright © 2009 by Maribeth H. Price 2-6 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

7. Return to Outline Copyright © 2009 by Maribeth H. Price 2-7 Generalization and Scale http://encarta.msn.com/map_701515760/portsmouth.html Polygons at one scale may be points or lines at a different scale Large scale map Intermediate scale map Small scale map

8. Return to Outline Copyright © 2009 by Maribeth H. Price 2-8 Scale and precision 1:100,000 scale map 3-pt highway symbol 1-pt local road symbol 1 inch = 72 points 3 pts = 1 S 100000 S = 300,000 pts S = 4166 inches S= 347 feet Let S be the size of the highway represented by a 3-point line symbol So the highway location has an uncertainty of nearly 350 ft due solely to the symbol used to portray it. The local roads have an uncertainty of about 115 feet.

9. Return to Outline Copyright © 2009 by Maribeth H. Price 2-9 Estimating precision from scale A 1-pt line is about 0.001 feet Map scale / 1000 gives approximate precision in feet for a 1-pt thick line 1:5,0005 ft 1:24,00024 ft 1:100,000100 ft 1: 1 million1000 ft Larger symbols would have lower precision.

10. Return to Outline Copyright © 2009 by Maribeth H. Price 2-10 Source scale and display scale Most GIS data have an intrinsic scale inherited from the source Display scale varies 1:24,000 USGS Topo Map (source scale)

11. Return to Outline Copyright © 2009 by Maribeth H. Price 2-11 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 1:25 million Original scale 1:5 million You should not display or analyze data at scales very different from the original source data.

12. Return to Outline Copyright © 2009 by Maribeth H. Price 2-12 Scale and resolution Resolution is the sampling distance of the stored x-y values. 1:5M scale source1:25M scale source A larger scale map generally has a finer sampling distance and better spatial resolution. It can represent features with better precision. Display scale approximately 1:500,000 Too fine a resolution wastes storage space and slows drawing— stores more points than needed at a particular display scale

13. Return to Outline Copyright © 2009 by Maribeth H. Price 2-13 Finding source scale Usually documented in the metadata –Scale of original paper map source –Scale or precision at which data were gathered

14. Return to Outline Copyright © 2009 by Maribeth H. Price 2-14 Ways to map data

15. Return to Outline Copyright © 2009 by Maribeth H. Price 2-15 Map Types and Data Types Single symbol maps Unique values maps Quantities maps –Graduated color –Graduated symbol –Dot density Nominal data Categorical data Ordinal data Interval and Ratio data

16. Return to Outline Copyright © 2009 by Maribeth H. Price 2-16 Nominal data Names or uniquely identifies objects –State names –Owner of parcel –Tax ID number –Parcel ID Number Each feature likely to have its own value Usually portrayed on a map as labels

17. Return to Outline Copyright © 2009 by Maribeth H. Price 2-17 Single symbol maps Display all features with the same symbol Combine with labels to portray nominal data

18. Return to Outline Copyright © 2009 by Maribeth H. Price 2-18 Categorical data Places features into defined number of distinct categories Category names may be text or numeric Portrayed by different symbol for each category

19. Return to Outline Copyright © 2009 by Maribeth H. Price 2-19 Unique values maps Different symbol for each category or value Geologic unitsVolcano typesRoad types

20. Return to Outline Copyright © 2009 by Maribeth H. Price 2-20 Types of unique values Nominal data Use to show different features Categorical data State subregion Use to show patterns

21. Return to Outline Copyright © 2009 by Maribeth H. Price 2-21 Ordinal data Places features into ranked categories or along an arbitrary scale –Low, Medium, High slope –Village, Town, City –Assistant, Associate, Full professor –Grade A, B, C, D, F A 0-40% B 40-70% C 70-100% Portrayed as categories but choosing variations in symbol size or color to indicate increase

22. Return to Outline Copyright © 2009 by Maribeth H. Price 2-22 Interval or Ratio data Interval data places values along a regular numeric scale –Supports addition/subtraction –Temperature, pH, elevation Ratio data places values along a regular scale with a meaningful zero point –Supports addition, subtraction, multiplication, division –Population, rainfall, median rent

23. Return to Outline Copyright © 2009 by Maribeth H. Price 2-23 Mapping numeric data Interval and ratio data must be divided into classes before mapping Mapped using variations in symbol size, thickness, or hue

24. Return to Outline Copyright © 2009 by Maribeth H. Price 2-24 Classed maps Graduated color map (choropleth map) Graduated symbol map

25. Return to Outline Copyright © 2009 by Maribeth H. Price 2-25 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

26. Return to Outline Copyright © 2009 by Maribeth H. Price 2-26 Normalizing classed maps If the size of the sample impacts the measured value, data should be normalized –By percent of total Percent of farms in each state Percent of mobile homes in each state –By another field Farms divided by area Mobile homes divided by total housing units Number of farms Number of farms per sq. mile

27. Return to Outline Copyright © 2009 by Maribeth H. Price 2-27 Unclassed maps Proportional symbol mapDot density map

28. Return to Outline Copyright © 2009 by Maribeth H. Price 2-28 Chart Maps Proportional chart map

29. Return to Outline Copyright © 2009 by Maribeth H. Price 2-29 Symbol psychology Where is the water? Where is there less rain? Which towns have more people? What’s there? Where’s the danger?

30. Return to Outline Copyright © 2009 by Maribeth H. Price 2-30 Choosing symbols Which one looks more aesthetic? Which one is easier to understand? Which one shows the roads better?

31. Return to Outline Copyright © 2009 by Maribeth H. Price 2-31 Classifying numeric data

32. Return to Outline Copyright © 2009 by Maribeth H. Price 2-32 Ways to classify data Choose number of classes Variety of different classification methods –Jenks Natural Breaks –Equal Interval –Defined Interval –Quantile –Standard Deviation –Manual (set your own) Best methods vary depending on data distribution

33. Return to Outline Copyright © 2009 by Maribeth H. Price 2-33 Common data distributions Value Number of samples Normal Uniform Skewed Bimodal

34. Return to Outline Copyright © 2009 by Maribeth H. Price 2-34 Jenks Natural Breaks Exploits natural gaps in the data Good for unevenly distributed or skewed data Default method, works well for most data sets Class breaks

35. Return to Outline Copyright © 2009 by Maribeth H. Price 2-35 Equal Interval Specify number of classes Divides into equally spaced classes Works best for uniformly distributed data

36. Return to Outline Copyright © 2009 by Maribeth H. Price 2-36 Defined interval User chooses the class size Data determines number of classes Works best for uniformly distributed data

37. Return to Outline Copyright © 2009 by Maribeth H. Price 2-37 Quantile Same number of features in each class May get very unevenly spaced class ranges Results depend on data distribution

38. Return to Outline Copyright © 2009 by Maribeth H. Price 2-38 Geometrical Interval Multiplies each succeeding class boundary by a constant Works well for normal and skewed distributions

39. Return to Outline Copyright © 2009 by Maribeth H. Price 2-39 Standard Deviation Shows deviation from mean User chooses units e.g. 0.5 standard deviations Assumes data are normally distributed