1. Announcement Lab 1 is on the class website. Due: 9/21/17
Also due that day is your project proposal.
Lecture 4B

2. Lab Deliverables
Report Sheet (in data folder) with the answers typed under the question.
Map layout on a “clean” page.
In general, layouts should include:
An appropriate title.
Legend
North arrow
Your name
Date
Lecture 4B

3. Lecture 4B

4. ArcInfo Coverages
Lecture 4B

5. A coverage is a geo-relational data model that stores vector data—it contains both the spatial (location) and attribute (descriptive) data for geographic features. Coverages use a set of feature classes to represent geographic features. Each feature class stores a set of points, lines (arcs), polygons, or annotation (text). Coverages can have topology, which determines the relationships between features.
Lecture 4B

6. More than one feature class is often required to define the features in a coverage. For example, line and polygon feature classes both exist in a coverage representing polygon features. Polygon features also have label points, which appear as a separate feature class. Every coverage has a feature class containing tic points, which represent known, real-world coordinates.
Lecture 4B

7. http://desktop. arcgis. com/en/arcmap/10
Lecture 4B

8. Ch. 4 Problems
4.1 Which is the larger scale map: a) 1:5000 or b) 1:15,000? 4.3 Describe three different types of generalization. 4.4 Categorize the generalizations in the following image as: fused, simplified, displaced, omitted or exaggerated.
Lecture 4B

9. fused, simplified, displaced, omitted or exaggerated
Lecture 4B

10. 4.9 Complete the following table:
Ground Distance
Map Distance
Map Scale
13,280 ft
6.4 in
1:24,900
126.4 km
25.28 cm
1:56110
123.6 mi
22.8 in
40.7 m cm
1:502.5 km
4.62 in
1:249,685
Lecture 4B

11. 4.12 Identify the digitizing error in the figure below.
Lecture 4B

12. Maps & Data Entry
Chapter 4 – pp 170-End
Lecture 4B

13. When do we need a coordinate transformation?
Digitizing from legacy maps
Digitizing from suitable aerial photos
Lecture 4B

14. Coordinate Transformation
Also called registration, as it registers the map layers to a map coordinate system.
Requires a set of control points.
Must be as accurate as the desired outcome.
Evenly distributed over the area.
Sufficient number.
The number of control points depends on the mathematical form of the transformation. Additional control points usually improves the overall accuracy,
Lecture 4B

15. Developing the Transformation Equations
Coordinates in the source system
Coordinates in the target system
Estimate equations that allow us to calculate the target coordinates given any set of source coordinates
Lecture 4B

16. Similarity Transformation
The method allows rotation of the rectangle and preserves its shape but not size.
Lecture 4B

17. Projective Transformation
The method allows angular and length distortion, thus allowing the rectangle to be transformed into an irregular quadrilateral.
Lecture 4B

18. Affine Transformation
The method allows angular distortion but preserves the parallelism of lines.
While preserving line parallelism, the affine transformation allows rotation, translation, skew, and differential scaling on the rectangular object.
Lecture 4B

19. Affine Transformation
Target coordinates a first-order linear function of source coordinates
Known as an Affine Transformation:
Easting = Te + a1x + a2y
Northing = Tn + b1x + b2y
There are 6 parameters to be estimated
T shifts origin
bi & ai changes scale & rotation
Lecture 4B

20. Example Road intersection:
Real world coordinates: E=500,000 & N=4,800,000
Digitized coordinates: x=125 & y= 100
There are six parameters to be estimated, so we need a minimum of six equations.
Each control point provides two equations.
Need a minimum of 3 control points
Lecture 4B

21. Lecture 4B

22. The Best Fit We start with as many control points as possible.
After the first iteration, check the residuals, and remove the control points with the largest error.
Repeat as necessary to reduce the RMSE to <5.
Lecture 4B

23. Root Mean Square Error
When the general formula is derived and applied to the control point, a measure of the error—the residual error—is returned.
The error is the difference between where the “from” point ended up as opposed to the actual location that was specified—the “to” point position.
The total error is computed by taking the root mean square (RMS) sum of all the residuals to compute the RMS error.
Lecture 4B

24. Root Mean Square Error (RMSE)
Lecture 4B

25. Root Mean Square Error (RMSE)
Although the RMS error is a good assessment of the transformation's accuracy, don't confuse a low RMS error with an accurate registration.
For example, the transformation may still contain significant errors due to a poorly entered control point, or errors in the image.
The more control points of equal quality used, the more accurately the polynomial can convert the input data to output coordinates.
Lecture 4B

26. Higher order polynomial transformation
Named for the largest exponent in the transformation equation.
ArcMap supports first to third order transformation.
Command line supports first to twelfth order transformation.
Use the lowest order transformation that provides acceptable results
Lecture 4B

27. Lecture 4B

28. Higher Order Polynomials and RMSE
Second order, RMSE = 3.82
First order (affine), RMSE = 7.2
Lecture 4B

29. Higher Order Polynomials and RMSE
First order (affine), RMSE = 7.2
Third order, RMSE =
Selecting the best transformation is a subjective process, guided by multiple goals.
Lecture 4B

30. Raster Geometry and Resampling
Data must often be resampled when converting between coordinate systems or changing the cell size of a raster data set.
Common methods:
Nearest neighbor
Bilinear interpolation
Cubic convolution
Lecture 4B

31. Nearest Neighbor
Nearest neighbor assignment does not change any of the values of cells from the input layer; for this reason it is often used to resample categorical or integer data (for example, land use, soil, or forest type), or radiometric values, such as those from remotely sensed images.
Lecture 4B

32. Nearest Neighbor Lecture 4B
Lecture 4B

33. Bilinear Interpolation
Distance
weighted
averaging
Lecture 4B

34. Cubic Convolution
Similar to bilinear interpolation, but it uses the 16 nearest cells in the calculation.
Lecture 4B

35. Types of GIS Output
Maps: Everyone recognizes this most common output from a GIS.
Cartograms: These special maps that distort geographic features based on their output values rather than their size.
Charts: GIS can produce pie charts, histograms (bar charts), line charts, and even pictures in addition to maps.
Lecture 4B

36. Types of GIS Output
Directions: Another common output, directions show you how to get from one place to another.
Customer lists: Business GIS applications often produce customer lists, sometimes with printed mailing labels.
3D diagrams and movies: These forms of GIS output help you see the results of your work realistically and dramatically.
Lecture 4B

37. Maps as Output The map is still the most common form of output.
Map design elements to be considered:
Frame of reference –title, graticule, inset map
Projection
Features to be mapped
Level of generalization
Annotations/Labels
Symbolism
Maps should show only as much detail as necessary to get the point across.
Lecture 4B 37

38. Cartographic Design Most design choices are compromises
Design is a process
Stage 1 – type of map, data to be represented, size, shape, basic layout.
Data type is the most important factor in determining map type and symbols
Stage 2 – kinds of symbolism, number of classes, class limits, color, line weights.
Stage 3 – define all symbols, typography (font, size, positions etc.)
Lecture 4B

39. Legends Qualitative Data Make symbols as intuitive as possible
Use professional standards whenever possible
Lecture 4B 39

40. Contrast – bad example
Lecture 4B

41. Contrast – good example
Lecture 4B

42. Typography & Lettering
Use concise formulated captions
Avoid using more than four fonts
Establish a typographic hierarchy
Develop legibility
Black lettering on yellow most legible
Red lettering on green least legible
Lecture 4B

43. Example:
hello world
hello world
Hard to read better
Lecture 4B

44. Text Placement
Placing text on a map is one of the most time consuming tasks, as much as 50% of the final map production time.
Poor placement of text affects the readability of the map, this is especially true in regions where map symbols are densely clustered.
Situations often arise in which text must overwrite other symbols with which it has no logical association.
Lecture 4B

45. Automated Name Placement
Components of name placement systems:
Specification of map features and text characteristics.
Generation of trial name positions.
Selection of optimal labels.
Scale at which labels will be displayed
Font type, color and size must also be specified.
It is desirable to name as many features as possible, while recognizing that some features will remain unlabeled.
Named features should be ranked in some way to resolve conflicts.
Lecture 4B

46. Non-Traditional Maps Cartogram Multimedia output
Hybrid – Map overlaying an image 3D
Virtual GIS
Lecture 4B

47. Cartogram
Shows different rail lines – the circles represent stops, the larger circles are stops where you can change lines.
This type of map is referred to as a cartogram
Lecture 4B 47

48. Results on A Population Cartogram
2008 Election Results by State
Lecture 4B

49.
Lecture 4B

50. Lecture 4B Figure 8.9 Example of multimedia content in GIS displays 50
Source: Screenshot shows ESRI Graphical User Interface (GUI). ArcMap, ArcView and ArcInfo Graphical User Interfaces are the intellectual property of ESRI and is used herein with permission. Copyright © 2005 ESRI all rights reserved
Lecture 4B 50

51. Making Great Maps
Lecture 4B

52. THE OUTPUT FROM GIS ANALYSIS
TABLES AND CHARTS
Used with maps or alone to improve understanding of cartographic results
Whenever map output is less immediately understood by audience
Tables and charts are generally more understood by general public than maps are
Map is not appropriate for the output
Show summaries of attribute data and relationships among them
Explicitly spatial
Implicitly spatial
Presents complex tabular information effectively.
Provides an immediate impact and takes less effort to understand.
Complements map information.
Shows the same information in a different way, or provides additional information about map features.
Lecture 4B

53. TABLE AND CHART DESIGN
Should be readily understood with minimal explanations
Appropriate titles for each table and chart presented
Label axes on all graphs
Provide legends wherever appropriate
Use fonts types and sizes that are easily read – Arial
Chose colors wisely as not to mislead audience
Avoid plotting more than 3 distinct attributes per plot
Lecture 4B

54. Good for comparing values and showing trends
Bar Chart
Good for comparing values and showing trends
Lecture 4B 54

55. Column Chart Good for comparing values and showing trends Lecture 4B55

56. Area Chart
Good for showing the relative value for each category as well as the total.
Lecture 4B 56

57. Cumulative Bar Chart Combines features of both the bar and area charts
Lecture 4B 57

58. Pie Charts
Shows relationships between the parts and the whole, particularly useful for showing proportions and ratios.
Lecture 4B 58

59. Line Charts
Emphasizes rate of change. Particularly good for representing trends over a period of time.
Lecture 4B 59

60. Scatter Charts
Reveals trends or patterns in the data. Can help reveal associations, sometimes cause-and-effect relationships.
Lecture 4B 60

61. What is Metadata? Data about data
Any information that makes data useful for another user
Background information that describes content, quality, condition, availability, use conditions, and distribution methods for data.
Structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource.
Lecture 4B

62. Types of Metadata?
Descriptive metadata – describes a resource for discovery and information.
Structural metadata – indicates how compound objects are put together .
Administrative metadata – provides information to help manage a resource.
Lecture 4B

63. Why is it Important? Supports data sharing Helps users to find data
Helps users to judge quality/utility of the data
Extends useful life-span of data
Saves time and money
Lecture 4B

64. Roles of Metadata Supports access (search, browse, and retrieval)
Supports transfer
Supports evaluation of fitness for use
Supports use
Lecture 4B

65. Assignment
Finish reading chapter 4.
Problems: 20, 21, 28.
Lecture 4B