GIS

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1. Data Acquisition and Input

Chapter 5
2. FGDC and NSDI
• Federal Geographic Data Committee
• establishes protocols for data integrity and coordination

• National Spatial Data Infrastructure
• shares data through clearinghouses

Usually prepared and entered during data production process.

Important to anyone using public data for a GIS project.
4. Snapping
Point (node or vertex) can be automatically snapped to another point if gap is smaller than specified snapping tolerance.
5. Digital Line Graphs
A Digital Line Graph (DLG) is digital vector data.

DLGs contain wide variety of information depicting geographic features (hypsography, hydrography, boundaries, roads, utility lines, etc).
Computer-generated image of an aerial photograph

Image displacement caused by terrain relief and camera tilts has been removed.
7. Rules for Adding Tables from Excel Into ArcMap
• Column Names: no more than 13 characters
• Alpha, then numeric, but no wonky characters
• No spaces
• Consider file size requirements
8. Converting GPS Data
Degrees, Minutes, Seconds

11°40‘ 3.90"N, 25°57‘ 8.68"E

11     40/60 = 0.666667     3.9/3600 = 0.001083

11 + 0.666667 + 0.001083 = 11.667749
9. Geometric Transformation

Chapter 6

10. Geometric Transformation
Using control points to register a scanned image onto a projected coordinate system.

Process of using control points and transformation equations to register digitized map, satellite image, or aerial photograph onto a projected coordinate system.

In GIS, geometric transformation includes map-to-map transformation and image-to-map transformation.

In ERDAS, image-to-image transformation.
11. Control Points
benchmarks, centerlines, ponds and other specific features

• Also called ground control points, GCPs
• Points where both image coordinates (in rows and columns) and real-world coordinates (x, y) can be identified.
• GCPs are selected directly from a satellite image:
• road intersections, rock outcrops, small ponds, etc.
• Selection is not as straightforward as selecting four tics for a digitized map.
12. Equiarea

Similarity

Affine

Projective
• Equiarea: preserves shape and size
• Similarity: Allows rotation; preserves shape, but not size
• Affine: allows angular distortion, preserves parallel lines
• Projective: angular and length distortion for irregular quadrilaterals
13. Affine
• Used for map-to-map or image to map transformations
• Skew: changes shape to a parallelogram
• Translation: shifts origin to new location
14. Geometric Transformation

3 Steps
• Step 1: update control points to real-world coordinates.
• Step 2: use control points to run an affine xformation.
• Step 3: create output by applying xformation equations to input features.
15. Root Mean Square Error
• RMS error is a common measure of the goodness of the control points.
• It measures the deviation between the actual (true) and estimated (digitized) locations of the control points.

• If a RMS error is within the acceptable range, we usually assume that the transformation of the entire map is also acceptable.
• This assumption can be quite wrong, however, if gross errors are made in digitizing the control points or in inputting the longitude and latitude readings of the control points.
16. Rubbersheeting
use pins as control points; resulting image is stretched in various directions to accomodate the pins.
17. Warp Image
• provides interactive functionality to establish the control points.
• When control points have been established, the distance between the transformed point and real position for that point (RMS residual error) is calculated.
18. Resampling
a process that fills each pixel of the new image derived from an image-to-map transformation with a value or a derived value from the original image.
19. Pyramiding
• Reduced resolution dataset (RRD files)
• Technique commonly used for displaying large raster data sets.
• Builds different pyramid levels to represent reduced or lower resolutions of a large raster.
• When viewing entire raster, we view it at the highest pyramid level; as we zoom in, we view more detailed data at a finer resolution.
20. Spatial Data Editing

Chapter 7
21. Applies to __________ Data
Vector
22. 2 Types of Location Errors
• Geometric inaccuracies of digitized features.
• Missing polygons, distorted lines
• Can be examined by referring to data source used for digitizing.

• Topological errors
• Dangling lines, unclosed polygons
• Violate relationships required by a GIS package or defined by user.
23. 3 Main Causes of Location Errors
• Human errors – hundreds of features need to be traced; it is reasonable that errors will be made
• Scanning and Tracing errors – duplicate lines, collapsed lines, misshapen lines
• Errors in spatial location of control points
24. Types of Topological Errors
• Undershoot
• line doesnt make it to another line

• Overshoot
• line goes past another line

• Dangling node
• overshoots and undershoots

• Pseudo node
• node that appears at an intersection and divides line unnecessarily

• Direction error
• Label error
25. Line simplification
process of simplifying or generalizing a line by removing some of its points.
26. Line smoothing
process of reshaping lines by using some mathematical functions such as splines.
27. Attribute Data Management

Chapter 8
28. Attribute Tables
• Every vector data set must have a feature attribute table.
• For the georelational data model, the feature attribute table uses feature ID to link to the feature’s geometry.
• For object-based data model, the feature attribute table has a field that stores the feature’s geometry.
29. Measurement Scales
• Nominal – Categorical, green, blue, male, female, elm, oak, Democrat, Republican, soil types
• Ordinal – ordered data - small, medium, large; first, second, third
• Interval – arbitrary 0; equal intervals between values; 10 degrees, 20 degrees
• Ratio – definite 0 point; age, height, length time
30. Types of Database Design
There are at least four types of database designs that have been proposed in the literature: flat file, hierarchical, network, and relational.
31. Normalization
• Process of decomposition of flat files or data
• Attribute data is broken down to small tables
• Still maintains necessary linkages between them.
32. Types of Relationships between tables
• One-to-one, (one capital per state)
• One-to-many, (one state to many counties)
• Many-to-one, (many people to one city)
• Many-to-many (many teachers to many students).
33. Join
operation uses a key that is common to both tables and can be saved as a new file.
34. Relate
operation temporarily connects two tables, but keeps the tables physically separate.
35. Classifying Data

Chapter 9
36. Map Types
• Single symbol maps
• Unique values maps
• Quantities maps- light to dark color
• Dot density- dots
• Chart maps- pie
• Multiple attribute maps- dots and light to dark color
37. Ways to Classify Data
Choose number of classes

• Variety of different classification methods:
• Jenks Natural Breaks
• Equal Interval
• Defined Interval
• Quantile
• Standard Deviation

Same data may appear differently
38. Jenks Natural Breaks
• Exploits natural gaps in data
• Breaks that best group similar values and maximize the differences between classes.
• Features are divided into classes with boundaries set where big jumps in the data values exit.
• Good for unevenly distributed data.
39. Equal Interval
• Divides range of values into equal-sized subranges, (i.e. 0–100, 101–200, and 201–300).
• Emphasizes amount of an attribute value relative to other values.
• Best applied to familiar data ranges, such as percentages and temperature.
40. Defined Interval
• User chooses class size.
• Specifies the interval value.
• Data determines number of classes based on the interval.
41. Quantiles
• Each class contains equal number of features.
• Well suited to linearly distributed data.
• Since features are grouped by number in each class, map can be misleading with unevenly spaced class ranges.
• Similar features can be placed in adjacent classes, or features with widely different values can be put in the same class. Minimize this distortion by increasing number of classes.
42. Standard Deviation
• Shows how much a feature's attribute value varies from the mean.
• ArcMap calculates the mean values and the standard deviations from the mean.
• Class breaks are then created using these values.
• A two-color ramp helps emphasize values above (shown in blue) and below (shown in red) the mean.
43. COLOR SCHEMES
44. Hue
Hue: Six major hues such as red, yellow, green
45. Chroma (Saturation)
purity of color
46. Value
47. MRG

BCG
Magneta Red Yellow

Blue Cyan Green
48. Map Typology and Labeling

Chapter 9...
49. Choosing a Font
• Legibility is paramount, especially with smaller type sizes. (c/e or i/j)
• Avoid extreme bold forms
• Be careful with decorative typefaces – difficult to read on map
• No smaller than 6 point font
• For most others 10 to 12 point font
• No more than 2 types of fonts on a map
• Use variations of italics, bold and letter spacing
50. 3 Labeling Options
• Dynamic
• Simple
• Annotation
51. Dynamic
placed automatically for an entire layer and behave as a group

• Turn on/off for entire layers
• Redrawn each time the map view changes
• Uses Autoplacement to ensure no overlaps between labels
• Can specify classes with own symbols
• Can specify placement priorities
• May change between screen and printing
52. Simple labels
Placed by user individually using text boxes from the DRAW tool bar.
53. Annotation
• Created from dynamic labels
• Stored permanently with feature class
• Provides significant control over independent labels and their positions.

• Can be stored three ways:
• As text in the map document
• As a feature class in a geodatabase
• As feature-linked annotation in a geodatabase:
• If the feature gets deleted, so does the label
• Cannot go back to dynamic labels
 Author: ichiban2008 ID: 180133 Card Set: GIS Updated: 2012-10-27 02:05:01 Tags: GIS Folders: Description: GIS Test 2 Show Answers: