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Data Acquisition and Input
Chapter 5
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FGDC and NSDI
- Federal Geographic Data Committee
- establishes protocols for data integrity and coordination
- National Spatial Data Infrastructure
- shares data through clearinghouses
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Metadata
Data about the data
Usually prepared and entered during data production process.
Important to anyone using public data for a GIS project.
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Snapping
Point (node or vertex) can be automatically snapped to another point if gap is smaller than specified snapping tolerance.
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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).
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Digital Orthophoto Quadrangle (DOQ)
Computer-generated image of an aerial photograph
Image displacement caused by terrain relief and camera tilts has been removed.
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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
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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
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Geometric Transformation
Chapter 6
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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.
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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.
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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
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Affine
- Used for map-to-map or image to map transformations
- Skew: changes shape to a parallelogram
- Translation: shifts origin to new location
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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.
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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.
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Rubbersheeting
use pins as control points; resulting image is stretched in various directions to accomodate the pins.
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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.
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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.
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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.
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Spatial Data Editing
Chapter 7
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Applies to __________ Data
Vector
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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.
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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
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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
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Line simplification
process of simplifying or generalizing a line by removing some of its points.
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Line smoothing
process of reshaping lines by using some mathematical functions such as splines.
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Attribute Data Management
Chapter 8
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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.
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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
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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.
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Normalization
- Process of decomposition of flat files or data
- Attribute data is broken down to small tables
- Still maintains necessary linkages between them.
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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).
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Join
operation uses a key that is common to both tables and can be saved as a new file.
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Relate
operation temporarily connects two tables, but keeps the tables physically separate.
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Classifying Data
Chapter 9
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Map Types
- Single symbol maps
- Unique values maps
- Quantities maps- light to dark color
- Graduated color- circles
- Graduated symbol- circles
- Dot density- dots
- Chart maps- pie
- Multiple attribute maps- dots and light to dark color
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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)
Same data may appear differently
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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.
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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.
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Defined Interval
- User chooses class size.
- Specifies the interval value.
- Data determines number of classes based on the interval.
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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.
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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.
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Hue
Hue: Six major hues such as red, yellow, green
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Chroma (Saturation)
purity of color
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MRG
BCG
Magneta Red Yellow
Blue Cyan Green
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Map Typology and Labeling
Chapter 9...
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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
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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
- Unavoidable overlaps are discarded
- Can specify classes with own symbols
- Can specify placement priorities
- May change between screen and printing
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Simple labels
Placed by user individually using text boxes from the DRAW tool bar.
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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
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