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National Spatial Data Infrastructureshares data through clearinghouses
- 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.
- 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.
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.
- 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.
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
- 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
Types of Database Design
- flat file
- Process of decomposition of flat files or data
- Attribute data is broken down to small tables
- Still maintains necessary linkages between them.
Ways to Classify Data
Variety of different classification methods:
- Jenks Natural Breaks
- Equal Interval
- Defined Interval
- Standard DeviationManual (set your own)
Same data may appear differently
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.
- 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.
- User chooses class size.
- Specifies the interval value.
- Data determines number of classes based on the interval.
- 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.
- 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.
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
Placed by user individually using text boxes from the DRAW tool bar.
- 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