Post midterm 13-20 plus 4, 11, parts of 5 and 11 Flashcards
Equal Intervals
Method of classification. Each class occupies an equal interval along the number line. The class interval is determined by dividing the range of the data by the number of classes.
Mean Standard Deviation
Method of classification. determined by repeatedly adding and subtracting the standard deviations from the data. Drawback: only works with data that are normally distributed. You can transform the data but this won’t work if the object is to analyze raw data.
Natural Breaks
Method of classification. Minimizes differences between the data. Decisions are subjective.
Quantiles
Method of classification.
Maximum Breaks
Method of classification
Fisher-Jenks optimal
Method of classification. Pros: Does best job of considering how the data are distributed Cons: legend is difficult to understand, not acceptable for ordinal data.
GADF
Goodness of Absolute Deviation Fit. A measure of optimal classifications, with the median used as the measure of central tendency. Then we get the sum of the absolute deviations of the medians for the classes and sum the deviations of the median for the entire data set.
GVF
Goodness of Variance Fit. Similar to GADF, it is a measure that is computed when the mean is used as the measure of central tendency (and the error in a class is the sum of squared deviations of the mean)
flat laxity
mathematically optimal solutions may be ignored if other partitions have particular features we deem important.
Raster Image Processor (RIP)
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Process Colors
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Color Gamut
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Offset Lithography
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Proofing methods
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Trapping
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EPS
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Screens
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Spot Colors
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Pre-Press
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Dot Gain
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PostScript
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Cloropleth Map
Ideal for a phenomena that is normally distributed within each enumeration unit. Also better if units don’t significantly differ in size and shape. Raw data need to be adjusted to account for varying sizes of units. (data should be standarized or turned into ratios.)
What are the arguments for and against classed and unclassed maps?
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What are the Munsell and Stevens curves for converting perceived blackness to percent area inked.
Munsell curve takes the largest perceived blackness and smallest perceived blackness and interpolates between for shades of grey. it is the most widely used method for deriving a grey scale. Stevens is more appropriate for unclassed maps because it uses magnitude estimation. one shade relative to the other.
How do you compare different kinds of Thematic maps?
What is the primary use? What are the Visual Variables? What are the data requirements and issues? What are the strengths and weaknesses? What are the design issues? What are the production issues? What are alternative forms?
What is he difference between Dasymetric mapping and dot mapping?
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What is the difference between Dasymetric maps and cloropleth maps?
Like the chloropleth map, the dasymetric map displays standarized data using aerial symbols but bounds of symbols do not match bounds of enumeration units. Need ancillary data.
What is the difference between Dasymetric maps and isarythmic maps?
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Ancillary Information
Other data which provides supporting data for the data being mapped. Limiting and Related attributes. Limiting gives you information about the limits where things should be placed. Related supports or correlates with the data being placed.
isarythms
Depict smooth, continuous data. Most common: a contour map.
isopleth map
uses conceptual data (assumption of continuity may not be met)
isometric map
uses true point data
triangulation
connecting neighboring points to form a set of triangles similar to those in manual contouring. Challenge is to find best set of triangles since entering the control points can result in different triangles. Delaunay triangles which minimize the distance over which interpolation must take place. by creating a Thessen polygon and connecting the control points of neighboring thiessen polygons.
inverse-Distance
Also called gridding. Lay a grid on top of control points, estimate the values at each grid point as a function of the distance to control points and interpolate between them.
hypsometric tints
shading the area between two contour lines
hill shading
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isometric lines
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control points
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interpolation
Estimating a line between two data points
Thiessen polygons
Drawing boundaries between control points such that all hypothetical points within a polygon are closer to that polygon’s control point than to any other control point.
Contour lines
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fishnet
a netlike structure that simulates the 3d character of a smooth continuous surface.
proportional symbols
Geometric or pictographic
geometric symbols
circles and squares
graduated symbols
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pictographic symbols
pictures and drawings representing the data in a proportional symbol map.
range grading
data grouped into classes and a single symbol is used for a class
perceptual scaling
a correction normally applied to compensate for mis-perception of larger symbols.
unit value
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mathematical scaling
symbols sized in direct proportion to the data.
Flannery’s constant
.57 added to the size of a circle in proportional symbol maps so that larger circles appear larger.
Cluster Analysis
Hierarchical and non hierarchical.
Integral symbols
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ray glyphs
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multivariate mapping
cartographic display of 2 or more variables
bi-variate mapping
Only two variables are displayed.
small multiples
Two or more maps displayed.
separable symbols
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distance cartogram
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contiguous cartogram
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dorlings cartogram
Used for “human geography” not a geographical extent.
network flow map
depict flows between networks like transportation networks.
area cartogram
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non-continuous cartogram
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distributive flow map
depict movement of goods and services between geographic areas.
continuous flow map.
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Raiz physiographic drawing
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block diagrams
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illuminated contours
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shaded relief
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panoramic views
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uncertainty
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positional accuracy.
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completeness
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intrinsic visual variable
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lineage
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attribute consistency
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logical consistency
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extrinsic visual variable.
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What are the factors in choosing the type of method of classification?
- Whether the method considers how data are distributed
- ease of understanding the method
- ease of computation
- ease of understanding the legend
- whether the method is acceptable for ordinal data
- whether the method can assist in selecting an appropriate number of classes.
Kriging
produces an optimal interporlation.
Pycnophylatic interpolation
raises each enumeration unit to a height proportional to the value of its associated control point.