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Cartography
The art and technique of making maps
The who, what, where, and how we may present information
What does Cartography depend on
Intended Audience
Information to communicate
Area of interest
Physical and resource limitations
Cartography must consider
Data to plot
Symbols shapes, sizes, or patterns
Labeling and font type and size
Legend properties, size, and borders
The placement of all these elements on a map
Slocum’s Map Elements: key map features
Scale, size, shape, and other general map properties
Map scale
relationship between distance on the map and actual distance on the ground
allows for navigation, calculating area, and comparing sizes
distortions mean scale can never be exactly correct on the entire map
Three map scale types
graphic map scale
fractional map scale
verbal map scale
large scale map
relatively large representative fraction
used to show a small portion of Earth’s surface
used to show detail
Small-scale map
small representative fraction
used to show a relatively large area
imits detail
Dot map
one of the first examples of GIS and the birth of modern Epidemiology
Isopleth Map
any line on a map connecting points with equal values of a particular variable (temperature, dew point, etc.).
Isobar
A line that connects points of equal pressure
Isotherm
A line that connects points of equal temperature
Isotach
A line that connects points of equal wind speed
Isohyet
A line that represents rainfall
Gestalt of map design
Map consists of elements
Human cognition is based on clumping
Elements seen both individually and holistically
Eye seeks similarity, proximity, continuity, closure
Symmetry, simplicity, balance favored
Examples of using maps to lie
propaganda, commercial lies, gerrymandering
Hue
the “color” of the color
Saturation
intensity of the color
desaturated colors tend to look
gray
value
the lightness or darkness of a color
Design principles
don’t rely only on color to convey information
Keep it simple
Avoid using these colors in close proximity
Red & green
Blue & purple
Blue & gray
Green & brown
Green & blue
Green & black
Qualitative color scheme
Good for nominal not appropriate for ordered or numerical
The distinction between classes becomes visible through variations in hue
Ideally with no or slight lightness differences between colors. If a class needs to be highlighted it is possible to use a darker or more saturated color to visualize it
sequential color scheme
applied to ordered, often numerical data
Lighter colors for lower values or rank
Diverging color scheme
often described as a combination of two sequential schemes with a critical break point in the middle.
The two sequences “diverge” from a shared light color that stresses important mid-ranges in the data. The two extremes are visualized by contrasting dark hues while changes in lightness are used to display intermediate values.
Common examples of data suitable for diverging color scales are temperature variations and stock exchange dynamics
NATURAL BREAKS
a kind of “optimal” classification scheme that finds class breaks that (for a given number of classes) will minimize within-class variance and maximize between-class differences.
One drawback of this approach is each dataset generates a unique classification solution, and if you need to make comparison across maps, such as in an atlas or a series (e.g., one map each for 1980, 1990, 2000)
you might want to use a single scheme that can be applied across all of the maps.
Manual data breaks
There are many times we need to manually set one or all of the class breaks.
Are there important breakpoints that need to be “hardwired” into your class breaks?
Does one of the class breaks need to be the mean?
Is this map part of a series that needs the same classes across all of the maps (so that the colors always refer to the same numbers on any map)?
Do any of the other methods get you close to a good solution that could be improved with a few slight adjustments in those classes?
If so, do not hesitate to set these class breaks yourself