Transcript Cartographic abstraction

Cartographic abstraction
Summary session
GEO381/550
October 5th, 2004
Outlines
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Basics
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Geographic phenomenon
Describing data distribution
Components of cartographic abstraction
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Data classification
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Quantitative classification methods
Simplification
Map symbolization
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Visual variables by measurement scale
Map types by the behavior of geographic phenomenon
Basics
Geographic phenomenon
Measurement scale
Data distribution
Geographic phenomenon
Location, Scale
 Spatial dimension
 Continuous vs. discrete
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Q. number, Mars, human organ
 Q. Tornado path, elevation
 Q. Temperature, cold/hot, population,
population density
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Measurement scale
of geographic phenomenon
Nominal
Ordinal
Interval/Ra
tio
Concept
Type,
category
Result of
ranking
Result of
measuring
Example
Male/female, Mega/large/ Temperature,
agricultural
medium/sma Mortality rate
region
ll city
Year, land use, elevation, strongly agree/strongly disagree, religion,
coffee consumption, national income, occupation
Describing data distribution
Central
tendency
Dispersion
Nominal
Ordinal
Interval/Rat
io
Mode: most
frequently
occurring
value
Variation ratio
Median:
value exactly
in half when
ranked
Quartile
deviation
Mean:
 = Σx / N
Standard
deviation
Σ (x-)2 / N
Histogram and descriptive statistics
Components of
cartographic abstraction
Selection
Classification
Simplification
Symbolization
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Selection
Classification
 Simplification
preliminary steps
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Symbolization
data processing
choosing symbols
Classification
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Group values into class such that
geographic pattern can be better revealed
How do you determine class
boundary?
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Equal interval
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Quantile
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put the same number of values into class
Natural break
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put any number of values into class with the same
interval
marginal change in values
Standard deviation
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how much deviated from the mean?
Data classification method
Equal interval
l1
l2
l3
l4
Quantile
l5
a1 a2 a5 a4
a5
σ

Natural break
Standard deviation
Simplification
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Alter geometry such that relevant details
are pronounced while irrelevant details are
suppressed
Line simplification
Area dissolution
Criteria for symbolization
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Measurement scale  visual variables
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Use ordering visual var. for quantitative scale
Use distinguishing visual var. for qualitative scale
The behavior of phenomenon  map types
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Observed in a discrete/continuous scale & in a
abrupt/smooth frequency
Maps sometimes reflect the way data collected rather
than phenomenon. (e.g. crime is reported in the unit
of jurisdiction)
Appropriate use of visual variables
- measurement scale qualitative
quantitative
point
Shape
Size
line
Shape, Hue
Size
area
Hue, Arrangement Value, Texture
Appropriate choice of map types
- behavior of phenomenon discrete
abrupt
smooth
Graduated
symbol map
Dot density
map
Chorodot
continuous Choropleth
map
Isopleth
map
Because of the discrepancy between phenomenon and data, we need to
process data by manipulating spatial scale…. Handling GIS data well is an
essential skill for advanced map-making!