Intro to GIS | Winter 2011
Data Visualization Part I
Cartographer “Code of Ethics”
• Always have a straightforward agenda and have a defining purpose or goal for each map
• Always strive to know your audience
• Do not intentionally lie with data
• Always show all relevant data whenever possible
• Data should not be discarded simply because they are contrary to the position held by the cartographer
• Data should not be discarded simply because they are contrary to the position held by the cartographer
• At a given scale, strive for an accurate portrayal of the data
• The cartographer should avoid plagiarizing; report all data sources
• Symbolization should not be selected to bias the interpretation of the map
• The mapped result should be able to be repeated by other cartographers
• Attention should be given to differing cultural values and principles
UNDERSTANDING YOUR
DATA
Qualitative & Quantitative
• Qualitative
– Data classified by category – Soil types, animals by species – Soil types, animals by species
• Quantitative
– Data grouped by measurement or numerical value – Population, % of forest cover
• Type of data will influence your choice of data symbolization/visualization
DATA ATTRIBUTE TYPES
Types of Attributes
• Ordinal
• Nominal Interval
• Interval
• Ratio
Nominal Data
• identify one instance from another;
• establish the group,
• establish the group, class, member, or
category with which the object is associated;
• these values are
qualities, not quantities
Ordinal (rank)
• determine position
• show place, such as first, second, third, and first, second, third, and so on, but they do not establish magnitude or relative proportions
• how much better, worse, healthier, and stronger cannot be demonstrated from ordinal numbers
Ratio
• values derived relative to a fixed zero point on a linear calibrated scale
• examples of ratio measurements are age, distance, cost
• examples of ratio measurements are age, distance, cost and elevation
• mathematical operations can be used on these values with predictable and meaningful results
Interval
• values on a linear calibrated scale but not relative to a true zero point in time or space
• time of day, years on a calendar, most temperature
• time of day, years on a calendar, most temperature scales are all examples of interval measurements
• because there is no true zero point, relative
comparisons can be made between the measurements, but ratio and proportion determination are not useful
Types of Attributes
• The computer does not decide between the 4 attribute types (you do)
• Most mathematical operations work well on ratio
• Most mathematical operations work well on ratio
values, but when interval, ordinal, or nominal values are multiplied or divided, the results are typically
meaningless
ArcMap Method Point Line Area Raster Feature (shows location) Nominal
Ordinal Interval Cyclic Ratio
Nominal Ordinal Interval Cyclic Ratio
Nominal Ordinal Interval Cyclic Ratio
Categories Nominal Nominal Nominal Nominal
Displaying data attributes in ArcMap
David Theobald Categories
- Unique values
Nominal Nominal Nominal Nominal
Quantities
-Graduated color -Graduated symbols -Proportional symbols
Ordinal Interval Cyclic Ratio
Ordinal Interval Cyclic Ratio
Ordinal Interval Cyclic Ratio
Ordinal Interval Cyclic Ratio
Charts Ratio Ratio Ratio
Multiple Ratio Ratio Ratio
Single Value
Each geographic feature is represented by a single color
Unique Value
Each geographic feature is represented by a different color
Unique Values
Geographic features are grouped and each group is represented by a color
DIFFERENT TYPES OF MAPS
Why Maps?
• Spatial visualization, as opposed to charts, graphs, tables
• Communicate information to others
• Explore, query, and analyze information
• Explore, query, and analyze information
• Used to generate hypotheses or questions
• Inform decision making
• Synthesize layers of information
COROPLETH MAPS & DATA
CLASSIFICATION
Choropleth Maps
• Widely used mapping method
• Based on numeric attributes of non-overlapping areas
• Areas are shaded based on the value of the attribute
• Areas are shaded based on the value of the attribute
• “spatially-sensitive” values should be normalized
• Different classification methods influence data visualization
Classification Methods
• Natural Breaks (Jenks)
• Quantile
• Equal Interval
• Equal Interval
• Defined Interval
• Standard Deviations
Classification Methods
• The purpose of classification
– Ease of reading & understanding the map
– Show info about an area that is not self evident – Show info about an area that is not self evident
• Must decide method & number of classes
– More classes show complex patterns – Less classes show distinct patterns
Natural Breaks
• Classes are based on
natural groupings of data
• Statistical methods that
• Statistical methods that minimizes the sum of
variance within each group
Intervals
• Equal: Divided equally into a set number of intervals (user sets # of classes)
• Defined: Divided into classes based on a set interval range (user sets Interval range)
Quantile
• Each class contains
(approx) the same number of features
• Best suited for data that is
• Best suited for data that is uniformly distributed; data that does not have a
disproportionate number of features with similar values
Standard Deviation
• Shows distance from the mean
• Places class breaks at
• Places class breaks at intervals (1/4, 1/5, or 1) based the standard
deviation
Symbology Demo | ArcMap
ISOLINE MAPS
Isoline Maps
• Used for continuous surfaces
• Lines joining points of equal value (usually generalized)
• Phenomena must vary smoothly across the map
• Phenomena must vary smoothly across the map
• two types:
– isometric (measured values) – isopleth (areal averages)
CARTOGRAMS
Cartograms
• Distort area, shape or distance for a specific purpose
• Reveal or enhance patterns that might not be visually apparent on a “normal” map
apparent on a “normal” map
• Sometimes used to promote legibility
DENSITY MAPS
Density Maps
• Repeated, uniform symbols representing spatial distribution
• Purpose to identify dense vs. sparse distribution
• Purpose to identify dense vs. sparse distribution
• Do not show exact quantities; instead give an overall impression of distribution/density
