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Review: Exam I, partII
GEOG 370
Christine Erlien, Instructor
Learning Goals: Ch. 3
To be able to define graphicacy and explain its
importance
To be able to explain the difference between the
communication and analytical paradigms and to
discuss the advantage of the analytical paradigm
over the communication paradigm.
To be able to discuss the processes of cartographic
abstraction and generalization (selection,
classification, simplification, symbolization)
To be able to define what a reference or thematic
map is as well as identify these map types
To be able to recognize different methods of
classifying interval/ratio data and describe the
qualities of each method
Learning Goals: Ch. 3
To be able to describe each of the basic methods of
illustrating scale on a map as well as advantages or
disadvantages associated with each method
To be able to discuss how analysis would be
impacted if data of different map scales were stored
in the same GIS database
To be able explain and identify major map elements.
In particular, to be able to discuss the purpose of a
map legend
To be able to explain the purpose of map projection,
describe the basic families of map projection, and
detail the types of distortions introduced by the
process of map projection
To be familiar with some basic grid systems and their
operation, recognizing their advantages and
disadvantages for GIS work
Graphicacy
Understanding graphic devices of
communication
– Maps
– Charts
– Diagrams
Why?
– Understanding usage of graphic devices
increases our abilities
• Describing spatial phenomena
• Making decisions
Maps as Models:
A paradigm shift in cartography
Communication paradigm -> analytical
paradigm
Communication paradigm
– Traditional approach to mapping
– Map itself was a final product
• Communication tool
– Limits access to original (raw) data
Maps as Models:
A paradigm shift in cartography
Analytical paradigm
– Maintains raw data in computer
– Display is based on user’s needs
– Transition ~ early ’60s
– Advantage:
Cartographic abstraction/generalization:
Selection
Decisions about
– Area to be mapped
– Map scale
– Map projection
– Data variables
– Data gathering/sampling
Cartographic abstraction/generalization:
Classification
Organizes mapped information
Qualitative or quantitative
– Qualitative: Spatial distribution of nominal
or ordinal data
– Quantitative: Spatial aspects of numerical
data
Cartographic abstraction/generalization:
Simplification
Elimination of unwanted features
Smoothing features
Aggregation of features
From How To Lie with Maps, M. Monmonier
Cartographic abstraction/generalization:
Symbolization
Symbols used to stand for real world
objects
Legend required to communicate
symbols’ meaning
Use of visual variables to assist in
communicating meaning (Bertin)
– Color (hue, value, saturation)
– Size
– Shape
– Texture
Map Types
Reference maps
– Purpose show location of variety of different
features
– Usually small scale
– Require conformity to standards
– Examples: USGS topographic maps, navigation
charts
Thematic maps
– Purpose display spatial characteristics of a
particular attribute
– Cartographer has control over map design
Map Scale
Map scale: Ratio between map distance &
ground distance
– large scale map vs. small scale map
• 1:250,000 > 1:1,000,000
• Large scale map more details
Scale-dependency
Methods of illustrating scale
– Verbal scale (1 inch equals 63,360 inches)
– Representative fraction scale (1:24,000)
– Graphic scale
Major Map Elements
Necessary components of a typical map
– Title
– Legend
– Scale bar & North arrow
– Cartographer & Date of production
– Projection
Elements used selectively
– Neatlines
– Inset maps
– Charts, Photos
– Additional text
Neat line
Map Elements
Border
Title
Figure
Legend
Ground
Scale
Credits
Inset
Place name
North Arrow
Geographic Data & Position
Important elements must agree:
– scale
– ellipsoid
– datum
– projection
– coordinate system
Geographic Data & Position: Scale
When is this is an issue?
– When data created for use at a particular
scale are used at another
Why is this an issue?
– All features are stored with precise
coordinates, regardless of the precision of
the original source data
– What does this mean?
• Data from a mixture of scales can be displayed
& analyzed in the same GIS project this can
lead to erroneous or inaccurate conclusions
Geographic Data & Position: Scale
Example:
– Location of same feature at different scales
– (-114.875, 45.675)
(-114.000, 45.000)
• Zoomed out look like same point
• Zoomed in look like separate points
Take-home message:
– Be aware of the scale at which data were
collected metadata
Geographic Data & Position:
Ellipsoid
Ellipsoid: Hypothetical, non-spherical shape
of earth
– Note: Earth’s ellipsoid is only 1/300 off from
sphere
– Datum: A system for anchoring an ellipsoid to
known locations (surveyed control points) on the
Earth
• Defines the origin of coordinate systems used for
mapping
Ellipsoids & Datums: Importance
Differences exist between different
ellipsoids & datums
– Coordinates different in each can be
significant distance
Note: Be aware of the ellipsoid & datum
for datasets you are working with
In this case, the boundaries are roughly 32 meters off: datum shifts are not uniform
Errors up to 1 km can result from confusing one datum for another
Geographic Data & Position:
Projection
Projection: Process by which the round
earth is portrayed on a flat map
To project
– Think of a light inside the globe, projecting
outlines of continents onto a piece of paper
wrapped around globe
Families of Projections
Planar/Azimuthal
Cylindrical
Conical
Cylindrical projections
http://www.progonos.com/furuti/MapProj/Normal/ProjCyl/projCyl.html
Conic Projections
Conic projections are created by setting a cone over a globe
and projecting light from the center of the globe onto the
cone.
Azimuthal/Planar Projections
Project map data onto
a flat surface
– Tangent to the globe at
one point
– North & South Poles
most common contact
points
Map projections: Distortion
Converting from 3-D globe to flat surface
causes distortion
Types of distortion
–
–
–
–
Shape: Maintained by conformal projections
Area: Maintained by equal area projections
Distance: Maintained by equidistant projections
Direction: Maintained by azimuthal projections
No projection can preserve all four of these
spatial properties
Projections: Patterns of Distortion
http://www.fes.uwaterloo.ca/crs/geog165
Learning Goals: Ch. 4
To know the different types of file structure and the
advantages/disadvantages of each for computer
search
To identify differences between hierarchical, network,
and relational database structures and know their
advantages/disadvantages
To be familiar with terminology related to relational
DBMS (primary key, tuple, relation, foreign key,
relational join, normal forms)
To describe how entities are represented on a map by
raster and vector data structures
To describe how methods of data compaction work for
both raster and vector data
To understand the difference between the spaghetti
and topological vector models and their
advantages/disadvantages
Basic computer file structures
What is where?
– Computer file structures allow the
computer to store, order, & search data
Types:
– Simple list
– Ordered sequential
– Indexed file (direct, inverted)
Databases & Database Structures
What is where?
– Geographic searches data retrieval
– Data retrieval requires data organization
Databases & Database Structures
Database: Collection of multiple files
– Requires more elaborate structure for
management
DBMS: Database Management System
Database structure types
– Hierarchical data structures
– Network systems
– Relational database systems
Hierarchical Database Structures
Hierarchical Database Structures
Advantages:
– Easy to search
Disadvantages:
– Knowledge of all questions that might be
asked necessary
• Unanticipated criteria make search impossible
– Large index files memory intensive, slow
access
Database Structures: Network Systems
Database Structures: Network
Systems
Advantages:
–
–
–
–
Less rigid than hierarchical structure
Can handle many-to-many relationships
Reduce data redundancy
Greater search flexibility
Disadvantages:
– In very complex GIS databases, the number of
pointers can get quite large storage space
Database Structures: Relational
Databases
Predominant in GIS
Joining tables Relational join
– Matching data from one table to
corresponding data in another table
– How? Link the primary key to the foreign
key
• Primary Key: Unique identifier in 1st table
• Foreign key: Column in 2nd table to which
primary key is linked
Relational DB & Normal Forms
Normal forms: A set of rules established to
indicate the form tables should take
Goal: Reduce database redundancy &
inconsistent dependency
– Database performance is better
• Redundancy wastes disk space & creates maintenance
problems
– Database more flexible
Representing Geographic Space
Methods: Raster
Raster
– Dividing space into a series of units
• Generally uniform in size
– Units connected to represent surface of
study area
– Do not provide precise locational
information
Raster Data Structure
columns
A B C D E
1 1 1 1 2 3
rows
2 1 3 6 6 6
Cell (x,y)
Cell value
3 1 5 5 4 3
4 1 2 1 1 1
5 1 1 1 1 1
Cell size = resolution
Values 1-6 based on
color gradation
Raster Graphic Data Structures:
Representing Entities
From Fundamentals of Geographic Information Systems, Demers (2005)
Representing Geographic Space
Methods: Vector
Vector (polygon-based)
– Spatial locations are specific
– How?
• Points: Single set of X,Y coordinates
• Lines: Connected sequence of coordinates
• Areas: Sequences of interconnected lines
– 1st & last coordinate pair must be same to close
polygon
– Attributes stored in a separate file
Representing Geographic Space
Methods: Vector
From Fundamentals of Geographic Information Systems, Demers (2005)
Data Structures vs. Data Models
Graphic data structures: Computer storage of
analog graphical data that enables close
approximation of analog graphic to be
reconstructed
Data models
– Allow links to attributes
– Allow interactions of objects in database
– Allow for analytical capabilities
• Multiple maps can be analyzed in combination
Raster Data Models
Minimizes #
maps
Multiple variables
associated with
each grid cell
Allows linkage to
programs using
vector data
model
From Fundamentals of Geographic Information Systems, Demers (2005)
Raster Data Models: Data Compression
Why?
– Save disk space by reducing information
content
– Methods
•
•
•
•
Run-length codes
Raster chain codes
Block codes
Quadtrees
Raster Data Compression Models:
Run-length Encoding
Reduces data volume on a row-by-row basis by indicating string
lengths for various values
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Raster Data Compression Models
Run-length codes
– Limited to operating row-by-row
What about areas?
Block encoding: Run-length encoding in 2-D
Raster chain codes: A chain of grid cells is
created around homogenous polygonal areas
Raster Data Compression Models:
Block Encoding
Run-length encoding in 2-D: Uses a series of square blocks to encode
data
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Raster Data Compression Models:
Raster Chain Codes
Reduces data by defining the boundary of entity
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Raster Data Compression Models
Quadtrees: Recursively divide an area into
quadrants until all the quadrants (at all
levels) are homogeneous
1
1
2
2
1
1
2
2
3
3
2
2
3
3
3
3
NW
1
NE
2
SW
3
SE
2
2
3
3
Raster Data Compression Models
From An Introduction to Geographic
Information Systems, Heywood et al.
(2002)
Representing Geographic Space:
Vector Data Structures
Represent spatial locations explicitly
Relationships between entities implicit
– Space between geographic entities not
stored
Vector Data Models
Multiple data models
– Examination of relationships
• Between variables in 1 map
• Among variables in multiple maps
Data models
– Spaghetti models
– Topological models
– Vector chain codes
Vector Data Model: Spaghetti
Simplest data structure
One-to-one translation of graphical image
– Doesn’t record topology relationships implied
rather than encoded
Each entity is a single piece of spaghetti
Point
very short
Line
longer
Area
collection of line segments
– Each entity is a single record, coded as variablelength strings of (X,Y) coordinate pairs
– Boundaries shared by two polygons stored twice
Vector Data Model: Spaghetti
From Fundamentals of Geographic Information Systems, Demers (2005)
Vector Data Model: Spaghetti
Measurement & analysis difficult
– All relationships among objects must be
calculated independently
Relatively efficient for cartographic
display
– CAC
Plotting: fast
www.gis.niu.edu/Cart_Lab_03.htm
Vector Data Model: Topological
Topology: Spatial relationships between
points, lines & polygons
Topological models record adjacency
information into data structure
– Line segments have beginning & ending
• Link: Line segment
• Node: Point that links two or more lines
– Identifies that point as the beginning or ending of line
– Left & right polygons stored explicitly
Vector
Data
Model:
Topological
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Compacting Vector Data Models
Compact data to reduce storage
Freeman-Hoffman chain codes
– Each line segment
• Directional vector
• Length
– Non-topological
• Analytically limited limits usefulness to
storage, retrieval, output functions
– Good for distance & shape calculations,
plotting