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