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
Legend
Scale
Credits
North ArrowPlace nameInset
Ground
Figure
Neat lineBorder
Title
Map Elements
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 uniformErrors 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 SpaceMethods: 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
1 1 1 2 3
1 3 6 6 6
1 5 5 4 3
1 2 1 1 1
1 1 1 1 1
Cell (x,y)
Cell value
Cell size = resolution
columns
row
s
A B C D E
1
2
3
4
5
Values 1-6 based on color gradation
From Fundamentals of Geographic Information Systems, Demers (2005)
Raster Graphic Data Structures: Representing Entities
Representing Geographic SpaceMethods: 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 SpaceMethods: 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
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Reduces data volume on a row-by-row basis by indicating string lengths for various values
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
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Run-length encoding in 2-D: Uses a series of square blocks to encode data
Raster Data Compression Models:Raster Chain Codes
From An Introduction to Geographic Information Systems, Heywood et al. (2002)
Reduces data by defining the boundary of entity
Raster Data Compression Models
Quadtrees: Recursively divide an area into quadrants until all the quadrants (at all levels) are homogeneous
1
1
3
3
2
1
1
3
2
2
3
2
2
3 3
2
NW NE1 2
SW3
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 spaghettiPoint Line Area
very short longer collection of line segments
– Each entity is a single record, coded as variable-length 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