Review: Exam I, partII GEOG 370 Christine Erlien, Instructor.

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


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


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


Run-length encoding in 2-D: Uses a series of square blocks to encode data

Raster Data Compression Models:Raster Chain Codes


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


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


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


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

Review: Exam I, partII GEOG 370 Christine Erlien, Instructor.

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