Course Outline

Prerequisites:

Facility with Windows 95, 98 or NT.
Familiarity with a windows-based text editor and a spreadsheet program.

Requirements for course completion:

	Demonstrated competence with ArcView and Spatial Analyst GIS software.
	Passing scores on homework and take-home quizzes covering software proficiency and theoretical knowledge.
	Successful completion of a GIS project by the last day of class.

Resources:

	ArcView 3.1 and Spatial Analyst 1.1 software (or upgrade to AV 3.2)
	Text book: Getting to Know ArcView GIS (a.k.a. "GTKAV")
	These web pages
	Class listserver (SYSEN597@listbot.com)
	E-mail support at whuber@quantdec.com
	Miscellaneous web references

Students only: join the private class mailing list. Enter your email address below, then click the 'Join List' button:
You will receive an e-mail confirmation when your request has been approved.

0. Author's note

Based on recent experiences teaching this course, I intend to integrate the raster and vector analysis portions by introducing Spatial Analyst much earlier in the syllabus, certainly by unit 3.  In other words, units 7 through 9 will be interwoven with units 2 through 6.

1. Introduction to PC-based GIS

Overview of course
Course requirements
Overview of learning resources, materials, data
Introduction to GIS

Homework: Explore GIS on the web.

2. Visualization, Symbolization, and Classification

Three class sessions

GTKAV Chapters 1-10

Kinds of visual representations
Topology
Data classification
Color models
Statistical graphics

Laboratory: Review of essential computing skills. Introduction to ArcView. Desktop mapping. Working with themes in ArcView.

3. Georeferenced Data

GTKAV Chapters 10-13

Measuring locations, distances, and areas
Map scale
Metadata
Mapping principles
Coordinate systems, datums, and projections

How to Lie with Maps
The Visual Display of Quantitative Information
Envisioning Data

Laboratory: Data visualization in ArcView.

Homework: Getting around in ArcView.

4. Managing Tabular Data

GTKAV Chapters 13-16

Data sources
Data formats
Querying and selecting data
Data summaries
Relational database introduction
Tables, relations, and normalization.
Relational data operations: joins and links
Data handling
The field calculator: operating on tables as objects
Controlling errors
File formats and file conversion
Data dictionaries
Examples of tabular data sets

Laboratory: Working with tabular data in ArcView and Excel.

Homework: Data visualization. Theme manipulation. Projections.

5. Spatial Query and Analysis (Vector Format)

GTKAV Chapters 17-20

Spatial selection
Spatial join
Buffering

Laboratory: Spatial query and analysis with ArcView.

Homework: Manipulating tables. Designing relational databases.

6. Charting and Mapping Quantitative Data. Graphical Data Editing

GTKAV Chapters 21-26

Charts
Maps
Digitizing vector features
Mapping by hand
Computerized mapping
Heads-up digitizing
Address geocoding
Map accuracy

Laboratory: Creating dynamic maps and charts with ArcView. Digitizing features.

Homework: Spatial analysis (vector data)

7. Introduction to Raster Datasets

GTKAV Chapter 29

Raster versus vector datasets
Gridded data
Surfaces
Interpolation and contouring
Hillshading
Integrating and interconverting raster and vector data
TIGER files

Laboratory: Grids in Spatial Analyst

Homework: Mapping and charting

8. Analysis of Raster Datasets

Two class sessions

GTKAV Chapter 29

Digital Elevation Models (DEMs and DTEDs)
Viewsheds
Watersheds
Digital Raster Graphics (DRGs)
Remote sensing
Images. Orthophotography. TIFF files and their relatives.
Map algebra
Distance models
Density calculations
Statistical analysis
Site selection and prioritization

Laboratories: Analysis of grids. Grid and image legends.

Homework: Managing raster data. Analyzing raster data sets.

Note: Final projects are selected by this date.

9. Transformation of Raster Datasets

Warping and rubber-sheeting
Reprojection
Mosaics
Raster models
Interfaces to external programs

Laboratory: Warping, mosaicing, importing and exporting.

Homework: Grid analysis and map algebra.

10. Managing GIS: Putting it All Together

A sample database
Data handling
Mapping
Analysis

Laboratory: Creating and using an environmental GIS with ArcView. Discussion of student project proposals.

Homework: Manipulating images and grids.

Special Topics

Optional, according to time and interest; not all will be covered

Three-Dimensional Visualization

Examples
Datasets
VRML
Animation

Global Positioning Systems

Incorporating GPS output in GIS

Extending a GIS through programming: Introduction to Avenue

GTKAV Chapter 28

Using scripts and programs
Customizing the software interface
Creating additional capabilities
Programming principles
Examples

Case Studies in GIS

Siting cellular communications towers
Open space and land use planning
Air emissions modeling on a regional and national scale
Acquiring legacy data
Designing sampling programs
Monitoring groundwater quality
Viewshed and visibility analyses: locating new infrastructure
Watershed analysis
Agricultural and forestry applications: natural resources management
Site assessment and investigation design
Sampling environmental media
Ecological risk assessment
Data visualization: decrypting encrypted text
Analyzing census and demographic data. Business applications.

Wrap-up

One to two class sessions

Students will formally present and discuss their completed GIS projects.