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Effective: Summer 2015
GIST 53ADVANCED GEOSPATIAL TECHNOLOGY & SPATIAL ANALYSIS4 Unit(s)

Prerequisites: Prerequisite: GEOG 12 or GIST 12.
Advisory: Advisory: GIST 11 and 52.
Grade Type: Letter Grade, the student may select Pass/No Pass
Not Repeatable.
FHGE: Non-GE Transferable: CSU
2 hours lecture, 6 hours laboratory. (96 hours total per quarter)

Student Learning Outcomes -
  • Run geoprocessing tools individually and implement a model to run several tools in sequence.
  • Organize the data sets resulting from analysis.
  • Student will be able to prepare data for use in Geospatial analysis.
  • Present the results of a geospatial analysis using appropriate terminology and visualizations.
  • Determine an appropriate approach to solving a problem using geospatial tools and methods.
Description -
Introduction to problem-solving and decision making using geospatial analysis techniques, applicable to a range of disciplines.

Course Objectives -
The student will be able to:
  1. Prepare data for use in analysis.
  2. Determine an appropriate approach to solving a problem using geospatial tools and methods.
  3. Run geoprocessing tools individually and implement a model to run several tools in sequence.
  4. Organize the data sets resulting from analysis.
  5. Present the results of a geospatial analysis using appropriate terminology and visualizations.
Special Facilities and/or Equipment -
Access to industry standard geospatial software and a computer with an Internet connection.

Course Content (Body of knowledge) -
  1. Reviewing the basics of geospatial data
    1. Data sources
    2. Data types
    3. Vector and raster data formats
    4. Basic cartographic data presentation techniques
  2. Introduction to Geospatial analysis
    1. Database joins
    2. Classifying and displaying data with a variety of statistical methods
    3. Presenting results as graphs and maps
  3. Using advanced attribute and spatial queries for data exploration
    1. Metadata and data dictionaries
    2. Formulating queries
    3. Types of selections
  4. Using advanced attribute and spatial queries for data exploration
    1. Using data dictionaries to interpret attribute tables
    2. Formulating Boolean queries
    3. Models
  5. Vector data analysis:
    1. Overlay techniques
    2. Creating a site selection model
    3. Networks and network analysis
  6. Building an automated model
    1. Uses and applications of models
    2. Planning and implementing models
  7. Raster data analysis: working with topographic data
    1. Topographic data
    2. Viewshed analysis for site selection
    3. Reclassification
    4. Map algebra
    5. Hydrographic data
    6. Density surfaces
  8. Database design and schema implementation
    1. Subtypes
    2. Default values
    3. Importing existing database schema
Methods of Evaluation -
  1. Labs
  2. Exams
  3. Portfolio
Representative Text(s) -
Bolstad, Paul, GIS Fundamentals: A First Text on Geographic Information Systems, 4th edition, Eider Press, 2012.

Disciplines -
Geography or Drafting or Environmental Technologies or Forestry/Natural Resources
 
Method of Instruction -
Lecture presentations and classroom discussion. Demonstrations and hands-on exercises. Laboratory exercises. Reading assignments.
 
Lab Content -
  1. Reviewing the basics of geospatial data
    1. Acquiring data
    2. Formatting data
    3. Reprojecting data and transforming coordinate systems
    4. Vector and raster data formats
    5. Basic cartographic data presentation techniques
  2. Introduction to Geospatial analysis
    1. Database joins
    2. Classifying and displaying data with a variety of statistical methods
    3. Presenting results as graphs and maps
  3. Using advanced attribute and spatial queries for data exploration
    1. Using metadata and a data dictionary
    2. Formulating queries
    3. Selection by location
  4. Using advanced attribute and spatial queries for data exploration
    1. Using data dictionaries to interpret attribute tables
    2. Formulating Boolean queries
    3. Selection by location
    4. Buffering
    5. Implementing a model
  5. Vector data analysis: Overlay techniques
    1. Union
    2. Intersect
    3. Converting from coverage format to modern GIS data format
  6. Vector data analysis: creating a site selection model
    1. Proximity analysis using line and polygon buffering
    2. Geospatial data model flow charts
    3. Creating a model that satisfies multiple location criteria
  7. Vector data analysis: network analysis
    1. Building topology
    2. Network routing
    3. Modeling of network impedance
    4. Generating service areas
  8. Building an automated model
    1. Setting environmental settings prior to running the model
    2. Setting model parameters in order to later model inputs
    3. Implementing a multi-step model using automation tools
    4. Exporting and editing model script
  9. Raster data analysis: working with topographic data
    1. Using elevation data to create slope, aspect, and hillshade surfaces
    2. Analyzing an environmental issue using elevation derived data sets
    3. Reclassifying raster data
    4. Map algebra
    5. Viewshed analysis for site selection
  10. Raster data analysis: working with hydrographic data
    1. Generating stream flow direction using accumulation surfaces
    2. Creating watersheds based on topographic data
    3. Using hydrographic data to analyze a scientific question
  11. Raster data analysis: density surfaces
    1. Interpolating density surfaces from point data
    2. Converting between vector and raster formats
  12. Database design and schema implementation
    1. Subtypes
    2. Default values
    3. Importing existing database schema
 
Types and/or Examples of Required Reading, Writing and Outside of Class Assignments -
A. Reading Assignments: Weekly reading assignments from text and outside sources ranging from 30 to 60 pages per week.
B. Lecture: Weekly lecture covering subject matter from text assignment with extended topic information. Class discussion is encouraged.
C. Hands on Exercises and demonstrations: Weekly computer labs. Each exercise covers assigned reading and lecture topics.