Discipline: Computer Information Systems (Geographic Information Systems) Degree Credit  [X]
Non Credit  [ ]
Nondegree Credit  [ ]
Comm Service  [ ]
 

Riverside Community College District
Integrated Course Outline of Record

Geographic Information Systems 9


COURSE DESCRIPTION
9 Spatial Analysis with GIS Units: 3.00
 
Prerequisite(s): GIS 1: Introduction to Geographic Info Systems

Advisory: CIS 96 or CIS 97
This course provides an introduction to spatial analysis. This course will briefly review the principles of statistics and relate them to methods used in analysis of geographically referenced data. This course will introduce sampling strategies for data used in Geographical Information Systems (GIS) using raster and vector data structures. The fundamentals of conventional estimation techniques will be compared with geostatistical techniques. The course will present single and multi-layer statistical operations including classification, coordination, and modeling analysis. Applications and problems in spatial correlation will be discussed including interpretation of results of spatial analysis. 54 hours lecture.
 
SHORT DESCRIPTION FOR CLASS SCHEDULE
An introduction to spatial analysis, which will briefly review the principles of statistics and relate them to methods used in analysis of geographically referenced data.
 
ADVISORY ENTRY SKILLS
Before entering the course, students will be able to:
  1. Compare and contrast conventional mapping technology to GIS automated technology.

  2. Apply and use basic cartographic principles to produce maps.

  3. Produce maps for use in interpreting geographically referenced data.

  4. Understand what spatial analysis is and how GIS can be used in scientific, business and government applications.

  5. Recognize the two basic GIS data structures (raster and vector).

  6. Manipulate both raster and vector data structures.

  7. Use the basic functions of GIS software to input data.

  8. Know how to overlay and produce maps combining the two basic GIS data structures.

  9. Use tabular data to analyze, manipulate, and construct charts and graphs using GIS.

  10. Use data bases to understand how to use GIS software to prepare and present map layouts.
STUDENT LEARNING OUTCOMES
 Upon successful completion of the course, students should be able to:

Compare and contrast conventional statistical analysis to spatial analysis.

Distinguish and use basic sampling methods used in GIS.

Formulate and interpret geographically referenced data.

Define problems associated with acquisition and accuracy of data used in spatial analysis.

Recognize the problems associated with statistical analysis and quantification f spatial features.

Compare and contrast ways data should be collected for a particular GIS analysis.

Analyze and manipulate geographically referenced data.

Understand the use of spatial analysis in the decision- making process.
 
COURSE CONTENT
  TOPICS
 
  1.   Brief Overview of Geographic Information Systems (GIS)
    1.  Survey of Applications Using Spatial Analysis
    2.  Use of GIS for Spatial Analysis
      1. Input
      2. Storage
      3. Manipulation
      4. Presentation and Display
  2.  Spatial Data
    1. Basic Principles of Probability and Statistics as Used in Non-Spatial Analysis
    2. Representation of Spatial Objects
      1. Zero, one, and two-dimensional objects
    3.  Basic Elements Used in Spatial Analysis
      1. Attribute
      2. Location
      3. Topology
    4. Data Structures
      1. Raster Data Structures
      2. Vector Data Structures
      3.  The Arc-Node Data Model
  3. Qualification of Spatial Features
    1. Measurement of Location
      1. Map Coordinate Systems
      2. Map Orientation
      3. Map Scale
      4. Map Projections
    2. Basic Measurements of Spatial Features
      1. Distance
      2. Area
    3.  Measurement of Attribute Data
      1. Measurement Scales
        1.  Nominal, Ordinal, Interval, and Ratio
      2. Central Tendency and Dispersion
  4. Single-Layer Operations
    1.  Feature Manipulations
      1. Boundary Operations
      2. Logical Operations
      3. Proximity Analysis
    2.  Feature Identification and Selection
    3.  Feature Classification
      1. Constant Interval
      2. Equal Frequency
      3. Progressions
      4. Clustering
      5. Fragmentation Index
  5.  Multi-Layer Operations
    1.  Overlay Analysis
      1. Boolean Algebra
      2. Union
      3. Intersect
      4. Identify
      5. Frequency/Density
    2.  Proximity Analysis
      1. Near
      2. Point Distant
    3.  Analysis of Spatial Correlation
      1. Contingency Table and Chi-Square Test of Goodness-of-Fit
      2. Correlation Coefficient
      3. Simple Regression
  6. Analysis of Point Features
    1. Description of Point Patterns
    2. Examination of Spatial Arrangement
    3. Spatial Autocorrelation
    4. Spatial Association
  7.  Network Analysis
    1. Data Requirement
    2. Evaluation of Network Structure
    3. Network Structure in a Valued Graph
      1. Minimal spanning Tree
      2. The Shortest Path Algorithm
  8. Spatial Modeling
    1. Description, Explanation, Prediction
    2. Analysis of spatial Relationships
      1. Multiple Regression
    3. Logistic Regression
  9. Surface Analysis
    1. Organization of Surface Information
      1. Irregularly Spaced Points
      2. Grid/Lattice Representations
      3. Digitized Contours
      4. Triangulated Irregular Networks (TIN)
    2.  Spatial Interpolation
      1. From Contours
      2. Gridding
      3. Local Estimation
      4. Triangulation
      5. Global Approximation
      6. Kriging
    3.  Applications of Surface Analysis
      1. Isarithmic Mapping
      2. Topographic Profile
      3. Perspective diagram
      4. Thiessen Polygons
  10. Grid Analysis
    1. Spatial Properties of Grid Data
    2. Data sources and Availability
    3. Generation of Grid
    4. Conversion Between Vector and Grid formats
    5. Analysis of Grid data
      1. Operations
        1.  Local, Focal, Zonal, Global
      2. Spatial Patterns
      3. Grid Resolution and Spatial Properties
    6.  Connectivity Analysis
      1. Classification of Grid Elements
      2. Evaluation of Distance
  11.  Spatial Analysis in Decision Making
    1. Qualitative approach
    2. Quantitative Approach
    3. Interpretation of Results
 
METHODS OF INSTRUCTION
 Methods of instruction used to achieve student learning outcomes may include, but are not limited to:

  • Present class lectures/discussions in order to assist students in achieving the learning outcomes by reviewing relevant course content.
  • Perform assigned lab activities in order to expose the student to situations/problems which reinforce lecture presentation material.
  • Show videos/films/slides/handouts in order to give the student a better feeling of exposure to activities within industry and related fields
  • Field trips to selected sites in order to give the students an experience the applications of the course concepts of design as applied in the professions and industries using GIS technologies.
  • Develop and assign problem solving tasks and activities in order to assist the student in achieving learning objectives and by offering for students opportunities to develop a variety of solution methods to specific problems, while providing individualized learning opportunities.
  • Off site meetings at selected locations in order to provide students the experience of seeing GIS course content in use.
  • Create and assign pair and small group activities such as preparing an analysis of a given composition using the relevant course content. A competition format may be used in this process. This is done in order to help students achieve outcomes by stimulating individual participation in group activities.
  • Invite or visit guest lecturers in order to bring current industry experience directly into the classroom and help students attain objectives through direct interface with active professionals.
  • Develop and assign web-based/web-enhanced tasks and activities in order to assist the student in achieving learning objectives by offering opportunities to interact with other students online, while also providing individualized learning opportunities.

 
 
METHODS OF EVALUATION
 Students will be evaluated for progress in and/or mastery of learning outcomes by methods of evaluation which may include, but are not limited to:

  • Individual and small group projects are evaluated based on the standards that would be applicable to success in the field or professions employing these skills.  Project Content, Student Accuracy and Procedures of producing the final product is evaluated within the context of the assigned problem and time allotted.
  • Oral reports and visual presentations designed to demonstrate student achievement of course learning objectives.
  • Laboratory projects are designed to evaluate their ability to properly apply the GIS concepts and use the related software and present their understanding of the concept learned in class.
  • Final notebook evaluation is designed to demonstrate student’s overall achievement of course learning objectives and to have students organize and demonstrate their understanding of the course learning outcomes.  This is an organized notebook and oral presentation by the student.
ASSIGNMENTS

Required Reading Assignments


Required Writing Assignments


Other Outside-of-Class Assignments
 
COURSE MATERIALS
 All materials used in this course will be periodically reviewed to ensure that they are appropriate for college level instruction. Possible texts include:

  • Ormsby. Extending ArcView GIS. 1 ed. Redlands: ESRI Press , 2001.
  • Mitchell, Andy. ESRI Guide to GIS Analysis. Redlands: ESRI Press , 2005.
  • 3. Paul A. Longley, Michael F. Goodchild, David J. Maguire, David W. Rhind. Geographic Information Systems and Science. 2 ed. any: John Wiley & Sons, 2005.
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