Geospatial Science 380
GEOS 380: Spatial Analysis Techniques (T)
Prerequisite: GEOS 250 and STAT 200 or STAT 219 or permission of the instructor.
Credit hours (4)
Three hours lecture and two hours asynchronous online laboratory.
The course, which will consist of both lecture and GIS lab applications, is devoted to description and application of methods for analyzing spatial distributions and to evaluation and assessment of geographic research problems in the context of GIS technology.
Note(s): Scientific and Quantitative Reasoning designated course.
Detailed Description of Course
The following topics will be discussed:
1) Geographic information analysis and spatial data
2) Special qualities of spatial data; Why classic procedures of statistics do not always fit spatial data? Proximity polygons, variograms, and the use of
matrices to summarize spatial relations.
3) Maps as outcomes of spatial processes (describing patterns reflected by maps and their underlying distributions)
4) Point pattern analysis (e.g.: settlement systems) in theory and practice
5) Lines and networks (e.g. transportation routes)
6) Spatial autocorrelation and spatial uncertainty
7) Describing and analyzing fields (e.g. phenomena reflected by isolines, like isotherms, and spatial interpolation and measuring gradients)
8) Trend surface analysis (reformulating regression in matrix terms that fit the needs of spatial analysis)
9) Polygon overlay as the most popular method of map combination
10) Use of classic (aspatial) multivariate statistics (e.g.: cluster analysis, principal components analysis, and factor analysis) to analyze spatial data
11) New approaches to spatial analysis (description of the most recent techniques in the GIS context).
Detailed Description of Conduct of Course
This course will include hands on exercises in statistical analysis of spatial data. The course can be taught through classroom lectures with accompanying labs, as an asychronous online class, or through synchronous classroom lectures and labs online or on-campus. The class will primarily involve hands-on experiences in the form of exercises that involve studying a geographic problem and drawing valid conclusions informed by quantitative data.
Goals and Objectives of the Course
Having successfully completed this course, the student will be able to understand and apply multiple techniques of spatial analysis such as point pattern analysis, trend surface analysis, and coefficients of autocorrelations. The student will also be able to apply such major multivariate statistical techniques as cluster and factor analysis to spatial data.
Assessment Measures
Assessment measures may include group projects, student reports, attendance, and exams.
Other Course Information
The most recent ARC GIS software from ESRI, for which the department already has a site license, will be used during lectures. Software for group projects can be acquired free from online sources.
Review and Approval
April 27, 2017
April 2014 Rick Roth, Chair
February 2010 Bernd H. Kuennecke, Chair
March 01, 2021