Geospatial Science 380
Spatial Analysis Techniques
Spatial Analysis Techniques
Four hours lecture/laboratory. (4)
Prerequisites: GEOS 250 and STAT 200
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.
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; 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; and 11) New approaches to spatial analysis (description of the most recent techniques in the GIS context).
Detailed Description of Conduct of Course
On average each of the above-mentioned topics or units will take one week (three MWF or two T&TR classes) to discuss. However, some topics will take less (e.g.: # 1, 3, and 11) and some more (e.g.: # 4, 8, and 10) than one week. The introduction to each unit will always be given in a lecture format using GIS recent packages for visualization of spatial concepts and for rationalizing the use of specific spatial analysis techniques. Following an introductory lecture (occasionally two lectures) and a corresponding reading assignment, students will be asked to either analyze sample data individually or as part of a group project. For group projects, three techniques will be selected: trend surface analysis, autocorrelation, and either cluster or principal component analysis. Results of individual data analyses will be regularly presented by students during the entire semester, whereas group projects results will be presented during one or two last classes of the semester.
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 understand and apply such major multivariate statistical techniques as cluster and factor analysis to spatial data.
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