G589 : Advanced Geospatial Data Analysis
Advanced methods of data analysis for evaluating spatial heterogeneity and spatial dependence. Topics include global and local spatial auto correlation, point pattern analysis, spatial cluster analysis, spatial regression analysis, and other multivariate approaches. Lecture and lab format with regular use of software. Emphasis on geographic applications.
Semester(s) Offered: Spring
Instructor: Scott Robeson - email@example.com
Other Contact(s): Scott Robeson - firstname.lastname@example.org
Prerequisites: Univariate descriptive and inferential statistics.
Algebra Required?: For notation only.
Calculus Required?: None.
Day(s) per week offered: 1 lecture and 1 computer lab exercise per week.
Substantive Orientation: environmental and social sciences
Books used: Rogerson, P.A. (2006). Statistical Methods for Geography: A Student's Guide (2nd ed). Sage Publications: New York.
Applied/Theoretical: Applied but emphasizing mathematics-based interpretation
Software Used: MatLab, R, SPSS
How the software is used: Data analysis
Problem Sets: No
Data Analysis: Yes, weekly exercises in computer lab.
Presentations: Final project.
Exams: Yes, final exam.
Keywords: spatial, autocorrelation, spatial regression, cluster analysis, multivariate