Indiana University Bloomington

Geography G589
Advanced Geospatial Data Analysis

Contact: Scott Robeson (srobeson@indiana.edu) 2012-10-29
Offered: Spring, 2017
Capacity: 20
Sequence: N/A
Pre-Requisites: Univariate descriptive and inferential statistics.
Algebra Required: For notation only.
Calculus Required: None.
Contact Person for Authorization: None.
Instructor: Scott Robeson
Days Per Week Offered: 1 lecture and 1 computer lab exercise per week
Recommended follow-up classes: N/A
Syllabus: Download Syllabus
Keywords: spatial, autocorrelation, spatial regression, cluster analysis, multivariate
Description: Advanced methods of data analysis for evaluating spatial heterogeneity and spatial dependence. Topics include global and local spatial autocorrelation, 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.
Books: Rogerson, P.A. (2006). Statistical Methods for Geography: A Student's Guide (2nd ed). Sage Publications: New York.
Substantive Orientation: environmental and social sciences
Applied/Theoretical: Applied but emphasizing mathematics-based interpretation
Software: MatLab, SPSS, R
How Software is Used: Data analysis.
Problem Sets: No.
Data Analysis: Yes, weekly exercises in computer lab.
Presentation: Final project.
Exams: Yes, final exam.
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