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Environmental Spatial Data Analysis

EAS
543
Description

 

This advanced GIS course focuses on frequently used quantitative methods in spatial analysis in the R programming environment.  These can help us answer questions such as: are features of interest in my study area spatially clustered or independently distributed? I have field-collected point data – how can I interpolate between points to make a spatially continuous map? Is my data spatially autocorrelated? And if so, how does it affect my analysis. What underlying variables best explain or predict the patterns of land cover in my study area?  Thus, course topics include:  use of R in spatial analysis, assessment of spatial autocorrelation, spatial point pattern analysis and clustering analysis, spatial interpolation, and spatial regression analysis. Course format emphasizes hands-on lab work, and uses R (and some ArcGIS).  Labs (homework) will require some additional time to finish outside of the lab time.  Prerequisites: one GIS course (e.g. EAS 531 or similar), one introductory statistics course (e.g. EAS 538 or similar), basic familiarity with the R environment (if no previous R experience contact instructor for permission to enroll, some preparatory work may be required).

Watch Faculty Introduction

Faculty/Instructor
Silvia Cordero-Sancho
Syllabus
File
EAS_543.001_Fall_2021.pdf
Credits
Minimum credits
3.00
Maximum credits
3.00
Pass / Fail
Pass/Fail or S/U optional
Undergrad
No
Graduate
Yes
Prerequisites
EAS 531 and EAS 538 or equivalent
Offered Fall Semester
Yes
Offered Winter Semester
No
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University of Michigan School for Environment and Sustainability
Dana Building
440 Church Street
Ann Arbor, MI 48109
(734) 764-6453
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