APPLIED GEOSTATISTICS
| Instructor: Ling Bian | Geog 597 Spring 2020 (3 credit hours) |
| Office: 301A Wilkeson Quad
Office Hours: T R 2:00-3pm or by appts |
T R 12:30:00-1:50pm 144 Wilkeson |
Purposes
The course is intended to introduce
the basic concepts and applications of applied geostatistics, which addresses
optimal spatial interpolation. Geostatistics are considered to be one of
the most sophisticated spatial interpolation methods. The method is commonly
used in many disciplines such as geology, engineering, hydrology, geography,
ecology, urban studies, and medical geography. Geostatistics are closely
related to statistics and GIS. Students with basic knowledge of statistics
or GIS can take a step further to learn how to use geostatistics. The course
emphasizes the applied side of geostatistics, and the method can be useful
in students' immediate and future needs such as students' own theses and
dissertations, or projects for their current or potential employers.
The course uses a well received textbook for the lectures and a popular GIS software package ArcGIS for the lab exercises. Three lab sections and several bi-weekly assignments will provide students with hands-on experience in using the geostatistical tool.
Texts
An Introduction to Applied Geostatistics.
Oxford University Press, New York, by Isaaks, Edward.H., and R.Mohan. Srivastava,
1989.
Prerequisites
The course is open to both graduate
students who have knowledge of univariate
statistics.
Requirements
During the semester, each student
should apply the geostatistical interpolation to a data set and present
the result.
| Assignments |
% Grade |
| Lab 1 |
10% |
| Lab 2 | 10% |
| Lab 3 | 10% |
| Project Report | 70% |
| Total | 100% |
Tentative
Schedule
1/28
Introduction
1/30
Spatial interpolation
2/04
Spatial desscription
2/06
Spatial description
2/11
Spatial continuity
2/13
Estimation
2/18 Random function models
2/20
Random function models
2/25 Lab section 1
2/27 Global estimation
3/03
Point estimate
3/05 Ordinary kriging
3/10
Ordinary kriging
3/12
Ordinary kriging
Spring Break
3/24 Block
kriging and Search strategy
3/26
Cross validation
3/31 Modeling
sample variogram
4/02
Modeling sample variogram
4/14 Lab section 2
4/16 Lab section 3
4/21 Co-kriging
4/23 Co-kriging
4/28
Class presentations
4/30
Class presentations
5/05 Class
presentations
5/07 Conclusions
Final paper due:
Recommended Data Sources
http://cugir.mannlib.cornell.edu/
http://gis.ny.gov
http://nationalmap.gov/viewer.html
http://datagateway.nrcs.usda.gov/
Map Library
Map Collection curator: Kim Plassche,
kf43@buffalo.edu
Map
Librarian: Dave Bertuca
dbertuca@buffalo.edu
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https://catalog.buffalo.edu/policies/academic_integrity_2019-20.html
Graduate
Academic Integrity Policy:
https://grad.buffalo.edu/succeed/current-students/policy-library.html#academic-integrity
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