bryceob7/esda_cee690-02

Environmental Spatial Data Analysis

CEE690-02: Environmental Spatial Data Analysis

Fall 2021

Course Information

Lectures are on Tuesdays and Thursdays from 8:30 AM - 9:45 AM. The course website is on GitHub (https://github.com/chaneyn/CEE690-02). Class announcements will be made via Sakai (CEE690.02.F21).

Instructor

Professor Nathaniel W. Chaney (Nate)
Email: [email protected]
Office: FCIEMAS 2463
Office hours: by reservation (calendly.com/nathaniel-chaney) Thursdays 12-2pm

TA

Tyler Waterman
Email: [email protected]
Office hours location: 2431 CIEMAS (preferable in person, by zoom: https://duke.zoom.us/j/91286900464)
Office hours: Tuesdays 2-3pm

Course Description

Environmental Spatial Data Analysis (ESDA) provides an introduction on how to leverage large volumes of spatial environmental data using primarily Python. The topics that will be covered include an overview of basic spatial statistics, spatial interpolation, kriging, conditional simulation, terrain analysis, dimensionality reduction, and spatial prediction. Existing software packages in Python will be introduced and used to explore the listed topics.

Prerequisites

Although there are no class prerequisistes, a strong foundation in programming will make this class much easier. Please contact Nate if you have concerns.

Readings

There are no required textbooks. Reading will be provided via journal articles, online materials, and tutorials.

Grades and workload

The course grade is based on three items:

• Homework: 50%
• Participation: 20%
• Final Project: 30%

Homework

There will be 4 homework assignments. Each assignment will be provided and completed within a corresponding Jupyter notebook. Completed assignments will be submitted via a private GitHub repository that each student will have for the course; assignments submitted via any other method will not be accepted. Each assignment must be submitted before class on the day listed on the schedule below. Late homeworks will not be accepted.

Participation

• Students should follow along the lecture on their personal jupyter lab Docker container that they will use for the course.
• Each student will present twice. The first presentation will involve describing a dataset and the second will be present a journal article.

Collaboration

Collaboration in completing assignments is permitted. However, each student must write up their assignment independently. We will be checking for identical homeworks.

Final Project

The final project can be done in groups of 2 or individually. The expectations for the project will increase with the group size. It will involve the following components:

• Proposal (November 2nd via email)
  • 3 pages max, single-spaced, 12 point font size, 1 inch margin
  • Contains: Title, introduction, objectives, data, methodology, and timeline of tasks
• Oral presentation (November 17th and 19th in class)
  • 12 minute oral presentation, 3 minutes for questions
  • Everyone needs to be present for each presentation
• Final report (November 24th via email)
  • 10 pages max, single-spaced, 12 point font size, 1 inch margin
  • Contains: Title, introduction, data, methods, results, discussion, and conclusion

Schedule

Note that the schedule is subject to change.

Date	Topic	New Software	Assignments	Article
08/24	Introduction	Jupyter/GitHub/Bash	-	-
08/26	Python overview	Python	-	Lin, J., 2012 (TBD)
08/31	Multi-dimensional arrays I	NumPy	-	Lu et al., 2018 (TBD)
09/02	Visualizing data	Matplotlib	-	Rougier et al., 2014 (TBD)
09/07	Data storage	Pickle/H5py/NetCDF/GeoTiff	-	Extance, 2016 (TBD)
09/09	Probability/Statistics I	Scipy	-	Holmes, 2018 (TBD)
09/14	Probability/Statistics II	-	HW #1 due	Walther and Moore, 2005 (TBD)
09/16	Bayesian Statistics	-	-	Prathvikumar, 2019 (TBD)
09/21	Map projections I	Cartopy	-	Lapaine, 2017 (Shawn Li)
09/23	Map projections II	GDAL	-	Asay, 2020 and Simmon, 2017 (Soumak Bhattacharjee)
09/28	Multi-dimensional arrays II	CDO/Xarray	-	Hoyer and Hamman, 2017 (Daniel Guyumus)
09/30	Vector Data	OGR/Shapely/GeoPandas	-	Kreveld, 2006 (Selena Galeos)
10/07	Cluster Analysis I	Scikit-Learn	-	Mishra, 2017 (Rachael Lau)
10/12	Cluster Analysis II	-	-	-
10/14	Dimensionality Reduction	-	HW #2 due	-
10/19	Decision Trees	-	-	Homer et al., 2004 (Selena Galeos)
10/21	Random Forests/Boosting	-	-	Kaminska, J., 2018, Cai, J., et al., 2020 (Cameron King)
10/26	Artificial Neural Networks	-	-	-
10/28	Convolutional Neural Networks	-	-	-
11/02	Simple Kriging	-	Proposal due	Wong, D., et al., 2004 (Zach Calhoun)
11/04	Ordinary Kriging	-	-	Pouladi, N., et al., 2019 (Aman Hingu)
11/09	Semivariogram	-	-	Hengl, T., et al., 2007 (Daniel Guyumus)
11/11	Regression Kriging	-	HW #3 due	-
11/16	Terrain Analysis I	-	-	Moore, I., et al., 1991 (Jiwei Xia)
11/18	Visualization II	-	-
11/23	Scaling up code	Numba/Mpi4py/Dask	-	Bakharia, A., 2018 and Grover, P., 2018 (Mazen Nakad)
11/30	Oral Presentations	-	-	-
12/2	Oral Presentations	-	-	-
12/13	Written report due	-	HW #4 due	-