Geospatial Data Analytics

CE 50701

Credit Hours

3

Instructor

Prof. Jie Shan

Learning Objectives

By successfully completing the course, students should be able to:

  1. LO1: Interpret and evaluate the theories and methods of geospatial data analytics.
    • LO 1.1: Mathematically formulate representative methods in geospatial data analytics.
    • LO 1.2: Interpret the notations and conditions of an analytical formulation.
    • LO 1.3: Explain the properties and limitations of popular geospatial methods.
  2. LO2: Design and implement typical geospatial methods through programming.
    • LO 2.1: Make a computer program to implement specified geospatial analytic methods with correct results.
    • LO 2.2: Discuss the uncertainty of the related implementation and calculation.
    • LO 2.3: Extend and combine basic analytic methods to solve a complex real-world problem through programming.
  3. LO3: Effectively visualize and interpret the outcome of geospatial data analytics.
    • LO 3.1: Access and visualize various geospatial data through a clean and comprehensive representation.
    • LO 3.2: Make spatial analysis over multiple geospatial data layers.
    • LO 3.3: Logically evaluate their mapping and analysis outcome based on the used methods and nature of the problem.

Description

The course will introduce fundamental theories, analytical methods and programming skills that are needed to work with geospatial data. Students will learn the theories, methods, and techniques to visualize, analyze and model various geospatial data through hands-on computer programming practice based on various open-source geospatial libraries. To be specific, the course will use R and its related packages as the basic tool for implementation. The goal is to enable the learners to develop their own geospatial analytical applications.

Topics Covered

R Basics, Temporal Exploration, Spatial Exploration, Thematic Mapping, Coordinate Systems and Transform, Spatial Point Patterns, Spatial Clustering, Spatial Autocorrelation, Spatial Operations and Query, Geographically Weighted Regression, Spatial Interpolation, Kriging Methods

Prerequisites

Graduate standing; prior GIS course; prior programming experience; college statistics and linear algebra

Web Address

https://purdue.brightspace.com/d2l/login

Web Content

Syllabus, grades, lecture notes, homework assignments, solutions and quizzes

The course will be graded based on the following criteria:

Assessment/
Learning Type		 Description % of Final Grade
Project Assignments There will be a total of nine (9) project assignments. They are designed for students to use R and its packages to experience both fundamental theories and practical applications. These projects will cover the entire range of subjects discussed in the lectures and will be assigned in conjunction with the lecture materials being delivered. It is expected that three (3) projects will be assigned for each five-week module. Refer to the Course Schedule below and in Brightspace for project due dates. Additional helpful feedback from the instructor will be shared along with the student’s score. Scores will be based upon provided rubrics for each project. 80%
Participation In-progress presentation discussion forums will accompany each course project. Refer to the Course Projects Procedure page in the Start Here section of Brightspace for more details about the process for posting in these discussion forums. Students that are assigned to a particular project are expected to share a video of their presentation. To achieve full participation points in this course, all students are expected to post substantive and thoughtful replies or comments to the project discussion forums. The instructor will monitor these discussions and may share additional feedback. 5%
Lab Tutorials Lab tutorials are important and necessary materials in conjunction with the topics and projects. They provide learners with live examples on how to apply the fundamentals for geospatial development. Lab tutorials will not be graded.  0%
Reading and Resources Reading and Resources can be in the form of readings and videos. They are provided for learners to extend their knowledge beyond what is covered in the class.  0%
Final Exam The final exam will be open book / open notes. Exams should be completed independently. The exam is comprehensive and will cover all materials discussed in class. It will focus on fundamental theories and methods. Specific exam dates will be announced. 15%

Grading Scale

Your course grade will be based on the following grading scale:

Letter Grade Percentage
A+ [97-100%]
A [93-97%]
A- [90-93%]
B+ [84-90%]
B [77-84%]
B- [70-77%]
C+ [64-70%]
C [57-64%]
C- [50-57%]
F <50%

*Late submissions may cause an up-to 20% deduction per day (no late submissions will be accepted after 48 hours).
*The first instance of academic plagiarism will cause a reduced grade for the related assignment/project. The max grade you may receive is 50%, depending on the severity of the plagiarism. The second instance of academic plagiarism will cause failure of the course.

Textbooks:

Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.

  • No required texts
  • Lecture notes
  • Personal computer/laptop with Windows
  • Brightspace learning management system

Reference Materials:

  1. Spatial Data Science with R
    • Introduction to R
    • ‘terra’ package as an update to ‘raster’
    • Many other materials
  2. Spatial Data in R, Robert J. Hijmans, 75 pp.
  3. Using Spatial Data with R, Claudia A Engel
    • Last updated: February 11, 2019
  4. Geocomputation with R, R. Lovelace, J. Nowosad, J. Muenchow, 2021-07-31
  5. Introduction to Spatial Data Programming with R,Michael Dorman，2021-08-04
  6. Spatial Data Science with applications in R，Edzer Pebesma, Roger Bivand， 2021-07-20
  7. R for Data Science, H. Wickham and G. Grolemud, and many authors, 2017
  8. Advanced R, by Hadley Wickham (it seems the link to 2nd Edition does not work)
  9. R Packages, Hadley Wickham
  10. 17 Best R Programming Books (2021 Update)

Many more resources can be found via Google and YouTube