Geospatial Analysis with R

Anto Aasa, PhD, Associate Professor in Human Geography
Department of Geography
University of Tartu

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The aim: This introductory course provides students with a diverse set of skills of spatial analysis in R environment. The aim is to understand and explore the benefits of using a non-gui (coding/scripting) approach to method development for spatial analytics and statistics, based on the standard approaches in R.

These lessons assume no prior knowledge of the skills or tools. It is a hands-on teaching course, so the majority of this course will be together in front of a computer and working on exercises.

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Learning outcomes

Student

• knows basic concepts, skills, and tools for working with the Python and R scripting environments;
• compiles an overview of practical Python and R libraries for everyday scientific and professional GIS use;
• understands how to make use of integration of Python and R environments from other software packages;
• is able to apply to solve common data-related tasks using Python and R in concrete GIS projects;
• is competent of using spatial and non-spatial data in order to answer a research question;
• knows how to conduct and automate different standard GIS-related tasks that support clear documentation of methods in the Python and R scripting environments;

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Brief description: Introductory course on concepts, skills, and tools for working with the Python and R scripting environments. Acquaintanceship with practical Python and R libraries for everyday scientific and professional GIS use, with a focus on automating different standard GIS-related tasks that support clear documentation of methods and productivity.
These lessons assume no prior knowledge of the skills or tools. It is a hands-on teaching course, so the majority of this course will be together in front of a computer and working on exercises.

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All the materials are available via Study Information System and course webpage: http://aasa.ut.ee/Rspatial/

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Period: Nov. 2 - Dec. 16, 2022

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Time schedule (R):

Type	Date	Time	Content
lecture	Nov 02	16:15…17:45	Introductive lecture
practical session 1	Nov 04	12:15…13:45	Introduction to R
practical session 2	Nov 09	16:15…17:45	Data wrangling
practical session 3	Nov 11	12:15…13:45	Thematic maps
practical session 4	Nov 18	12:15…13:45	Orthophoto / satellite image as base map
practical session 5	Nov 23	16:15…17:45	Spatial interpolation
practical session 6	Nov 25	12:15…13:45	Mobile data collection; crime data analysis
practical session 7	Nov 30	16:15…17:45	3D maps
practical session 8	Dec 02	12:15…13:45	Interactive plots & maps
practical session 9	Dec 07	16:15…17:45	Animations
lecture	Dec 09	12:15…13:45	Principles of scientific visualization
practical session 10	Dec 14	16:15…17:45	Calling Python from R
practical session 11	Dec 16	12:15…13:45	Consultation

Literature:

• Short introduction to R
  
• R for Data Science by Hadley Wickham & Gerrett Golemund
• Introduction to GIS with R by Jesse Sadler
  
• Tutorial of the statistical analysis (and graphs) in R
  
• Book “Geocomputaion with R”
  
• GIS in R by Nick Eubank
  
• Simple Features for R

Slides:

• Introductive lecture
• Principles of scientific visualization

Practical session:

1. Introduction to R
2. Data wrangling
3. Thematic maps
4. Orthophoto / satellite image as base map
5. Spatial interpolation
6. Mobility analysis
   1. GPS & mobile data
   2. crime data
7. 3D maps
8. Interactive plots & maps
9. Animations
10. Calling Python from R

Passing the course:

To pass the course, students must upload the results of their homework to Moodle.

• If a student submits the homework after the deadline - we will remove 20% of the final score.
  
• In Moodle, students have to press the button “Submit” after uploading the file. Otherwise, we don’t know whether the uploaded work is ready for evaluation.
  
• If students have a general question (they want to clarify something about homework or receive an error in R) - they should ask questions in Moodle. Other students can answer those questions and, by doing this, they will receive additional scores.
  
• R code should be submitted as .R file. Neither .txt nor .docx are accepted. Please, do not paste the code as a text to the comment window in Moodle.

Homewok list:

• After practical session 1: Thematic map of Estonian municipalities (2 points; deadline: November 10, 2022)
  
• After practical session 2: Pigs in Estonia (2 points; deadline: November 16, 2022)
  
• After practical session 3: Thematic maps (2 points; deadline: November 18, 2022)
• After practical session 4: Orthophoto / satellite image as base map (2 points; deadline: November 25, 2022)
• After practical session 5: Interpolated map of snow cover duration in Estonia (4 points; deadline: November 30, 2022)
• After practical session 6: Map of crimes in Saaremaa county (2 points; deadline: December 7, 2022 December 12, 2022)
• After practical session 7: 3D map of the highest peak of Estonia, Suur-Munamägi (4 points; deadline: December 14, 2022 December 19, 2022)
• After practical session 8: Interactive dashboard about the COVID-19 situation in Estonia or some other country (10 points; deadline: January 7, 2023)
• After practical session 9: Animation of The Flying Dot (4 points; deadline: December 20, 2022 December 23, 2022)

Maximum possible score: 32 points.

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Author: Anto Aasa
Supervisors: Anto Aasa & Lika Zhvania
LTOM.02.041
Last update: 2024-04-15 11:37:37

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