Intro to GIS

Author

Bogdan G. Popescu

Course Information

Course Number: PL/CS 362-1
Course Title: Applied Computational Methods for Social Sciences
Hours: MW 10:00-11:15AM
Location: G.K.1.4-Guarini Campus, Kushlan Wing, First Floor, Room 4
Credits: 3
Prerequisites: None

Instructor Details

Instructor: Bogdan G. Popescu
E-Mail: bogdan.popescu@johncabot.edu
Office Hours: By appointment

Note: You should have a computer, either Windows or Mac, with at least 8GB of RAM.

Course Description

This is an introductory course to spatial data in R, a free programming language and environment developed for statistical computing and graphics. The first part of the course will introduce you to the R programming language. The second part will focus on R’s ability to manipulate spatial data, including how to process vector layers and rasters. Through hands-on exercises, this course introduces students to the concepts and applications of geographic information systems (GIS). It provides a basic foundation of spatial analysis and GIS with laboratory applications in particular techniques and methodology utilizing R. Students will learn to perform spatial analyses and communicate their results through cartography, along with an introduction to concepts such as spatial data collection, remote sensing, and spatial statistics.

Summary of Course Content

This introductory course on spatial data in R offers a comprehensive exploration of the R programming language’s capabilities in statistical computing and graphics, with a specific focus on spatial data manipulation. The initial segment of the course acquaints students with the fundamentals of the R programming language, emphasizing its status as a versatile and free tool. Subsequently, the course delves into R’s ability to handle spatial data, covering the processing of both vector layers and raster data. Participants learn to perform spatial analyses using R, with a particular emphasis on laboratory applications that apply techniques and methodologies to real-world scenarios. A critical component of the course is the integration of cartography, enabling students to visually communicate spatial analysis results through map creation. The practical applications extend to various domains, allowing students to develop a solid foundation in spatial analysis skills. Overall, this course equips students with a robust skill set for handling spatial data, making it valuable for those entering fields such as geography, environmental science, and urban planning.

Learning Outcomes

Upon successful completion of this course the students will be able to:

  • execute basic programming tasks in R (e.g. loops, conditional statements, while statements, etc.)
  • understand basic GIS terms and concepts
  • utilize GIS for mapping and conducting spatial analyses
  • create a professional website containing a portfolio

Assessment

You will be graded on four problem sets during the semester (each 12.5% of your grade) and a final report and presentation (each 25% of your grade).

  • 4 problem sets: 50% of the final grade (12.5% each)
  • final presentation: 25% of the final grade
  • final project report: 25% of the final grade

Problem Sets

  1. Initial Individual Submission: This component contributes 50% of the overall grade for the problem set. When you first complete the problem set independently and submit it to the instructor, your grade for this component will be calculated based on the quality of your independent work. This grade will be weighted at 50% of the total assignment grade.

  2. Final Submission After Group Consultation: This component also contributes 50% of the overall grade for the problem sets. After discussing the problem set with your group members and documenting the correct answers, you will submit this revised version individually. Note: no group submission is permitted. Each one of you has to submit the second attempt of the assignment individually. Your grade for this component will be based on the quality of your final submission after group consultation. This grade will also be weighted at 50% of the total assignment grade.

Final Project

Students will undertake a GIS project emphasizing the practical application of spatial analysis techniques using the R programming language. This project offers an opportunity to showcase the acquired skills in manipulating spatial data and conducting meaningful analyses. Participants are encouraged to choose a topic of interest or relevance, utilizing datasets provided in the course or exploring new ones. The project entails a few steps:

  • choosing a topic that involves spatial data
  • acquiring data either from the course materials or from external sources
  • employing at least five GIS procedures in R such as: st_join, st_centroid, st_area, st_distance, st_buffer, st_voronoi, st_union, st_combine, st_cast, st_intersection, st_difference, dplyr for vector layer aggregation, st_crop, st_rasterize, raster::aggregate, etc.

OR

  • producing some descriptive visualizations (maps, barplot, scatterplot, boxplots) that tell the same story AND making a GitHub website (for data, you can explore https://ourworldindata.org)
  • crafting a well-structured two-page report containing an intro to the problem, objectives, data sources, methodology, results, and conclusion
  • an appendix with the R code for the GIS procedures

Here are examples of projects that former students produced:

Examples of Project Topics:

  1. Understanding economic development in Eastern Europe since 1992 using satellite luminosity
  2. Exploring temperature changes: what are the countries in Europe that have experienced the most dramatic changes in temperature since 1901
  3. Exploring temperature changes: what are the regions within a country (e.g. Italy) that have experienced the most dramatic changes in temperature since 1901
  4. Exploring business opportunities (e.g. restaurants, retail) in your favorite city using OSM
  5. Exploring areas worst affected by an earthquake

Students have to submit a two-page report in Quarto and give a 20-minute, in-class presentation in Quarto.

Grading criteria for the project:

  • Relevance
  • Methodology: demonstration of at least five distinct GIS procedures in R
  • Analysis
  • Presentation
  • Code Quality

You can find below an example of a project: Wildfires in 2023

Attendance Requirements

Students are required to attend classes following the University’s policies. Students with more than four unexcused absences (two weeks) will get an F on the course. Thus, students must attend classes.

Academic Honesty

As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.

Students with Learning or Other Disabilities

John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.