Applied Computational Methods for Social Sciences

By the end, you’ll know what this course offers and how to set up your own R workflow.

• Room: G.K.1.4-Guarini Campus, Kushlan Wing, First Floor, Room 4

• Office Hours: By appointment

• execute basic programming tasks in R (e.g. loops, conditional statements, while statements, etc.)

• understand basic GIS terms and concepts

• utilize GIS for conducting spatial analyses

• appreciate the design and structure of a geographic information system (GIS) as a decision-making tool

• produce maps

• GIS Analyst / GIS Specialist

• Environmental Scientist

• Market Research Analyst

• Remote Sensing Specialist

• Transportation Planner

• This component contributes 50% of the overall grade for the problem set.
• When you 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 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.

• choose a topic that involves spatial data

• acquire data either from the course materials or from external sources

• employ at least five GIS procedures in R

• craft a well-structured two-page report containing: intro to the problem, objectives, data sources, methodology, results, and conclusion

• appendix with the R code for the GIS procedures

• You have two attempts for every assignment

• The final grade for each assignment will be the average of the first and second attempts

• For the second attempt, you should meet up with your assigned team to discuss solutions

We will then move to work with spatial data in R, including how to process vectors, rasters, and combine the two.

In the final part, we will also learn how to deal with spatio-temporal data and point pattern analysis.

It is an open-source ecosystem (i.e. everyone can contribute and it’s free).

It is compatible with Windows, Mac, and Linux.

• clean and process data
• visualize data
• create interactive web-apps
• typeset: write visually appealing articles and presentations (this presentation is made in R Quarto)

• Reproducibility — using the same commands repeatedly and obtaining the same output

• Visualization — making and presenting graphs and maps

• R for Data Science
  http://r4ds.had.co.nz/
  Introduction to data analysis using R, focused on the tidyverse packages
  Good substitute for Stata

• Advanced R
  https://adv-r.hadley.nz/
  Programming in R

• Markdown Basics
  https://quarto.org/docs/authoring/markdown-basics.html
  Useful to create professionally-looking presentations and documents

GIS is a system that creates, manages, analyzes, and maps all types of data.

It helps us understand patterns, relationships, and geographic context.

• Identify problems
• Manage and respond to events
• Set priorities
• Monitor change
• Perform forecasting
• Understand trends

Spatial analysis focuses on a variety of aspects:

• data manipulation
• data querying
• statistical analysis of geographic patterns

GIS comprises both mapping (visualization) and geographic data manipulation and analysis.

In this course, we will use R — free, open-source, and fully scriptable.

• Intro to GIS and Spatial Analysis

• Introduction to Spatial Data Programming with R

• PROJ handles Coordinate Reference Systems (CRS), in both sf and stars

It will help us deal with:

• Numerical Operations to calculate: Areas, Length, Distances, etc.

• GIS Logical Operations: Overlaps, Equals, Intersects, etc.

• Geometry Operations: Centroid, Buffer, Intersection, Union, Difference, etc.

• Accessing cell values — as a matrix or as a dataframe, extracting pixels to points

• Performing raster algebra — raster arithmetic and logic

• Changing the resolution and extent — cropping, mosaicing, resampling, and reprojecting

• Transforming rasters — to points and polygons

• it has a well designed and consistent syntax that supports visualization for both vectors and rasters

• it has highly customizable publication-quality figures and maps

• Programming allows us to edit code and re-use it in the future and obtain the same results in the future.

• Each object belongs to a class: an abstract structure that has specific properties

Example:

• All cars in the parking lot are instances of the “car” class
• The “car” class has specific properties: make, color, year and methods: start, drive, stop

• columns

ncol(restaurants2)

[1] 3

• dimension

• bounding box

• crs

Inheritance is what makes it possible for one class to extend another class, by adding other properties.

Example:

• A “taxi” is an extension of a “car” class, inheriting all of its properties and methods.
• A taxi could have new properties like taxi company name.

We first need to install R: R-project

We will then install an R interface that allows us to interact with R in a more user-friendly manner: RStudio

• You should save your code and not your workspace: the workspace takes a lot of space

• Always start R with a blank state

• Restart R once in a while to clear memory

• Create a folder for every week: week1, week2, week3, assignment1, assignment2
• Within each folder, you should have:
  1. data
  2. graphs
  3. tables (or output)

• Always start R with a blank state

• Always save your R scripts

• Reduces mistakes

• It is easy to implement in the long run

• If your work is reproducible, other people can see what you have done: other people can replicate your analyses

• Comments are the what
• Code is the how

• If you cannot find a solution, have another try in another 15 minutes after taking a break

• Take another break and try again

• Finally, go and ask for help

• Stackoverflow

• ChatGPT

• Fellow students

• Me

Next week we roll up our sleeves and start programming in R.