Chapter 1 Introduction

Increasingly, the study of structural disparities is an interdisciplinary affair. Researchers, academics, journalists, policy makers, and others have seen the rise of quantitative and qualitative, visual and analytic, and technological and theoretical tools for examining the differences in social and economics outcomes between demographic groups. The use of maps, for example, has risen greatly among academics and journalists to convey gaps between groups with clarity and conciseness.

During 2021, I started a course at Babson College called Quantitative Analysis of Structural Injustice. Babson College is a small, highly competitive, business-oriented college in Wellesley, MA (USA) that has a goal of emphasizing sustainability in its curriculum. The course was started with the goal of providing advanced quantitative literacy beyond foundational frequentist statistics. At a minimum, the course would be the third quantitative analytics course taken by undergraduates. And it would come to provide additional practice for advanced regression techniques. It would also provide a foundation in philosophies of causality, the strengths and weaknesses of machine learning, and regression considerations when incorporating geographic data into your analysis. As part of the course, students found a light dive into maps and GIS programs to be highly useful for their knowledge and career.

Given the dearth of an undergraduate textbook for simple GIS use for analytics, data merging, and visualizing purposes, especially for those in non-analytics or non-engineering careers, I proceeded to write this short book as part of a series of lecture notes. The goal of this text is to provide students with a foundation in GIS terminology, handling shapefile data, and basic map visualization skills. We use QGIS, a free, open-source software, but we also utilize R and RStudio for the purpose of analysis.

This book came to life thanks to a generous teaching grant from the Babson Teaching Innovation Fund (TIF). I thank Dean Ken Matsuno, Kathy Esper, Cathleen Chaves, Davit Khachatryan, Erin Escobedo and the rest of the former and current TIF committee for selecting my project to fund and supporting it. I also thank Vikki Rodgers, Rick Cleary, Dessi Pachamanova, Denise Troxell, and Nathaniel Karst for their constant guidance, mentoring, and feedback. Much of their support, shown through class visits and mentorship, have allowed teaching and the furthering of the quant side of the Babson curriculum to be my focus.

Finally, none of this would be possible without the generous support of Ms. Amanda Strong (Babson ’87). Given the dearth of teaching materials specific to using GIS for the purpose of analyzing structural injustice at the undergraduate level, this teaching material have made my life much easier and my students much happier. Thank you, Ms. Strong, for funding this endeavor.

This book is published using the bookdown package (Xie 2022), which was built on top of R Markdown and knitr (Xie 2015).

1.1 About the Author

Eric W. Chan is an Assistant Professor of Statistics & Public Policy at Babson College in Wellesley, MA. He devotes much of his time to the research and teaching of structural disparities, particularly in education, housing, and the environment. He received his B.S. from Babson College, an M.A. and M.Phil. from Teachers College, Columbia University, and his Ph.D. from Columbia University. He cherishes his time with his wife, two daughters, and church community.

References

Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.name/knitr/.
———. 2022. Bookdown: Authoring Books and Technical Documents with r Markdown. https://CRAN.R-project.org/package=bookdown.