Spatial Analysis open books

I am teaching a straight forward, stand-alone Spatial Analysis class for the first time in a couple of decades. That means that I have been looking at resources to share with the class, especially reference materials that they can access given that they will mostly forget what I tell them by February once the next semester is in swing.

I have generally utilized OER materials for the last decade (don’t get me started on the modern shift of the textbooks-as-a-service subscription model to get around the growth of OER), so I wanted to find a solid open source item to point to. Instead, between the ones I had used before and those that I found over the last few months, I have found an embarrassment of riches. In the end I am pointing to a number of sources for class readings. By no means inclusive of all of the sources out there, these sources, ranging from PDFs of print books to texts available through our library to GitHub publications, are ones that I am pulling from for student readings (in the order that I have tabs open, aka, no particular order):

• Geospatial Analysis, 6th edition (2021)
  • I have used portions of this book a number of times for various classes, and it remains a solid source for broad concepts
• The Language of Spatial Analysis (2013)
  • A short Esri publication that provides concepts in a rudimentary manor. For me, this a good resource to give students who haven’t had the general GIS class (or not had it recently) to contextualize some content
• The SAGE Handbook of Spatial Analysis (2009)
  • A reference text that I am using as a glossary for the course
• Intro to GIS and Spatial Analysis (2023)
  • The first GitHub document in the list. The focus is on R and ArcGIS. It provides a concise text and a series of R exercises
• Introduction to Spatial Data Programming with R (2023)
  • Another GitHub document that focuses on R. There is more R embedded in the text than in the previous document, and also includes R exercises.
• Spatial Analysis Methods and Practice (2020)
  • A focused text which provides exercises in ArcGIS and GeoDa
• Spatial Modelling for Data Scientists (2023)
  • Worth it for a chapter titled Data Wrangling alone 🙂
• R for Geographic Data Science (2023)
  • This book highlights ML more explicitly in a couple of the later chapters.
• Geographic Data Science with Python (2020)
  • A late addition to the list, but I will be using the Geographic Thinking for Data Scientists chapter

And these are just the book shaped resources for the class. The class is going to incorporate the Esri Spatial Data Science MOOC at the beginning as well (since it is taking place during the course). We will also spend a few weeks focusing on QGIS and R. I am likely going to jump to deep into things this time around, but that will just help me pace the course better next time. Right? 🙂