Lesson 1: Introduction to Geospatial Analytics

Content

• Introduction to Geospatial Analytics
  • Demystifying Geospatial Analytics
  • Geospatial Analytics vs Mapping
  • Geospatial Analytics vs GIS
  • Geospatial Analytics vs Statistical Analysis, Data Mining & Machine Learning
• Motivation of Geospatial Analytics
• A Tour Through the Geospatial Analytics Zoo
• Geospatial Analytics and Social Consciousness

Lesson Slides

• Lesson 1 slides in html and pdf formats.

In-class Exercise:

• Installing and configuring R and RStudio
• Installing Rtools and devtools
• Installing and configuring git
• Creating a github account
• Building a course website using Quarto
• Publishing the course website on Netlify

Slides

• My First Date with Quarto

Hands-on Exercise

References

• “Spatial Data, Spatial Analysis, Spatial Data Science” by Prof. Luc Anselin. (This is a long lecture 1hr 15minutes but don’t turn away just because it is lengthy.)
• Xie, Yiqun et. al. (2017) “Transdisciplinary Foundations of Geospatial Data Science” ISPRS International Journal of Geo-information, 2017, Vol.6 (12), p.395.
• Paez, A., and Scott, D.M. (2004) “Spatial statistics for urban analysis: A review of techniques with examples”, GeoJournal, 61: 53-67. Available in SMU eLibrary.
• “Geospatial Analytics Will Eat The World, And You Won’t Even Know It”.

R packages for Data Science

• tidyverse: a family of modern R packages specially designed to meet the tasks of Data Science and Analytics.
• readr: a fast and effective library to parse csv, txt, and tsv files as tibble data.frame in R. To get started, refer to Chapter 11 Data import of R for Data Science book.
  
• tidyr: an R package for tidying data. To get started, refer to Chapter 12 Tidy data of R for Data Science book.
  
• dplyr: a grammar of data manipulation. To get started, read articles under Getting Started and Articles tabs.
• ggplot2: a grammar of graphics. To get started, read Chapter 2: Data Visualization and Chapter 13 Communication of R for Data Science (2ed) book.
• pipes: a powerful tool for clearly expressing a sequence of multiple operations. To get started, read Chapter 5 Workflow: pipes of R for Data Science (2ed) book.