Lesson 1: Introduction to Geospatial Analytics
Overview
This lesson consists of three parts. First, it provides an overview of geospatial analytics. Second, it explains the popular geospatial models used to store geographical data. The methods used to import, integrate, wrangle, process geospatial data will be discussed too. Lastly, the basic principles and concepts of thematic mapping and geovisualisation will be introduced.
The hands-on exercises will allow you to gaion hands-on experience on using:
Content
- Introduction to Geospatial Analytics
- Demystifying Geospatial Analytics
- Motivation of Geospatial Analytics
- A Tour Through the Geospatial Analytics Zoo
- Geospatial Analytics and Social Consciousness
- Fundamentals of Geospatial Data Models
- Vector and raster data model
- Coordinate systems and map projection
- Handling and wrangling vector data in R: sf methods ````- Handling and wrangling raster data in R: terra methods
- Fundamentals of Geospatial Data Visualisation and tmap Methods
- Classification of maps
- Principles of map design
- Thematic mapping techniques
- tmap methods
Lesson Slides
Self-reading Before Lesson
- “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.
Hands-on Exercise
Hands-on Exercise 1: Geospatial Data Wrangling with R
Hands-on Exercise 1: Choropleth Mapping with R
All About R
R packages for Data Science
sf package.
tidyverse: a family of modern R packages specially designed to meet the tasks of Data Science in R.
- 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 5 Data tidying 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 1: Data Visualization, Chapter 10 Exploratory Data Analysis and Chapter 11 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.
- 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.
R Package for GeoVisualisation and Thematic Mapping
Tennekes, M. (2018) “tmap: Thematic Maps in R”, Journal of Statistical Software, Vol 84:6, 1-39.
tmap: thematic maps in R package especially:
- tmap: get started!,
- tmap: version changes, and
- Chapter 8 Making maps with R of Geocomputation with R.
References
Geospatial Analytics
- 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”.