Lesson 1: Geospatial Data Science and Visualisation

Overview

In this lesson, R objects used to import, integrate, wrangle, process and visualise geospatial data will be discussed. The discussion will focus on sf and tmap packages. Other R packages for storing (i.e. sp), transforming (i.e. rgdal) and processing (i.e. rgeos) geospatial data will be discussed briefly too.

Content

• Geospatial Data Science
  • An overview of Geospatial Data Models
  • Map Projection and Georeferencing
  • Geocoding
  • Classes of Spatial Data in R: Simple features class
• Geospatial Visualisation
  • Classification of maps
  • Principles of map design
  • Thematic mapping techniques
  • Analytical mapping techniques

Lesson Slides

• Lesson 1: Geospatial Data Science with R.
• Lesson 1: GeoVisualisation and Analytical Mapping.

Hands-on Exercise

• Geospatial Data Wrangling with R
• Choropleth Mapping with R

Self-reading Before Meet-up

To read before class:

• Chapter 2. Codifying the neighbourhood structure of Handbook of Spatial Analysis: Theory and Application with R.

Alternatively

• Chapter 9: Modelling Areal Data of Applied Spatial Data Analysis with R (2nd Edition). This book is available in smu digital library. Until section 9.3.1.

References

• François Bavaud (2010) “Models for Spatial Weights: A Systematic Look” Geographical Analysis, Vol. 30, No.2, pp 153-171.

• Tony H. Grubesic and Andrea L. Rosso (2014) “The Use of Spatially Lagged Explanatory Variables for Modeling Neighborhood Amenities and Mobility in Older Adults”, Cityscape, Vol. 16, No. 2, pp. 205-214.

All About R

• sf package.

  • Simple Features for R
  • Reading, Writing and Converting Simple Features
  • Manipulating Simple Feature Geometries
  • Manipulating Simple Features

• tmap: thematic maps in R

• sfdep package.

  • The Basics of sfdep
  • dnearneigh()
  • knearneigh()