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Spatial Statistics for GIS Using R

Spatial Statistics for GIS Using R

This course will teach you spatial statistical analysis methods to address problems in which spatial location. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.


Spatial data are everywhere. Spatial statistical analysis gets behind the map to ask about the data that are mapped and pose questions about the patterns we see. In this course you will learn about the relationship between maps and the data they represent and how such data are coded in the R environment. You will explore point pattern analysis, spatial autocorrelation statistics, and geostatistical interpolation to estimate values across a continuous contour type map.

  • Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

After completing this course, you will be able to describe spatial data using maps and correctly implement spatial data in R. Students will analyze patterns in point, area, and field data. You will learn to detect non-randomness, measure spatial autocorrelation and create contour maps.

  • Describe spatial data using maps
  • Describe and implement the ways spatial data is represented in R
  • Use spastat to analyze patterns in point data, and detect non-randomness
  • Use spdep to analyze patterns in area data, and measure spatial autocorrelation in lattice data
  • Use gstat to analyze continuous field data and create contour maps

Who Should Take This Course

GIS users, scientists, business analysts, engineers and researchers who need to create, use and analyse maps of geographic data.

Our Instructors

Prof. David Unwin

Prof. David Unwin

Prof. David Unwin, until his retirement in 2002, was Professor of Geography at Birkbeck College, University of London, where he retains an Emeritus Chair in the subject. His work using and developing spatial statistics in research stretches back some 40 years, and he has authored over a hundred academic papers in the field, together with a series of texts and a series of edited collections at the interface between geography and computer science. Having developed the world’s first wholly internet-delivered Master’s program in GIS in 1998, David Unwin has considerable experience of teaching and tutoring online.  Most recently, in 2012 David has been awarded the Ron F Abler Honor of the Association of American Geographers for distinguished service to their discipline.

Course Syllabus

Week 1

Introducing geo-data and their representation in R

  • Introducing geographical data
  • Representing geographical data in R

Week 2

Analyzing point events using spatstat

  • Introductory methods for detecting non-randomness in dot/pin map distributions

Week 3

Analyzing lattice data using spdep

  • Detecting and measuring spatial autocorrelation in lattice data

Week 4

Analyzing geostatistical data using gstat

  • Creating contour-type maps using inverse distance weighting and geostatistical methods

Class Dates


01/12/2024 to 02/09/2024
Instructors: Prof. David Unwin
07/12/2024 to 08/16/2024
Instructors: Prof. David Unwin


01/10/2025 to 02/07/2025
07/11/2025 to 08/15/2025
Instructors: Prof. David Unwin


You should be familiar with introductory statistics to the level of correlation and regression.

You should also be familiar with basic operations in R, as covered in R Programming – Introduction Part 1.

Introduction to R Programming

This course provides an easy introduction to programming in R.
  • Skill: Intermediate
  • Credit Options: ACE, CAP, CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Spatial Statistics for GIS Using R

Additional Information


Each week has an associated assignment, which, together with following the Lesson, should take 15 or so hours to complete. The assignments are designed to complement and extend the materials in the lesson and will be marked and commented upon by the instructor.

In addition to assigned readings, this course has an end of course final project (required for PASS and ACE candidates).

Course Text

The required text for this course is Geographic Information Analysis, 2nd revised edition by O’Sullivan, D. and Unwin,D. J.


R software is used in this course.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Spatial Statistics for GIS Using R