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.
Overview
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, 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
2024
Instructors: Prof. David Unwin
Instructors: Prof. David Unwin
2025
Instructors:
Instructors: Prof. David Unwin
Prerequisites
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
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
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Additional Information
Homework
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.
Software
R software is used in this course.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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