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.
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
2025
Instructors:
Instructors:
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
Time Requirements
This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.
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 DataSciencePro 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.
Course Fee & Information
Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
Group Rates
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Discounts
Academic affiliation? In most courses you are eligible for a discount at checkout.
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Invoice or Purchase Order
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Supplemental Information
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