Spatial Statistics for GIS - Using R

taught by David Unwin

Aim of Course:

Spatial data are everywhere.  Everything has a location and so can be mapped, but making maps of data is not easy and the questions they pose often cannot be answered solely by ‘reading’ them.   Spatial statistical analysis gets behind the map to ask about the data that are mapped, posing questions about the patterns we see on them such as

• Is this pattern of point events, such as crimes, residences of customers of a store, trees of a specific type in a wood, and so on, ‘clustered’?
• Does this patchwork of data such as census returns or votes in an election, aggregated into zones like the Counties and States of the USA, show a ‘pattern’?
• Given a sample of observations of a spatially continuous variable such as air temperature, average rainfall or soil nutrient content, can we interpolate between the data points to estimate the values of these same variables at every location?

In this course you will learn about the relationship between maps and the data they represent, how such data are coded in the R environment, and how we can answer these types of question.  Point pattern analysis enables us to say whether or not a pattern is clustered, spatial autocorrelation statistics enable us to detect spatial heterogeneity in a patchwork of zone values, and geostatistical interpolation enables us to estimate values across a continuous contour type map.

After completing this course, you will be able to:

• 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
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

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

Note that the course does not concentrate on the analysis of spatially continuous data using methods that are collectively referred to as geostatistics. Lesson 4 has a brief introduction to the basic concepts as used in interpolation, but this is all.

The course contains required readings from the instructor's joint text Geographic Information Analysis, O'Sullivan and Unwin, 2010, John Wiley and Sons.

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).

Spatial Statistics for GIS - Using R

Who Should Take This Course:

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

Level:
Intermediate
Prerequisite:
You should be
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.
5. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses

College credit:
Spatial Statistics for GIS - Using R has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree category, 3 semester hours in spatial statistics or geospatial analysis. Note: The decision to accept specific credit recommendations is up to each institution. More info here.

INFORMS CAP:
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP® analysts accrue Professional Development Units to maintain their certification .
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 will be used
Instructor(s):

Dates:

October 25, 2019 to November 22, 2019 June 05, 2020 to July 03, 2020 October 23, 2020 to November 20, 2020

Spatial Statistics for GIS - Using R

Instructor(s):

Dates:
October 25, 2019 to November 22, 2019 June 05, 2020 to July 03, 2020 October 23, 2020 to November 20, 2020

Course Fee: \$589

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

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