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Spatial Analysis Techniques in R

Instructor(s):

Dates:

December 12, 2014 to January 09, 2015 December 11, 2015 to January 08, 2016

Thank you for your submission.

Spatial Analysis Techniques in R

taught by Professor David Unwin

Aim of Course:

The R environment provides a consistent and stable platform for spatial statistical analysis and is the computing environment of choice for most researchers in the field.

The course aims to

  • Introduce the use of R for geographic information analysis.  Although much of what will be covered can be accomplished using a GIS, such use is awkward and often highly inefficient;
  • Develop understanding of some topics beyond the basic courses or most standard texts.
After following the course and doing the assignments you will be able to:
  • Install and use the basic R environment;
  • Select an appropriate R package for point, lattice and geostatistical data and enter spatial data into it;
  • Create sensible maps of these same data;
  • Undertake both global and local spatial analysis of the patterns these maps reveal, using the idea of complete spatial randomness as benchmark;
  • Most important of all, critically assess the results of these analyses.

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

Course Program:

WEEK 1: Introducing R

  • spatial and displaying some geographic data including
    • visualization of point,
    • lattice and
    • geostatistical data using simple maps.


WEEK 2: Point Pattern Analysis

  • global tests against the hypothesis of complete spatial randomness,
  • kernel density estimation, and
  • dealing with non-homogeneity using the spatstat package


WEEK 3: Area (lattice) objects

  • spatial autocorrelation and local statistics including
    • global autocorrelation,
    • local indicators of spatial association and
    • geographical weighted regression using the spdep package.


WEEK 4: Geostatistical data

  • the analysis of continuous ‘field’ data by variography including
    • interpolation by inverse distance decay,
    • trend surface analysis,
    • variography and
    • kriging using the gstat package

Note that the course does not concentrate on the analysis of spatially continuous data using methods that are collectively referred to as geostatistics but that Lesson 4 covers the basics.

Homework Assignments

There are four assignments in this course. These will be marked by the course leader himself, but whether you need to be concerned with these marks depends on your purpose in taking the course. Some students are interested just in learning for learning's sake, others may require a certificate showing they have completed a course, some may need academic credit (offered in selected courses only).  Students enrolled in a Program in Advanced Statistical Studies also complete a guided project using spatial data.

In addition to assigned readings, this course also has an end of course data modeling project, and supplemental readings available online.


Spatial Analysis Techniques in R

Instructor(s):

Dates:
December 12, 2014 to January 09, 2015 December 11, 2015 to January 08, 2016

Course Fee: $629

Do you meet course prerequisites? What about book & software? (Click here to learn more)

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

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.

Spatial Analysis Techniques in R

taught by Professor David Unwin

Who Should Take This Course:

The course is aimed at anyone with experience either in spatial analysis using a standard GIS (such as ArcGIS), or who already uses R for basic non-spatial analysis wishing to extend their skills in spatial analysis using R.  Although it covers some of the same ground, students who have followed the course Spatial Statistics with GIS given by the same instructor will find that this course usefully extends their skills.

Level:

intermediate

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

If you are unclear as to whether you have mastered the above requirements, try these placement tests.

(See the Software section)

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.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. 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. 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, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The required text for this course is Geographic Information Analysis, 2nd ed by David O'Sullivan and David J. Unwin. Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount – try calling your regional Wiley representative.) If you already have a copy of the first edition, with the exception of materials on visualization of spatial data using maps, most of the readings can still be found, but the page numbers will be different. As and where necessary, the instructor has also prepared additional comments to extend the materials or point to newer work that you should know about.

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

In addition, students might also like to purchase Bivand, R.S., Pebesma, E. and V. Gomez Rubio (2008) Applied Spatial Data Analysis with R (Springer, NY, in the UseR! Series). This contains all you need to know, but at relatively advanced level. It can be ordered from the publisher. Springer offers a generous discount on this book after providing the code AECT15 (this code is case sensitive) in the Promotion Code field when prompted during checkout time if you are from North or South America. The same code will work for the rest of the world if you order from the North American site, but may result in longer ship time and higher ship cost (alternatively, you can buy from local site with no discount.)

Software:

You must have a copy of R for the course. Click Here for information on obtaining a free copy. If you are new to R and doubtful about your ability to learn R quickly enough to follow along in the ocurse, we recommend first taking one of the introductory R courses.


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