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SSGUndergraduate courses

Spatial Statistics with Geographic Information Systems

Getting behind the map: point pattern analysis, spatial autocorrelation and geostatistical interpolation, for problems where location is the explanatory variable.

  • Intermediate
  • 4 weeks
  • Approx. 15 hours per week
  • 3 semester hours

About this course

Spatial data are everywhere. Spatial statistical analysis gets behind the map to ask about the data that are mapped and to pose questions about the patterns we can see in them — it adapts conventional methods to problems in which spatial location is the most important explanatory variable. This course covers the relationship between a map and the data it represents, how such data are coded and handled, point pattern analysis, spatial autocorrelation statistics, and geostatistical interpolation to estimate values across a continuous contour-type map. It is aimed at people with a computing or statistics background who have not worked with geospatial data, and the problems it addresses are the ones a geographic information system such as ArcGIS or MapInfo is used to pose.

Who this course is for

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

What you will learn

6 outcomes

By the end of this course, you will be able to:

  • Describe spatial data using maps
  • Describe and implement the ways spatial data are represented in R
  • Use spatstat 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
  • Interpret correctly the relationship between a map and the data set it represents

Week by week

4-Week curriculum overview

This curriculum is identical across all start dates. Expand a week to explore the topics covered.

Weeks
4
Your week
Approx. 15 hours
Level
Intermediate
Introducing geo-data and their representation in RWeek 1
  • Introducing geographical data
  • Representing geographical data in R
Analyzing point events using spatstatWeek 2
  • Introductory methods for detecting non-randomness in the distributions shown by dot and pin maps
Analyzing lattice data using spdepWeek 3
  • Detecting and measuring spatial autocorrelation in lattice data
Analyzing geostatistical data using gstatWeek 4
  • Creating contour-type maps using inverse distance weighting and geostatistical methods

Instructors

Expert-Led Guidance

Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.

  • Mr. Kuber Deokar

    Mr. Kuber Deokar is Data Science Lead at UpThink EduTech Services. He holds a master's degree in Statistics from the University of Pune, India, where he previously taught undergraduate statistics. He co-authored Machine Learning for Business Analytics with Galit Shmueli, Peter Bruce and Nitin Patel. With over a decade of experience, he specializes in course design, development, delivery and management, coordinating online courses and the communication between course creators, instructors, teaching assistants and students. His interests are machine learning and responsible artificial intelligence.

Before you start

What you need to know first

Introduction to R Programming

Course Format & Schedule

This is a 4-week, 100% online, asynchronous course.

  • No mandatory live sessions: Log in and complete your work at times that fit your schedule.
  • Weekly Releases: At the start of each week, you will receive new lecture materials and answer keys for the previous week's exercises.
  • Interactive Community: Work through exercises, submit assignments, and engage with your instructor and peers via a private discussion board.
Homework

Each week carries an assignment that goes with that week's lesson; between the lesson and its assignment, expect the week to take around fifteen hours. Assignments are marked and returned with individual feedback. There is also an end-of-course project.

Texts

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

Software

This is a hands-on course worked in R, which is free, using the spatstat, spdep and gstat packages, which are free as well — the coursework costs you nothing in software. You do not need a GIS package to do it: the maps and the questions are framed the way a GIS user meets them, but the analysis itself is done in R. If you want to run a GIS alongside the course, that is yours to arrange. ArcGIS is commercially licensed and LearnStatistics does not supply it or a license for it; QGIS is free and open source if you would rather not pay for one.

FAQ

Do I need ArcGIS, QGIS or another GIS package to take this course?This course

No. All the coursework is done in R with the spatstat, spdep and gstat packages, all of which are free. A GIS is where spatial data usually comes from and where the results usually go back to, so the course is framed around the questions a GIS user asks, but the statistics are done outside it.

How much R do I need to know?This course

Enough to read in data, work with vectors and data frames, install and load a package, and run and adjust a script — the ground an introductory R course covers. The spatial data structures and the three packages are taught from scratch.

Is there a money-back or satisfaction guarantee?Every course

We handle cancellation and refund requests on an individual basis. If a course is not meeting your expectations or your circumstances change, please reach out to our team (support@learnstatistics.org) so we can work with you on a solution.

Can I transfer to a later start date or withdraw after the course begins?Every course

Yes. If you need to defer your enrollment to a future start date, withdraw mid-course, or transfer your seat to a colleague, please contact us directly. We address these requests flexibly on a case-by-case basis.

Who teaches the course?Every course

Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.

Something not answered here? Ask us before you book — and the terms set out what a purchase covers.

Credit, and where it counts

This course carries a verified figure of 3 semester hours. A credit recommendation from the American Council on Education says what a course is worth; the institution receiving it decides whether to award it.

Anyone can take this subject: there is no application, and you do not have to be studying at a university. It also counts toward a degree at Thomas Edison State University, which decides what to award for it. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.