Interactive Course

Spatial Statistics in R

Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.

  • 4 hours
  • 16 Videos
  • 60 Exercises
  • 4,502 Participants
  • 4,950 XP

Loved by learners at thousands of top companies:

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Course Description

Everything happens somewhere, and increasingly the place where all these things happen is being recorded in a database. There is some truth behind the oft-repeated statement that 80% of data have a spatial component. So what can we do with this spatial data? Spatial statistics, of course! Location is an important explanatory variable in so many things - be it a disease outbreak, an animal's choice of habitat, a traffic collision, or a vein of gold in the mountains - that we would be wise to include it whenever possible. This course will start you on your journey of spatial data analysis. You'll learn what classes of statistical problems present themselves with spatial data, and the basic techniques of how to deal with them. You'll see how to look at a mess of dots on a map and bring out meaningful insights.

  1. Point Pattern Analysis

    Point Pattern Analysis answers questions about why things appear where they do. The things could be trees, disease cases, crimes, lightning strikes - anything with a point location.

  2. Geostatistics

    Originally developed for the mining industry, geostatistics covers the analysis of location-based measurement data. It enables model-based interpolation of measurements with uncertainty estimation.

  1. 1

    Introduction

    Free

    After a quick review of spatial statistics as a whole, you'll go through some point-pattern analysis. You'll learn how to recognize and test different types of spatial patterns.

  2. Point Pattern Analysis

    Point Pattern Analysis answers questions about why things appear where they do. The things could be trees, disease cases, crimes, lightning strikes - anything with a point location.

  3. Areal Statistics

    So much data is collected in administrative divisions that there are specialized techniques for analyzing them. This chapter presents several methods for exploring data in areas.

  4. Geostatistics

    Originally developed for the mining industry, geostatistics covers the analysis of location-based measurement data. It enables model-based interpolation of measurements with uncertainty estimation.

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Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

Barry Rowlingson
Barry Rowlingson

Research Fellow at Lancaster University

Barry Rowlingson is a Research Fellow in the Lancaster Medical School, part of the Faculty of Health and Medicine at Lancaster University. His primary field of research is statistical applications in epidemiology and other health-related areas. He plays assorted instruments, takes pictures in exotic places, and drives an old Land Rover.

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