# Spatial Statistics in R

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

4 Hours16 Videos60 Exercises8,895 Learners4950 XPSpatial Data Track

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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. 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.

Problems in spatial statistics
50 xp
Simple spatial principles
100 xp
Plotting areas
100 xp
Uniform in a circle
100 xp
Simulation and testing with spatstat
50 xp
100 xp
Creating a uniform point pattern with spatstat
100 xp
Simulating clustered and inhibitory patterns
100 xp
Point pattern testing
50 xp
Further testing
50 xp
Nearest-neighbor distributions
100 xp
Other point pattern distribution functions
100 xp
Tree location pattern
50 xp
2. 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. 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. 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.

In the following tracks

Spatial Data

Collaborators

Tom JeonRichie Cotton

#### 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.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden