Spatial Statistics in R

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

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4 Hours16 Videos60 Exercises8,381 Learners
4950 XP

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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.

    Play Chapter Now
    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
    Quadrat count test for uniformity
    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.

    Play Chapter Now
    Bivariate point patterns
    50 xp
    Crime in Preston
    100 xp
    Violent crime proportion estimation
    100 xp
    Spatial segregation
    50 xp
    Bandwidth selection
    100 xp
    Segregation probabilities
    100 xp
    Mapping segregation
    100 xp
    Space-time data
    50 xp
    Sasquatch data
    100 xp
    Spatial pattern of bigfoot sightings
    50 xp
    Temporal pattern of bigfoot sightings
    100 xp
    Temporal pattern analysis
    50 xp
    Space-time clustering
    50 xp
    Preparing data for space-time clustering
    100 xp
    Monte-carlo test of space-time clustering
    100 xp
    Space-time clustering explanation
    50 xp
  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.

    Play Chapter Now
    Areal statistics
    50 xp
    London EU referendum data
    100 xp
    Cartogram
    100 xp
    Spatial autocorrelation test
    100 xp
    Spatial health data
    50 xp
    London health data
    100 xp
    Binomial confidence intervals
    100 xp
    Exceedance probabilities
    100 xp
    Generalized linear models in space
    50 xp
    A Poisson GLM
    100 xp
    Residuals
    100 xp
    Correlation in spatial GLMs
    50 xp
    Fit a Bayesian GLM
    100 xp
    Adding a spatially autocorrelated effect
    100 xp
    Mapping the spatial effects
    100 xp
  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.

    Play Chapter Now
    Geostatistical data
    50 xp
    Canadian geochemical survey data
    100 xp
    Fitting a trend surface
    100 xp
    Predicting from a trend surface
    100 xp
    The variogram
    50 xp
    Variogram estimation
    100 xp
    Variogram with spatial trend
    100 xp
    Variogram model fitting
    100 xp
    Kriging predictions
    50 xp
    Filling in the gaps
    100 xp
    Making a prediction grid
    100 xp
    Gridded predictions
    100 xp
    Automatic kriging
    50 xp
    Auto-kriging at point locations
    100 xp
    Auto-kriging over a grid
    100 xp
    Last words
    50 xp

In the following tracks

Spatial Data

Datasets

Canadian geological survey soil acidityBounding region for ca_geo.rdsFlu incidents by London borough, 2017EU referendum voting by London borough, 2016An OpenStreetMap map of Preston, UKCrime in Preston, UKPh gridSasquatch sightings, 1990 to 2016

Collaborators

Tom JeonRichie Cotton

Prerequisites

Visualizing Geospatial Data in RFoundations of Probability in R
Barry Rowlingson Headshot

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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