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  • Spatial Statistics for Data Science: Theory and Practice with R Book Club
  • Welcome
    • Book club meetings
    • Pace
  • 1 Types of spatial data
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 2 Spatial data in R
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 3 The sf package for spatial vector data
    • Simple features (1)
    • Simple features (2)
    • Simple features in R (1)
    • Simple features in R (2)
    • Simple features in R (3)
    • Simple features in R (4)
    • Simple features in R (5)
    • Simple features in R (6)
    • Simple features in R (7)
    • Simple features in R (8)
    • Simple features in R (9)
    • Simple features in R (10)
    • Static maps with ggplot2 (1)
    • Static maps with ggplot2 (2)
    • Interactive maps with mapview (1)
    • Interactive maps with mapview (2)
    • Another st_coordinates() example
    • Writing an sf object to file
    • Subsetting simple features (1)
    • Subsetting simple features (2)
    • Subsetting simple features (3)
    • Subsetting simple features (4)
    • Subsetting simple features (5)
    • Subsetting simple features (6)
    • Subsetting simple features (7)
    • Generate sf objects (1)
    • Generate sf objects (2)
    • Generate sf objects (3)
    • Generate sf objects (4)
    • Generate sf objects (5)
    • Generate sf objects (6)
    • Generate sf objects (7)
    • Generate sf objects (8)
    • Generate sf objects (9)
    • Generate sf objects (10)
    • Manipulating sf objects (1)
    • Manipulating sf objects (2)
    • Manipulating sf objects (3)
    • Manipulating sf objects (5)
    • Manipulating sf objects (4)
    • Manipulating sf objects (6)
    • Manipulating sf objects (7)
    • Binary logical operations (1)
    • Binary logical operations (2)
    • Binary logical operations (3)
    • Binary logical operations (4)
    • Binary logical operations (5)
    • Binary logical operations (6)
    • Binary logical operations (7)
    • Binary logical operations (8)
    • Binary logical operations (9)
    • Binary logical operations (10)
    • Binary logical operations (11)
    • Joining sf object with data (1)
    • Joining sf object with data (2)
    • Joining sf object with data (3)
    • Joining sf object with data (4)
    • Meeting Videos
      • Cohort 1
  • 4 The terra package for raster and vector data
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 5 Making maps with R
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 6 R packages to download open spatial data
    • 6.1 Packages
      • 6.1.1 Example with rnaturalearth
    • 6.2 geodata
    • 6.3 chirps
    • 6.4 elevatr
    • 6.5 osmdata
      • 6.5.1 with leaflet
    • 6.6 wbstats
      • 6.6.1 with mapview
    • 6.7 spocc
    • Meeting Videos
      • Cohort 1
  • 7 Spatial neighbourhood matrices
    • Areal data
    • Spatial neighbourhood
    • Spatial neighbourhood
    • Spatial neighbourhood in R
    • Read example data
    • Example data
    • Example data
    • Spatial neighbourhood
    • Spatial neighbours
    • Spatial neighbours list
    • Defining who is a neighbour and who isn’t
    • Creating a neighbours list (‘nb’) from geometries
    • Neighbours list: type Queen contiguity
    • Neighbours list: type Rook contiguity
    • Plotting
    • Plotting
    • Neighbours list based on distance bounds
    • Neighbours list based on distance bounds
    • Neighbours list based on distance bounds
    • Neighbours list based on distance bounds
    • Neighbours list based on k nearest neighbours
    • Neighbours list based on k nearest neighbours
    • Neighbours list based on k nearest neighbours
    • Creating higher order neighbours lists
    • Creating higher order neighbours lists
    • Creating higher order neighbours lists
    • Creating higher order neighbours lists
    • Creating higher order neighbours lists
    • Creating higher order neighbours lists
    • Cumulating neighbours lists
    • Cumulating neighbours lists
    • Further things to do with a neighbours list
    • Count neighbours
    • Compute distances between neighbours
    • Compute distances between neighbours
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Neighbourhood matrix
    • Meeting Videos
      • Cohort 1
  • 8 Spatial autocorrelation
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 9 Bayesian spatial models
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Bayesian spatial models in INLA
    • Latent models referred in this chapter
    • Latent models referred in this chapter
    • Case: housing prices in Boston
    • Case: housing prices in Boston
    • Case: housing prices in Boston
    • Case: housing prices in Boston
    • Meeting Videos
      • Cohort 1
  • 10 Disease risk modeling
    • 10.1 Introduction
    • 10.2 Modeling of lung cancer risk in Pennsylvania
      • 10.2.1 Expected cases
      • 10.2.2 Standardized Mortality Ratios
    • 10.3 Modeling diseased risk
      • 10.3.1 Neighborhood structure
      • 10.3.2 Model
      • 10.3.3 Relative Risk
    • Meeting Videos
      • Cohort 1
  • 11 Areal data issues
    • 11.1 Areal data issues
    • Meeting Videos
      • Cohort 1
  • 12 Geostatistical data
    • The part about geostatistical data
    • The part about geostatistical data
    • Gaussian random fields: what
    • Gaussian random fields: properties
    • Covariance functions of GRFs
    • Covariance functions of GRFs
    • Covariance functions of GRFs
    • Covariance functions of GRFs
    • Covariance functions of GRFs
    • Simulating GRFs
    • Simulating GRFs
    • Simulating GRFs
    • Simulating GRFs
    • Simulating GRFs
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Summarizing the GRF’s correlation structure
    • Meeting Videos
      • Cohort 1
  • 13 Spatial interpolation methods
    • Recap: geostatistical data
    • Recap: geostatistical data
    • Recap: geostatistical data
    • Aim of spatial interpolation
    • Considered methods
    • Considered methods
    • Considered methods
    • Considered methods
    • Packages and data
    • Packages and data
    • Packages and data
    • Packages and data
    • Packages and data
    • Packages and data
    • Packages and data
    • Prediction grid
    • Prediction grid
    • Prediction grid
    • Closest observation method
    • Closest observation method
    • Closest observation method
    • Closest observation method
    • Approaches for IDW and nearest neighbours
    • Approaches for IDW and nearest neighbours
    • Approaches for IDW and nearest neighbours
    • Approaches for IDW and nearest neighbours
    • Approaches for IDW and nearest neighbours
    • Inverse Distance Weighting method (IDW)
    • Inverse Distance Weighting method (IDW)
    • Inverse Distance Weighting method (IDW)
    • Inverse Distance Weighting method (IDW)
    • Nearest neighbours method
    • Nearest neighbours method
    • Nearest neighbours method
    • Ensemble approach
    • Ensemble approach
    • Ensemble approach
    • Assessing performance with cross-validation
    • Assessing performance with cross-validation
    • Meeting Videos
      • Cohort 1
  • 14 Kriging
    • 14.1 What is Kriging
    • 14.2 Types of Kriging
    • 14.3 Performing Kriging in R
      • 14.3.1 Example: Simple Kriging
      • 14.3.2 Variogram Model
      • 14.3.3 Perform Simple Kriging
      • 14.3.4 Plotting the Results
    • 14.4 Summary
    • 14.5 Additional Resources
    • Meeting Videos
      • Cohort 1
  • 15 Model-based geostatistics
    • Aim
    • INLA and GRFs
    • INLA and GRFs
    • Projection matrix
    • Example in the book
    • Preparations before fitting with inla()
    • Preparations before fitting with inla()
    • Preparations before fitting with inla()
    • Preparations before fitting with inla()
    • Preparations before fitting with inla()
    • Preparations before fitting with inla()
    • Fit the model
    • Extract results
    • Meeting Videos
      • Cohort 1
  • 16 Methods assessment
    • Overview
    • Obtaining training and testing datasets
    • Predictive performance measures
    • Meeting Videos
      • Cohort 1
  • 17 Spatial point patterns
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 18 The spatstat package
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 19 Spatial point processes and simulation
    • Spatial point process vs. spatial point pattern
    • Intensity function of a spatial point process
    • Intensity function of a spatial point process
    • Intensity function of a spatial point process
    • Commonly used special cases
    • Commonly used special cases
    • Commonly used special cases
    • Poisson processes: what’s next
    • Simulating spatial point patterns
    • Simulating spatial point patterns
    • Example: homogeneous
    • Example: homogeneous
    • Example: homogeneous
    • Example: homogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Example: heterogeneous
    • Meeting Videos
      • Cohort 1
  • 20 Complete spatial randomness
    • Complete spatial randomness
    • Randomness of a given spatial point pattern
    • Test statistic
    • Test statistic
    • Possible outcomes
    • spatstat functions
    • Example: longleaf
    • Example: longleaf
    • Example: longleaf
    • Example: longleaf
    • Example: longleaf
    • Example: longleaf
    • Example: longleaf
    • Meeting Videos
      • Cohort 1
  • 21 Intensity estimation
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 22 The K-function
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 23 Point process modeling
    • Considering intensity as a stochastic variable
    • Considering intensity as a stochastic variable
    • Considering intensity as a stochastic variable
    • Considering intensity as a stochastic variable
    • Similarities with chapter 15 (model-based geostatistics)
    • Differences with chapter 15 (model-based geostatistics)
    • Aim in chapter 15 (model-based geostatistics)
    • How to model a spatial point process with INLA?
    • How to model a spatial point process with INLA?
    • Projection matrix for spatial point processes
    • Projection matrix for spatial point processes
    • Projection matrix for spatial point processes
    • Example in the book
    • Example in the book
    • Example in the book
    • Example in the book
    • Example in the book
    • Preparations before fitting with inla()
    • Create the mesh
    • Create the mesh
    • Create the dual mesh
    • Create the dual mesh
    • Calculate the offsets (surface areas)
    • Calculate the offsets (surface areas)
    • Define the SPDE model
    • Define the SPDE model
    • Construct the projection matrices
    • Construct the projection matrices
    • Construct the projection matrices
    • Preparations before fitting with inla()
    • Stack with data for estimation and prediction
    • Stack with data for estimation and prediction
    • Fit the model
    • Extract results
    • Extract results
    • Extract results
    • Extract results
    • Meeting Videos
      • Cohort 1
  • Published with bookdown

Spatial Statistics for Data Science: Theory and Practice with R Book Club

14.5 Additional Resources

  • gstat package documentation
  • Geostatistics with R
  • Introduction to Geostatistics