grid100: Simulated Spatial Autocorrelation

In spatialanalysis/geodaData: Spatial Analysis Datasets for Teaching

Description

Simulated random variables with spatial autocorrelation, on 10x10 grid.

Usage

grid100

Format

An sf data frame with 100 rows, 37 variables, and a geometry column:

POLYID

Grid cell identifier

Z

Random variables for standard normal distribution

ZMAxx

Standard normal variates transformed to follow a spatial moving average with parameter xx (xx is 02, 05, 07, 09 for 0.2, 0.5, 0.7, 0.9)

RANZMAxx

Randomly permuted observations matching the variable ZMAxx (xx defined above)

ZMANxx

Standard normal variates transformed to follow a spatial moving average with negative parameter xx (xx defined above)

RANZMANxx

Randomly permuted counterpart of ZMANxx (xx defined above)

ZARrxx

Standard normal variates transformed to follow a spatial autoregressive process with parameter xx (xx defined above)

RANZARxx

Randomly permuted counterpart of ZARxx (xx defined above)

ZARNxx

Standard normal variates transformed to follow a spatial autoregressive process with negative parameter xx (xx defined above)

RANZARNxx

Randomly permuted counterpart of ZARNxx (xx defined above)

Details

Sf object, Coordinate Reference System not defined.

Source

The grid was created in GeoDa. The variables were simulated using R and its spdep package.

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  library(sf)
  data(grid100)

  plot(grid100["RANZMA07"])
}

spatialanalysis/geodaData documentation built on Oct. 6, 2020, 12:09 p.m.