README.md

In gisUTM/lur: Land use regression package.

lur: A package for land use regression modelling in R

Land use regression modelling is commonly applied for spatial modelling of air pollution concentrations. The principle is that given a set of air pollution observations, that their surrounding land use conditions can be used to explain the variation in concentrations. The statistical model that defines this relationship is often a linear regression model, but other regression techniques area common.

The process can be broken into five major steps that include:

1. Collection of air pollution observations.
2. Identify surronding land use conditions within buffers of the air pollution observations.
3. Fit a statisitcal model to explain the variation in air pollution observations based on the land use conditions. Tradionally this has been a linear regresion model.
4. Cross-validation including spatial and/or temporal blocking
5. Produce a an air pollution surface.

gisUTM/lur documentation built on May 31, 2019, 1:56 p.m.