A spatial dataset of of the boundaries of the 33 London Boroughs extracted from the <code>GWModel</code> package, cleaned and converted to <code>sf</code>.

datasets

A framework for specifying spatially, temporally and spatially-and-temporally varying coefficient models using Generalized Additive Models with smooths. The smooths are parameterised with location, time and predictor variables. The framework supports the investigation of the presence and nature of any space-time dependencies in the data by evaluating multiple model forms (specifications) using a Generalized Cross-Validation score. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Evaluate all possible spatial and/or temporal models in which each predictor is specified in different ways. iii) Evaluate each model and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. For more details see: Comber et al (2023) <doi:10.4230/LIPIcs.GIScience.2023.22>, Comber et al (2024) <doi:10.1080/13658816.2023.2270285> and Comber et al (2004) <doi:10.3390/ijgi13120459>.

Lex Comber

stgam

Spatially and Temporally Varying Coefficient Models Using
Generalized Additive Models

Paul Harris

Chris Brunsdon

lb function

A <code>sf</code> polygon (MULTIPOLYGON) dataset with 33 observations and 2 fields.<dl>
<dt>name</dt>
<dd>The name of the London borough</dd><dt>lad</dt>
<dd>The ONS lcoal authrority district code for the borough</dd>
</dl>

Format

London borough boundaries — lb

A <code>sf</code> polygon (MULTIPOLYGON) dataset with 33 observations and 2 fields.<dl>
<dt>name</dt>
<dd>The name of the London borough</dd>

<dt>lad</dt>
<dd>The ONS lcoal authrority district code for the borough</dd>


</dl>

lb: London borough boundaries

Description

Usage

Arguments

Format

Examples