chaco

A point dataset of NDVI and climate data. The data are sample of 2000 observations of Normalised Difference Vegetation Index (NDVI) (2012-2022) of the Chaco dry rainforest in South America with some climate data. These are found via Google Earth Engine (Gorelick et al., 2017). The NDVI data is sourced from the PKU GIMMS NDVI v1.2 dataset, which provides NDVI observations at 1/12° spatial resolution at bi-monthly intervals from 1982 to 2022 (Li et al., 2023). The climate data was derived from the TerraClimate dataset (IDAHO_EPSCOR/TERRACLIMATE). Maximum temperature (<code>tmax</code>) and Precipitation (<code>pr</code>) were selected and means calculated for each monthly image across all pixels.

datasets

A framework for undertaking space and time varying coefficient models (varying parameter models) using a Generalized Additive Model (GAM) with smooths approach. The framework suggests the need to investigate for the presence and nature of any space-time dependencies in the data. It proposes a workflow that creates and refines an initial space-time GAM and includes tools to create and evaluate multiple model forms. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Create all possible space and/or time models in which each predictor is specified in different ways in smooths. iii) Evaluate each model via their AIC value 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. The number of knots used in each smooth can be specified directly or iteratively increased. This is illustrated with a climate point dataset of the dry rain forest in South America. This builds on work in 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

Gonzalo Irisarri

Chris Brunsdon

chaco function

A <code>sf</code> POINT dataset with 2000 observations and 12 fields.<dl>
<dt>id</dt>
<dd>An observation identifier</dd><dt>ndvi</dt>
<dd>Normalised Difference Vegetation Index (NDVI)</dd><dt>tmax</dt>
<dd>Maximum temperature (°C)</dd><dt>pr</dt>
<dd>Preciptation</dd><dt>month</dt>
<dd>A continous integer variable from 1 to 120</dd><dt>year</dt>
<dd>The year of observation</dd><dt>lon</dt>
<dd>Longitude in degrees (WGS84)</dd><dt>lat</dt>
<dd>Latitude in degrees (WGS84)</dd><dt>X</dt>
<dd>Easting in metres from the SIRGAS 2000 / Brazil Mercator projection (EPSG:5641)</dd><dt>Y</dt>
<dd>Northing in metres from the SIRGAS 2000 / Brazil Mercator projection (EPSG:5641)</dd><dt>geometry</dt>
<dd>The spatial geometry of the observation in the SIRGAS 2000 / Brazil Mercator projection (EPSG:5641)</dd>
</dl>

Format

Chaco dry rainforest data (2012-2022) — chaco

A <code>sf</code> POINT dataset with 2000 observations and 12 fields.<dl>
<dt>id</dt>
<dd>An observation identifier</dd>

<dt>ndvi</dt>
<dd>Normalised Difference Vegetation Index (NDVI)</dd>

<dt>tmax</dt>
<dd>Maximum temperature (°C)</dd>

<dt>pr</dt>
<dd>Preciptation</dd>

<dt>month</dt>
<dd>A continous integer variable from 1 to 120</dd>

<dt>year</dt>
<dd>The year of observation</dd>

<dt>lon</dt>
<dd>Longitude in degrees (WGS84)</dd>

<dt>lat</dt>
<dd>Latitude in degrees (WGS84)</dd>

<dt>X</dt>
<dd>Easting in metres from the SIRGAS 2000 / Brazil Mercator projection (EPSG:5641)</dd>

<dt>Y</dt>
<dd>Northing in metres from the SIRGAS 2000 / Brazil Mercator projection (EPSG:5641)</dd>

<dt>geometry</dt>
<dd>The spatial geometry of the observation in the SIRGAS 2000 / Brazil Mercator projection (EPSG:5641)</dd>


</dl>

chaco: Chaco dry rainforest data (2012-2022)

Description

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Examples