trainsample

The dataset of 2014 training sample for the Shandong with missing values imputed using SVD.

datasets

The approach of constrained spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high spatiotemporal
resolution (Li, L., Zhang, J., Meng, X., Fang, Y., Ge, Y., Wang, J., Wang, C., Wu, J., Kan, H. (2018) <doi.org/10.1016/j.rse.2018.09.001>;
Li, L., Lurmann, F., Habre, R., Urman, R., Rappaport, E., Ritz, B., Chen, J., Gilliland, F., Wu, J., (2017) <doi:10.1021/acs.est.7b01864>).
This package is an extensive tool for this modeling approach with support of block Kriging (Goovaerts, P. (1997) <http://www.gbv.de/dms/goettingen/229148123.pdf>)
and uses the PM2.5 modeling as examples. It provides the following functionality:
(1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster;
(2) Generation of temporal basis functions to simulate the seasonal trends in the study regions;
(3) Generation of the regional monthly or yearly means of air pollutant concentration;
(4) Generation of Thiessen polygons and spatial effect modeling;
(5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing;
(6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing;
(7) Constrained optimization to interpolate the missing values;
(8) Generation of the grid surfaces of air pollutant concentration estimates at high resolution;
(9) Block Kriging for regional mean estimation at multiple scales.

Lianfa Li

sptemExp

Constrained Spatiotemporal Mixed Models for Exposure Estimation

trainsample function

<code>DataFrame</code><dl class="dl-horizontal">
<dt>provence</dt><dd>provence</dd>
<dt>city</dt><dd>city</dd>
<dt>geocode</dt><dd>geocode</dd>
<dt>name</dt><dd>name</dd>
<dt>code</dt><dd>code</dd>
<dt>x</dt><dd>x</dd>
<dt>y</dt><dd>y</dd>
<dt>siteid </dt><dd>siteid</dd>
<dt>rid </dt><dd>rid</dd>
<dt>ndvi </dt><dd>ndvi</dd>
<dt>aod </dt><dd>aod</dd>
<dt>wnd_avg</dt><dd>wind speed</dd>
<dt>monthAv</dt><dd>regional monthy average</dd>
<dt>date</dt><dd>date</dd>
<dt>month </dt><dd>month</dd>
<dt>pm25 </dt><dd>pm2.5</dd>
<dt>logpm25</dt><dd>log PM2.5</dd>
<dt>tid</dt><dd>tid</dd>
</dl>

Format

The dataset of 2014 training sample for the Shandong with missing values imputed using SVD. — trainsample

<code>DataFrame</code><dl class='dl-horizontal'>
<dt>provence</dt><dd>provence</dd>
<dt>city</dt><dd>city</dd>
<dt>geocode</dt><dd>geocode</dd>
<dt>name</dt><dd>name</dd>
<dt>code</dt><dd>code</dd>
<dt>x</dt><dd>x</dd>
<dt>y</dt><dd>y</dd>
<dt>siteid </dt><dd>siteid</dd>
<dt>rid </dt><dd>rid</dd>
<dt>ndvi </dt><dd>ndvi</dd>
<dt>aod </dt><dd>aod</dd>
<dt>wnd_avg</dt><dd>wind speed</dd>
<dt>monthAv</dt><dd>regional monthy average</dd>
<dt>date</dt><dd>date</dd>
<dt>month </dt><dd>month</dd>
<dt>pm25 </dt><dd>pm2.5</dd>
<dt>logpm25</dt><dd>log PM2.5</dd>
<dt>tid</dt><dd>tid</dd>
</dl>

trainsample: The dataset of 2014 training sample for the Shandong with missing values imputed using SVD.

Description

Usage

Arguments

Format

Examples