From ceddc59f30792dba95a5dbc3c49ad3d3ce114bb8 Mon Sep 17 00:00:00 2001
From: Adrian Kriger <59996720+AdrianKriger@users.noreply.github.com>
Date: Thu, 7 Sep 2023 12:34:06 +0200
Subject: [PATCH] Update index.md

---
 docs/index.md | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 6c29a2e..37db87a 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -8,6 +8,11 @@ description: "Spatial Statistics with R."
 # (Geo)spatial Statistics with R (Meuse)
 {: .fs-9 }
 
+<figure><center>
+  <img src="{{site.baseurl | prepend: site.url}}/img/preds.png" style="width: 800px; height: 300px; border: 0px">
+  <figcaption>Fig.1 - Inverse Distance Weighting, 2nd-order Ordinary Least Squares and Ordinary Kriging interpolation </figcaption>
+</center></figure> 
+
 In this exercise, we will explore the concepts and applications of Deterministic and Stochastic Interpolation Methods. We traverse such technics as:
 
 <!-- <iframe src="{{site.baseurl | prepend: site.url}}/img/plotly.html" style="width: 800px; height: 300px; border: 0px"></iframe> -->
@@ -17,17 +22,8 @@ In this exercise, we will explore the concepts and applications of Deterministic
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b. Linear Regression  
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;c. Inverse Distance Weighting  
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d. Ordinary Least Squares  
-
-<!-- <img src="/img/preds.png" alt="predictions" img> -->
-
 &nbsp;&nbsp;&nbsp;**2) Stochastic methods**  
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a. Variograms and Kriging  
-
-<figure><center>
-  <img src="{{site.baseurl | prepend: site.url}}/img/preds.png" style="width: 800px; height: 300px; border: 0px">
-  <figcaption>Fig.1 - Inverse Distance Weighting, 2nd-order Ordinary Least Squares and Ordinary Kriging interpolation </figcaption>
-</center></figure> 
-
 &nbsp;&nbsp;&nbsp;**3)** we also briefly highlight ways we can **interrogate the quality** of an interpolation with;  
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;a. $N$-fold cross validation; and  
 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;b. Residual Mean Squared Error (rmse).
@@ -36,6 +32,6 @@ In this exercise, we will explore the concepts and applications of Deterministic
   <img src="{{site.baseurl | prepend: site.url}}/img/5-fold.png" style="width: 800px; height: 300px; border: 0px">
   <!-- <figcaption>Fig.2 - Inverse Distance Weighting, 2nd-order Ordinary Least Squares and Ordinary Kriging interpolation </figcaption> -->
 </center></figure> 
-
+_____
 For this assignment we use a dataset that is well-suited to illustrate these concepts. The [meuse](https://search.r-project.org/CRAN/refmans/sp/html/meuse.html) dataset which comes with the `gstat` package. 
 **meuse**: gives locations (on a regular grid) and topsoil heavy metal concentrations, along with a number of soil and landscape variables at the observation locations, collected in a flood plain of the river Meuse, near the village of Stein (NL). Heavy metal concentrations are from composite samples of an area of approximately 15 m x 15 m.