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      <title>Mathematical Transformations of Spatially Balanced Samples</title>
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      <pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate>
      
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      <description>library(&amp;#34;dplyr&amp;#34;) library(&amp;#34;magrittr&amp;#34;) library(&amp;#34;sf&amp;#34;) library(&amp;#34;glue&amp;#34;) library(&amp;#34;spbal&amp;#34;) library(&amp;#34;ggplot2&amp;#34;) library(&amp;#34;ggExtra&amp;#34;) # set specific seed for reproducability location_id &amp;lt;- 42 set.seed(location_id) 
Introduction Is Randomness Chaos? Occasionally, one requires a set of points which are randomly distributed in a given area.
n_samples &amp;lt;- 2^8 x &amp;lt;- runif(n_samples) y &amp;lt;- runif(n_samples) data &amp;lt;- data.frame(&amp;#34;x&amp;#34; = x, &amp;#34;y&amp;#34; = y) par(pty = &amp;#34;s&amp;#34;) plot(data, pch = 21, col = &amp;#34;black&amp;#34;) But then, you know, if we are honest, &amp;ldquo;random&amp;rdquo; might be a bit too random.</description>
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