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copied!<p>Do you actually need to calculate the <a href="http://en.wikipedia.org/wiki/Euclidean_distance" rel="noreferrer">Euclidean distance</a>? If you are just comparing relative magnitudes of distances, you can probably get away with using the <a href="http://en.wikipedia.org/wiki/Manhattan_distance" rel="noreferrer">Manhattan distance</a>, which will save you two calls to <code>pow()</code> and one to <code>sqrt()</code>: </p> <pre><code>function pixelDistance($lat1, $lon1, $lat2, $lon2, $zoom) { $x1 = $lon1*10000000; //This is what I did to compensate for using lat/lon values instead of pixels. $y1 = $lat1*10000000; $x2 = $lon2*10000000; $y2 = $lat2*10000000; return ($x1-$x2) + ($y1-$y2) >> (21 - $zoom); } </code></pre> <p>Not sure if you need the <code>>> (21 - $zoom)</code> bit for your calculations, so I left it in. But unless you actually need to use the calculated distance values elsewhere, you can probably get away with just using the latitude/longitude directly (no need to multiply by anything) and taking the Manhattan distance, assuming you pre-calculate <code>$distance</code> to fit in with that measure, which will be a lot cheaper in computational terms than coercing all the distances to fit in with the units and magnitude of <code>$distance</code>.</p> <p>EDIT: When I was researching this problem last year, I found some useful stuff on Wikipedia - yes, it can happen ;-)</p> <ul> <li><a href="http://en.wikipedia.org/wiki/Cluster_analysis" rel="noreferrer">Cluster analysis</a></li> <li><a href="http://en.wikipedia.org/wiki/Hierarchical_clustering" rel="noreferrer">Hierarchical clustering</a></li> </ul> <p>I can also highly recommend the book <em><a href="http://www.amazon.co.uk/Programming-Collective-Intelligence-Building-Applications/dp/0596529325/ref=sr_1_5?ie=UTF8&s=books&qid=1253123239&sr=1-5" rel="noreferrer">Programming Collective Intelligence: Building Smart Web 2.0 Applications </a></em> which goes into clustering in great depth, as applied not only to geographical data but also to other areas of data analysis.</p>

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