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	<title>Alex Little &#187; geolocation</title>
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		<title>MSG on Google Maps</title>
		<link>http://alexlittle.net/blog/2006/12/20/msg-on-google-maps/</link>
		<comments>http://alexlittle.net/blog/2006/12/20/msg-on-google-maps/#comments</comments>
		<pubDate>Wed, 20 Dec 2006 15:50:38 +0000</pubDate>
		<dc:creator>Alex</dc:creator>
				<category><![CDATA[Maps & Geolocation]]></category>
		<category><![CDATA[Web dev & programming]]></category>
		<category><![CDATA[algorithm]]></category>
		<category><![CDATA[geolocation]]></category>
		<category><![CDATA[google maps]]></category>
		<category><![CDATA[msg]]></category>

		<guid isPermaLink="false">http://alexlittle.net/blog/2006/12/20/msg-on-google-maps/</guid>
		<description><![CDATA[Have just done my first version of overlaying geolocation info for MSG users onto Google Maps. At the moment it&#8217;s pretty rough and ready (only on my dev server), but seems to do the basics, eg changes the &#8216;pin&#8217; when a users state changes. Quick screenshot here with users having different presence states: Another problem [...]]]></description>
			<content:encoded><![CDATA[<p>Have just done my first version of overlaying geolocation info for MSG users onto Google Maps. At the moment it&#8217;s pretty rough and ready (only on my dev server), but seems to do the basics, eg changes the &#8216;pin&#8217; when a users state changes. Quick screenshot here with users having different presence states:</p>
<p><img src="http://alexlittle.net/blog/images/googlemap.jpg" alt="" /></p>
<p>Another problem for me to work on is how to &#8216;cluster&#8217; users, as things stand with my map, users will only be clustered if they have exactly the same latitude and longitude &#8211; which fairly quickly starts to break down &#8211; on the image above there are several pins around Milton Keynes and it&#8217;s not really obvious exactly how many users are there &#8211; or whether there are more pins hidden behind the pins you can already see.</p>
<p>So I&#8217;m going to look to see how we can do the clustering more sensibly (perhaps at different resolutions of Google maps), this was solved quite well in BuddySpace, so I&#8217;m going to have a look at the algorithm used there (see the <a href="http://kmi.open.ac.uk/publications/techreports.php?techreport=kmi-01-18">tech report</a> on it) and see if I could apply the same algorithm, or if other algorithms have been developed in the last few years.</p>
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