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	<title>Map services - Vivid Maps</title>
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	<title>Map services - Vivid Maps</title>
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		<title>Mapping smoke alarms</title>
		<link>https://vividmaps.com/mapping-smoke-alarms/</link>
					<comments>https://vividmaps.com/mapping-smoke-alarms/#respond</comments>
		
		<dc:creator><![CDATA[Alex]]></dc:creator>
		<pubDate>Thu, 08 Oct 2015 12:16:00 +0000</pubDate>
				<category><![CDATA[Internet]]></category>
		<category><![CDATA[Map services]]></category>
		<category><![CDATA[Nature]]></category>
		<category><![CDATA[Japan]]></category>
		<category><![CDATA[Turkey]]></category>
		<category><![CDATA[United States]]></category>
		<guid isPermaLink="false">https://vividmaps.com/2015/10/08/mapping-smoke-alarms/</guid>

					<description><![CDATA[<p>Do you have a smoke alarm? 25,000 people are killed or injured each year in 1 million fires across the</p>
<p>The post <a href="https://vividmaps.com/mapping-smoke-alarms/">Mapping smoke alarms</a> appeared first on <a href="https://vividmaps.com">Vivid Maps</a>.</p>
]]></description>
										<content:encoded><![CDATA[<p>Do you have a smoke alarm? 25,000 people are killed or injured each year in 1 million fires across the U.S.</p>
<p>Prioritizing outreach for fire prevention is difficult without access to data and analytics targeting at-risk houses. By drawing together different public data sets, <a href="http://labs.enigma.io/smoke-signals/" target="_blank">Enigma</a> developed a predictive model that identifies hot spots of homes that are unlikely to have smoke alarms.</p>
<p><a href="http://labs.enigma.io/smoke-signals/" target="_blank"><img decoding="async" border="0" src="http://3.bp.blogspot.com/-0t1bJWY53hg/VhZebL6ipjI/AAAAAAAApN4/DZomAYcb_Qw/s1600/smokemap.jpg" /></a></p>
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			</item>
		<item>
		<title>Predicting the perceived safety of one million streetscapes</title>
		<link>https://vividmaps.com/predicting-perceived-safety-of-one/</link>
					<comments>https://vividmaps.com/predicting-perceived-safety-of-one/#respond</comments>
		
		<dc:creator><![CDATA[Alex]]></dc:creator>
		<pubDate>Thu, 08 Oct 2015 05:47:00 +0000</pubDate>
				<category><![CDATA[Cities]]></category>
		<category><![CDATA[Interactive maps]]></category>
		<category><![CDATA[Internet]]></category>
		<category><![CDATA[Map services]]></category>
		<category><![CDATA[Nature]]></category>
		<category><![CDATA[Japan]]></category>
		<category><![CDATA[Turkey]]></category>
		<category><![CDATA[United States]]></category>
		<guid isPermaLink="false">https://vividmaps.com/2015/10/08/predicting-perceived-safety-of-one/</guid>

					<description><![CDATA[<p>StreetScore is an algorithm that assigns a score to a street view based on how safe it looks to a</p>
<p>The post <a href="https://vividmaps.com/predicting-perceived-safety-of-one/">Predicting the perceived safety of one million streetscapes</a> appeared first on <a href="https://vividmaps.com">Vivid Maps</a>.</p>
]]></description>
										<content:encoded><![CDATA[<p><a href="http://streetscore.media.mit.edu/" target="_blank">StreetScore</a> is an algorithm that assigns a score to a street view based on how safe it looks to a human — but using a computer (see FAQ). This website is a collection of map visualizations of perceived safety of street views from cities in the US as predicted by StreetScore. We will be releasing a map of perceived safety for a new city each week. The StreetScore algorithm was created by Nikhil Naik as part of a collaboration between the Macro Connections group and the Camera Culture group at MIT Media Lab. Jade Philipoom created the visualizations presented in the StreetScore website.</p>
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