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<item><guid isPermaLink="true">http://tags.library.upenn.edu/makerecord/project/43506</guid>
<link>http://tags.library.upenn.edu/makerecord/project/43506</link>
<title>Heatwave/BlockCaptain Project</title>
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<item><guid isPermaLink="true">http://tags.library.upenn.edu/makerecord/url/43507</guid>
<link>http://tags.library.upenn.edu/makerecord/url/43507</link>
<title>The socio-spatial dynamics of extreme urban heat events: The case of heat-related deaths in Philadelphia.</title>
<description>&lt;div class="mlacite"&gt;"The socio-spatial dynamics of extreme urban heat events: The case of heat-related deaths in Philadelphia." &lt;span style="text-decoration: underline;"&gt;Applied geography&lt;/span&gt; [0143-6228] 29.3 (2009).  419-.&lt;/div&gt;
&lt;div class="mlacite"&gt;&lt;br /&gt;&lt;/div&gt;
&lt;div class="mlacite"&gt;Abstract: &lt;strong&gt;&lt;em&gt;Heat&lt;/em&gt;&lt;/strong&gt; is the number one weather-related cause of mortality in the United States; typically punctuated by extreme &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt; waves. This study examines the relationship between the spatial distribution of vulnerable populations, satellite-detected &lt;strong&gt;&lt;em&gt;urban&lt;/em&gt;&lt;/strong&gt; &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt; island (UHI) and &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt;-related mortality distributions during a 1993 extreme &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt; event in Philadelphia, PA. Geostatistical methods are used to compare spatial distributions of vulnerability and to determine concentration of mortality within surface UHI intensity levels. The results suggest the spatial distribution of &lt;strong&gt;&lt;em&gt;urban&lt;/em&gt;&lt;/strong&gt; poor is congruent with &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt;-related death. Additionally, deaths are concentrated in higher order surface UHI intensity levels. The findings suggest that surface UHI measures and population in poverty are important variables in spatially measuring risk from extreme &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt; events. Coupling surface UHI measures with socioeconomic indicators of vulnerability may enable creation of risk models with improved spatial specificity to assist public health professionals. This approach is demonstrated by developing a linear regression model of potential risk in Philadelphia for the 1993 extreme &lt;strong&gt;&lt;em&gt;heat&lt;/em&gt;&lt;/strong&gt; event. [Copyright 2009 Elsevier]&lt;/div&gt;
&lt;div class="mlacite"&gt;&lt;br /&gt;&lt;/div&gt;
&lt;div class="mlacite"&gt;&lt;img src="http://www.sciencedirect.com/scidirimg/clear.gif" border="0" alt="" width="1" height="10" /&gt;&lt;a href="http://dx.doi.org/10.1016/j.apgeog.2008.11.004" target="doilink" onclick="var doiWin; doiWin=window.open('http://dx.doi.org/10.1016/j.apgeog.2008.11.004','doilink','scrollbars=yes,resizable=yes,directories=yes,toolbar=yes,menubar=yes,status=yes'); doiWin.focus()"&gt;doi:10.1016/j.apgeog.2008.11.004&lt;/a&gt;&amp;nbsp;&amp;nbsp; &lt;br /&gt;&lt;/div&gt;</description>
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