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    <title>topic GWR bandwidth vs. AIC calculated kernel in Spatial Statistics Questions</title>
    <link>https://community.esri.com/t5/spatial-statistics-questions/gwr-bandwidth-vs-aic-calculated-kernel/m-p/22297#M86</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;SPAN&gt;Using a dataset of ~ 1500 locations, I found substantial spatial autocorrelation and nonstationarity (using Moran's I) between 1 single set of dependent vs. independent variables. Using Incremental Spatial Autocorrelation (ISA) in ArcGIS 10, I calculated a distance at which maximum spatial clustering occurs and used this distance as a bandwidth for both Hot Spot analysis and Geographically Weighted Regression. &lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN&gt;When I run the GWR for using the ISA derived bandwidth I have greater biological significance in the trends of my coefficient distributions, yet higher AICc and lower Adjusted R-square scores then when I simply run the GWR with an adaptive kernel as calculated using AIC within the ArcGIS 10 program. When I test the GWR residuals using Moran's I, some of the GWR ISA bandwidth models still show significant spatial autocorrelation, which is absent in the GWR AIC adaptive kernel models. &lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN&gt;I feel like the ISA derived bandwidth models are the 'correct' ones due to the separate calculation using ISA to determine max clustering, but I cannot ignore that the model fit appears higher using the AIC adaptive kernel. &lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN&gt;Any suggestions or insights would be very appreciated!&lt;/SPAN&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Thu, 13 Jun 2013 15:47:38 GMT</pubDate>
    <dc:creator>BarbaraFrei</dc:creator>
    <dc:date>2013-06-13T15:47:38Z</dc:date>
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      <title>GWR bandwidth vs. AIC calculated kernel</title>
      <link>https://community.esri.com/t5/spatial-statistics-questions/gwr-bandwidth-vs-aic-calculated-kernel/m-p/22297#M86</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;SPAN&gt;Using a dataset of ~ 1500 locations, I found substantial spatial autocorrelation and nonstationarity (using Moran's I) between 1 single set of dependent vs. independent variables. Using Incremental Spatial Autocorrelation (ISA) in ArcGIS 10, I calculated a distance at which maximum spatial clustering occurs and used this distance as a bandwidth for both Hot Spot analysis and Geographically Weighted Regression. &lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN&gt;When I run the GWR for using the ISA derived bandwidth I have greater biological significance in the trends of my coefficient distributions, yet higher AICc and lower Adjusted R-square scores then when I simply run the GWR with an adaptive kernel as calculated using AIC within the ArcGIS 10 program. When I test the GWR residuals using Moran's I, some of the GWR ISA bandwidth models still show significant spatial autocorrelation, which is absent in the GWR AIC adaptive kernel models. &lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN&gt;I feel like the ISA derived bandwidth models are the 'correct' ones due to the separate calculation using ISA to determine max clustering, but I cannot ignore that the model fit appears higher using the AIC adaptive kernel. &lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN&gt;Any suggestions or insights would be very appreciated!&lt;/SPAN&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 13 Jun 2013 15:47:38 GMT</pubDate>
      <guid>https://community.esri.com/t5/spatial-statistics-questions/gwr-bandwidth-vs-aic-calculated-kernel/m-p/22297#M86</guid>
      <dc:creator>BarbaraFrei</dc:creator>
      <dc:date>2013-06-13T15:47:38Z</dc:date>
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