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    <title>topic Re: Row standardization option with K nearest neighbors? in Spatial Statistics Questions</title>
    <link>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72191#M297</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The answer is in the interpretation of your results...&lt;/P&gt;&lt;BLOCKQUOTE class="jive_macro_quote jive-quote jive_text_macro"&gt;&lt;P&gt;&lt;/P&gt;For polygon features, you will almost always want to choose &lt;SPAN&gt;Row&lt;/SPAN&gt; for the &lt;SPAN&gt;Row Standardization&lt;/SPAN&gt; parameter. &lt;A href="https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/modeling-spatial-relationships.htm#GUID-DB9C20A7-51DB-4704-A0D7-1D4EA22C23A7"&gt;&lt;SPAN style="color: #0066cc; text-decoration: underline;"&gt;Row Standardization&lt;/SPAN&gt;&lt;/A&gt; mitigates bias when the number of neighbors each feature has is a function of the aggregation scheme or sampling process, rather than reflecting the actual spatial distribution of the variable you are analyzing.&lt;/BLOCKQUOTE&gt;&lt;P&gt;Which means that this wasn't an issue in your case.&lt;/P&gt;&lt;P&gt;&lt;A href="https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/modeling-spatial-relationships.htm#GUID-DB9C20A7-51DB-4704-A0D7-1D4EA22C23A7"&gt;Standardization is explained here &lt;/A&gt;(PRO help, but it is the same in either package)&lt;/P&gt;&lt;P&gt;So, your results show no bias and the differences in the resultant Moran's are completely insignificant whether it is on or off.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Tue, 28 Feb 2017 21:46:25 GMT</pubDate>
    <dc:creator>DanPatterson_Retired</dc:creator>
    <dc:date>2017-02-28T21:46:25Z</dc:date>
    <item>
      <title>Row standardization option with K nearest neighbors?</title>
      <link>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72190#M296</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P style="background-color: #ffffff; border: 0px;"&gt;I am looking at the Generate Spatial Weights matrix tool. I am trying to understand how it works. When K nearest neighbors option&amp;nbsp;is selected, row standardization can still be clicked. How does that change anything? If I select K nearest neighbors, enter 8 as the number of neighbors, the weight will always be 0.125 no matter row standardization is selected or not right? In that case, I don't understand why row standardization option is available. In addition, however, I see very slightly different results with such configurations using my dataset. Here they are:&lt;/P&gt;&lt;P style="background-color: #ffffff; border: 0px;"&gt;&amp;nbsp;&lt;/P&gt;&lt;P style="background-color: #ffffff; border: 0px;"&gt;&lt;SPAN style="border: 0px; font-weight: inherit;"&gt;K nearest neighbors option, 8 neighbors,&amp;nbsp;row standardization NOT enabled, Moran's Index I:&amp;nbsp;0.243726&lt;/SPAN&gt;&lt;/P&gt;&lt;P style="background-color: #ffffff; border: 0px;"&gt;&lt;SPAN style="border: 0px; font-weight: inherit;"&gt;K nearest neighbors option, 8 neighbors,&amp;nbsp;row standardization enabled, Moran's Index I: 0.243721&lt;/SPAN&gt;&lt;/P&gt;&lt;P style="background-color: #ffffff; border: 0px;"&gt;&amp;nbsp;&lt;/P&gt;&lt;P style="background-color: #ffffff; border: 0px;"&gt;&lt;SPAN style="border: 0px; font-weight: inherit;"&gt;Why are there such differences? Thanks!&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 28 Feb 2017 17:41:38 GMT</pubDate>
      <guid>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72190#M296</guid>
      <dc:creator>NaciDilekli</dc:creator>
      <dc:date>2017-02-28T17:41:38Z</dc:date>
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    <item>
      <title>Re: Row standardization option with K nearest neighbors?</title>
      <link>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72191#M297</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The answer is in the interpretation of your results...&lt;/P&gt;&lt;BLOCKQUOTE class="jive_macro_quote jive-quote jive_text_macro"&gt;&lt;P&gt;&lt;/P&gt;For polygon features, you will almost always want to choose &lt;SPAN&gt;Row&lt;/SPAN&gt; for the &lt;SPAN&gt;Row Standardization&lt;/SPAN&gt; parameter. &lt;A href="https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/modeling-spatial-relationships.htm#GUID-DB9C20A7-51DB-4704-A0D7-1D4EA22C23A7"&gt;&lt;SPAN style="color: #0066cc; text-decoration: underline;"&gt;Row Standardization&lt;/SPAN&gt;&lt;/A&gt; mitigates bias when the number of neighbors each feature has is a function of the aggregation scheme or sampling process, rather than reflecting the actual spatial distribution of the variable you are analyzing.&lt;/BLOCKQUOTE&gt;&lt;P&gt;Which means that this wasn't an issue in your case.&lt;/P&gt;&lt;P&gt;&lt;A href="https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/modeling-spatial-relationships.htm#GUID-DB9C20A7-51DB-4704-A0D7-1D4EA22C23A7"&gt;Standardization is explained here &lt;/A&gt;(PRO help, but it is the same in either package)&lt;/P&gt;&lt;P&gt;So, your results show no bias and the differences in the resultant Moran's are completely insignificant whether it is on or off.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 28 Feb 2017 21:46:25 GMT</pubDate>
      <guid>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72191#M297</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2017-02-28T21:46:25Z</dc:date>
    </item>
    <item>
      <title>Re: Row standardization option with K nearest neighbors?</title>
      <link>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72192#M298</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I know why row standardization is advised for polygon features, I just don't understand why it makes any difference at all when K nearest neighbors option is used. When K nearest neighbors option&amp;nbsp;is used, the weights will be the same across all the features&amp;nbsp;&lt;SPAN style="background-color: #ffffff;"&gt;no matter row standardization is selected or not.&amp;nbsp;&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 28 Feb 2017 22:22:28 GMT</pubDate>
      <guid>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72192#M298</guid>
      <dc:creator>NaciDilekli</dc:creator>
      <dc:date>2017-02-28T22:22:28Z</dc:date>
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    <item>
      <title>Re: Row standardization option with K nearest neighbors?</title>
      <link>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72193#M299</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;So you are questioning&amp;nbsp;the 6th decimal place? ... could be simply floating point math carry-forward, but that would require dissecting the equations in detail to see if that is the case.&amp;nbsp; Other than that, it would be possibly the influence on what was chosen for the 'Number_of_Neighbors'&amp;nbsp; option and whether that wasn't being met, whereby the selection becomes "For polygons with fewer than this number of contiguous neighbors, additional neighbors will be based on feature centroid proximity".&amp;nbsp; I suppose that muddies the interpretation but there is no mention of row standardization being ignored just because k nearest neighbours is chosen&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 28 Feb 2017 22:42:59 GMT</pubDate>
      <guid>https://community.esri.com/t5/spatial-statistics-questions/row-standardization-option-with-k-nearest/m-p/72193#M299</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2017-02-28T22:42:59Z</dc:date>
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