<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Hi, I am looking for a solution to detect a feature in one FC that crosses a &amp;quot;sinlge&amp;quot; feature from another FC more than once. ie. A feature is valid if it crosses once and invalid if crossing the same feature more than once. the feature is also valid if i in Python Snippets Questions</title>
    <link>https://community.esri.com/t5/python-snippets-questions/hi-i-am-looking-for-a-solution-to-detect-a-feature/m-p/776050#M138</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi, I am looking for a solution to detect a feature in one FC that crosses a "single" feature from another FC more than once. ie. A feature is valid if it crosses once and invalid if crossing the same feature more than once. the feature is also valid if it crosses multiple features as long an single feature is only crossed once.&lt;/P&gt;&lt;P&gt;&lt;IMG src="data:image/png;base64,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" /&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Wed, 15 Nov 2017 23:25:49 GMT</pubDate>
    <dc:creator>ScottFitzsimmons</dc:creator>
    <dc:date>2017-11-15T23:25:49Z</dc:date>
    <item>
      <title>Hi, I am looking for a solution to detect a feature in one FC that crosses a "sinlge" feature from another FC more than once. ie. A feature is valid if it crosses once and invalid if crossing the same feature more than once. the feature is also valid if i</title>
      <link>https://community.esri.com/t5/python-snippets-questions/hi-i-am-looking-for-a-solution-to-detect-a-feature/m-p/776050#M138</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi, I am looking for a solution to detect a feature in one FC that crosses a "single" feature from another FC more than once. ie. A feature is valid if it crosses once and invalid if crossing the same feature more than once. the feature is also valid if it crosses multiple features as long an single feature is only crossed once.&lt;/P&gt;&lt;P&gt;&lt;IMG src="data:image/png;base64,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" /&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 15 Nov 2017 23:25:49 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-snippets-questions/hi-i-am-looking-for-a-solution-to-detect-a-feature/m-p/776050#M138</guid>
      <dc:creator>ScottFitzsimmons</dc:creator>
      <dc:date>2017-11-15T23:25:49Z</dc:date>
    </item>
    <item>
      <title>Re: Hi, I am looking for a solution to detect a feature in one FC that crosses a "sinlge" feature from another FC more than once. ie. A feature is valid if it crosses once and invalid if crossing the same feature more than once. the feature is also valid</title>
      <link>https://community.esri.com/t5/python-snippets-questions/hi-i-am-looking-for-a-solution-to-detect-a-feature/m-p/776051#M139</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Not a python solution, but a gp suggestion:&amp;nbsp; Take a look at intersection tool.&amp;nbsp; If I recall correctly, you can set the out put to a point and those points will carry identifying attributes of the features intersected.&amp;nbsp; You might be able to then summarize them in a way that denotes your [in] validation.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 16 Nov 2017 01:05:17 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-snippets-questions/hi-i-am-looking-for-a-solution-to-detect-a-feature/m-p/776051#M139</guid>
      <dc:creator>JoeBorgione</dc:creator>
      <dc:date>2017-11-16T01:05:17Z</dc:date>
    </item>
  </channel>
</rss>

