<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20230310</CreaDate>
<CreaTime>13104300</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>BoulderHexagons</resTitle>
</idCitation>
<searchKeys>
<keyword>Boulder</keyword>
<keyword>H3</keyword>
<keyword>Uber</keyword>
<keyword>Hexagons</keyword>
<keyword>Hex</keyword>
<keyword>Analysis</keyword>
<keyword>Visualization</keyword>
<keyword>Analytics</keyword>
</searchKeys>
<idPurp>A geographic representation of the Uber H3 hexagons that overlap Boulder's city limits, OSMP land, and Boulder Valley Comprehensive Plan (BVCP) area.</idPurp>
<idAbs>&lt;DIV STYLE="text-align:Left;font-size:12pt"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;H3 is a Hexagonal Hierarchical Spatial Index developed by Uber. It is a grid system that partitions the Earth into uniquely identified grid cells. More information on H3 can be found on &lt;/SPAN&gt;&lt;A href="https://www.uber.com/blog/h3/" STYLE="text-decoration:underline;"&gt;&lt;SPAN&gt;Uber's engineering blog&lt;/SPAN&gt;&lt;/A&gt;&lt;SPAN&gt;.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;These delineations were extracted from Uber's open source H3 library using Python bindings. They are geographic representations of the global hexagonal index over areas of interest to the City of Boulder.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Each hex has a unique string index that matches its original 64-bit integer identifier. There are 6 different resolutions in this dataset, ranging from 6-12; the larger the number, the smaller the hexagon. Visible ranges are applied to higher Resolutions, so be sure to zoom in to see those smaller polygons.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
<idCredit>Uber, City of Boulder</idCredit>
<resConst>
<Consts>
<useLimit>&lt;DIV STYLE="text-align:Left;font-size:12pt"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;See Uber's &lt;/SPAN&gt;&lt;A href="https://github.com/uber/h3/blob/master/LICENSE" STYLE="text-decoration:underline;"&gt;&lt;SPAN&gt;license &lt;/SPAN&gt;&lt;/A&gt;&lt;SPAN&gt;and &lt;/SPAN&gt;&lt;A href="https://github.com/uber/h3/blob/master/NOTICE" STYLE="text-decoration:underline;"&gt;&lt;SPAN&gt;notice&lt;/SPAN&gt;&lt;/A&gt;&lt;SPAN&gt;.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</useLimit>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
</Thumbnail>
</Binary>
</metadata>
