<?xml version="1.0" encoding="UTF-8" standalone="no"?><metadata xml:lang="en">
    <Esri>
        <CreaDate>20230508</CreaDate>
        <CreaTime>15430000</CreaTime>
        <ArcGISFormat>1.0</ArcGISFormat>
        <SyncOnce>TRUE</SyncOnce>
    </Esri>
    <dataIdInfo>
        <idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
        <searchKeys>
            <keyword>Advanced GIS</keyword>
        </searchKeys>
        <idPurp>Service area network analysis by census tract</idPurp>
        <idCredit>Department of City Planning (DCP)</idCredit>
        <resConst>
            <Consts>
                <useLimit>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;This dataset is being provided by the Department of City Planning (DCP) on DCP’s website for informational purposes only. DCP does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of the dataset, nor are any such warranties to be implied or inferred with respect to the dataset as furnished on the website. DCP and the City are not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use the dataset, or applications utilizing the dataset, provided by any third party.&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/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
        </Thumbnail>
    </Binary>
</metadata>