<?xml version="1.0" encoding="UTF-8"?><metadata xml:lang="en">
    <Esri>
        <ArcGISFormat>1.0</ArcGISFormat>
        <ArcGISProfile>DCPLUS</ArcGISProfile>
        <CreaDate>20241204</CreaDate>
        <CreaTime>20161300</CreaTime>
        <ModDate>20241205</ModDate>
        <ModTime>20161300</ModTime>
        <DataProperties>
            <itemProps>
                <portalDetails>
                    <thumbnailURL>https://www.arcgis.com/sharing/rest/content/items/5b7afb391fc54ed285f6ae4798a81fbc/info/thumbnail/ago_downloaded.png</thumbnailURL>
                    <itemDetailsURL>https://www.arcgis.com/home/item.html?id=5b7afb391fc54ed285f6ae4798a81fbc</itemDetailsURL>
                    <itemIdentifier>5b7afb391fc54ed285f6ae4798a81fbc</itemIdentifier>
                    <itemType>Feature Service</itemType>
                    <resourceURL>https://services.arcgis.com/8df8p0NlLFEShl0r/arcgis/rest/services/Overlayy/FeatureServer</resourceURL>
                </portalDetails>
            </itemProps>
        </DataProperties>
    </Esri>
    <dataIdInfo>
        <idCitation>
            <resTitle>Deer Density and hot spots</resTitle>
            <date>
                <createDate>2024-12-04T20:16:13</createDate>
                <reviseDate>2024-12-05T17:45:57</reviseDate>
            </date>
        </idCitation>
        <searchKeys>
            <keyword>Deer</keyword>
            <keyword>Minnesota</keyword>
            <keyword>Wildlife corridor</keyword>
            <keyword>DVC</keyword>
        </searchKeys>
        <idPurp>Combines high deer-density areas with identified deer-vehicle collision hotspots for targeted analysis.</idPurp>
        <idAbs>&lt;strong&gt;Description&lt;/strong&gt;:&lt;br /&gt;This overlay layer merges the deer density dataset with DVC hotspots, providing a comprehensive view of where high deer populations and frequent collision incidents intersect. Using spatial analysis tools, the layer highlights critical areas where deer movement and human activity overlap, making it an essential component for identifying ideal locations for wildlife corridors. By focusing on these intersections, planners can prioritize mitigation strategies in the most impactful areas.&lt;br /&gt;&lt;strong&gt;Source&lt;/strong&gt;:&lt;br /&gt;Deer Density Dataset (2018), Wilson et al.&lt;br /&gt;DVC Hot Spots Layer (derived from DVC dataset, 2022, University of Minnesota).&lt;br /&gt;&lt;strong&gt;Date Accessed&lt;/strong&gt;: November 2024.&lt;br /&gt;&lt;strong&gt;Purpose&lt;/strong&gt;: This layer aids in refining wildlife corridor planning by pinpointing regions with both high deer activity and significant collision risks, ensuring resources are directed to areas of greatest need.</idAbs>
        <dataExt>
            <geoEle>
                <GeoBndBox>
                    <westBL Sync="FALSE">-97.05500035985463</westBL>
                    <eastBL Sync="FALSE">-89.59963692272554</eastBL>
                    <northBL Sync="FALSE">48.86952956695644</northBL>
                    <southBL Sync="FALSE">43.48745600196848</southBL>
                    <exTypeCode Sync="TRUE">1</exTypeCode>
                </GeoBndBox>
            </geoEle>
        </dataExt>
    </dataIdInfo>
    <Binary>
        <Thumbnail>
            <Data EsriPropertyType="PictureX">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</Data>
        </Thumbnail>
    </Binary>
    <mdFileID>5b7afb391fc54ed285f6ae4798a81fbc</mdFileID>
    <distInfo>
        <distTranOps>
            <onLineSrc>
                <linkage>https://services.arcgis.com/8df8p0NlLFEShl0r/arcgis/rest/services/Overlayy/FeatureServer</linkage>
            </onLineSrc>
        </distTranOps>
        <distFormat>
            <formatName>Feature Service</formatName>
        </distFormat>
    </distInfo>
    <refSysInfo>
        <RefSystem>
            <refSysID>
                <identCode code="102100"/>
                <idCodeSpace>EPSG</idCodeSpace>
            </refSysID>
        </RefSystem>
    </refSysInfo>
</metadata>
