<?xml version="1.0" encoding="UTF-8"?><metadata xml:lang="en">
    <Esri>
        <ArcGISFormat>1.0</ArcGISFormat>
        <ArcGISProfile>DCPLUS</ArcGISProfile>
        <CreaDate>20220419</CreaDate>
        <CreaTime>18543700</CreaTime>
        <ModDate>20220419</ModDate>
        <ModTime>18543700</ModTime>
        <DataProperties>
            <itemProps>
                <portalDetails>
                    <thumbnailURL>https://www.arcgis.com/sharing/rest/content/items/2cf61e9d78b54c13adf08c5da236fac5/info/thumbnail/ago_downloaded.png</thumbnailURL>
                    <itemDetailsURL>https://www.arcgis.com/home/item.html?id=2cf61e9d78b54c13adf08c5da236fac5</itemDetailsURL>
                    <itemIdentifier>2cf61e9d78b54c13adf08c5da236fac5</itemIdentifier>
                    <itemType>Feature Service</itemType>
                    <resourceURL>https://services9.arcgis.com/2ynJbr9BE17vXxR8/arcgis/rest/services/Find_Outliers_Classes/FeatureServer</resourceURL>
                </portalDetails>
            </itemProps>
        </DataProperties>
    </Esri>
    <dataIdInfo>
        <idCitation>
            <resTitle>Find_Outliers_Classes</resTitle>
            <date>
                <createDate>2022-04-19T18:54:37</createDate>
                <reviseDate>2022-04-19T18:55:42</reviseDate>
            </date>
        </idCitation>
        <searchKeys>
            <keyword>Analysis Result</keyword>
            <keyword>Find Outliers</keyword>
            <keyword>LA_County_apartment_and_hotel_inspections_Prediction</keyword>
            <keyword>Classes</keyword>
        </searchKeys>
        <idPurp>Feature layer generated from Find Outliers</idPurp>
        <idAbs>&lt;b&gt;The following report outlines the workflow used to optimize your Find Outliers result:&lt;/b&gt;&lt;br /&gt;&lt;u&gt;&lt;b&gt;Initial Data Assessment.&lt;/b&gt;&lt;/u&gt;&lt;br /&gt;&lt;ul&gt;&lt;li&gt;There were 104 valid input features.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;CLASSES Properties:&lt;/li&gt;&lt;/ul&gt;&lt;table style='width: 200px;margin-left: 2.5em;border: none;'&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td style='border: none;'&gt;Min&lt;/td&gt;&lt;td style='float:right;border: none;'&gt;0.0000&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style='border: none;'&gt;Max&lt;/td&gt;&lt;td style='float: right;border: none;'&gt;9.0000&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style='border: none;'&gt;Mean&lt;/td&gt;&lt;td style='float: right;border: none;'&gt;5.2212&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td style='border: none;'&gt;Std. Dev.&lt;/td&gt;&lt;td style='float: right;border: none;'&gt;2.9120&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;ul&gt;&lt;li&gt;There was 1 outlier location; it will not be used to compute the optimal fixed distance band.&lt;/li&gt;&lt;/ul&gt;&lt;u&gt;&lt;b&gt;Scale of Analysis&lt;/b&gt;&lt;/u&gt;&lt;br /&gt;&lt;ul&gt;&lt;li&gt;The optimal fixed distance band selected was based on peak clustering found at 595.4353 Meters.&lt;/li&gt;&lt;/ul&gt;&lt;br /&gt;&lt;u&gt;&lt;b&gt;Outlier Analysis&lt;/b&gt;&lt;/u&gt;&lt;br /&gt;&lt;ul&gt;&lt;li&gt;Creating the random reference distribution with 499 permutations.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;There are 10 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;There are 0 statistically significant high outlier features.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;There are 1 statistically significant low outlier features.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;There are 2 features part of statistically significant low clusters.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;There are 7 features part of statistically significant high clusters.&lt;/li&gt;&lt;/ul&gt;&lt;br /&gt;&lt;u&gt;&lt;b&gt;Output&lt;/b&gt;&lt;/u&gt;&lt;br /&gt;&lt;ul&gt;&lt;li&gt;Pink output features are part of a cluster of high CLASSES values.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;Light Blue output features are part of a cluster of low CLASSES values.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;Red output features represent high outliers within a cluster of low CLASSES values.&lt;/li&gt;&lt;/ul&gt;&lt;ul&gt;&lt;li&gt;Blue output features represent low outliers within a cluster of high CLASSES values.&lt;/li&gt;&lt;/ul&gt;&lt;br /&gt;</idAbs>
        <dataExt>
            <geoEle>
                <GeoBndBox>
                    <westBL Sync="FALSE">-118.2231947740388</westBL>
                    <eastBL Sync="FALSE">-118.1794000004079</eastBL>
                    <northBL Sync="FALSE">33.93054227078618</northBL>
                    <southBL Sync="FALSE">33.905487742734955</southBL>
                    <exTypeCode Sync="TRUE">1</exTypeCode>
                </GeoBndBox>
            </geoEle>
        </dataExt>
    </dataIdInfo>
    <Binary>
        <Thumbnail>
            <Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKbI4iieRvuqCx+goAjubuG0j3zPjPQdz9BT4pPNjDhWUHswwazrSGW9uUv7hUCbf3SAk49zxWpSWpUklp1GPIqFQ3G44B9/Sn0jAMpUjIIwaozz3VkH2W5nhA3Bt+Co7g55NMSV9C/RTIpBNCkoBAdQwB96fQIKKKKACiiigAqrcahBbSeW5JbGcAZxVliQpIGSBwK5bEtxPzzK7frVwinuZ1JuOx00cySwiYHCEZy3FQTajbQMAX3E/wBznFZt5f7oWtEjARcLuye3t+FZ1VGnfVkTrW0R0UWp200qxqWDMcDIq5XIbgCBuAP1rXstW2qI7kk+j4/nRKnbYcKt9JGxRTIpY5k3xuGX1FPrI2CiiigAooooAKKKKACoL2JprKaNPvMpwPU1PRQNOzuUtLnWWxjQcPEAjKeoxxV2qs9mHk86FhFOP4wOvsR3pRPLEwFyqBT/AMtEPH4g9KSHKzd0WaKKKZJBEwW4khHQAOMds54/TNT1VtgpubmQZO5hh8cEAYwPoc/nVqgAooooAKKKKACsLWIIoHV4gRJISx54GP65rdqhq0CyWZkx88eCD7d6qLsyJq8TAJJJJOSeSaRjhSaCecAUhy3GMV0nGKABn360bMAMBgHvjg0p4GauPqXmWAtmhAIAAbII49vWk3YcUne7F0mcx3gUthX+Uj37V0Nc1ptv9pvACcAfOfUgYrpaxqbnTRvyhRRRWZqFFFFABRRRQAUUVHPPFbR+ZK+1emaA3CeeO2haWQ4VetMiu7a44jlR85G3PJ/Cuav79r+VWK7FUYVc5/Gqmcc9Mc5rJ1ddDsjhbxu3ZnVpL9kl8iXiI5MUhOAB/dPpjt7U8Tm4bZDuCFSTIUI+m3PWqNnJBfRGO8jH2kAZMgGeem38PSrOkk/YAhOdjMoPqM1ocrWly6qqiBVGFAwBS1C9xsuFhCMSV3EjsOn+FSeYo65H1FMkdRSKysMqQR7UtABRRRQAVma1Ltt0iDD525Xvgf8A160mOFJ9BXLTXEty/mStk44HpV043ZlVlaNiL+P8KDwDzigj06ikyCc9gM10HKALAZIq3ZWEt1IcgpH1LEfpWrY6atviSXDSEdMcLWhWMqnY6IUesita2MNpkxglyMFieTVmiism7mySWwUUUUDCiiigAooooAKZNCk8LRSDKsMGn0UAcZNEYJ3ibqjEfWoyMgj1rV1zRJL+4ZghkjkKsQr7SCuMdx6CmR6HduCW2RnsGbOfyzXO4PoelGvHlvJhp0El3cwyPESI2BaQoMEADA/Tt0zW59mkjdvJlCRyMWYbckHvj/69Q6ehsY/s07KDuJRsYBH19avZGM5GPWt0tNTgnJOTtsRxQiNi5dpHPG5sZx6cCpaRWV1DKwZT0IOQaWmQQlmhd2YZjPzFh/Dx6fh1qamSoZIXQdWUiiFxJCrDjjBB7EdRQA+iiigArlrlQlzIFUqm47QRjit+a+igulhk+XK7t3Ydf8Kwr2bz7uSQNlc4U+1a0r3MKzViuSc4GKTbjkHml6P9RVuxe1SZjdLlSOMjIH4Vq3ZXMEruxPpl/KsyQOSyE7QD1X0x7Vu1yplEV20kHyjnZkZI5rVtNSl2IblAUY7RIvr7ispx6o6Kc+jNWiiisjYKKKKACiiigAooooAKy5tZEF48DwkBeA2epx6VqdBk1kXenvfX0dxFJEYcAZB9D7damV+hpT5b+8RJrcsKILmJWZwHXacfKa2o5FliWRDlWAIrmdWtltrvbHFsjIyMHr61uaXxpsAPUqTz9aUW72ZdSEVBTXUkvZxbWjy4VivQHuay7FTKXaeIjf8A6u3GVU+pxnGPXNR6n9sk1FYWLNGSCipwMf41r2mZQ1w6bTIcpnqEwMfT1xT3ZLXLD1II11C3gWOOK3IQYxvOT+lRSahdQRu08axsvRfLJDf8CBwK0ZJCpVVHzOcDPTpmo5LKKZWEuWZgAWzj8vSnbsQmr6o5+XWLyRjtkEanjaoH8+tXdJ1MHZbSr8xPDj+I+/8AjWmlhaIoUW0WB6qCfzNURHH/AG+iwpEqxIS21cYJB/M8ioSkne5u505JpRsa1FFFaHMUdQsBdoHXAlUce49KwCCpIIII6g11tVLywjuxn7kg6MB1+vrWkJ20ZlUp82qObIzRk/3asXNnNat+8X5c4DDoadFYXUyK6RZVuQdwrbmW9zm5ZXtYqgc5PWt3Ro5Et3ZlKqxBXPfjrSWmkoiq9x8z9duflH+NadZTmnojop02ndhRRRWRsFFFFABRRRQAUUUUAZmqi+3KLUMUZSHAAP8AOucZWQsjAqc4INdtWff6Ul9KsnmeWwGD8uc1nODeqOmjWUdJLQzbb+0p4V/crPEPuiZQR+Gear3l1Mb9XePynhwAgPTHNaVrc3FrqX2GZzOp+6x6jjNN1yW2KbBtNwCASByB7mk17u5cZfvLOO/Y045Y7uy8wkqjoc89PXmnwZ8hNwwcCsnSIZmsZQ27YzKVU8Ajv+fSrsU966gfZVBXhjLJtyfbANaJ3RzTjaTSJpiXljhBx/GWx6EcCp6rQxTG4aafYDtCqqEkAZyev4VZpkMKxbGGU6vNNJFIpySCwwADn9en61tUUmrlKTSaXUKKKKZIUUUUAIyq67WUMD2IzQqqihVUKo6ADFLRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBjTzR2OsTTzAnfEDH7Hpj9Ks2FiiwebOqyTS/OxZQcZ7CrksEU23zY1facjcM4qSpS1Lc7qwUUUVRAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//Z</Data>
        </Thumbnail>
    </Binary>
    <mdFileID>2cf61e9d78b54c13adf08c5da236fac5</mdFileID>
    <distInfo>
        <distTranOps>
            <onLineSrc>
                <linkage>https://services9.arcgis.com/2ynJbr9BE17vXxR8/arcgis/rest/services/Find_Outliers_Classes/FeatureServer</linkage>
            </onLineSrc>
        </distTranOps>
        <distFormat>
            <formatName>Feature Service</formatName>
        </distFormat>
    </distInfo>
    <refSysInfo>
        <RefSystem>
            <refSysID>
                <identCode code="102100"/>
                <idCodeSpace>EPSG</idCodeSpace>
            </refSysID>
        </RefSystem>
    </refSysInfo>
</metadata>
