<?xml version="1.0" encoding="UTF-8" standalone="no"?><metadata xml:lang="en">
    <Esri>
        <CreaDate>20220406</CreaDate>
        <CreaTime>18301000</CreaTime>
        <ArcGISFormat>1.0</ArcGISFormat>
        <SyncOnce>TRUE</SyncOnce>
    </Esri>
    <dataIdInfo>
        <idCitation>
            <resTitle>Map</resTitle>
        </idCitation>
        <idAbs>&lt;div&gt;This data shows the population change over the latest 7 days when Population Density data from Meta are available. The change is calculated using a linear regression over the daily data showing population change from 1600 to 2359 hrs UTC.&lt;/div&gt;&lt;div&gt;&lt;br /&gt;&lt;/div&gt;&lt;div&gt;Hotspots, or areas of interest, are visualized using the following color levels:&lt;/div&gt;&lt;div&gt;&lt;ul&gt;&lt;li&gt;&lt;b&gt;&lt;font color='#8b4513' style='font-family:inherit;'&gt;Decreasing &lt;/font&gt;&lt;/b&gt;of population density change are shown in &lt;font color='#8b4513' style='font-family:inherit;'&gt;&lt;b&gt;brown&lt;/b&gt;&lt;/font&gt;, which signify a small but consistent decrease in population density in these regions. &lt;b&gt;&lt;font color='#8b4513' style='font-family:inherit;'&gt;Three shades of brown&lt;/font&gt;&lt;/b&gt; indicates statistical significance from the linear regression applied to detect the trend over time (&lt;font color='#f4a460' style='font-family:inherit;'&gt;&lt;b&gt;p &amp;lt; 0.1&lt;/b&gt;&lt;/font&gt;, &lt;font color='#d2691e' style='font-family:inherit;'&gt;&lt;b&gt;p &amp;lt; 0.05&lt;/b&gt;&lt;/font&gt;, &lt;font color='#8b4513' style='font-family:inherit;'&gt;&lt;b&gt;p &amp;lt; 0.01&lt;/b&gt;&lt;/font&gt;).&lt;/li&gt;&lt;li&gt;&lt;font color='#008b8b' style='font-family:inherit;'&gt;&lt;b&gt;Increasing &lt;/b&gt;&lt;/font&gt;levels of population density changes are shown in &lt;b&gt;&lt;font color='#008b8b' style='font-family:inherit;'&gt;cyan&lt;/font&gt;&lt;/b&gt;, which indicates a small but consistent increase in population densities in these regions. &lt;b&gt;&lt;font color='#008b8b' style='font-family:inherit;'&gt;Three shades of cyan&lt;/font&gt;&lt;font color='#8b4513' style='font-family:inherit;'&gt; &lt;/font&gt;&lt;/b&gt;indicates statistical significance from the linear regression applied to detect the trend over time (&lt;b&gt;&lt;font color='#8fbc8f' style='font-family:inherit;'&gt;p &amp;lt; 0.1&lt;/font&gt;&lt;/b&gt;, &lt;b&gt;&lt;font color='#20b2aa' style='font-family:inherit;'&gt;p &amp;lt; 0.05&lt;/font&gt;&lt;/b&gt;, &lt;b&gt;&lt;font color='#008b8b' style='font-family:inherit;'&gt;p &amp;lt; 0.01&lt;/font&gt;&lt;/b&gt;).&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;div&gt;&lt;br /&gt;&lt;/div&gt;&lt;div&gt;This analysis of population density changes uses anonymized and aggregated Meta (Facebook) data can indicate that people are leaving or coming-into a specific location at the time period of the analysis. This is not necessarily indicative of specific forced displacement, but likely one of the following scenarios from our remote analyses:&lt;/div&gt;&lt;div&gt;&lt;ul&gt;&lt;li&gt;Seasonal changes&lt;/li&gt;&lt;li&gt;Fluctuations in the general population&lt;/li&gt;&lt;li&gt;Changes in population, which may be related to displaced people from Ukraine&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;</idAbs>
        <searchKeys>
            <keyword>ukraine</keyword>
            <keyword>refugee</keyword>
            <keyword>facebook</keyword>
        </searchKeys>
        <idPurp>Population change linear regression over time calculated for GADM admin level 3 units (updated to April 27, 2022).</idPurp>
        <idCredit>Data for Good at Meta, GADM</idCredit>
        <resConst>
            <Consts>
                <useLimit/>
            </Consts>
        </resConst>
    </dataIdInfo>
    <Binary>
        <Thumbnail>
            <Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
        </Thumbnail>
    </Binary>
</metadata>