{"id":1084231,"date":"2020-12-18T08:45:45","date_gmt":"2020-12-18T16:45:45","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1084231"},"modified":"2020-12-18T09:26:17","modified_gmt":"2020-12-18T17:26:17","slug":"city-palettes-redlining","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining","title":{"rendered":"Explore Imagery-Derived Color Palettes in Redlined Neighborhoods"},"author":8492,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[22941],"tags":[25781,30791,115262,24381,756502],"industry":[],"product":[36581,36561,42661,380802],"class_list":["post-1084231","blog","type-blog","status-publish","format-standard","hentry","category-mapping","tag-3d","tag-color","tag-imagery","tag-lidar","tag-redlining","product-arcgis-living-atlas","product-arcgis-pro","product-arcgis-solutions","product-arcgis-storymaps"],"acf":{"short_description":"Use imagery and Lidar to understand tree cover disparities of Redlined neighborhoods in Montgomery, Alabamba.","flexible_content":[{"acf_fc_layout":"content","content":"<p>This fall the <a href=\"https:\/\/www.richmond.edu\/\">University of Richmond<\/a>, <a href=\"https:\/\/www.smv.org\/\">The Science Museum of Virginia<\/a>, and Esri worked together to produce a new story focused on the environmental legacy of redlining policies from the 1930s.<\/p>\n<p>This collaboration was born out of new research by the Science Museum and the Digital Scholarship Lab examining the environmental disparities related to redlined neighborhoods. Researchers found the environmental factors like heat islands had a striking relationship to the Home Owners&#8217; Loan Corporation (HOLC) grades. Our teams joined forces to elevate this under told story and explore this relationship further by performing geospatial analysis on environmental data layers in relation to these HOLC grades. These collaborations allow researchers and museum staff to have more capacity and raise awareness about the legacy of redlining today.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091111,"id":1091111,"title":"eem_2020_Red4","filename":"eem_2020_Red4.jpg","filesize":369542,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/eem_2020_red4","alt":"Picture of the Story Map main page","author":"8492","description":"","caption":"","name":"eem_2020_red4","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 22:55:17","modified":"2020-12-16 22:55:37","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1794,"height":928,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4.jpg","medium-width":464,"medium-height":240,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4.jpg","medium_large-width":768,"medium_large-height":397,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4.jpg","large-width":1794,"large-height":928,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4-1536x795.jpg","1536x1536-width":1536,"1536x1536-height":795,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4.jpg","2048x2048-width":1794,"2048x2048-height":928,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4-826x427.jpg","card_image-width":826,"card_image-height":427,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red4.jpg","wide_image-width":1794,"wide_image-height":928}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/storymaps.arcgis.com\/stories\/0f58d49c566b486482b3e64e9e5f7ac9"},{"acf_fc_layout":"content","content":"<p><a href=\"https:\/\/storymaps.arcgis.com\/stories\/0f58d49c566b486482b3e64e9e5f7ac9\">The lines that shape our cities<\/a> explores how redlining policies from the 1930s and 40s have had an environmental legacy on cities today. The story identifies four cities that haven\u2019t received major redlining coverage and performs novel GIS methods to see how environmental characteristics like tree coverage, impervious surfaces, topography, and heat islands relate to HOLC grades. The story profiles the research of the Digital Scholarship Lab at the University of Richmond and The Science Museum of Virginia.<\/p>\n<p>As part of this Story Map we wanted to create a color-based metric for characterizing two different neighborhoods in Montgomery, Alabama. The following video from the Story Map summarizes the results of our analysis and in this blog we\u2019ll explain in detail our methodology.<\/p>\n"},{"acf_fc_layout":"youtube","start_time":"0","end_time":"","youtube_video_url":"<iframe title=\"Montgomery, Alabama: Imagery and Tree Canopy GIS Analysis in HOLC Redlined neighborhoods\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/pRJF5oYSbwY?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>"},{"acf_fc_layout":"content","content":"<p>&nbsp;<\/p>\n<h2>Units of Analysis for RGB Imagery<\/h2>\n<p>One of the first challenges in exploring the palettes of each neighborhood was to determine the appropriate unit of spatial analysis. While the original HOLC grade polygons were logical containers, they didn\u2019t provide us with a way to look at the range or variation in tones within each neighborhood. To remedy this, we used a more granular regular tessellated grid. We decided on a resolution that was approximately twice the size of a standard city block (200m x 200m). Essentially, this created a low-resolution, <em>very large pixel<\/em> version of the source imagery.<\/p>\n<p>Unfortunately, this tessellation approach introduced some problems with the unpredictable shape, size, and orientation of the HOLC neighborhood polygons. While exploring ways to get around the alignment issue, the answer was looking us in the face; why use a regular grid to <em>approximate<\/em> the size of a city block when we can use the physical barriers of the street network to generate <em>actual<\/em> city blocks. Individual blocks tend to be fairly homogeneous in their land use and vegetation cover in a city, so they made sense as the unit of analysis. This block-based analytical approach was then applied to several of the other cities in the story.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091261,"id":1091261,"title":"RGBAnalysisUnitsSmall","filename":"RGBAnalysisUnitsSmall.gif","filesize":216507,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/rgbanalysisunitssmall","alt":"This shows several approaches we used to summarize imagery colors in neighborhoods.","author":"8492","description":"","caption":"This shows several approaches we used to summarize imagery colors in neighborhoods.","name":"rgbanalysisunitssmall","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 23:38:32","modified":"2020-12-17 19:08:15","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":540,"height":600,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","medium-width":235,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","medium_large-width":540,"medium_large-height":600,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","large-width":540,"large-height":600,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","1536x1536-width":540,"1536x1536-height":600,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","2048x2048-width":540,"2048x2048-height":600,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall-419x465.gif","card_image-width":419,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif","wide_image-width":540,"wide_image-height":600}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/RGBAnalysisUnitsSmall.gif"},{"acf_fc_layout":"content","content":"<h2>Extracting and Summarizing Imagery Bands<\/h2>\n<p>National Agricultural Imagery Program (NAIP) is \u201cleaf on\u201d <a href=\"https:\/\/www.arcgis.com\/apps\/Cascade\/index.html?appid=506fd7098cef4bb2a25a7eac2693012c\">Four Band<\/a> imagery that covers the United States. The first 3 bands contain the <strong>R<\/strong>ed, <strong>G<\/strong>reen, and <strong>B<\/strong>lue values in the <strong>RGB<\/strong> color spectrum, which is what we used to calculate the mean values among the pixels inside each of our blocks. To do so, we first broke these three bands out individually, then ran our analysis in the following workflow:<\/p>\n<p>1. Use the Raster Function Extract Bands to separate the NAIP imagery into Band 1 (Red), Band 2 (Green), and Band 3 (Blue) rasters.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091191,"id":1091191,"title":"Extract Bands Function","filename":"Extract-Bands-Function.jpg","filesize":13796,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/extract-bands-function","alt":"Extract Bands function window in ArcGISPro.","author":"8492","description":"","caption":"","name":"extract-bands-function","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 23:13:16","modified":"2020-12-16 23:13:41","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":429,"height":246,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","medium-width":429,"medium-height":246,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","medium_large-width":429,"medium_large-height":246,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","large-width":429,"large-height":246,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","1536x1536-width":429,"1536x1536-height":246,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","2048x2048-width":429,"2048x2048-height":246,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","card_image-width":429,"card_image-height":246,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg","wide_image-width":429,"wide_image-height":246}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Extract-Bands-Function.jpg"},{"acf_fc_layout":"content","content":"<p><i><span data-contrast=\"none\">2. Clip<\/span><\/i><span data-contrast=\"none\">\u00a0out street lines within the HOLC boundaries, then merge these with the line version of each HOLC.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">3.\u00a0 Create polygons (<\/span><i><span data-contrast=\"none\">Feature to Polygon<\/span><\/i><span data-contrast=\"none\">) from these lines to generate polygon blocks and add a\u00a0UniqueID.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">4. Run <\/span><i><span data-contrast=\"none\">Zonal Statistics as Table<\/span><\/i><span data-contrast=\"none\">\u00a0using the\u00a0<\/span><i><span data-contrast=\"none\">Mean<\/span><\/i><span data-contrast=\"none\">\u00a0statistics type for the Red, Green, and Blue\u00a0single-band\u00a0rasters, within each\u00a0UniqueID\u00a0block zone.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">5. Join each of the mean RGB values back to the polygon blocks and populate three new average color value fields: Red, Green, and Blue. <\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">6. Add an additional text field to store the list of 3 average RGB values to use as a polygon color fill, using the format: \u201c<\/span><span data-contrast=\"none\">rgb<\/span><span data-contrast=\"none\">(R, G, B)\u201d<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">7. Add and calculate a new field for Brightness, which is just the mean (of the mean) of the R, G, and B bands for each block. This will be used in a legend to order the range of blocks in each id according to their relative brightness.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">8. Run <\/span><i><span data-contrast=\"none\">Identity<\/span><\/i><span data-contrast=\"none\">\u00a0on the polygon blocks against the original HOLC polygons to re-attach the HOLC grades (A, B, C, D) and individual\u00a0<\/span><i><span data-contrast=\"none\">holc_id<\/span><\/i><span data-contrast=\"none\">\u00a0values (A1, A2, B8, D4, etc.)<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">With an RGB attribute value associated with each block polygon, we are ready to symbolize them.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h2><span data-contrast=\"none\">Symbology Using Mean RGB Color Values<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"none\">The attribute containing the average RGB color values for each block can be read directly in ArcGIS Pro and used to color each block polygon\u2019s fill:<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">1. Select a <em>Single Symbol<\/em> for the block polygons.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW146095973 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW146095973 BCX0\">2. In the <\/span><\/span><em><span class=\"TextRun SCXW146095973 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW146095973 BCX0\">Vary Symbology by Attribute<\/span><\/span><\/em><span class=\"TextRun SCXW146095973 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW146095973 BCX0\">\u00a0tab of the Symbology pane, enable\u00a0<\/span><\/span><span class=\"TextRun SCXW146095973 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW146095973 BCX0\"><em>Allow symbol property connections<\/em>.<\/span><\/span><span class=\"EOP SCXW146095973 BCX0\" data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091201,"id":1091201,"title":"Single Symbology","filename":"Single-Symbology.jpg","filesize":14084,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/single-symbology","alt":"Vary symbol by attribute window in ArcGIS Pro.","author":"8492","description":"","caption":"","name":"single-symbology","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 23:14:35","modified":"2020-12-16 23:15:01","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":357,"height":197,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology-213x197.jpg","thumbnail-width":213,"thumbnail-height":197,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","medium-width":357,"medium-height":197,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","medium_large-width":357,"medium_large-height":197,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","large-width":357,"large-height":197,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","1536x1536-width":357,"1536x1536-height":197,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","2048x2048-width":357,"2048x2048-height":197,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","card_image-width":357,"card_image-height":197,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg","wide_image-width":357,"wide_image-height":197}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Single-Symbology.jpg"},{"acf_fc_layout":"content","content":"<p>3. <span class=\"TextRun SCXW54312618 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW54312618 BCX0\">In the\u00a0<\/span><\/span><span class=\"TextRun SCXW54312618 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW54312618 BCX0\">Primary Symbology<\/span><\/span><span class=\"TextRun SCXW54312618 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW54312618 BCX0\">\u00a0tab, click on the database icon for the polygon fill, then select the attribute containing the\u00a0<\/span><span class=\"NormalTextRun SpellingErrorV2 SCXW54312618 BCX0\">rgb<\/span><span class=\"NormalTextRun SCXW54312618 BCX0\">(<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2 SCXW54312618 BCX0\">R,G<\/span><span class=\"NormalTextRun SCXW54312618 BCX0\">,B) string value, and click Apply.<\/span><\/span><span class=\"EOP SCXW54312618 BCX0\" data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091281,"id":1091281,"title":"FormatPolygon SymbolFill2","filename":"FormatPolygon-SymbolFill2.jpg","filesize":25952,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/formatpolygon-symbolfill2","alt":"ArcGIS Pro window to set the attribute mapping.","author":"8492","description":"","caption":"","name":"formatpolygon-symbolfill2","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 23:43:27","modified":"2020-12-16 23:43:47","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":487,"height":539,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","medium-width":236,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","medium_large-width":487,"medium_large-height":539,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","large-width":487,"large-height":539,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","1536x1536-width":487,"1536x1536-height":539,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","2048x2048-width":487,"2048x2048-height":539,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2-420x465.jpg","card_image-width":420,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg","wide_image-width":487,"wide_image-height":539}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/FormatPolygon-SymbolFill2.jpg"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW67599799 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW67599799 BCX0\">Summarizing the colors of blocks in a neighborhood is an interesting way to \u201cfingerprint\u201d the range of colors seen in the imagery \u2013 and their variation. As we saw in Montgomery, there is a stark contrast between the lush, dark greens of \u201cA\u201d rated neighborhoods compared to the light greys and browns of \u201cD\u201d rated neighborhoods.<\/span><\/span><span class=\"EOP SCXW67599799 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091581,"id":1091581,"title":"eem_2020_Red9","filename":"eem_2020_Red9-scaled.jpg","filesize":186487,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/eem_2020_red9","alt":"Mean color by block for two HOLC grades.","author":"8492","description":"","caption":"Mean color by neighborhood block for two HOLC grades.","name":"eem_2020_red9","status":"inherit","uploaded_to":1084231,"date":"2020-12-17 00:35:27","modified":"2020-12-17 19:19:02","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1515,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-scaled.jpg","medium-width":441,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-scaled.jpg","medium_large-width":768,"medium_large-height":455,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-scaled.jpg","large-width":1825,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-1536x909.jpg","1536x1536-width":1536,"1536x1536-height":909,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-2048x1212.jpg","2048x2048-width":2048,"2048x2048-height":1212,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-786x465.jpg","card_image-width":786,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-1825x1080.jpg","wide_image-width":1825,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red9-scaled.jpg"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW266619386 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW266619386 BCX0\">As a final, easy-to-read summary of the color palettes of each HOLC neighborhood, we sorted the RGB values for our \u201cA\u201d and \u201cD\u201d study areas by their brightness, from low to high, to create a custom-made semi-continuous color ramp, inspired in part by the color analysis in the New York Times\u2019 article<\/span><\/span><span class=\"TextRun SCXW266619386 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW266619386 BCX0\">\u00a0<\/span><\/span><a class=\"Hyperlink SCXW266619386 BCX0\" href=\"https:\/\/www.nytimes.com\/interactive\/2020\/09\/02\/upshot\/america-political-spectrum.html\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW266619386 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW266619386 BCX0\" data-ccp-charstyle=\"Hyperlink\">The True Colors of America\u2019s Political Spectrum are Grey and Green<\/span><\/span><\/a><span class=\"TextRun SCXW266619386 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW266619386 BCX0\">.<\/span><\/span><span class=\"EOP SCXW266619386 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091141,"id":1091141,"title":"eem_2020_Red2","filename":"eem_2020_Red2-scaled.jpg","filesize":79275,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/eem_2020_red2","alt":"Palettes created from the average mean RGB value and ranked by brightness","author":"8492","description":"","caption":"Palettes created from the average mean RGB value and ranked by brightness","name":"eem_2020_red2","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 23:03:40","modified":"2020-12-17 19:19:28","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":877,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-scaled.jpg","medium-width":464,"medium-height":159,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-scaled.jpg","medium_large-width":768,"medium_large-height":263,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-scaled.jpg","large-width":1920,"large-height":658,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-1536x526.jpg","1536x1536-width":1536,"1536x1536-height":526,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-2048x701.jpg","2048x2048-width":2048,"2048x2048-height":701,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-826x283.jpg","card_image-width":826,"card_image-height":283,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-1920x658.jpg","wide_image-width":1920,"wide_image-height":658}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red2-scaled.jpg"},{"acf_fc_layout":"content","content":"<h2>Lidar Tree Height Analysis<\/h2>\n<h3>Colorizing the Lidar<\/h3>\n<p><span data-contrast=\"auto\">Lidar has become more readily available nationwide through the USGS\u00a0<\/span><a href=\"https:\/\/www.usgs.gov\/core-science-systems\/ngp\/3dep\"><span data-contrast=\"none\">3D Elevation Program<\/span><\/a><span data-contrast=\"auto\">\u00a0(3DEP) and is a great data source to extract and model 3D features \u2013 including bare-earth terrain models, buildings, trees, or even\u00a0<\/span><a href=\"https:\/\/www.youtube.com\/watch?v=vZQqNU-TW5k\"><span data-contrast=\"none\">power lines<\/span><\/a><span data-contrast=\"auto\">. For these reasons, Lidar was a great candidate to better understand the differences between tree cover in Montgomery\u2019s HOLC neighborhoods.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:257}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The first goal was to create some \u201cdrone-like\u201d flyover animations, to juxtapose the presence of trees in different HOLC grades at a bird\u2019s-eye view. To do this, we used the RGB information present in the point cloud to render the 3D\u00a0<\/span><span data-contrast=\"auto\">points<\/span><span data-contrast=\"auto\">\u00a0so they appeared as a continuous surface. But what if you don\u2019t have RGB information in your Lidar? Don\u2019t despair! Take a short trip to this Esri\u00a0<\/span><a href=\"https:\/\/community.esri.com\/t5\/imagery-and-remote-sensing-blog\/fun-with-colorizing-lidar-with-imagery\/ba-p\/884803\"><span data-contrast=\"none\">blog post<\/span><\/a><span data-contrast=\"auto\">\u00a0to learn how to colorize and transform your drab, colorless Lidar using NAIP imagery in ArcGIS Pro \u2013 from a city to an entire state. It\u2019s an easy way to create a \u201c3D\u00a0<\/span><span data-contrast=\"auto\">basemap<\/span><span data-contrast=\"auto\">\u201d to add real-world context to your projects.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:257}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1092011,"id":1092011,"title":"eem_2020_Red10","filename":"eem_2020_Red10.jpg","filesize":416432,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/eem_2020_red10","alt":"Lidar symbolized by height (left) and by RGB value from leaf-on NAIP imagery (right).","author":"8492","description":"","caption":"Lidar symbolized by height (left) and by RGB value from leaf-on NAIP imagery (right).","name":"eem_2020_red10","status":"inherit","uploaded_to":1084231,"date":"2020-12-17 05:48:50","modified":"2020-12-17 05:49:07","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1200,"height":800,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","medium-width":392,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","medium_large-width":768,"medium_large-height":512,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","large-width":1200,"large-height":800,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","1536x1536-width":1200,"1536x1536-height":800,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","2048x2048-width":1200,"2048x2048-height":800,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10-698x465.jpg","card_image-width":698,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg","wide_image-width":1200,"wide_image-height":800}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red10.jpg"},{"acf_fc_layout":"content","content":"<h3>Classifying Trees<\/h3>\n<p><span data-contrast=\"auto\">While Montgomery\u2019s Lidar was already classified for vegetation, all is not lost if yours is unclassified and you want to extract vegetation information. In an exciting recent development, the\u00a0<\/span><a href=\"https:\/\/doc.arcgis.com\/en\/arcgis-solutions\/reference\/introduction-to-3d-basemaps.htm\"><span data-contrast=\"none\">3D Basemap Solution<\/span><\/a><span data-contrast=\"auto\">\u00a0has released a set of tools to classify tree points in a Lidar point cloud using deep learning. This brings a lot of new utility to existing Lidar datasets, particularly for high-resolution environmental analysis.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:257}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">By filtering out just the vegetation returns, we created a digital surface model and\u00a0<\/span><span data-contrast=\"auto\">hillshade<\/span><span data-contrast=\"auto\">\u00a0from the Montgomery Lidar, as a quick visualization of the tree distribution in different HOLC neighborhoods. To dig a little deeper, we wanted to next look at specific tree heights and counts for each HOLC grade.<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:257}\">\u00a0<\/span><\/p>\n<h3>Extracting Tree Heights<\/h3>\n<p><span class=\"TextRun SCXW232411554 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW232411554 BCX0\">To put a metric on average tree heights in the different HOLC grades, we used the 3D\u00a0<\/span><\/span><span class=\"TextRun SCXW232411554 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 SCXW232411554 BCX0\">Basemap<\/span><\/span><span class=\"TextRun SCXW232411554 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW232411554 BCX0\">\u00a0Solution to extract individual tree points from the Lidar. In this workflow, a normalized digital surface model was created, which represents the height of objects above the ground. From this surface, the solution uses hydrologic tools to detect \u201creverse sinks\u201d, which represent the center (top) of each tree and includes individual tree height attributes. These points can then be visualized as realistic 3D models \u2013 or analyzed, as we showed by determining the average tree heights in grade \u201cA\u201d versus grade \u201cD\u201d HOLC neighborhoods.<\/span><\/span><span class=\"EOP SCXW232411554 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:257}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091321,"id":1091321,"title":"Tree Extraction from Lidar","filename":"Tree-Extraction-from-Lidar.gif","filesize":1032131,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/tree-extraction-from-lidar","alt":"Classified lidar can be used to represent vegetation as a 2D surface, or for the extraction of individua 3D trees with heights.","author":"8492","description":"","caption":"Classified lidar can be used to represent vegetation as a 2D surface, or for the extraction of individua 3D trees with heights.","name":"tree-extraction-from-lidar","status":"inherit","uploaded_to":1084231,"date":"2020-12-17 00:01:16","modified":"2020-12-17 00:01:59","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":800,"height":533,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","medium-width":392,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","medium_large-width":768,"medium_large-height":512,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","large-width":800,"large-height":533,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","1536x1536-width":800,"1536x1536-height":533,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","2048x2048-width":800,"2048x2048-height":533,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar-698x465.gif","card_image-width":698,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif","wide_image-width":800,"wide_image-height":533}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/Tree-Extraction-from-Lidar.gif"},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW188730079 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW188730079 BCX0\">While some individual neighborhoods had greater height differences, the averages across all HOLC grades in Montgomery shows a consistent relationship between HOLC neighborhood desirability and tree height. This is enforced by the neighborhood descriptions of the period, which often use words like \u201cshady\u201d or \u201cwooded\u201d for \u201cA\u201d grades. Almost 100 years after these neighborhood descriptions were written, these environmental disparities persist.<\/span><\/span><span class=\"EOP SCXW188730079 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:257}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1091151,"id":1091151,"title":"eem_2020_Red3-01","filename":"eem_2020_Red3-01-scaled.jpg","filesize":161601,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\/eem_2020_red3-01","alt":"Graphic showing the average tree height by HOLC grades.","author":"8492","description":"","caption":"","name":"eem_2020_red3-01","status":"inherit","uploaded_to":1084231,"date":"2020-12-16 23:06:37","modified":"2020-12-16 23:07:12","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1436,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-scaled.jpg","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-scaled.jpg","medium_large-width":768,"medium_large-height":431,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-scaled.jpg","large-width":1920,"large-height":1077,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-1536x862.jpg","1536x1536-width":1536,"1536x1536-height":862,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-2048x1149.jpg","2048x2048-width":2048,"2048x2048-height":1149,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-826x463.jpg","card_image-width":826,"card_image-height":463,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-1920x1077.jpg","wide_image-width":1920,"wide_image-height":1077}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red3-01-scaled.jpg"},{"acf_fc_layout":"content","content":"<h2>Sharing this\u00a0Analysis<\/h2>\n<p><span data-contrast=\"auto\">After processing\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">imagery and Lidar for this\u00a0<\/span><span data-contrast=\"auto\">project and\u00a0<\/span><span data-contrast=\"auto\">then\u00a0<\/span><span data-contrast=\"auto\">reflecting on the results, w<\/span><span data-contrast=\"auto\">e\u00a0<\/span><span data-contrast=\"auto\">thought the best medium to<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">communicate\u00a0<\/span><span data-contrast=\"auto\">what we were seeing<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">would be\u00a0<\/span><span data-contrast=\"auto\">through a<\/span><span data-contrast=\"auto\">n<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">embedded\u00a0<\/span><span data-contrast=\"auto\">video<\/span><span data-contrast=\"auto\">.<\/span><span data-contrast=\"auto\">\u00a0\u00a0<\/span><span data-contrast=\"auto\">We wanted to take viewers<\/span><span data-contrast=\"auto\">\u00a0on a<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">visual<\/span><span data-contrast=\"auto\">\u00a0journey<\/span><span data-contrast=\"auto\">\u00a0using<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">Esri<\/span><span data-contrast=\"auto\">\u2019s<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">tools and\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">data resources that are\u00a0<\/span><span data-contrast=\"auto\">publicly\u00a0<\/span><span data-contrast=\"auto\">available\u00a0<\/span><span data-contrast=\"auto\">to<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">d<\/span><span data-contrast=\"auto\">i<\/span><span data-contrast=\"auto\">ve deeper into understandin<\/span><span data-contrast=\"auto\">g how<\/span><span data-contrast=\"auto\">\u00a0the vegetation<\/span><span data-contrast=\"auto\">\u00a0of Montgomery<\/span><span data-contrast=\"auto\">\u00a0created\u00a0<\/span><span data-contrast=\"auto\">very specific<\/span><span data-contrast=\"auto\">\u00a0color palette<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">\u00a0that can be correlated back to the HOLC grades<\/span><span data-contrast=\"auto\">.\u00a0\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We began by storyboarding\u00a0<\/span><span data-contrast=\"auto\">our scenes,\u00a0<\/span><span data-contrast=\"auto\">writing\u00a0<\/span><span data-contrast=\"auto\">the\u00a0<\/span><span data-contrast=\"auto\">narrative,\u00a0<\/span><span data-contrast=\"auto\">identifying the different\u00a0<\/span><span data-contrast=\"auto\">transitions or <\/span><span data-contrast=\"auto\">fades and then\u00a0<\/span><span data-contrast=\"auto\">to put\u00a0<\/span><span data-contrast=\"auto\">it all\u00a0<\/span><span data-contrast=\"auto\">together\u00a0<\/span><span data-contrast=\"auto\">we\u00a0<\/span><span data-contrast=\"auto\">moved<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">into the video editing software Camtasia<\/span><span data-contrast=\"auto\">.\u00a0\u00a0<\/span><span data-contrast=\"auto\">We were mindful to keep with the same look and feel of the Story Map through\u00a0<\/span><span data-contrast=\"auto\">maintaining the\u00a0<\/span><span data-contrast=\"auto\">overall stru<\/span><span data-contrast=\"auto\">cture,\u00a0<\/span><span data-contrast=\"auto\">similar graphic<\/span><span data-contrast=\"auto\">s,<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">and font<\/span><span data-contrast=\"auto\">.\u00a0 We\u00a0<\/span><span data-contrast=\"auto\">also\u00a0<\/span><span data-contrast=\"auto\">kept the video under 50 MB so that we could host the video within the story, no small feat with the Lidar addition!<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We hope that this blog\u00a0<\/span><span data-contrast=\"auto\">prompts<\/span><span data-contrast=\"auto\">\u00a0you to<\/span><span data-contrast=\"auto\">\u00a0not only<\/span><span data-contrast=\"auto\">\u00a0r<\/span><span data-contrast=\"auto\">ead <a href=\"https:\/\/storymaps.arcgis.com\/stories\/0f58d49c566b486482b3e64e9e5f7ac9\">The lines that shape our cities<\/a><\/span><span data-contrast=\"auto\">, but\u00a0<\/span><span data-contrast=\"auto\">that you also<\/span><span data-contrast=\"auto\">\u00a0try this analysis in your own neighborhood or city because it extends so much further\u00a0<\/span><span data-contrast=\"auto\">than<\/span><span data-contrast=\"auto\">\u00a0this one application.<\/span><span data-contrast=\"auto\">\u00a0What does your\u00a0<\/span><span data-contrast=\"auto\">hometown<\/span><span data-contrast=\"auto\">\u00a0look like?\u00a0<\/span><span data-contrast=\"auto\">We hope you use this workflow to find out!<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<h2>Continued Reading<\/h2>\n<p><a href=\"https:\/\/www.esri.com\/en-us\/racial-equity\/overview\">Esri&#8217;s Racial Equity Website<\/a><\/p>\n<p><a href=\"https:\/\/gis-for-racialequity.hub.arcgis.com\/\">Racial Equity GIS Hub<\/a><\/p>\n<p><a href=\"https:\/\/dsl.richmond.edu\/\">Digital Scholarship Lab<\/a><\/p>\n<p>&nbsp;<\/p>\n"}],"authors":[{"ID":8492,"user_firstname":"Emily","user_lastname":"Meriam","nickname":"Emily Meriam","user_nicename":"emeriam","display_name":"Emily Meriam","user_email":"EMeriam@esri.com","user_url":"https:\/\/www.instagram.com\/meriamaps\/","user_registered":"2018-10-26 16:33:49","user_description":"Emily Meriam has a diverse GIS background that spans more than two decades. Her portfolio includes mapping elephants in Thailand, wildlife poachers in the Republic of Palau, land-use issues around Yosemite National Park, and active wildfire incidents for the State of California. Since 2018, Emily has been with Esri's ArcGIS Living Atlas of the World. In this role she serves as lead Cartographer and Senior GIS Engineer for the Environment Team where she styles and designs layers, maps, and applications for the global GIS community. Outside of her professional endeavors, Emily is a passionate geographer who enjoys exploring the world with her family.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/11\/EM-465x465.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"},{"ID":8672,"user_firstname":"Ross","user_lastname":"Donihue","nickname":"Ross Donihue","user_nicename":"rdonihue","display_name":"Ross Donihue","user_email":"rdonihue@esri.com","user_url":"","user_registered":"2018-12-14 18:25:52","user_description":"Ross leads geospatial storytelling content development at Esri, helping users tell stories with maps from conception to publication. He spends his time developing technical workflows, directing projects, and improving geospatial storytelling technology. He\u2019s also a National Geographic Explorer with a background in Environmental Management. When he\u2019s not at his computer, he\u2019s usually in the garden, wandering through the woods, or making something by hand.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/12\/Ross-Donihue-150x150.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"},{"ID":10472,"user_firstname":"Craig","user_lastname":"McCabe","nickname":"Craig McCabe","user_nicename":"cmccabe","display_name":"Craig McCabe","user_email":"CMcCabe@esri.com","user_url":"","user_registered":"2020-01-24 21:48:27","user_description":"Craig is a GIS Engineer, geologist, and geographer on the ArcGIS Living Atlas Environment team at Esri, with a passion for 3D visualization, analysis, and telling stories with data.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/03\/Craig_McCabe_Profile_Full-213x200.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"related_articles":[{"ID":947861,"post_author":"7121","post_date":"2020-08-07 10:13:05","post_date_gmt":"2020-08-07 17:13:05","post_content":"","post_title":"Historical redlining data now in ArcGIS Living Atlas","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"redlining-data-now-in-arcgis-living-atlas","to_ping":"","pinged":"","post_modified":"2025-08-21 14:25:46","post_modified_gmt":"2025-08-21 21:25:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=947861","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"1","filter":"raw"},{"ID":930041,"post_author":"7071","post_date":"2020-07-10 09:47:07","post_date_gmt":"2020-07-10 16:47:07","post_content":"","post_title":"Fast and Simple NAIP Imagery","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"fast-and-simple-naip-imagery","to_ping":"","pinged":"","post_modified":"2022-09-27 15:26:06","post_modified_gmt":"2022-09-27 22:26:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=930041","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"3","filter":"raw"},{"ID":760941,"post_author":"9882","post_date":"2020-03-10 12:12:53","post_date_gmt":"2020-03-10 19:12:53","post_content":"","post_title":"Dev Summit 2020: Use AI to extract data from LiDAR point clouds","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"dev-summit-2020-use-ai-to-extract-data-from-lidar-point-clouds","to_ping":"","pinged":"","post_modified":"2020-03-13 10:16:55","post_modified_gmt":"2020-03-13 17:16:55","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=760941","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red15.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red14.jpg"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Explore Imagery-Derived Color Palettes in Redlined Neighborhoods<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" 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Her portfolio includes mapping elephants in Thailand, wildlife poachers in the Republic of Palau, land-use issues around Yosemite National Park, and active wildfire incidents for the State of California. Since 2018, Emily has been with Esri's ArcGIS Living Atlas of the World. In this role she serves as lead Cartographer and Senior GIS Engineer for the Environment Team where she styles and designs layers, maps, and applications for the global GIS community. Outside of her professional endeavors, Emily is a passionate geographer who enjoys exploring the world with her family.","sameAs":["https:\/\/www.instagram.com\/meriamaps\/","https:\/\/www.linkedin.com\/in\/emilymeriam\/"],"knowsAbout":["GIS","Mapping","Cartography"],"knowsLanguage":["English"],"jobTitle":"Product Engineer, Cartographer, Geographer","worksFor":"Esri","url":"https:\/\/www.esri.com\/arcgis-blog\/author\/emeriam"}]}},"text_date":"December 18, 2020","author_name":"Multiple Authors","author_page":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/city-palettes-redlining","custom_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/eem_2020_Red14.jpg","primary_product":"ArcGIS Pro","tag_data":[{"term_id":25781,"name":"3D","slug":"3d","term_group":0,"term_taxonomy_id":25781,"taxonomy":"post_tag","description":"","parent":0,"count":342,"filter":"raw"},{"term_id":30791,"name":"color","slug":"color","term_group":0,"term_taxonomy_id":30791,"taxonomy":"post_tag","description":"","parent":0,"count":31,"filter":"raw"},{"term_id":115262,"name":"Imagery","slug":"imagery","term_group":0,"term_taxonomy_id":115262,"taxonomy":"post_tag","description":"","parent":0,"count":152,"filter":"raw"},{"term_id":24381,"name":"Lidar","slug":"lidar","term_group":0,"term_taxonomy_id":24381,"taxonomy":"post_tag","description":"","parent":0,"count":45,"filter":"raw"},{"term_id":756502,"name":"redlining","slug":"redlining","term_group":0,"term_taxonomy_id":756502,"taxonomy":"post_tag","description":"","parent":0,"count":2,"filter":"raw"}],"category_data":[{"term_id":22941,"name":"Mapping","slug":"mapping","term_group":0,"term_taxonomy_id":22941,"taxonomy":"category","description":"","parent":0,"count":2683,"filter":"raw"}],"product_data":[{"term_id":36581,"name":"ArcGIS Living Atlas","slug":"arcgis-living-atlas","term_group":0,"term_taxonomy_id":36581,"taxonomy":"product","description":"","parent":0,"count":1164,"filter":"raw"},{"term_id":36561,"name":"ArcGIS Pro","slug":"arcgis-pro","term_group":0,"term_taxonomy_id":36561,"taxonomy":"product","description":"","parent":0,"count":2035,"filter":"raw"},{"term_id":42661,"name":"ArcGIS Solutions","slug":"arcgis-solutions","term_group":0,"term_taxonomy_id":42661,"taxonomy":"product","description":"","parent":0,"count":348,"filter":"raw"},{"term_id":380802,"name":"ArcGIS StoryMaps","slug":"arcgis-storymaps","term_group":0,"term_taxonomy_id":380802,"taxonomy":"product","description":"","parent":0,"count":322,"filter":"raw"}],"primary_product_link":"https:\/\/www.esri.com\/arcgis-blog\/?s=#&products=arcgis-pro","_links":{"self":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog\/1084231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/users\/8492"}],"replies":[{"embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/comments?post=1084231"}],"version-history":[{"count":0,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog\/1084231\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/media?parent=1084231"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/categories?post=1084231"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/tags?post=1084231"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/industry?post=1084231"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/product?post=1084231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}