{"id":2084,"date":"2016-06-10T15:54:19","date_gmt":"2016-06-10T20:54:19","guid":{"rendered":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/?p=2084"},"modified":"2017-05-26T15:00:52","modified_gmt":"2017-05-26T20:00:52","slug":"mapping-connecticut-census-tract-data-in-tableau","status":"publish","type":"post","link":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/06\/10\/mapping-connecticut-census-tract-data-in-tableau\/","title":{"rendered":"Mapping Connecticut Census Tract data in Tableau"},"content":{"rendered":"<p>This is the second\u00a0in a series of posts on using <a href=\"http:\/\/get.tableau.com\/trial\/data-analysis-software.html?cid=701600000005cS4&amp;ls=Paid%20Search&amp;lsd=Google%20AdWords%20-%20Tableau%20-%20Free%20Trial&amp;adgroup=Tableau%20-%20Analytics&amp;kw=tableau%20data%20analysis&amp;adused=98805906975&amp;distribution=search&amp;kcid=29f0d3e2-a43b-4600-a907-4ef474b003e6&amp;gclid=CMjEm-qDrc0CFU07gQodBLYK5g\" target=\"_blank\">Tableau Desktop<\/a> or <a href=\"https:\/\/public.tableau.com\/s\/\" target=\"_blank\">Tableau Desktop Public Edition<\/a> to map Connecticut data using custom polygons, to accommodate geographic entities not recognized innately in the Tableau mapping functionality. See these other posts for more information on\u00a0creating filled maps for\u00a0Connecticut towns and school districts\u00a0in Tableau:<\/p>\n<ul>\n<li><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/05\/12\/creating-a-custom-polygon-map-for-connecticut-towns-in-tableau\/\" target=\"_blank\">Creating a custom polygon map for Connecticut towns in Tableau<\/a><\/li>\n<li><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/06\/16\/mapping-connecticut-school-district-data-in-tableau\/\" target=\"_blank\">Mapping Connecticut School District data in Tableau<\/a><\/li>\n<\/ul>\n<p>The U.S. Census Bureau aggregates and publishes Decennial Census and American Community Survey data at geographic levels large and small, including Census Tracts &#8211; small statistical subdivisions of roughly 1200-8000 people. Connecticut has 833 Census tracts; many small towns comprise a single Census Tract, while Hartford has more than 30. There are more than 7,500 data tables available through American FactiFinder for Connecticut Census tracts\u00a0on demographic and economic measures including income and poverty, educational attainment, health insurance coverage, housing characteristics, and more. The steps below will let you create a choropleth map in Tableau for any Census Tract-level data downloaded from American FactFinder, enhancing the functionality of Tableau to analyze and visualize ACS and Decennial Census data.<\/p>\n<p>Tableau support documentation includes <a href=\"http:\/\/kb.tableau.com\/articles\/knowledgebase\/tableau-polygons-arcgis-shapefiles\" target=\"_blank\">instructions on converting ArcGIS shape files<\/a> into spreadsheet files that Tableau can use to <a href=\"http:\/\/kb.tableau.com\/articles\/knowledgebase\/polygon-shaded-maps\" target=\"_blank\">construct custom polygon maps<\/a>.\u00a0The Connecticut State Data Center has converted a number of <a href=\"http:\/\/www.census.gov\/geo\/maps-data\/data\/tiger-line.html\" target=\"_blank\">U.S. Census Bureau TIGER\/Line shape files<\/a> into polygon data files for use in Tableau, including polygon\u00a0 files for Connecticut towns, school districts, Census tracts, and legislative district boundaries. If you would like to visualize Connecticut Census Tract-level data on a map <a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/05\/17\/median-household-income-and-household-income-distribution-within-connecticut-census-tracts\/\" target=\"_blank\">(as in this dashboard)<\/a> in Tableau, this Excel workbook:<\/p>\n<p style=\"text-align: center\"><a href=\"https:\/\/drive.google.com\/open?id=0By5Y7_XnHrPtblMxZTZpX2xlNTA\"><strong>CT_Census_Tract_Polygons_for_Tableau.xlsx<\/strong><\/a><\/p>\n<p>of Connecticut Census Tract polygons contains a Polygon spreadsheet that will render a map of all Census tracts within Connecticut, and an additional sheet of housing value\u00a0data from the American Community Survey that you can link with the polygon map, following the steps below, to create a choropleth map like the one below (click image to see full size). Additional information will help you download more tract-level data from American FactFinder, and join and map it with the Polygon data.<\/p>\n<p style=\"padding-left: 30px\"><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/05\/8.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2038 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/05\/8-300x190.jpg\" alt=\"8\" width=\"300\" height=\"190\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/05\/8-300x190.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/05\/8-1024x648.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/05\/8-474x300.jpg 474w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/05\/8.jpg 1308w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Each of the 29,202 rows of the Excel <strong>Polygon<\/strong> sheet describes a single point on the outline of a single tract\u2019s boundaries, with a field for longitude and latitude of the point. Another field for each row is Point Order, which tells Tableau in which order to \u2018connect the dots\u2019 to form each tract on the map. For example, Mansfield\u2019s shape has 208 points in the data set. Tableau draws each polygon by starting with the latitude and longitude of point 1, then continues drawing the shape until it gets to point 208, completing the outline of the town. The software does this for all 29,000+ points on the map instantly, whether in Tableau Desktop or Public, and map tools seem to work as quickly with a polygon map published to Tableau Public as any boundaries innately recognized in Tableau.<\/p>\n<h2><strong>Step 1: Setting up the polygon map:<\/strong><\/h2>\n<p><!--more--><\/p>\n<ol>\n<li>Save a copy of the <a href=\"http:\/\/production-web.lib.uconn.edu\/classwebimages\/batt\/CT_Tract_Polygons_for_Tableau.xlsx\" target=\"_blank\">Excel workbook of Connecticut Census tract polygons<\/a> locally. In Tableau Public or Desktop, from the <strong>Data<\/strong> menu navigate to the workbook and drag the<strong> Polygon<\/strong>\u00a0sheet into the data space. Click <strong>Go to Worksheet<\/strong> or open a new New Sheet on the task bar.<br \/>\n.<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/import-polygons.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2086 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/import-polygons-300x285.jpg\" alt=\"import polygons\" width=\"300\" height=\"285\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/import-polygons-300x285.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/import-polygons-316x300.jpg 316w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/import-polygons.jpg 861w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<li>Under <strong>Measures<\/strong> in the Data pane, drag <strong>Lo<\/strong><strong>ngitude<\/strong> to the <strong>Columns<\/strong> shelf. Note that the Aggregation for this pill should be <em>average<\/em>; i.e. the pill should say <strong>AVG(Longitude)<\/strong>. Aggregations can be changed if necessary (e.g. from Sum to Average) from the carrot menu for the measure in the rows or columns shelf.<\/li>\n<li>Drag <strong>Latitude<\/strong> to the<strong> Rows<\/strong> shelf. The aggregation should also be average (AVG) in the pill.<\/li>\n<li>In the<strong> Marks<\/strong> card, change the view type menu from <strong>Automatic<\/strong> to <strong>Polygon<\/strong><\/li>\n<li>If <strong>Pointorder<\/strong> appears in the <strong>M<\/strong><strong>easures<\/strong> pane, it must be converted to a Dimension &#8211; simply drag it from Measures to <strong>Dimensions<\/strong>. Then, drag <strong>Pointorder<\/strong> to <strong>Path<\/strong> on the <strong>Marks<\/strong> card. (Don&#8217;t panic, it is normal to see a strange Rorschach test-like image on the map at this stage!)<\/li>\n<li>Drag <strong>Id2<\/strong>\u00a0from <strong>Dimensions<\/strong> and place it on <strong>Color<\/strong> on the Marks card. The Id2 field contains the unique 10-digit Census identification number for each tract, and will be used below to join American Community Survey data with the map.<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-tracts.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2087 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-tracts-300x185.jpg\" alt=\"map tracts\" width=\"300\" height=\"185\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-tracts-300x185.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-tracts-1024x632.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-tracts-486x300.jpg 486w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-tracts.jpg 1334w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a>You should now see the outlines of all 833 Census Tracts (click the image above to see the full view) and you can now\u00a0 link or join this base map to other data following steps in either Option 1 or Option 2 below.<\/li>\n<\/ol>\n<h1><strong>Step 2: Linking the custom polygon map to Census Tract data\u00a0<\/strong><\/h1>\n<h4><strong>Option 1 \u2013 connecting separately to the data source<br \/>\n<\/strong><\/h4>\n<ol>\n<li>From the Data menu, <strong>New Data Source<\/strong><\/li>\n<li>Navigate again to the local copy of the CT_Tract_Polygons_for_Tableau.xlsx workbook. Drag the <strong>Sample data<\/strong> sheet into the data area. Then return to the sheet on the taskbar in Tableau where you created the map.<\/li>\n<li>Because the Sample data sheet has an\u00a0<strong>Id2<\/strong>\u00a0column, Tableau should indicate that the new data sheet has been joined to the Polygon sheet by the common Id2 field (with the chain icon as in the Dimension pane below). <a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connect-sheets.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2089 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connect-sheets-300x182.jpg\" alt=\"connect sheets\" width=\"300\" height=\"182\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connect-sheets-300x182.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connect-sheets-1024x621.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connect-sheets-495x300.jpg 495w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connect-sheets.jpg 1346w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<li>In the map above, <strong>Id2<\/strong>\u00a0is on Color on the Marks card. We&#8217;ll want to create a choropleth map based on the one of the measures in the <strong>Sample data<\/strong> data source, but we will still need to keep we want to keep the Tract number &#8211; Id2 field &#8211; in the visualization, letting Tableau that town is the unit of analysis or \u201clevel of detail\u201d. Move<strong> Id2\u00a0<\/strong>from<strong> Color <\/strong>onto<strong> Detail<\/strong> on the <strong>Marks<\/strong> card. You should see a the outline of the state but the state is changed to a solid color.<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/tracts-with-outline.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2090 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/tracts-with-outline-300x181.jpg\" alt=\"tracts with outline\" width=\"300\" height=\"181\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/tracts-with-outline-300x181.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/tracts-with-outline-1024x619.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/tracts-with-outline-497x300.jpg 497w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/tracts-with-outline.jpg 1346w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a>(If you like, you can show the borders of the tracts as in the image above by selecting a Border color from the Color menu on the Marks card: Click Color, then under Effects change Border from Automatic to a color. This step isn\u2019t necessary, but illustrates that Tableau really is showing the individual tracts at this point)<\/li>\n<li>Now drag\u00a0any\u00a0measure you want to show on the map, e.g.<strong>\u00a0&#8216;Owner-occupied housing units with a mortgage; Estimate, VALUE &#8211; $500,000 or more<\/strong>\u00a0to <strong>Color <\/strong>on the Marks card:<strong><br \/>\n<\/strong><\/li>\n<\/ol>\n<p style=\"padding-left: 30px\"><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/choropleth.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2092 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/choropleth-300x176.jpg\" alt=\"choropleth\" width=\"300\" height=\"176\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/choropleth-300x176.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/choropleth-1024x599.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/choropleth-500x293.jpg 500w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/choropleth.jpg 1391w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p><strong>Option 2 &#8211; join the polygon sheet with the data sheet<\/strong><\/p>\n<p>Joining the Polygon sheet with the data sheet is what Tableau recommends in its support pages and may improve performance with some data sets. (It may not be the best option if your data sheet includes data for multiple years for each tract.)<\/p>\n<ol>\n<li>Drag down on the Data menu, and from the carrot menu for the <strong>Polygons (CT_Tract_Polygons_for_Tableau)<\/strong> data source select Edit Data Source. Drag the <strong>Sample data<\/strong> sheet into the data workspace.<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/joining-data.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2094 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/joining-data-300x193.jpg\" alt=\"joining data\" width=\"300\" height=\"193\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/joining-data-300x193.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/joining-data-1024x660.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/joining-data-465x300.jpg 465w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/joining-data.jpg 1264w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<li>Normally you will want to join the Polygon sheet with your data sheet using a Left join. Click on the venn diagram in the data workspace to bring up the Join dialog box. Click on the image illustrating Left join. The Polygon Data Source sheet should be joined to the Sample data sheet with the join clause <strong>Id2 = Id2 (Sample data)<\/strong>:<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/left-join.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2095 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/left-join-300x208.jpg\" alt=\"left join\" width=\"300\" height=\"208\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/left-join-300x208.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/left-join-1024x710.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/left-join-433x300.jpg 433w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/left-join.jpg 1179w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<\/ol>\n<p><strong>Step 3: downloading additional Census Tract data from the Census Bureau&#8217;s American FactFinder data engine<\/strong><\/p>\n<p>An enormous amount of data from the American Community Survey and the 2000 and 2010 Decennial Census are available through American FactFinder. With a couple of simple steps, tables can be downloaded and joined to the polygon map you created with the above steps.<\/p>\n<ol>\n<li><strong>Browsing data in American FactFinder<\/strong><br \/>\n<a href=\"http:\/\/factfinder.census.gov\/bkmk\/table\/1.0\/en\/ACS\/14_5YR\/S0101\/0400000US09.14000\" target=\"_blank\">The link to this table<\/a> from the American Community Survey can serve as a helpful starting point to browse for more data at the Connecticut Census Tract level. In the upper left corner, above the data, lick the Advanced Search button to return to the main American FactFinder search page. Note that &#8216;All Census Tracts within Connecticut&#8217; will be pre-selected in the Your Selections window, and you can use the Topics menu to browse by topic among the more than 7,000 data tables available.<img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2097 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/browse-AFF-300x201.jpg\" alt=\"browse AFF\" width=\"300\" height=\"201\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/browse-AFF-300x201.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/browse-AFF-447x300.jpg 447w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/browse-AFF.jpg 913w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/li>\n<li><strong>Download the data in .csv format<\/strong><br \/>\n<span style=\"text-decoration: underline\">Be sure to always download data in .csv format<\/span>\u00a0from American FactFinder instead of using the &#8216;Presentation-ready&#8217; Microsoft Excel format. The .csv format will swap columns and rows, presenting the data in a vertical format that Tableau will be happy with. (The &#8216;presentation ready&#8217; format often brings the data into the spreadsheet as text, further complicating matters). From the Download icon above any table in American FactFinder, choose\u00a0<strong>Comma delimited (.csv) format (data rows only)\u00a0<\/strong>and leave the default settings\u00a0<strong>Data and annotations in a single file\u00a0<\/strong>and\u00a0<strong><strong>Include descriptive data element names:<\/strong><\/strong><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-2100 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/download-300x249.jpg\" alt=\"download\" width=\"261\" height=\"219\" \/>The zipped folder will contain 4 files for each table; the metadata.csv file contains data about the table, and the larger of the 2 .csv files contains the actual data.<\/li>\n<li><strong>Clean up the spreadsheet and join it with your polygon map<\/strong><br \/>\nTables downloaded from American FactFinder need very little cleanup for use in Tableau (as long as they&#8217;re downloaded in .csv format!). Simply delete the first row of the table, with the short field codes (GEO.id, GEO.id2, GEO.display-label, etc.) leaving the longer, eye-readable field labels as the first row (Id, Id2, Geography, etc.). Save the sheet, and join it with your polygon map using the Id2 field following the steps outlined above.<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p>Hope these instructions are helpful \u2013 your comments, corrections or suggestions are welcomed!<\/p>\n<p>Steve Batt<br \/>\nsteven.batt@uconn.edu<\/p>\n<!-- AddThis Advanced Settings generic via filter on the_content --><!-- AddThis Share Buttons generic via filter on the_content -->","protected":false},"excerpt":{"rendered":"<p>This is the second\u00a0in a series of posts on using Tableau Desktop or Tableau Desktop Public Edition to map Connecticut data using custom polygons, to accommodate geographic entities not recognized innately in the Tableau mapping functionality. See these other posts &hellip; <a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/06\/10\/mapping-connecticut-census-tract-data-in-tableau\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><!-- AddThis Advanced Settings generic via filter on get_the_excerpt --><!-- AddThis Share Buttons generic via filter on get_the_excerpt --><\/p>\n","protected":false},"author":74,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/posts\/2084"}],"collection":[{"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/comments?post=2084"}],"version-history":[{"count":5,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/posts\/2084\/revisions"}],"predecessor-version":[{"id":2336,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/posts\/2084\/revisions\/2336"}],"wp:attachment":[{"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/media?parent=2084"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/categories?post=2084"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/tags?post=2084"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}