{"id":2109,"date":"2016-06-16T12:37:40","date_gmt":"2016-06-16T17:37:40","guid":{"rendered":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/?p=2109"},"modified":"2017-10-09T12:29:33","modified_gmt":"2017-10-09T17:29:33","slug":"mapping-connecticut-school-district-data-in-tableau","status":"publish","type":"post","link":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/06\/16\/mapping-connecticut-school-district-data-in-tableau\/","title":{"rendered":"Mapping Connecticut School District Data in Tableau"},"content":{"rendered":"<p>This is the third in 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 Census Tracts in Tableau:<\/p>\n<ul>\n<li style=\"padding-left: 30px\"><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/06\/10\/mapping-connecticut-census-tract-data-in-tableau\/\" target=\"_blank\">Mapping Connecticut Census Tract data in Tableau<\/a><\/li>\n<li style=\"padding-left: 30px\"><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<\/ul>\n<p>Tableau and Tableau Public offer robust mapping capabilities, including the ability to recognize geographic entities in your data and instantly create choropleth (filled) maps with shapes for counties, states, and countries. For users that want to create filled\u00a0maps for\u00a0geographic entities not recognized innately by the software, Tableau <a href=\"http:\/\/kb.tableau.com\/articles\/knowledgebase\/polygon-shaded-maps\" target=\"_blank\">supports the creation of polygon-shaded maps<\/a>,\u00a0allowing users to map data onto polygon shapes which correspond\u00a0to sales regions, marketing areas, etc. The Connecticut State Data Center has created a number of custom polygon map files corresponding to Connecticut geographies not innately supported in Tableau, including Connecticut school districts:<\/p>\n<p style=\"text-align: center\"><strong><a href=\"https:\/\/drive.google.com\/file\/d\/0By5Y7_XnHrPtVVFIR2wySVFoREU\/view?usp=sharing\" target=\"_blank\">CT_School_District_Polygons_for_Tableau<\/a><\/strong><\/p>\n<p>The directions below show you how to connect\u00a0to this file to create a custom polygon map for school districts, and then join the Polygon data with some sample demographic data from the American Community Survey to create a filled\/choropleth map. This is followed by additional tips to joining the polygon shapes with additional data sources, such as data from Connecticut Open Data, CTDataCollaborative, and\u00a0Connecticut Department of Education.<\/p>\n<h2>Step 1: Setting up the School District polygon map:<\/h2>\n<ol>\n<li>Save a copy of the CT_School_District_Polygons_for_Tableau Excel workbook (linked above) to your computer. Open Tableau Public or Desktop, and from the <strong>Data<\/strong> menu navigate to the Excel workbook, and drag the<strong> Polygons<\/strong> sheet into the data space. Click <strong>Go to Worksheet<\/strong> or open a new New Sheet on the task bar.<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connecting.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2114 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connecting-300x175.jpg\" alt=\"connecting\" width=\"300\" height=\"175\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connecting-300x175.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connecting-1024x599.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connecting-500x292.jpg 500w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/connecting.jpg 1050w\" 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>\u00a0was placed in the\u00a0<strong>M<\/strong><strong>easures<\/strong> pane, it must be converted to a Dimension \u2013 simply drag it from Measures to <strong>Dimensions<\/strong>. Then, drag <strong>Pointorder<\/strong> to <strong>Path<\/strong> on the <strong>Marks<\/strong> card.<\/li>\n<li>Drag\u00a0<strong>Polygon Number<\/strong> onto\u00a0<strong>Detail<\/strong> on the Marks card.<\/li>\n<li>Drag\u00a0<strong>District Name<\/strong> from Dimensions onto\u00a0<strong>Color<\/strong> on the Marks card. (If a dialog window appears, confirm that you want to\u00a0<strong>Add all members<\/strong>).<\/li>\n<li>To see the boundaries of districts more clearly, it is helpful to display borders around the polygons. To do this, click\u00a0<strong>Color<\/strong> on the Marks card, and from the\u00a0<strong>Border<\/strong>\u00a0carrot menu, select a color. You should now see a map like this:<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2115 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-300x214.jpg\" alt=\"map\" width=\"300\" height=\"214\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-300x214.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map-421x300.jpg 421w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/map.jpg 1022w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<li>At this point, it&#8217;s important to note that mapping Connecticut school district data is a little tricky, because the varying administrative structures among districts creates overlapping geographies that makes it impossible to show <strong>all<\/strong> districts on a single map. There are three basic administration models for school\u00a0districts in Connecticut, and geographically speaking, they aren&#8217;t mutually exclusive &#8211; as many towns send children\u00a0to more than one\u00a0district. Here are the three types (the names of the district types are from the Census Bureau (which publishes the shape files from which the polygons are derived); and may not correspond to local or state terminology):<\/li>\n<\/ol>\n<p><!--more--><\/p>\n<ul>\n<li style=\"text-align: left\"><strong><em>Unified<\/em><\/strong> school include traditional town-based districts which administer schools for all grades K-12 in their town; most medium- and larger-sized towns in Connecticut fall under this category. However, Unified districts also include <strong>regional<\/strong> districts which administer schools at all levels within their member towns. (For example, Regional District 17 includes all elementary, middle, and high schools in Haddam and Killingworth).<\/li>\n<li style=\"text-align: left\"><em><strong>Elementary<\/strong>\u00a0<\/em>districts administer only K-6, or K-8, schools &#8211; such as the Mansfield School District, which administers elementary schools and a middle school. These districts send their high-school aged students to a regional high school (Regional District 19, in the case of Mansfield) where they join students from other towns.<\/li>\n<li style=\"text-align: left\"><em><strong>Secondary<\/strong><\/em> districts are Regional districts 1,4,5, 7, 8, 9, 11 and 19, which consist only of high schools. These districts include towns which administer their own k-6 or k-8 schools, but share a regional high school with neighbor towns. Regional School District 19, for example, is a single high school shared by Mansfield, Ashford, and Willington students.So, how can we handle these overlapping geographies in Tableau? We could either break out the\u00a0district types onto 3 separate maps, or use a Tableau <strong>Quick Filter<\/strong> on the District Type dimension, making the user view a single district type at a time. To set this up, click\u00a0<strong>District Type\u00a0<\/strong>in the Dimensions pane, and from the carrot menu select\u00a0<strong>Show Quick Filter<\/strong>. A District Type check box menu should appear adjacent to the map. Finally, we&#8217;ll alter the menu to make it so the user can only display a single District Type at one time. Click the carrot next to the District Type title in the Quick Filter, and change the menu type from <strong>Multiple Values (List)<\/strong> to <strong>Single Value (List)<\/strong>. Now, click the carrot once again to return to the same menu, and from the\u00a0<strong>Customize<\/strong> menu,<strong> uncheck<\/strong> the <strong>Show &#8220;All&#8221; value<\/strong> selection. You map should now look like this, and is ready to display data:<\/li>\n<\/ul>\n<p><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/quickfilter.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-2117 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/quickfilter-300x186.jpg\" alt=\"quickfilter\" width=\"300\" height=\"186\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/quickfilter-300x186.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/quickfilter-1024x634.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/quickfilter-485x300.jpg 485w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/quickfilter.jpg 1129w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<h2>Step 2: Linking the custom polygon map to school district-level data<\/h2>\n<p>Here we&#8217;ll link to the Sample data sheet in the Excel workbook, containing data on the number of families below the poverty level within school districts, to get the hang of connecting the polygon map to actual data which we want to visualize. There are two ways to do this:<\/p>\n<h1>Option 1 &#8211; connecting separately to the data source<\/h1>\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_School_District_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\u00a0be ready to join the Sample data with the\u00a0Polygon sheet by the common <strong>Id2<\/strong> field. Click the <strong>chain<\/strong> icon, circled\u00a0in the Dimension pane below, to set up this relationship:<img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2122 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/linkingdata-300x178.jpg\" alt=\"linkingdata\" width=\"300\" height=\"178\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/linkingdata-300x178.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/linkingdata-1024x607.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/linkingdata-500x296.jpg 500w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/linkingdata.jpg 1250w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/li>\n<li>In the map above, <strong>District Name<\/strong>\u00a0is on Color on the Marks card. We\u2019ll want to create a choropleth map based on one of the measures in the <strong>Sample data<\/strong> data source, but we will still need to keep the School District dimension\u00a0in the visualization, letting Tableau that School District is the unit of analysis or \u201clevel of detail\u201d. Move<strong>\u00a0District Name\u00a0<\/strong>from<strong> Color <\/strong>onto<strong> Detail<\/strong> on the <strong>Marks<\/strong> card. You should now see the districts are\u00a0the same color, but still outlined:<br \/>\n<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/districtsoutlined.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2123 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/districtsoutlined-300x161.jpg\" alt=\"districtsoutlined\" width=\"300\" height=\"161\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/districtsoutlined-300x161.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/districtsoutlined-1024x551.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/districtsoutlined-500x269.jpg 500w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/districtsoutlined.jpg 1249w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<li>Now drag any measure you want to show on the map, e.g. &#8216;<strong>All families- percent below poverty level; Estimate; With children under 18<\/strong>&#8216; onto Color on the Marks card to create a filled map.<\/li>\n<\/ol>\n<h1>Option 2 &#8211; join the polygon sheet with the data sheet<\/h1>\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 school district.)<\/p>\n<ol>\n<li>Drag down on the Data menu, and from the carrot menu for the <strong>Polygons (CT_School_District_Polygons_for_Tableau)<\/strong> data source select <strong>Edit Data Source<\/strong>. Drag the <strong>Sample data<\/strong> sheet into the data workspace:<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join1.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2126 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join1-300x186.jpg\" alt=\"join1\" width=\"300\" height=\"186\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join1-300x186.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join1-1024x634.jpg 1024w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join1-484x300.jpg 484w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join1.jpg 1253w\" 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 <strong>Left<\/strong> 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>:<br \/>\n<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join2.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2127 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join2-300x155.jpg\" alt=\"join2\" width=\"300\" height=\"155\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join2-300x155.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join2-500x258.jpg 500w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/join2.jpg 854w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/li>\n<li>Follow steps 4 &amp; 5 above to create the filled map.<\/li>\n<\/ol>\n<h2>Tips for connecting the school district polygons to additional data sources:<\/h2>\n<p>There\u00a0are\u00a0many great data sources that can be joined with the Polygons file to map CT\u00a0school district data visualizations in Tableau:<\/p>\n<ul>\n<li><strong><a href=\"http:\/\/ctdata.org\/\" target=\"_blank\">Connecticut Data Collaborative<\/a><\/strong> offers a great selection of school district data on Connecticut school districts. Check out their collection\u00a0of\u00a0<strong><a href=\"http:\/\/data.ctdata.org\/dataset\" target=\"_blank\">Raw Data<\/a><\/strong> for<a href=\"http:\/\/data.ctdata.org\/dataset?organization=ctsde\" target=\"_blank\"> Department of Education<\/a> datasets at the school district level, including special education, mastery test results, and more. Instead of joining files from CTdata.org with the Polygon sheet on the Id2 field, try joining the District field in the CTdata.org data with the Alternate District Name field in the Polygons sheet. (The difference between the District Name and Alternate Name fields in the Polygon data is that the Alternate Name column contains commonly-used names for the secondary school districts, i.e. <em>Regional School District 19<\/em> instead of the slightly different name assigned by the Census Bureau: \u00a0<em>Regional High School District 19).<\/em><\/li>\n<li><strong><a href=\"https:\/\/data.ct.gov\/\" target=\"_blank\">Connecticut Open Data<\/a>\u00a0<\/strong>provides a growing number of datasets from the <a href=\"https:\/\/data.ct.gov\/browse?category=Education\" target=\"_blank\">State Department of Education<\/a> on district-level enrollment, absenteeism, standardized tests, and more. As with CTdata.org data, use the Alternate Name field in the Polygons sheet to join it with the District Name field in datasets from Connecticut Open Data.<\/li>\n<li><strong><a href=\"http:\/\/edsight.ct.gov\/SASPortal\/main.do\" target=\"_blank\">Connecticut State Department of Education EdSight<\/a> <\/strong>data portal includes fiscal data, staffing levels, student and instructor demographics, graduation rates, standardized test scores, and more. Use the Alternate Name field in the Polygons sheet to join it with the District Name field in datasets downloaded from EdSight.<\/li>\n<li>The U.S. Census Bureau&#8217;s<a href=\"http:\/\/factfinder.census.gov\/faces\/nav\/jsf\/pages\/index.xhtml\" target=\"_blank\"><strong> American FactFinde<\/strong>r<\/a>\u00a0data portal provides close to 15,000 data tables for Connecticut school districts from various Census Bureau programs, including the 2000 and 2010 Decennial Census, the American Community Survey, and the Annual Survey of School System Finances. Use <a href=\"http:\/\/factfinder.census.gov\/bkmk\/table\/1.0\/en\/ACS\/14_5YR\/S0101\/0400000US09.95000|0400000US09.96000|0400000US09.97000\" target=\"_blank\">this bookmarked table\u00a0<\/a>as a starting point. Above the Age and Sex table, click the Advanced Search button (circled below). This will take you to the main American FactFinder search page, and all Connecticut school districts are pre-selected in the Your Selections window.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/AFFreturntosearch.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2134 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/AFFreturntosearch-300x241.jpg\" alt=\"AFFreturntosearch\" width=\"300\" height=\"241\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/AFFreturntosearch-300x241.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/AFFreturntosearch-374x300.jpg 374w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/AFFreturntosearch.jpg 696w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Use\u00a0the\u00a0<strong>Topics<\/strong> menu to browse a subject index of population, demographic, and economic tables at the school district level. To download tables you&#8217;ve located, from the T<strong>able, File, or Document Title<\/strong> results window, put a check next to the table ID to select any table or tables, and use the <strong>Download<\/strong> icon above the list to download the table(s) for all selected geographies in a .csv format that Tableau will be happy with. Use the Id2 field in datasets downloaded from American FactFinder to join the data with the Polygons sheet, as we did with the Sample data sheet above.<a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/download-aff-data.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-2111 aligncenter\" src=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/download-aff-data-300x170.jpg\" alt=\"download aff data\" width=\"300\" height=\"170\" srcset=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/download-aff-data-300x170.jpg 300w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/download-aff-data-500x283.jpg 500w, https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/files\/2016\/06\/download-aff-data.jpg 843w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Hope these instructions are helpful &#8211; your comments, corrections or suggestions are welcomed!<\/p>\n<p>Steve Batt<br \/>\nsteven.batt@uconn.edu<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/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 third in 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 &hellip; <a href=\"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/2016\/06\/16\/mapping-connecticut-school-district-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\/2109"}],"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=2109"}],"version-history":[{"count":5,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/posts\/2109\/revisions"}],"predecessor-version":[{"id":2367,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/posts\/2109\/revisions\/2367"}],"wp:attachment":[{"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/media?parent=2109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/categories?post=2109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs-dev.lib.uconn.edu\/outsidetheneatline\/wp-json\/wp\/v2\/tags?post=2109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}