According to the U.S. Census Bureau’s American Community Survey estimates, income inequality increased significantly in 142 U.S. counties between the 2008-10 and 2011-13 survey periods. While this is relatively small compared to the number of counties where there was no significant change (1,688), almost 92 million people – nearly 30% of the U.S. population at the time – resided in these counties in 2013. Eleven counties – with a combined population of 565,00 – had significantly reduced income inequality over the 2011-2013 ACS survey period, compared with 2008-2010. Click any county in the map below to see a link to the original ACS data in the American FactFinder data engine.

This visualization uses table B19083 Gini Index of Income Inequality  from the 2010 and 2013 ACS 3-Year Estimates and compares the values for each county, and their margins of error, between the survey periods. Counties with very close Gini Index values from the two surveys (where the confidence intervals overlap) are considered not to have experienced a statistically significant change in income inequality. Counties which have an upper bound of the 2008-10 confidence interval which is smaller than the lower bound of the 2011-13 confidence interval are considered to have had a statistically significant increase in income inequality income between the two survey periods. Conversely, those counties which have a lower bound of the 2008-10 confidence interval which is greater than the upper bound of the 2011-13 confidence interval are considered to have experienced a statistically significant decline in median household income.

The 3-Year Estimates data series (now discontinued) reported data for counties with populations of 20,000 or more, so counties with smaller populations are excluded from the analysis. The counties in the map below had an aggregate total population of 301 million in 2013, compared with the total U.S. population 314 million at the time. The release of the ACS 2011-2015 5-Year data set in December 2016 will allow similar analysis for all U.S. counties, including those with populations under 20,000. Data from this survey will be able to be compared to results from the 2006-2010 5-Year data.

The Gini Index represents the concentration of income in a given state or country, in a range from 0 to 1. A higher Gini index indicates greater inequality – where income is concentrated among a relatively few individuals or households; a lower Gini score represents more even income distribution. The Gini index is a commonly used economic measure, reported by organizations such as the World Bank and CIA, in its World Factbook.

[xyz-ihs snippet=”Change-in-GINI-index-in-US-counties”]

This is the first in a series of posts on using Tableau Desktop or Tableau Desktop Public Edition to visualize Connecticut data with custom polygons, which let Tableau create filled maps for geographic entities not recognized innately in the software’s mapping functionality. See these other posts for more information on creating choropleth/filled maps for Connecticut Census Tracts and school districts in Tableau:

• Mapping Connecticut Census Tract data in Tableau
• Mapping Connecticut School District Data in Tableau

Tableau can instantly recognize and map the boundaries of many types of geographic entities (e.g. states, counties, countries) –  but for states like Connecticut with town-based governments, displaying town-level data on a map in Tableau isn’t quite as easy. Fortunately, a workaround exists that allows Tableau users of town-level data to create custom polygons maps to represent these areas. Tableau support documentation on this feature includes instructions on converting ArcGIS shape files into spreadsheet files that Tableau can use to construct custom polygon maps.

The Connecticut State Data Center has converted a number of U.S. Census Bureau TIGER/Line shape files into polygon data files for use in Tableau, including polygon  files for Connecticut towns, school district, Census tracts, and legislative district boundaries. If you would like to visualize Connecticut town-level data on a map (as in this dashboard) in Tableau, this Excel workbook of Connecticut town polygons:

CT_Town_Polygons_for_Tableau.xlsx

contains a Polygon spreadsheet that will render a map of Connecticut towns in Tableau, and an additional sheet of town tax mill rate data that you can link with the polygon map (following the steps below, to create a choropleth map like this (click image to see full size):

Each row of the Excel Polygon sheet describes a single point on the outline of a single Connecticut town’s 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 ‘connect the dots’ to form each town’s boundaries on the map. For example, Mansfield’s 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 16,000+ 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. Amazing, and really useful, functionality!

Setting up the polygon map:

1. Save a copy of the Excel workbook of Connecticut town polygons locally. In Tableau Public or Desktop, from the Data menu navigate to the workbook and drag the Polygons sheet into the data space. Click Go to Worksheet or open a new New Sheet on the task bar.
2. Under Measures in the Data pane, drag Longitude to the Columns shelf. Note that the Aggregation for this pill should be average; i.e. the pill should say AVG(Longitude). 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.
3. Drag Latitude to the Rows shelf. The aggregation should also be average (AVG) in the pill.
4. In the Marks card, change the view type menu from Automatic to Polygon
5. If Pointorder appears in the Measures pane, it must be converted to a Dimension – simply drag it from Measures to Dimensions. Then, drag Pointorder to Path on the Marks card. (Don’t panic, it is normal to see a strange Rorschach test-like image on the map at this stage! Until we tell Tableau that we want Town to be the level of detail, it’s plotting the average latitude/longitude of each Pointorder value for all the rows in the data set, resulting in a bizarre polygon approximately where Madison is).
6. Drag Town from Dimensions and place it on Color on the Marks card. (Select Add all members in the dialog window).You should now see the outlines of all 169 Connecticut towns (click the image above to see the full view) and you can now  link or join this base map to other data following steps in either Option 1 or Option 2 below.

Continue reading →

The latest data from the American Community Survey 2010-2014 Estimates shows a clear correlation between educational attainment and median earnings in Connecticut towns. This visualization includes data on educational attainment levels for all 169 towns for persons 25 and older, as well as median earnings for men and women of different education levels. Follow the links that appear in the mouseover tooltip to download the original tables from the Census Bureau’s American FactFinder data tool.

[xyz-ihs snippet=”Educational-attainment-and-earnings-in-CT-towns”]

In advance of the Connecticut primary on April 26, the U.S. Census Bureau presents a variety of statistics that give an overall profile of the state’s voting-age population and industries. Statistics include:

• Voting-age population and estimate of eligible voters (i.e., citizens age 18 and older).
• Breakdown of voting-age population by race and Hispanic origin (Hispanic origin and race alone) (race alone or in combination).
• Selected economic characteristics, including median household income and poverty.
• Selected social characteristics, including educational attainment.
• County Business Patterns (providing information on employment by specific industries).
• Statistics on voting and registration.

[xyz-ihs snippet=”Demographic-and-Economic-Profiles-of-Connecticut-Electorate”]

This visualization uses data from the 2013 Annual Survey of School System Finances which provides per-pupil expenditures for various instructional and support services functions for over 13,000 school districts, including salaries, wages, and benefits of instructional staff, as well as administrative, instructional staff support, and other support service costs.

(Note that total per-student expenditures include expenses not included separately in Instruction and Support Services costs (see footnote [1] in original American FactFinder table).

[xyz-ihs snippet=”Per-pupil-spending-by-district”]

More great data sets seem to be added to the U.S. Census Bureau’s  American FactFinder data engine all the time. This visualization uses data from the 2013 Annual Survey of School System Finances: Per pupil amounts for current spending of public elementary-secondary school systems by state , allowing comparison of expenditures on  instruction and support services among states.

[xyz-ihs snippet=”Per-pupil-spending-by-state”]

This is a draft Tableau dashboard displaying various Google Analytics usage metrics f for the Archives & Special Collections digital repository at the Thomas J. Dodd Research Center:  http://archives.lib.uconn.edu

[xyz-ihs snippet=”UConn-archives-Google-analytics-dash”]

According to the the latest American Community Survey data, the period from 2007 to 2014 saw a statistically significant increase in income inequality – represented by the Gini index – in 32 states. The Gini Index represents the concentration of income in a given state or country, in a range from 0 to 1. A higher Gini index indicates greater inequality – where income is concentrated among a relatively few individuals or households; a lower Gini score represents more even income distribution.

The ACS 1-Year reports the Gini index for households in table B19083 as the middle point of a 90% confidence interval, along with a corresponding margin of error. The visualization below calculates significant change in Gini index scores from 2007-2014 – shifts outside the margin of error. In 32 states, the lower end of the range of the 2014 Gini estimate was greater than the upper range of the 2009 Gini Index estimate. In the remaining states, there was no statistically significant change from 2009 to 2014: in these cases, taking into account the margin of error, the estimates from 2009 overlapped with those from 2014.

Gini index is a commonly used economic measure, reported by organizations such as the World Bank and CIA, in its World Factbook.

[xyz-ihs snippet=”GINI-increase-by-state”]