A Through the Cycle Geo-Spatial Analysis of CT Town Finances

[This article was first published on R on Redwall Analytics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.


In an earlier post, Reviewing Fairfield County Municipal Fiscal Indicators Since 2001, we used 17 years of individual Town Comprehensive Annual Financial Reports (CAFR) aggregated in Connecticut’s Municipal Fiscal Indicator’s to compare 15 Fairfield County towns. The challenge was that the graphs became crowded even with that small number of towns. In this analysis, we will expand our look at the similar variables over all 169 Connecticut towns using Geo-spatial mapping.

We find a few surprising trends:

  • Education expenses have risen rapidly and broadly, but declining school age populations may be pushing towards unsutainable levels in some towns
  • Three of Connecticut’s four largest cities (other than Stamford) show ongoing struggles with employment, mill rates, debt levels and credit ratings in contrast to the national trend for cities to generate employment and thrive
  • There has been a broad based capital spending boom, but some towns stand out above all others for spending

The map in Figure 1 is a snapshot of 2017 financial data for each town in CT with color scaled by population. The map shows the largest town populations along Metronorth and the I-95 Corridor through New Haven, and then North to Hartford along I-91 in the central part of the state. It is possible click to see the town names along with per cap data on taxes, spending, state transfers, equalized grand lists, mill rates, debt and capital investment. School spending makes up about 60% of town spending on average (although ranging widely between 40-80%). Spending per student and students per population are shown. Unemployment, population density and Moody’s ratings are also shown. The point in time in Figure 1 is well into the recovery, but below we will “animate” by year to give a feel for changes through the cycle.

To leave a comment for the author, please follow the link and comment on their blog: R on Redwall Analytics.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)