Trego's Mountain Ear

"Serving North Lincoln County"

Tag: Data

  • Party Affiliation over Time

    Party Affiliation over Time

    It seems, especially in the midst of an election year that the political parties are long established and permanent. While we don’t have an especially high rate of turnover in major political parties we do have one.

    The Republicans became the Democratic Republicans, which eventually become the Democrats (an extremely brief summary of a rather lengthy chunk of American History). The Republican party, as we know it today, actually came out of the Whig party (well, a splinter faction, sort of. No one said politics was straight forward).

    At any rate, political parties are not constant and neither is their membership. Gallup has a nice collection of data on party affiliation that we referenced last week in things that make surveys hard. Since they’ve provided it as a table, here is the graph:

    Party Affiliation over time; data from Gallup

    Looking at Gallup’s data, we can make several observations. Since 2004, the general trend has been an increase in people identifying as Independents, and a decrease in both Republicans and Democrats. We also notice that declines in either major party tend to coincide with increases in Independents.

    The top of the graph is 50%, and while none of our three categories make it that high, Independents come the closest (highest percent independents was 47%, which occurred both in October of 2013 and October of 2014).

    A “Zoomed in” version of the previous graph, from 2012 on

    Taking a closer look at things (note that I’ve changed the vertical scale as well, the bottom is now 15%) from 2012 on, we can see a large drop in both Republicans and Democrats in 2013 that has a corresponding rise in independents. 2018 had a decline in Independents that mirrors a rise in Democrats.

    The difficulty with examining trends is “How far do we have to zoom out?” Over a large amount of time, it’s difficult to see the impacts of smaller events but easier to examine long term trends. Another consideration is that what looks like a clear trend on the small scale may not reflect the trend in the long term.

    Political polling doesn’t give us all that much long term data. Do we have enough to make predictions from? Well, the people making predictions certainly seem to think so!

  • If LCHS District were a County

    After the article on searching Lincoln County data, the question came in: “What if North Lincoln County was its own county?”  The answer is available, but it takes the sort of personality that enjoys digging through data.  Here’s a few facts that would describe the thought experiment that would be county 57.

    County 57, sharing boundaries with the Lincoln County High School District, would rank 31st in population of Montana’s 57 counties (6,260 residents).  The remainder of Lincoln County would drop from the tenth largest population (19,980) to twelfth (12,694 residents). Data from 2010 Census and, of course, will likely change with completion of the 2020 Census. 

    Looking at market and taxable valuations we find that High School District 13 (LCHS) has a market value set at $1,202,098,056 and taxable valuation set at $17,042,130.  By my count, and this is the type of sort where it is easy to miss something, County 57 would have market and taxable valuations larger than 17 Montana counties. 

    The same source shows that Lincoln County’s market valuation is $2,741,812,498 and taxable valuation is $37,491,358.  44 Montana counties show lower market values than Lincoln County – not a great variance from the population rank.  County 57 would be 22nd in market valuation.  The remainder of County 56 – consisting of Troy and Libby High School districts, would be 21st.

    A more detailed study might include roads, total area, access to county services and a number of other items.  For a simple, “what if” analysis, looking at population and market value seems adequate.

  • Talking about Risk

    A headline read, “What if we could live for a million years?”  My casual answer was “not a chance.”  Even if we could eliminate aging and disease, we still have to consider risk.  The National Safety Council reports 1.25 deaths for every 100 million miles of highway travel.  Assume 10,000 miles per year of highway travel, in the first thousand years of life, you’re up to 10 million miles.  That turns out to be a 1 out of 8 chance of dying – just from highway transportation.  The National Geographic gives odds of being struck by lightning as 1 in 700,000 each year, and one in 3,000 over a lifetime.  Increase the lifetime to that same 1,000 years, and the odds go up.  We live in a risk society, and often ignore that fact. 

    I know a guy who survived a grizzly mauling, and I’ve known one who died from wasp stings.  Even in Trego, I encounter more wasps than bears.  CDC stats show 89 people who died from bee stings in 2017.  It isn’t a large risk – but 80% of the fatalities are male.  Bee stings are a greater risk to the male half of the population. 

    Just about every endeavor includes misunderstood risks.  In 2018, the police fatality rate was 13.7 per 100,000 (bureau of labor and statistics).  CDC shows the 2017 fatality rate for farmers was 20.4 per 100,000.  BLS shows a rate of 132.7 per 100,000 loggers in 2015.  CDC shows bartenders have a higher on job fatality rate than police.  In some places policing is a relatively risky job – but in farm country and logging country it seems fairly safe.  Here’s the link to more data if this interests you.

    When we started looking a Covid-19, we didn’t have the statistical data to calculate risk.  Now, we do have some data – and we’re seeing disagreements, mostly because folks see risks differently.  As I write, RealClearPolitics shows 5,746,272 confirmed cases in the US, and 177,424 deaths, yielding a 3.09% confirmed case fatality rate.  Still, that number goes from 8.25% in New Jersey to 0.79% in Utah.  We have solid numbers – but not numbers that allow us to calculate risk.  When we look at the charts that include age, we can begin to calculate risk – 70 is higher risk than 40.  80 is even higher.  New York numbers suggest that dense populations and public transportation increase risk – but not enough to calculate. 

    The numbers let us develop ordinal data about covid risk – ranking things as more or less, rather than develop statistical data.  At 70, I’m at more risk than someone who is 50. The age data is good enough to develop some statistics – but the comorbidities that make it covid fatal mask that. Riding mass transportation is more dangerous than driving a car alone.  The virus doesn’t differentiate between bars and churches – open spaces are safer than crowded enclosed spaces.  So far we know that Covid is a bit more infectious than flu, and less infectious than measles.  Ordinal data. Buzz Hollander, a physician in Hawaii, has a good, readable article on covid at this site.