jim o'neill | austin texas

Category: datasets

LISD COVID-19 stats & analysis

We’ve been keeping an eye on the reported cases coming out of our kids’ school district, Leander ISD. Living in Texas, the mitigation steps taken to contain the spread of COVID-19 is minimal:

  • an unenforced mask mandate
  • no social distancing
  • no pods / containment strategies for limiting contact in secondary school
  • no staggered bell schedules to reduce hallway crowding
  • no outside lunch options available for increased distancing

The relative lack of mitigation efforts alarmed us, so we started taking a closer look at what data was available from the district that we could use to better inform ourselves of what was going on in our school, and the schools around us.

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COVID19 / US War comparison

Here’s a visualization of COVID19 average daily death rates compared to US wars. Approximately 866 Americans have died each day during the pandemic from COVID19, eclipsing the American Civil War’s average daily death rate of 449.

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southern border data

With all the talk about southern border security and a security crisis, I figured I’d poke around and see what data is available that illustrates long term trends in apprehensions along the southern border, and plot those statistics by presidency. Fun project, and I’ll burn through a few iterations of the graphic as I get some ideas. Here’s my first attempt:

So far the only trend I see is a sharp decline in apprehensions beginning with George W. Bush’s presidency and continuing through Barak Obama’s. The current apprehension levels on the southern border have not been seen since the Nixon era.

There’s likely multiple modifications I can make to the infographic. The trend line was a quick fit with a black line. It’s likely too heavy of a contrast and I’ll have to lighten that to fit the color scheme. I’m not sure I like the bar next to the president names. I think the alternating colors for different presidents on the bar graphs are likely sufficient rendering the vertical line redundant. I’d also like to add more summary statistics and maybe numerical representations of the trend numbers, but that could clutter the plot.

Good experiment design – queues from the sciences

experimentDesign

I was reading this interesting study on the impact of fear on the stability of a food web which led me to start thinking about principles of sound experimental design, and how such designs can yield valuable insight into a variety of systems, natural or man made. From the authors:

When it comes to conserving biodiversity and maintaining healthy ecosystems, fear has its uses. By inspiring fear, the very existence of large carnivores on the landscape, in and of itself, can provide a critical ecosystem service human actions cannot fully replace, making it essential to maintain or restore large carnivores for conservation purposes on this basis alone.

The experimental design behind this study was fascinating. Using two islands off the coast of British Columbia, Canada, the team setup an experiment:

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Weather data…and frogs

saolourenco837360_tempRHI am a dart frog addict…er…hobbyist. You can read more about my involvement in the hobby here, but one aspect of the hobby that intersects with my analytics background is weather data. I try to locate weather stations near the regions in South America where the frogs I keep were originally collected. This has led to some interesting visualizations, including this one, regional weather data for Sao Lourenco:

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visualizing scale testing projects

timeline2015v1.0

Time for a new twist on an old concept…the Gantt Chart. Tried and true, we’re used to seeing it used for old waterfall planning models. However, we can take a project chart for a large scale POCĀ of an MDM application housing 250 million records on a new clustered database. The schedule was extremely aggressive with an original target of three weeks, which, due to delays in database deployments and JDBC/ODBC connectivity issues stretched to 5 weeks.

Can you build a visualization that conveys the complexity of a project involving a dozen technical people, hundreds of tests, with distinct goals?

Hey, I’m a data guy – you know what the answer is going to be…

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