Today’s “data in motion” post is a visualization of earthquakes over time. I’ll use seismic data from the National Science Foundation. Keeping with the theme, I’ll use F# and FFmpeg to convert the raw data into a video of the data over time.

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Today’s “data in motion” post is a quick population over time visualization. I’ll use U.S. census data over the last one-hundred-ish years. As is the theme in this series, I’ll convert the raw data into a video of the data over time using primarily F#.

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It has been awhile since I posted about Anomaly detection using F# and ML.NET. Since the ML.NET framework continues to evolve, so it is worth a revisit to investigate changes. This also provides a good opportunity to dig deeper into the anomaly detection options that are provided.

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Persisting data can be a subtle art. Today I am taking a look at how F#’s Discriminated Unions can interact with Dapper. I’ve discussed Dapper before, but never really discussed its facilities for interacting with Discriminated Unions. It is a useful bit of knowledge when determining project data structures.

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Today is again a lighter post playing with visualizations. The data focus is on the Standardized Precipitation Index data for the U.S. over that last one-hundred years. Static images and data can be useful, but visualizing data over time can be a welcome addition for analysis. So I’ll be converting data ultimately into a video of the data over time using primarily F#.

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