[OC] 6 years of daily steps: data analysis found reaction to COVID-19 regulations Analysis

What This Visualization Shows

This data visualization displays "[OC] 6 years of daily steps: data analysis found reaction to COVID-19 regulations" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on So I keep exploring patterns in my daily biometric data. This time: daily steps.

GAMs (generalized additive models) are great, but their nature is to smooth things out. So I decided to try BEAST, a Bayesian changepoint detector that looks for abrupt shifts instead. And yeah it works.

**Panel A (BEAST).** Purple segments show regime means between detected changepoints. The lowest one lines up almost exactly with San Diego's first Stay-at-Home order in spring 2020, right when everyone else got locked down too. The bounce right after shows me adapting, and then there's a nice jump in steps when the restrictions fully lifted in 2021. Celebrated with more walking, it seems. After that it slowly settles into a stable regime for three years, with a small uptick at the very end of 2025.

**Panel B (GAM).** Same data with the smooth trend from the GAM overlaid on a kernel density heatmap. The GAM captures most of it but as a slow trend rather than sharp breaks. It misses the 2020 cliff and turns the bounce into a gentle sag. Smooths can't do cliffs.

**Panel C (cyclic smooths).** This is where the GAM shines. Weekdays are higher, weekends are lower. Apparently weekend is rest and recovery time for me. And the seasonal smooth shows a gentle summer bump. I'm more active in summer.

None of the dates (lockdowns, reopening) were fed to any of the models. They found them on their own. So indeed body keeps the score/remebers/reacts.

**Tools:** R, Rbeast for the Bayesian changepoint detection, mgcv for the GAM, ggplot2 and patchwork for composition. Full write-up with [code](https://jbogomolovas2.github.io/Julius-s-Blog/posts/steps/).

**Data:** my own Garmin Connect export., which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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