Dr. Katya Steiner — The Categorical Imperative: When 67 Beats 69, CO₂ Breathes a Sigh, and Tourists Outnumber You

# Why you should care (and why you shouldn’t be intimidated)

There’s a wonderful, messy civic thing happening online: strangers post charts, strangers call out terrible axes, and strangers — occasionally very kindly — hold your hand through why your plot is lying to you. If you work with data, this is the modern salon: equal parts snark, pedagogy, and community debugging. Pull up a chair.

Below I riff on three tiny, glorious case studies — one delightfully petty, one deeply domestic, and one geopolitically useful — and I sprinkle in math and logic because I can’t help myself. Think of this as a field guide written by someone who likes both category theory and coffee-shop sarcasm.

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## 1) When 67 overtook 69 (and why numbers get jealous)

Someone plotted relative search interest and pronounced: “67 beat 69.” It’s a headline that reads like a joke and a chart that gets everyone in the comment thread to argue whether the plot is funny or fraud.

Mathematical bones:
– Probability & normalization: Google Trends–style curves are relative; they rescale to a baseline. This is measure theory in a hoodie — you’re not looking at counts but at densities over time. A spike can make a baseline shrink by comparison.
– Ordinal vs. cardinal: Is that 67 > 69 as ranks or as raw magnitude? We confuse order with size all the time.
– Category theory whisper: view labels (’67’, ’69’) as objects and your transformations (normalization, smoothing) as morphisms. If you change the morphism, you change the story.

Practical nudge:
– Annotate what the y-axis actually means. If you normalized to max=100, say so. If you smoothed with a 7-day filter, say that too.

The argument side: a neat comparison can be revelatory — it flips expectations and prompts questions. The counter: clever visuals can be confidently misleading. The fix is pedagogy: show the transformation pipeline, so the reader can judge whether they’re seeing a personality shift or a math trick.

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## 2) The CO₂ drop when the human leaves the room (and why sensors are both intimate and deceitful)

A home sensor records a dip in CO₂ after the occupant steps outside. It’s earnest and relatable and makes for great low-stakes data theatre: the apartment exhales relief.

Mathematical & logical lenses:
– Time series & signal processing: raw readings, sampling frequency, and the Nyquist intuition matter. If your sensor samples every five minutes, you miss the half-minute drama of opening a window.
– Causality vs. correlation: walking out of the room coincides with a drop, but is it the cause? Enter do-calculus and counterfactual reasoning — what would the CO₂ have been had you stayed?
– Bayesian thinking: treat the drop as evidence and update your belief about occupancy effects, but include prior skepticism about HVAC cycles and sensor drift.
– Fuzzy logic: “occupied” is not always binary. Maybe the cat was there, maybe the neighbor’s kitchen was producing a CO₂ cloud.

How to make the anecdote teachable:
– Show raw and smoothed series; mark departure and return times.
– Discuss confounders: ventilation, HVAC cycles, sensor placement.
– Quantify uncertainty: error bars, not drama.

Both sides: the anecdote makes environmental monitoring intimate and relatable — a damn good thing. The danger: if you treat a single episode as proof, you’ve invited overconfidence.

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## 3) When tourists outnumber locals (and why your country matters)

A choropleth showing regions where visitors outnumber residents can trigger righteous anger or wistful travel envy. It’s also policy-relevant: infrastructure, housing markets, and culture feel the squeeze.

Math & logic takeaways:
– Ratios over counts: measure theory’s lesson — absolute measures can mislead; normalize by population or by person-weeks to capture burden better.
– Seasonality & Fourier intuition: annual totals hide weekly peaks. A small island with low population can be overwhelmed for a fortnight even if annual averages look boring. Decompose the signal.
– Spatial stats & topology: density matters. A choropleth can smooth away local hot spots. Think of point processes and kernel density estimation.
– Simpson’s paradox: aggregated tourist counts can hide opposite trends in subregions. Don’t aggregate away the story you want to see.
– Modal/fuzzy logic: who qualifies as a “tourist”? Passport stamp, a night at a motel, or someone who spent three weeks in town because they liked the coffee? Your predicate matters.

Visualization prescriptions:
– Use tourists-per-resident ratios (or better: tourists-per-capita-days).
– Show seasonality (peak-week heatmaps).
– Present density maps and local insets for hotspots.

The debate: maps persuade, and rightly so when used well. Misuse? They turn nuance into outrage.

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## A short note to would-be askers (and responders) in open threads

If you want help, ask a specific question. Think like a type signature: Data: CSV; Goal: show seasonality; Tool: Python/Excel/D3; Audience: stakeholders or trolls. Provide a minimal reproducible example. If you’re answering, be generous and specific — and if you must be cheeky, let it be a single, well-aimed insult.

Category theory offers a charming metaphor here: think of your analysis as a functor from raw data to a claim. If you can’t describe the functor (transformations), the claim has no provenance.

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## Final takeaway (Katya’s parting injunction)

Charts are persuasive because they mix narrative with math. The responsible thing to do is simple and hard at once: label your transformations, be explicit about definitions, and quantify uncertainty. Bring receipts — raw data, code, and an honest caption. The internet will remain sarcastic, but at least your data can be honest.

And for you, dear reader: when you look at a sexy chart that answers a question you didn’t know you had, do you trust your instincts — or do you first follow the morphisms backward to see what the author actually measured?