Sure thing. First step: treat the “hotspots” like any other phenomenon you want to map. Get a decent GPS or use a smartphone’s built‑in location services to log each incense burn, noting time, weather, and any observable effect you’re measuring (temperature rise, scent diffusion, odd sounds). Store that in a CSV or a lightweight database. Next, add environmental variables: humidity, wind direction, building layout if indoors, ground material if outdoors. That’s your independent variables; the “hotspot intensity” is your dependent variable. Once you have at least a few dozen data points, you can feed them into a simple regression or a small clustering algorithm like k‑means to see if there are patterns—like a higher density of hotspots near vents or in corners. If you want to go fancy, pull in satellite imagery or building plans and run a GIS overlay. You’ll see if there’s a correlation between structural features and hotspot frequency. And don’t forget to keep a log of any “mystery” incidents—those can’t be modeled, but they’re good sanity checks. So, gather the data cleanly, add the context variables, and let a bit of statistical muscle do the rest. If you need help setting up the database schema or the code, just let me know.

Alright, let’s keep this lean. I’ll give you a minimal SQLite schema, plus a quick Python snippet that pulls the data, runs a simple linear regression, and plots the residuals. That should let you see if there’s a systematic bias in your placement. **Schema (SQLite)** ```sql CREATE TABLE burns ( id INTEGER PRIMARY KEY AUTOINCREMENT, timestamp DATETIME NOT NULL, lat REAL NOT NULL, lon REAL NOT NULL, humidity REAL, -- percent wind_deg REAL, -- direction in degrees wind_spd REAL, -- m/s temp_before REAL, -- ambient temp before burn temp_after REAL, -- temp after burn scent_intensity REAL, -- your custom scale notes TEXT ); ``` Add indices on `timestamp`, `lat`, and `lon` if you plan to query ranges. **Python (pandas + statsmodels)** ```python import pandas as pd import sqlite3 import statsmodels.api as sm import matplotlib.pyplot as plt conn = sqlite3.connect('demon.db') df = pd.read_sql_query("SELECT * FROM burns", conn) # Create a simple feature set df['temp_diff'] = df['temp_after'] - df['temp_before'] features = ['humidity', 'wind_deg', 'wind_spd', 'temp_before'] X = df[features] y = df['scent_intensity'] X = sm.add_constant(X) # intercept model = sm.OLS(y, X).fit() print(model.summary()) # Residual plot resid = model.resid plt.scatter(range(len(resid)), resid) plt.axhline(0, color='red', ls='--') plt.title('Residuals of scent intensity model') plt.xlabel('Observation') plt.ylabel('Residual') plt.show() ``` Run that in a Jupyter notebook or plain script. If the residuals look randomly scattered, your model’s fine. If you see a trend, you probably omitted a key variable—maybe room volume or floor material. Happy hacking, and keep the logs tidy.

Sounds good. Here’s a compact, ready‑to‑use schema you can paste into your SQLite console. Just keep it tight and consistent. CREATE TABLE burns ( id INTEGER PRIMARY KEY AUTOINCREMENT, timestamp DATETIME NOT NULL, lat REAL NOT NULL, lon REAL NOT NULL, humidity REAL, wind_deg REAL, wind_spd REAL, temp_before REAL, temp_after REAL, scent_intensity REAL, notes TEXT ); Add indexes if you’ll query by time or location: CREATE INDEX idx_burns_timestamp ON burns(timestamp); CREATE INDEX idx_burns_location ON burns(lat, lon); That’s it. Once you start spotting biases, just add the new variable to the table and run the regression again. Keep the logs tight, and you’ll get clean residuals.

Nice, you’ve got everything in place. Just a quick sanity tip: keep a separate “meta” table where you log the version of your schema and any extra variables you add. That way, if you ever need to merge old data with new columns, you won’t scramble the data set. Happy hunting, and keep those logs tidy.