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.