THE RESULT
What the data shows
The largest computed metric is 0.0787 for temp; the smallest is -0.06407 for rain. Metric: spearman_rho (Spearman correlation).
The sample is selected on recorded fires, and many area values are zero. Associations do not measure ignition risk.
| feature | spearman rho | absolute rho | paired rows |
|---|---|---|---|
| temp | 0.0787 | 0.0787 | 517 |
| dmc | 0.07192 | 0.07192 | 517 |
| rain | -0.06407 | 0.06407 | 517 |
| dc | 0.06163 | 0.06163 | 517 |
| wind | 0.0532 | 0.0532 | 517 |
| ffmc | 0.0253 | 0.0253 | 517 |
| rh | -0.02422 | 0.02422 | 517 |
| isi | 0.0125 | 0.0125 | 517 |
THE METHOD
From source to answer
Pairwise Spearman correlations and sample counts.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Treat the associations as hypotheses and obtain event-level temporal exposure data before forecasting.
Where the conclusion stops
The sample consists of recorded fire observations, not all places and times at risk. Zero recorded area is not evidence of no ignition. No precise dates or event identifiers are supplied. This is not an emergency forecasting system.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.