# 094. Weather and burned-area associations

**Question:** Which recorded weather and fire-index variables associate with burned area?

## Result

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.

![Weather and burned-area associations](outputs/chart.png)

| 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 chart shows 8 of 8 result rows; the table previews the first 8 in the analysis-defined order. [Download the full result table](outputs/results.csv). Numerical values are computed from the source; missing results stay unavailable.

## Method

Pairwise Spearman correlations and sample counts.

The study uses shared source preparation and reusable statistical routines. Its specific transformations are in [analysis.py](analysis.py), and common model/evaluation code is in [portfolio/methods.py](../../portfolio/methods.py). The [notebook](analysis.ipynb) executes the study and displays the saved results.

## Decision and limitations

Treat the associations as hypotheses and obtain event-level temporal exposure data before forecasting.

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 share observations or holdouts. These are focused analytical studies, not independent replications or deployed business systems. Any model refinements informed by these results need new untouched evaluation data. No commercial impact is inferred from an association or backtest.

## Reproduce

From the repository root, after installing `requirements.txt`:

```powershell
python projects/094-weather-and-burned-area-associations/analysis.py
```

Source data are downloaded automatically if absent. Original archives are retained unchanged and checked by SHA-256. The cleaned cache normalizes column names; field-specific changes are visible in [data preparation](../../portfolio/data.py). Runtime evidence is in [receipt.json](outputs/receipt.json).

## Source

[Forest Fires](https://archive.ics.uci.edu/dataset/162/forest+fires), Cortez and Morais (2007). [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Source data are transformed and aggregated in this study. [Source provenance](../../data/provenance/fires.json) and [prepared-data audit](../../data/provenance/fires_prepared.json) record the downloaded files, field coverage and hashes.