Deer Harvest
Analysis 2015–2025
Statewide harvest records across 11 seasons. 2,213,979 individual kill records normalized, cleaned, and ready for weather correlation analysis.
Season Totals
Harvest Composition
Antler Quality Trend
Only includes bucks with antler_reported = true. 2025 data reflects partial season. Count labels show number of qualifying bucks.
Top 20 Counties / Independent Cities
Showing top 20 jurisdictions by cumulative harvest.
Weather Correlation Layer
Loading weather summary...
Loading weather windows...
| Month | Weekday | Moon | Temp | Wind | Rain | Lift |
|---|---|---|---|---|---|---|
| Loading... | ||||||
How to Read This Dashboard
This page shows harvest patterns over time. Read each chart as a pattern indicator, not a proof of cause. A higher bar or line means more recorded harvests in that category.
Season Totals: compares total harvest by year.
Monthly Profile: shows when harvest concentrates within a season.
Day of Week Pattern: highlights weekend pressure differences.
Composition charts: show the mix of animal and weapon types.
County chart: view Top 20 or All FIPS, filter by season year, and search by county/city name or FIPS.
Weather Correlation Layer: coverage summary, relative-rate charts by rain/temp/wind band, and top weather windows.
If one year is much higher, first compare month and day-of-week patterns before drawing conclusions.
If Saturday is much higher, that usually reflects hunter participation, not only deer activity.
If a county moves up or down in rank, first verify year mode (single year vs combined) and Top 20 vs All FIPS view.
Use weather windows as directional context; always check weather coverage and sample size before treating a band as robust.
Harvest: number of recorded deer taken.
Season year: the hunting season label used in the dataset.
FIPS: county or city code used for location grouping.
Relative rate: weather-band performance compared against overall baseline (100 = baseline).
Composition: percentage split across categories (example: animal type).
Correlation is not causation: matching movement in two metrics does not prove one causes the other.
Data quality flags in the notice box can affect specific years and fields.
Partial seasons and reporting changes can shift comparisons.