Deer Harvest Correlation Explorer

2,212,952 records · Holiday-adjusted effort weights · Moon phase, holiday, weapon, animal type, and weather correlations · Use the filters on the left to slice the dataset and explore patterns.
Best Days to Hunt (Current Filters)
Top 5 day windows ranked by normalized kill rate.
Top day windows by normalized kill rate for current filters.
# Month Weekday Window Moon Lift Rate
Filtered Kill Distribution by Moon Phase
Filtered Kill Distribution by Holiday Impact Level
Filtered Kills by Weapon
Filtered Kills by Month
Full Moon Signal: Raw DOW-Only vs Holiday-Adjusted Normalization

Phase Rates — Holiday-Adjusted (bars) vs DOW-Only (line) Comparison

Filled bars = holiday-adjusted rate. Line = DOW-only rate. The gap between bar and line reveals how much holiday coincidence inflates or deflates each phase. Full Moon loses ~6 percentage points of its signal once Thanksgiving Full Moon years are properly weighted.

Average Daily Harvest by Holiday — vs Baseline Days
Holiday Kill Totals 2015–2025
Moon Phase Distribution on Thanksgiving Days 2015–2025

2015 & 2018 were Full Moon Thanksgivings — the highest-effort day of the year aligned with the highest-activity moon phase. This accounts for ~6.3 percentage points of the raw Full Moon signal.

Normalized Kill Rate by Phase — Weapon (Filtered)
Normalized Kill Rate by Phase — Animal Type (Filtered)
Holiday-Adjusted Kill Rate · Moon Phase × Season Year · Color Scaled Per Year

Each cell shows normalized kills per effort-unit for that phase in that season. Color is scaled within each year (green = highest that year, dark = lowest). This isolates whether a given phase was productive in a given year independent of overall season size.

Filterable Aggregate Table — Click column headers to sort
Active filters: none
Filterable aggregate harvest data table.

🌡 Weather Correlation

These charts summarize effort-adjusted harvest rates by daily weather bands. If no weather data is present in the payload, this section will show empty state placeholders.

Weather coverage: —

python3 pipeline/02_weather_integration.py \
  --harvest ./data/cleaned/harvest_clean.csv \
  --output ./data/cleaned/harvest_with_weather.csv

python3 pipeline/03_moon_phase_analysis.py \
  --harvest ./data/cleaned/harvest_with_weather.csv \
  --output-dir ./data/cleaned

python3 pipeline/05_build_correlation_data.py \
  --harvest ./data/cleaned/harvest_with_moon.csv \
  --output ./public/correlation_data.bbf9200e49a8.json
Harvest Rate vs Daily High Temp
Harvest Rate by Wind Speed Band
Rain/No-Rain Harvest Rate Comparison
Wind Direction × Harvest Rate
Best Hunt Windows (Weather + Moon)
Top windows ranked by normalized kill rate.
Best hunt windows by weather and moon conditions.
# Month Weekday Window Moon Temp Wind Rain Lift Rate Days Kills
How to Read This Page

Start with Overview for broad patterns, then use filters one at a time. Compare normalized rates first, then totals.

Read Order

Overview: quick directional pattern check.

Moon × Adjustments: shows how much holiday timing inflates or reduces moon effects.

Holiday Effects: compares holiday days against baseline days.

Weapon & Animal: checks whether the phase pattern is consistent across groups.

Year Heatmap: compares phase performance within each season year.

How to Interpret

Higher normalized rate means stronger harvest performance after effort adjustment.

A positive lift means above baseline; negative lift means below baseline.

If a pattern disappears after adjustment, holiday or day-of-week timing likely explained part of the raw signal.

Use Weather as supporting context, not standalone proof.

Glossary

Normalized rate: kills adjusted by estimated hunting effort.

Lift: percent above or below baseline comparison level.

Holiday-adjusted: rates corrected so holiday timing does not dominate moon comparisons.

Filtered kills: records that match your current left-sidebar filters.

Important Caveats

Correlation is not causation: aligned trends do not prove one factor causes another.

Small subgroup filters can produce noisy swings; check record counts before concluding.

Year-to-year regulation and reporting changes can affect totals and composition.