Aviation Wildlife Strikes: An Analysis of Wildlife Strikes Across Airports Throughout the United States

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Project Description

This project analyzes 342,890 wildlife strike incidents reported to the Federal Aviation Administration (FAA) of the United States between 1990 and 2025. Using SQL for data exploration and Power BI for visualization, it examines which US airports face the highest strike frequency, which phases of flight are most dangerous, and how strike volume and repair costs have evolved over 35 years. Data is sourced from the FAA National Wildlife Strike Database — one of the most comprehensive aviation safety datasets in the world.

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GitHub Project Repository & Canva Stakeholder Report

GitHub - cloudrosemage/faa-wildlife-strike-analysis: Analysis of 342,890 FAA wildlife strike records (1990–2025) using SQL and Power BI. Explores strike frequency by airport, phase of flight risk, and 35-year cost trends. Data sourced from the FAA National Wildlife Strike Database.

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Key Findings

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Steps Undertaken

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Project Purpose & Beneficiaries

The primary purpose of this project is to demonstrate a complete end-to-end analytics workflow — from raw data acquisition and SQL-based exploration to interactive dashboard visualization and professional documentation.

Who this is relevant to:

Airport Operations & Safety Teams — Strike concentration data by airport and geographic location directly informs where wildlife hazard management resources are most needed, and which flight corridors warrant the greatest attention.
Aviation Safety Professionals & Regulators — The 35-year trend analysis provides longitudinal context for evaluating the effectiveness of existing wildlife mitigation programs and identifying periods where reporting behavior or actual strike frequency shifted.
Airlines & Fleet Operators — Phase-of-flight risk data helps frame operational risk during approach, take-off, and climb — the three phases that together account for nearly 75% of all recorded strikes.
Researchers & Data Analysts — The methodology and SQL queries used in this analysis are fully documented and publicly available, providing a reproducible foundation for further investigation into species-specific risk, seasonal patterns, or regional trends.
General Aviation Community — Understanding the scale, geography, and trend of wildlife strikes builds broader awareness of a hazard that affects every segment of flight operations, from commercial carriers to general aviation pilots operating at non-towered airports. </aside>

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Tools & Technology Used

Tool	Purpose
JetBrains DataGrip	SQL querying, data exploration, and aggregated CSV exports
SQLite	Local database engine storing and querying the full 342,890-row dataset
Microsoft Power BI Desktop	Interactive dashboard and data visualization
Microsoft Excel	Initial file inspection of the raw .xlsx download
CloudConvert	Independent XLSX to CSV conversion for cross-reference validation
Anthropic Claude	Data verification, CSV conversion cross-checking, and workflow guidance
Canva	Visual report design and PDF creation for portfolio sharing
Notion	Project documentation and process writeup
GitHub	Version control and public project repository
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Main Data Source

https://wildlife.faa.gov/search

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FAA Wildlife Strike Database Dictionary

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Key Column Reference

This dictionary covers the most analytically relevant fields used in this project, plus additional columns of interest for future research.

Source	FAA National Wildlife Strike Database (wildlife.faa.gov)
Total Columns in Full Dataset	102
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Sections

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Data Quality Notes

Voluntary Reporting	Strike reporting is not mandatory for most operators. The database represents reported incidents only — actual strike frequency is estimated to be significantly higher.
Null Values	Many fields contain null or blank values, particularly for older records (pre-2000) where reporting standards were less detailed. Key columns with high null rates include TIME, TIME_OF_DAY, SPECIES, HEIGHT, SPEED, and cost fields.
Cost Fields	Zero values in COST_REPAIRS and COST_OTHER may represent no damage or unreported costs — these cannot be distinguished without reviewing individual records.
Species Identification	Species names in older records rely on visual identification by crew or reporters. DNA-confirmed identifications from the Smithsonian Feather Identification Laboratory are more common in recent records.
Inflation Adjustment	Always use COST_REPAIRS_INFL_ADJ rather than COST_REPAIRS for year-to-year cost comparisons. The nominal cost field does not account for inflation and will understate the relative cost of older incidents.
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