Aviation Safety
Trend Analysis

Project Overview

This project performs a time-series analysis of NASA's Aviation Safety Reporting System (ASRS) — a voluntary, confidential reporting program that collects safety-related incident data from pilots, air traffic controllers, and other aviation personnel.

The goal is to surface meaningful patterns in aviation incident reports over time, broken down by event type, aircraft category, and reporting year. The methodology directly mirrors the Continuous Analysis and Surveillance System (CASS) approach used in FAA regulatory compliance work, making this a practical demonstration of applied aviation safety analytics.

Methodology

The analysis follows a structured pipeline from raw data ingestion to visual reporting:

• Data sourced from the NASA ASRS database — publicly available and updated monthly
• Ingestion and cleaning performed in Python using Pandas, handling missing fields and normalizing event categories
• Time-series decomposition applied to isolate trend, seasonality, and irregular components in incident frequency
• Incident records grouped by event type (runway incursion, airspace deviation, mechanical, etc.) and aircraft category
• Year-over-year comparison calculated to identify statistically significant shifts in reporting volume
• Final outputs built as a Power BI dashboard for interactive filtering by date range, event type, and aircraft class

Dashboard & Charts

Power BI dashboard and supporting Python charts will be embedded here upon project completion.

Key Findings

Findings will be documented here upon analysis completion. Expected outputs include:

• Trend direction in total ASRS submissions over the past decade
• Event categories with the highest year-over-year growth rates
• Aircraft categories most frequently represented in reports
• Seasonal patterns in incident reporting frequency
• Comparison of reporting volume pre- and post-COVID disruption period

Source Code

Full Python analysis code, data pipeline, and documentation available on GitHub.