Airpot Network Analysi

Introduction

This project analyzes flight network data from March 2020 through October 2021, covering a critical period during the COVID-19 pandemic. The dataset includes comprehensive flight tracking information across multiple months.

Dataset Overview

The analysis uses flight data spanning:

Time Period: March 2020 - October 2021
Total Files: 20 monthly datasets
Data Format: CSV files with flight tracking information

Data Collection Period

Data Structure

Each monthly dataset contains flight information including:

Flight identifiers
Origin and destination airports
Departure and arrival times
Flight status information
Aircraft details

Project Goals

Network Analysis: Examine the structure and connectivity of the airport network
Temporal Trends: Analyze how flight patterns changed during the pandemic period
Hub Identification: Identify major airport hubs and their characteristics
Route Analysis: Study popular routes and their evolution over time

Sample Data Analysis

Code

# Example: Loading and exploring a dataset
library(readr)
library(dplyr)

# Load a sample month
flights <- read_csv("dataset/flightlist_20210601_20210630.csv")

# Basic summary
summary(flights)

# Example analysis: Top airports by flight count
# top_airports <- flights %>%
#   count(origin_airport, sort = TRUE) %>%
#   head(10)

Network Visualization

Future sections will include:

Interactive network graphs showing airport connections
Heat maps of flight density by time period
Comparison of pre-pandemic vs pandemic flight patterns
Geographic visualizations of routes

Methodology

The analysis employs:

R for data processing and statistical analysis
Network analysis techniques to understand connectivity
Time series analysis to identify trends
Geospatial analysis for route visualization

Next Steps

Load and combine all monthly datasets
Clean and standardize the data
Perform exploratory data analysis
Build network graphs
Analyze temporal patterns
Create interactive visualizations

Data Source

Flight data collected from public flight tracking sources covering the period of March 2020 through October 2021.

Last updated: 2026-02-05

--- title: "Airpot Network Analysi" format: html: code-fold: true code-wrap: true code-overflow: wrap code-tools: true tidy: true theme: litera execute: echo: true warning: false mainfont: Helvetica editor_options: markdown: wrap: sentence canonical: true page-footer: | © 2025 Farhan Sadeek. All Rights Reserved. --- ```{=html} <style> @import url('https://fonts.googleapis.com/css2?family=Funnel+Sans:ital,wght@0,300..800;1,300..800&display=swap'); @import url('https://fonts.googleapis.com/css2?family=Figtree:ital,wght@0,300..900;1,300..900&family=Fragment+Mono:ital@0;1&display=swap'); p { font-family: 'Funnel Sans'; } pre, code { font-family: 'Fragment Mono'; font-size: 14px; } </style> ``` ## Introduction This project analyzes flight network data from March 2020 through October 2021, covering a critical period during the COVID-19 pandemic. The dataset includes comprehensive flight tracking information across multiple months. ## Dataset Overview The analysis uses flight data spanning: - **Time Period**: March 2020 - October 2021 - **Total Files**: 20 monthly datasets - **Data Format**: CSV files with flight tracking information ### Data Collection Period ```{r} #| echo: false #| warning: false library(lubridate) library(dplyr) library(ggplot2) # Create a timeline of available data months <- data.frame( date = seq(ymd("2020-03-01"), ymd("2021-10-01"), by = "month"), available = TRUE ) ggplot(months, aes(x = date, y = 1)) + geom_point(size = 4, color = "#2E86AB") + geom_line(color = "#2E86AB", size = 1) + scale_x_date(date_breaks = "2 months", date_labels = "%b %Y") + labs(title = "Flight Data Collection Timeline", subtitle = "March 2020 - October 2021", x = "Month", y = "") + theme_minimal() + theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(), plot.title = element_text(size = 16, face = "bold"), axis.text.x = element_text(angle = 45, hjust = 1)) ``` ## Data Structure Each monthly dataset contains flight information including: - Flight identifiers - Origin and destination airports - Departure and arrival times - Flight status information - Aircraft details ## Project Goals 1. **Network Analysis**: Examine the structure and connectivity of the airport network 2. **Temporal Trends**: Analyze how flight patterns changed during the pandemic period 3. **Hub Identification**: Identify major airport hubs and their characteristics 4. **Route Analysis**: Study popular routes and their evolution over time ## Sample Data Analysis ```{r} #| eval: false #| echo: true # Example: Loading and exploring a dataset library(readr) library(dplyr) # Load a sample month flights <- read_csv("dataset/flightlist_20210601_20210630.csv") # Basic summary summary(flights) # Example analysis: Top airports by flight count # top_airports <- flights %>% # count(origin_airport, sort = TRUE) %>% # head(10) ``` ## Network Visualization Future sections will include: - Interactive network graphs showing airport connections - Heat maps of flight density by time period - Comparison of pre-pandemic vs pandemic flight patterns - Geographic visualizations of routes ## Methodology The analysis employs: - **R** for data processing and statistical analysis - **Network analysis** techniques to understand connectivity - **Time series analysis** to identify trends - **Geospatial analysis** for route visualization ## Next Steps 1. Load and combine all monthly datasets 2. Clean and standardize the data 3. Perform exploratory data analysis 4. Build network graphs 5. Analyze temporal patterns 6. Create interactive visualizations ## Data Source Flight data collected from public flight tracking sources covering the period of March 2020 through October 2021. --- *Last updated: `r Sys.Date()`*