This project constructs and explores a directed, weighted network of global trade flows for 2023, applying graph‐theoretic analyses (centrality, community detection, trade‐dependency metrics) and presenting results via an interactive Dash dashboard. https://wtrademap.onrender.com Link above! Disclaimer, it uses Render's free tier, so it might take a while to load and start up.

Overview

A snapshot of world trade in 2023 is modeled as a directed graph, where nodes are countries and edges are bilateral trade volumes. Key features include:

• Data cleaning and aggregation of Observatory of Economic Complexity (OEC) trade records
• Integration of GDP data to compute trade‑to‑GDP ratios
• Application of the disparity filter and Louvain algorithm for backbone extraction and community detection
• Computation of export/import rankings, trade balances, centrality measures, and concentration indices
• An interactive, multi‑tab dashboard built with Plotly and Dash

Datasets

All source data is packaged in global_trade_datasets.zip, containing:

1. bilateral_value_clean_23_withid.csv
2. API_NY.GDP.MKTP.CD_DS2_en_csv_v2_26433.csv
3. extra_country_coords.json

You can download the archive via your course’s MarkUs page, Send.UToronto.ca link, or the provided OneDrive URL.

Installation (Docker)

1. Clone repo

git clone https://github.com/farrellsid/macromap_alpha.git

2. Build the Docker image

docker build -t global-trade-app .

3. Run the container and map to local port 8050

docker run -p 8050:8050 global-trade-app

Then, click the links in the CLI!

What’s happening under the hood?

The app and its dataset files are bundled into a Docker container.

All dependencies (Dash, Pandas, NetworkX, etc.) are installed during build.

The server is configured to run with host=0.0.0.0 and port from the PORT environment variable—making it flexible and cloud-ready.

Installation (No Docker)

1. Clone the repository

git clone https://github.com/your‑username/global‑trade‑network.git
cd global-trade-network

2. Prepare your data folder

Unzip global_trade_datasets.zip into a folder named Data at the project root:

unzip global_trade_datasets.zip -d Data

3. Install dependencies

This project requires Python 3.13. Install all necessary libraries via the provided script:

python install_packages.py

(Or, if you prefer, pip install -r requirements.txt)

Usage

1. Launch the Dash application:

   python main.py
   

2. Open your browser and navigate to

   http://127.0.0.1:8050/
   

3. Explore the tabs for global network, community views, country‑specific maps, and choropleth visualizations.

Project Structure

├── Data/                              # Unzipped datasets
│   ├── bilateral_value_clean_23_withid.csv
│   ├── API_NY.GDP.MKTP.CD_DS2_en_csv_v2_26433.csv
│   └── extra_country_coords.json
├── cleaner.py                         # Cleans and aggregates raw trade data
├── data_processing.py                 # Merges in GDP & coordinates, preprocesses for graph
├── graph_builder.py                   # Builds NetworkX graph, applies disparity filter
├── analysis.py                        # Computes rankings, centrality, dependency metrics
├── visualization.py                   # Defines Plotly/Dash dashboard layout & callbacks
├── install_packages.py                # Installs Python dependencies
├── requirements.txt                   # Pin‑listed libraries
├── main.py                            # Entry point to launch Dash server
└── global_trade_datasets.zip          # Archive of all input data