Montandon Data Fetching Examples

Example notebooks demonstrating usage of the Montandon API / STAC data catalog.

Authentication Required

The Montandon STAC API staging environment now requires authentication using a Bearer Token from the IFRC OpenID Connect system.

API Endpoint: https://montandon-eoapi-stage.ifrc.org/

To get your API token:

1. Visit: https://goadmin-stage.ifrc.org/
2. Log in with your IFRC credentials
3. Navigate to your account settings to generate an API token
4. When running notebooks, you'll be prompted to enter your token securely

Setting token as environment variable (optional):

# PowerShell
$env:MONTANDON_API_TOKEN = "your_token_here"

# Bash/Linux
export MONTANDON_API_TOKEN="your_token_here"

Run in Binder

Click to open the notebooks in an interactive environment:

Notes:

• When using Binder, you'll need to provide your own API token when prompted in the notebooks.
• First build may take 5-10 minutes. Subsequent launches use cached builds and start in ~1-2 minutes.
• Environment configuration is in .binder/ directory for optimized caching.

Run in Google Colab

You can run any notebook directly in Google Colab:

1. Open a notebook in Colab:

   • Navigate to the notebook in the montandon_notebooks/ folder on GitHub
   • Click the "Open in Colab" badge, or
   • Use this URL pattern: https://colab.research.google.com/github/arunissun/Montandon-Data-Fetching-Examples/blob/master/montandon_notebooks/<notebook_name>.ipynb

2. Install dependencies first: Add and run this cell at the top of the notebook:

   # Install required packages for Google Colab
   !pip install -q pystac-client pandas folium matplotlib seaborn plotly statsmodels pymannkendall geopandas shapely requests ipywidgets

3. Set your API token: Add this cell before running the notebook:

   import os
   from getpass import getpass
   
   # Set your Montandon API token
   os.environ['MONTANDON_API_TOKEN'] = getpass('Enter your Montandon API token: ')

Quick Links to Notebooks:

Notebook	Open in Colab
01 - Getting Started
02 - Data Analysis
03 - Time Series Analysis
04 - Cyclone Tracking
05 - Earthquakes Visualization
06 - Snow/Cold Wave Analysis
07 - Cascading Impacts
08 - Queryables Deep Dive
09 - EM-DAT Impact Analysis

Run Locally

Prerequisites

• Python 3.12+
• uv (recommended) or pip

Option 1: Using uv (Recommended)

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/arunissun/Montandon-Data-Fetching-Examples.git
cd Montandon-Data-Fetching-Examples

# Install dependencies and run JupyterLab
uv sync
uv run jupyter lab

Option 2: Using pip

# Clone the repository
git clone https://github.com/arunissun/Montandon-Data-Fetching-Examples.git
cd Montandon-Data-Fetching-Examples

# Create a virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run JupyterLab
jupyter lab

Set API Token as Environment Variable

PowerShell (Windows):

$env:MONTANDON_API_TOKEN = "your_token_here"

Bash/Linux/Mac:

export MONTANDON_API_TOKEN="your_token_here"

Notebooks

All notebooks are located in the montandon_notebooks/ folder:

#	Notebook	Description	Key Packages
01	01_Getting_Started_Montandon_STAC_API.ipynb	Introduction to the Montandon STAC API and basic data retrieval	pystac-client, pandas, matplotlib, seaborn
02	02_Montandon_data_analysis.ipynb	Data analysis and interactive map visualization	pystac-client, pandas, folium, matplotlib
03	03_Time_Series_Analysis.ipynb	Time series analysis with parallel processing	pystac-client, statsmodels, pymannkendall, pandas
04	04_Recent_Cyclone_Tracking.ipynb	Tracking recent cyclone events	pystac-client, pystac, folium, pandas
05	05_Earthquakes_visualization.ipynb	Interactive earthquake visualization	pystac-client, folium, ipywidgets, pandas
06	06_Snow_Cold_Wave_Impact_Analysis.ipynb	Winter hazard impact analysis	pystac-client, plotly, pandas, seaborn
07	07_cascading_impacts_analysis.ipynb	Cascading disaster impact analysis	pystac-client, geopandas, folium, pandas
08	08_Queryables_Deep_Dive.ipynb	Deep dive into STAC API queryables and CQL2 filtering	pystac-client, requests, plotly, pandas
09	09_EMDAT_Impact_Analysis.ipynb	EM-DAT people impact analysis with memory-optimized CSV export	pystac-client, csv, pandas

Data Sources

The Montandon STAC API aggregates disaster and hazard data from multiple sources:

• GDACS - Global Disaster Alert and Coordination System
• EM-DAT - Emergency Events Database
• GLIDE - Global Library of Insurance Disaster Events
• DesInventar - Disaster Inventory System
• GFD - Geospatial Facility Database
• IBTRACS - International Best Track Archive for Climate Stewardship
• IDMC - Internal Displacement Monitoring Centre
• IFRCEVENT - IFRC Event Database
• UNDRR-ISC - UN Disaster Risk Reduction Registry