CIESIN GRID3 Data Science User Guide
Access, analyze, and visualize GRID3 data with Python
1 Welcome to the CIESIN GRID3 Data Science User Guide!
This website is a collection of user guides demonstrating ways to access, analyze, and visualize CIESIN GRID3 datasets!!!
These guides support CIESINβs effort to strengthen GRID3 sustainability by making spatial data more findable, reproducible, interoperable, and accessible in alignment with open science principles. Designed for users ranging from beginners to advanced practitioners, the guides cover key topics in spatial data workflows, including data access, analysis, and visualization. Each guide is delivered as a Jupyter Notebook and includes code examples, explanations, and visualizations to help users effectively work with GRID3 spatial data in a variety of contexts.
For more information, please visit the GRID3 Data Catalog and the CIESIN, Columbia Climate School websites.
2 How this guide is organized
This Data User Guide is organized into four main sections:
- User Guides β step-by-step notebooks
- Data Access β APIs, Feature Services, metadata
- Visualization β maps, plots, spatial workflows
- Reference β reusable patterns and examples
2.1 π Getting started
If youβre new, start here. This is the full catalog of notebooks in the guide.
2.1.1 π User Guide Template
A template for creating GRID3 data user guides.
2.1.2 π£οΈ Friction Surface by LGA
Access and analyze GRID3 friction surfaces at the Local Government Area (LGA) level in Nigeria.
2.1.3 π£οΈ Accessing GRID3 Road Networks
Learn how to access and analyze GRID3 road network datasets using the ArcGIS API for Python. GRID3 COD - Roads v1.0 Data Release Notes
2.1.4 π Road Network Analysis
Common patterns for spatial and tabular analysis.
2.1.5 πΊοΈ Visualization
Create interactive and static maps with Folium and GeoPandas.