Spatial Data Science

About this course

Explore spatial data science with geospatial Python, spatial statistics, interpolation, network analysis and reproducible pipelines.

This practitioner course is part of the GeoAI & Geospatial Intelligence school. Work through the curriculum below in a self-paced / instructor-led format, with an estimated study time of 16 h.

Course curriculum

• Module 1 — Geospatial Python in Practice
• Module 2 — Spatial Joins and Aggregation at Scale
• Module 3 — Point Patterns
• Module 4 — Spatial Autocorrelation, Global and Local
• Module 5 — Interpolation
• Module 6 — Network Analysis and Accessibility
• Module 7 — Areal Units, the MAUP and the Ecological Fallacy
• Module 8 — Reproducible Spatial Pipelines

Applied learning

• Case Study — Pennard: Three Numbers the Council Nearly Published
• Lab 1 — Accessibility for Pennard's services
• Lab 2 — Hotspot analysis with a test of significance
• Lab 3 — Interpolation with cross-validated error
• Capstone — A Spatial Analysis You Can Rerun and Defend

Who this course is for

GIS / Remote Sensing Professional; Data Professional; Researcher

Prerequisites

NE-GEO-102

Course details

• Course code: NE-GEO-203
• Level: Practitioner
• Estimated duration: 16 h
• Delivery: Self-Paced / Instructor-Led