Geospatial Data Analysis
Maps, GeoPandas, spatial joins and location intelligence.
Taught by Sabina Tamang · Data Engineer
What you'll learn
- Distinguish vector from raster data and find open spatial datasets for any region
- Choose and apply the right coordinate reference system so distances, areas and joins are correct
- Load, inspect, filter and plot GeoDataFrames from GeoJSON, GeoPackage and GeoParquet
- Perform spatial joins, buffers, nearest-neighbour and overlay operations to answer location questions
- Build honest choropleth maps and interactive Folium and Kepler.gl maps for stakeholders
- Apply location intelligence to logistics, retail and climate problems using H3 hexagonal aggregation
About this course
Where something happens is often the most valuable column in a dataset, and most analysts never use it properly. This intermediate course teaches geospatial data analysis in Python for people who already know pandas: reading vector data with GeoPandas, understanding coordinate reference systems so distances and areas are correct, performing spatial joins, buffers and overlays, making choropleth maps that do not mislead, building interactive maps with Folium and Kepler.gl, and applying it all to location intelligence problems in logistics, retail and climate such as delivery zones, store catchments and flood exposure. You will work with open data from OpenStreetMap and administrative boundaries, aggregate points into H3 hexagons, and finish with a reusable spatial analysis workflow. Prerequisites are comfortable pandas and matplotlib; no GIS background is needed.
Course content
6 lessons- 1Thinking SpatiallyVector and raster data, where spatial datasets come from, and the Python toolkit for working with them.Preview
- 2Coordinate Reference SystemsGeographic versus projected coordinates, EPSG codes, and the rules that keep distances and joins correct.
- 3GeoPandas: Reading, Inspecting and PlottingThe GeoDataFrame, common file formats, attribute and spatial filtering, and quick static maps.
- 4Spatial Joins and OperationsPredicates such as within and intersects, buffers, nearest-neighbour joins and overlays that answer location questions.
- 5Choropleths and Interactive MapsClassification and normalisation for honest choropleths, and interactive maps with Folium and Kepler.gl.
- 6Location Intelligence: Logistics, Retail and ClimateDelivery zones and isochrones, store catchments and cannibalisation, flood exposure, and H3 hexagon aggregation.