Overview

In this lesson, we will continue to learn how to manage and analyse spatial data using Shapely, Geopandas and other relevant Python packages:

  1. Geocoding / Geocoding in Geopandas
  2. Conducting Point in Polygon queries
  3. How to boost spatial queries using spatial index
  4. Making Spatial joins
  5. Nearest neighbour analysis

Learning goals

After this weeks’ lesson you should be able to:

  • Do geocoding, i.e. transform addresses into coordinates
  • Conduct point-in-polygon queries
  • Read data from WFS services and KML files
  • Make spatial joins
  • Conduct nearest neighbour analysis (finding the closest point).

Sources

Lesson materials are partly based on documentation of Geopandas, geopy, Pandas, Shapely, and Lawhead, J. (2013), Chapters I and V.

Lesson videos

Lesson 3 - Geocoding

Vuokko Heikinheimo, University of Helsinki @ AutoGIS channel on Youtube.

Contents:

  • quiz 0:00
  • Lesson 3 overview 4:02
  • Geocoding intro 5:23
  • Geocoding in geopandas 9:22
  • Table join 22:42

Lesson 3 Point-in-polygon & Intersect, Spatial join

Vuokko Heikinheimo, University of Helsinki @ AutoGIS channel on Youtube.

Contents:

  • Point-in-polygon using Shapely objects 04:35
  • Point-in-polygon using geopandas 14:20
  • Reading KML files 17:40
  • Quick overview of using spatial index in geopandas 36:55
  • Spatial join 44:10
  • Getting data from wfs to geopandas 46:15

Lesson 3 - Nearest neighbour analysis

Vuokko Heikinheimo, University of Helsinki @ AutoGIS channel on Youtube.

Contents: