Overview¶
In this lesson, we will continue to learn how to manage and analyse spatial data using Shapely, Geopandas and other relevant Python packages:
- Geocoding / Geocoding in Geopandas
- Conducting Point in Polygon queries
- How to boost spatial queries using spatial index
- Making Spatial joins
- 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:
- Shapely nearest_points 1:00
- Nearest points in geopandas 6:00
- See also extra materials about nearest neighbour analysis on the course webpages
- Quick overview of exercise 3 17:40