This document introduces GeoPySpark, an open source geospatial library for big data. It discusses how GeoPySpark can be used to process geospatial raster and vector data at scale, provides examples of local operations, focal operations, and distance calculations that can be performed. The document concludes with a demo of GeoPySpark capabilities and outlines future work including improving performance by integrating with Apache Arrow.

1. Introducing GeoPySpark, a Big Data
GeoSpatial Library
Jacob Bouffard / @jbouffard

2. Goals For This Talk

3. Goals For This Talk
● Introduce GeoPySpark

4. Goals For This Talk
● Introduce GeoPySpark
● What GeoPySpark can do

5. Goals For This Talk
● Introduce GeoPySpark
● What GeoPySpark can do
● A demo of GeoPySpark in action

6. Goals For This Talk
● Introduce GeoPySpark
● What GeoPySpark can do
● A demo of GeoPySpark in action
● Next steps for GeoPySpark

7. Introducing

8. But First, a Little Background

10. GeoSpatial Processing and Analysis at
Scale

11. Raster, Vector,
VectorTile, Point Cloud
+

12. How Does
Relate to
?

13. GeoPySpark is a Python Binding of
GeoTrellis!

14. GeoPySpark is a Python Binding of
GeoTrellis!

17. localhost (AWS)

18. localhost (AWS)

19. Processing GeoSpatial Data at Scale
With GeoPySpark

20. Map Algebra

21. Local Operations

22. Focal Operations

23. Euclidean Distance

24. Cost Distance

25. Rasterization

26. And Now, the Demo

27. Future Work

28. Future Work

29. Future Work
● Provides a Pandas like interface to
working with data

30. Future Work
Apache Arrow

31. Future Work
Apache Arrow:
● Language agnostic framework

32. Future Work
Apache Arrow:
● Language agnostic framework
● Can store data in-memory

33. Future Work
Apache Arrow:
● Language agnostic framework
● Can store data in-memory
● Zero-copy reads of data

34. Future Work
Apache Arrow:
● Language agnostic framework
● Can store data in-memory
● Zero-copy reads of data
● Improved performance