Vector Data Processing using Python Tools: Reference

Key Points

Geospatial Concepts
  • A common terminology and set of concepts has been defined by the OGC Simple Feature Access, and is widely used across open source geospatial libraries

  • Core geometric objects (point, line/linestring and polygon), and their multi-part collections (multi-point, multi-line, multi-polygon)

  • A “feature” is made up of a geometry and attributes

Encodings, Formats and Libraries
  • Python vector packages implement community standards for vector encoding. While these can seem complex, tools exist for conversion into various forms, and many of the tools including common interfaces for exchanging data across tools.

  • Packages exist for easily reading data from file-based and other serialized data formats.

  • Most of these packages involve a series of steps to handle data, such as stepping through features via a loop, etc. Most tools do one or a couple of things only. GeoPandas solves these problems by enabling operations on feature collections in one step, and bundling multiple tools via a coherent interface that builds on Pandas.

GeoPandas Introduction
  • Leveraging Pandas and core vector geospatial libraries, Python GeoPandas greatly simplifies the use of vector geospatial data and provides powerful capabilities.

GeoPandas Advanced Topics
  • While still a young package, GeoPandas offers powerful capabilities.

  • GeoJSON and the common __geo_interface__ enable convenient and widespread geospatial data object exchange across geospatial packages in Python.

Useful References

Most of these links are blog posts. The date shown is the blog post date, or else some other document date. A couple of links are in French, all from the same blog; apologies, but they’re excellent resources – if you know French! They’re probably useful even if you don’t know French.