Posts

One of the most fun parts of being involved in GeoParquet is that I get to hear about all the amazing tools that people are writing to scale geospatial analysis above and beyond what the stack I’m familiar with can handle.

There’s a great discussion going on in the GeoParquet repo about how exactly to split up GeoParquet datasets for optimal querying. This post is my adventure giving the partition strategies a shot for practicality.

A little over a two years ago I wrote about point-in-polygon joins in R using an example of parking violations in Philedelphia. This example has been written about a few times involving a GPU example, using it as example for tuning PostGIS, a GeoPandas version for comparison, and a version comparing ArcPro, QGIS, PostGIS, and Manifold.

GDAL’s 3.6 release is a huge deal. As with every GDAL release, there are a lot of bugfixes and incremental updates that represent a huge amount of maintainer time and effort.

Another release of the wk package is upon us! This release is a mix of exciting new features and bugfixes for some corner cases I encountered when working on the geoarrow package.

The first post I wrote about GeoArrow builds on a lot of great work in the GeoArrow specifcation repo that’s accumulated a lot of really exciting discussion over the past year or so.

A new version of the geos package for R is upon us! Buckle up and, if you so choose, get some of the example data to follow along! library(sf) library(geos) curl::curl_download( "https://github.

This release of the s2 package for R is an exciting one! Most users will use s2 implicity through its use in sf; however, the s2 package can be used on its own and these improvements will likely be available in future releases of sf.

There’s lots of cool chatter about the potential for Apache Arrow in geospatial and for geospatial in Apache Arrow. Some recent blog posts have touched on some of the opportunities that are unlocked by storing geospatial vector data in Parquet files (as opposed to something like a Shapefile or a GeoPackage), like the ability to read directly from JavaScript on the web, the ability to register a Parquet file as a virtual table in Postgres/PostGIS, and the general support for Parquet in cloud-based data providers.

Inspired by Martin Fleischmann’s post on spaital joins using Dask and geopandas, I thought I’d give it a go in R! I’ve been playing with bigish point datasets for a while and this particular bit of profiling seemed like a good opportunity to illustrate a few things to think about when dealing with them in the r-spatial universe.