Paul takes a recent example of GPU-assisted spatial joins project to see how PostGIS stacks up in the same situation. He goes through the steps of joining a large record set of parking data with neighborhood data.
Recently Crunchy Data added pg_featureserv support for most of CQL2. Here we'll describe the powerful new capability it provides.
Paul walks through spatial indexes and queries to find a solution to a recent postgis-user query.
Learn about how structuring an index based on the inputs it receives and the order of inputs can have a strong effect on the quality of the final index. This post includes a lot of example code and geometry.
Highlights from the 3rd annual PostGIS Day, hosted virtually November 18th, 2021.
The cool thing about foreign data wrappers is that they're an alternative to needing to have everything in the same data store. With spatial data being stored and shared in so many different formats, imagine being able to abstract that conversion away and just focus on analysis. Read on for a couple of quick demos.
Raster data access from the spatial database is an important feature, and the coming release of PostGIS will make remote access more practical, by allowing access to private cloud storage.
One theme of the 3.2 release is new analytical functionality in the raster module, and access to cloud-based rasters via the "out-db" option for rasters. Let's explore two new functions and exercise cloud raster support at the same time.
Crunchy Data has developed a suite of spatial web services that work natively with PostGIS to expose your data to the web, using industry-standard protocols.
One of the less visible improvements coming in PostGIS 3.2 (via the GEOS 3.10 release) is a new algorithm for repairing invalid polygons and multipolygons.
A common situation in the spatial data world is having discrete measurements of a continuous variable. Every place in the world has a temperature, but there are only a finite number of thermometers - how should we reason about places without thermometers and how should we model temperature?
The simple story of spatial indexes is - if you are planning to do spatial queries (which, if you are storing spatial objects, you probably are) you should create a spatial index for your table.
We at Crunchy Data put as much development effort into improving GEOS as we do improving PostGIS proper, because the GEOS library is so central to much geospatial processing.
As a GIS newbie, I've been trying to use local open data for my own learning projects. I've recently relocated to Tampa, Florida, and was browsing through the City of Tampa open data portal and saw that they have a Public Art map. That sounded like a cool dataset to work with but I couldn't find the data source anywhere in the portal. I reached out to the nice folks on the city's GIS team and they gave me an ArcGIS-hosted URL.
There are a lot of ways to load data into a PostgreSQL/PostGIS database and it's no different with spatial data. If you're new to PostGIS, you've come to the right place. In this blog post, I'll outline a few free, open source tools you can use for your spatial data import needs.
The PostGIS raster has a steep learning curve, but it opens up some unique possibilities for data analysis and accessing non-standard data from within PostgreSQL. Here's an example that shows how to access raster data from PostGIS running on Crunchy Bridge.
While we talk about "PostGIS" like it's one thing, it's actually the collection of a number of specialized geospatial libraries, along with a bunch of code of its own.
Crunchy Data's second annual PostGIS Day took place a couple weeks ago on November 19th, and as a first-time attendee I was blown away by the knowledge-sharing and sense of community that I saw, even as I was tuning in remotely from my computer at home.
Summarizing data against a fixed grid is a common way of preparing data for analysis. Fixed grids have some advantages over natural and administrative boundaries.