Most businesses have databases of previous customers, and data analysts will frequently be asked to join arbitrary data to the customer tables in order to provide analysis.
New features and better performance get a lot of attention, but one of the relatively unsung improvements in PostGIS over the past ten years has been inclusion in standard software repositories, making installation of this fairly complex extension a "one click" affair.
Once you've got PostgreSQL/PostGIS installed though, how are upgrades handled? The key is having the right versions in place, at the right time, for the right scenario and knowing a little bit about how PostGIS works.
This is the third and final post of the series intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language. The information below provides sample use of R Functions against the NDVI dataset.
This is the second in a series of posts intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language. This post builds on the example introduced in the initial post by demonstrating the steps associated with preprocessing the Normalized Difference Vegetation Index (NDVI) satellite raster data in preparation for spatial analytics.