Posts about Spatial

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.

Install PostgreSQL 10 and PostGIS 2.4

Many applications these days want us to know how close we are to things:

• What are the three closest coffee shops to my current location?
• Which is the nearest airport to the office?
• What are the two closest subway stops to the restaurant?

and countless more examples.

Another way of asking these questions is to say “who are my nearest neighbors to me?” This maps to a classic algorithmic problem: efficiently finding the K-nearest neighbors

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.

As introduced in the previous posts, the combination of PostgreSQL and R provides users with the ability to leverage the power and efficiency of PostgreSQL and the rich analytic functionality of R. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database. It is our hope that these posts will cause those building analytic applications to give PostgreSQL a second look.

The first post in this series

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

This is the first 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. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database. This initial post introduces PL/R and R, provides set up instructions for following the Spatial Analytics example to be used in this series of posts, and provides introductory instruction on Geocoding with PostGIS, R and PL/R.

While PostgreSQL is known for and widely popular as a transactional database due to its SQL compliance, reliability, data integrity and ease of use, it is less commonly associated with analytic applications.

The combination of PostgreSQL and R provide users with the ability to leverage the power and efficiency of PostgreSQL and the rich analytic functionality of R. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database.

This series of blog posts will provide users with information about:

• Introduction to R and PL/R
• Geocoding with PostGIS, R and PL/R
• Example of Spatial Analytics – NDVI Processing
• Example of Spatial Analytics – Sample Analytics Functions