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MCP (Model Context Protocol) Server. Provides direct PostgreSQL database integration with PostGIS spatial extension support, enabling SQL query execution, spatial data analysis, geographic information systems operations, and location-based queries for both traditional relational data and geographic datasets.

A new way to work with data is here. Posit AI embeds a specialized agent directly in RStudio, transforming your IDE into a powerful collaborator for data analysis.

We’re launching a data science agent and next edit suggestion system for RStudio today.

Easy programmatic access to geographic administrative boundaries - wherobots/wkls

The Azure Architecture Center provides guidance for designing and building solutions on Azure by using established patterns and practices.

Everything you need to know about geospatial metadata — from why it matters to how the major standards work in practice.

Entropy, Lehman’s Laws of Software Evolution, and Conway’s Law - Entropy_Lehman-Laws_Conway-Law.md

The Arazzo Specification provides a mechanism that can define sequences of calls and their dependencies to be woven together and expressed in the context of delivering a particular outcome or set of outcomes when dealing with API descriptions (such as OpenAPI descriptions)

Build Open Geospatial Consortium GeoPackage files (). GDAL utilities for reading and writing spatial data are provided by the terra package. Additional GeoPackage and SQLite features for attributes and tabular data are implemented with the RSQLite package.

This set of functions can be used to access and run gdal command line interface (CLI) algorithms. Requires GDAL >= 3.11.3 Experimental (see the section Development Status below)

Experimental functions for interacting with public-facing Dynamic Soils Hub tools and APIs.

An R Frontend for the GDAL CLI

Contribute to PawanRamaMali/shinyoats development by creating an account on GitHub.

Learn everything about Data Acquisition, a key knowledge category of the GISCI Geospatial Core Technical Exam. Click to start studying.

The generation and consumption of spatial information in the hydrographic field is a fundamental part of daily work, as not only are many decisions taken based on this information but, additionally, information is produced in real time based on these decisions and processes. Another of the particular characteristics of hydrographic information is the great amount of detail and its size, which traditionally makes it necessary to use desktop tools for the execution of geoprocesses and the production of derived information. In this context, the use and edition of information through web clients using OGC processes and standards (WMS, WMST, WPS), solves the casuistry and provides the necessary power for daily work, focused on an architecture where the server centralises operations and processes. However, the irruption of new technologies with the use of vector tiles presented as an evolutionary leap presents great advantages in the management of data at both server and client level. At the server side, it allows for lighter and more resilient infrastructures through the use of STAC and enables the process load to be distributed while at client level, it provides the browsers with the vector data for the execution of geoprocesses locally using specific tools. This use case presents a practical application for the production and customisation of vector tiles in the specific case of hydrological information, presenting an application where, on the one hand, spatial information is integrated into the data production processes by generating the appropriate services and, on the other hand, client tools are used for the management, geoprocessing and consolidation of spatial information using vector tiles. Additionaly, the information in vector tile format has specific styles, developed using a tool that manages the spatial information in vector format and establishes an associated style through a simple user interface. Thus, the viewer requests the vector information and the associated style for its adequate rendering to the user. This environment has been based on Open Source technologies and standards, which allows its scalability and sustainability in the future in a modular way. Finally, it is worth highlighting the good performance obtained, as the project, through the interfaces developed, achieves both the updating of spatial information in an integrated way in the production and decision-making processes, as well as its dissemination in different formats. Technologies: PostGIS, GeoServer, Maputnik, OpenLayers, Mapea, vector tiles, STAC

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This blog featured a post on mocking, the art of replacing a function with whatever fake we need for testing, years ago. Since then, we’ve entered a new decade, the second edition of Hadley Wickham’s and Jenny Bryan’s R packages book was published, and mocking returned to testthat, so it’s time for a new take/resources roundup! Thanks a lot to Hannah Frick for useful feedback on this post!

Introduction to Spatial Analytics with PostgreSQL, PostGIS, PL/R and R Programming Language. Example use of R Analytic functions in PostgreSQL and PostGIS.

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