Large
Streaming FlatGeobuf
This is a 14.1 MB flatgeobuf! If your browser supports streaming fetch, it will be displayed progressively as it downloads. See flatgeobuf.org for more information about this format.
Tricks for Faster Spatial Indexes | Crunchy Data Blog
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.
Spatial Index: R Trees | Towards Data Science
If you have been following the Spatial Index Series, it started with the need for multi-dimensional indexes and an introduction to space-filling curves, followed by a deep dive into grid systems (GeoHash and Google S2) and tessellation (Uber H3). In this post, let’s explore the R-Tree data structure (data-driven structure), which is popularly used to […]
Crafting Interpreters
A Bytecode Virtual Machine · Crafting Interpreters
Specification - stac-geoparquet
Convert STAC items between JSON, GeoParquet, pgstac, and Delta Lake.
C-Loftus/fgbdump: Quickly explore metadata about a FlatGeobuf file
Quickly explore metadata about a FlatGeobuf file
FlatGeobuf – Cloud-Optimized Geospatial Formats Guide
oz123/awesome-c: A curated list of awesome C frameworks, libraries, resources and other shiny things. Inspired by all the other awesome-... projects out there.
A curated list of awesome C frameworks, libraries, resources and other shiny things. Inspired by all the other awesome-... projects out there. - oz123/awesome-c
A list of open source C libraries - cppreference.com
Well-Known Binary (WKB)
“Well-known binary” is a scheme for writing a simple features geometry into a platform-independent array of bytes, usually for transport between systems or between programs. By using WKB, systems can avoid exposing their particular internal implementation of geometry storage, for greater overall interoperability.
GEOS can read and write three “flavours” of WKB:
Standard WKB Extended WKB ISO WKB When reading WKB, GEOS will transparently detect the flavour of WKB and automatically read it.
Introduction - DBHub, Minimal Database MCP Server
Getting Started with SQLite Cloud | SQLite Cloud Docs
SQLite Cloud is a distributed relational database system built on top of the SQLite database engine.
GeoPackage format - GeoPackage JS
A GeoPackage is a self-contained SQLite database that stores vector features, raster tiles, and tabular attributes in a single portable file.
geopackage
An asciidoc version of the GeoPackage specification for easier collaboration
Like other relational databases, GeoPackages contain a number of tables. These tables fall into two categories, user-defined data tables and metadata tables. GeoPackages contain two mandatory metadata tables, gpkg_contents and gpkg_spatial_ref_sys. The presence of other metadata tables is dictated by the content being stored (see Content Types). The name of the user-defined data table is the primary key for gpkg_contents and generally is a foreign key for content-specific metadata tables.
Vector feature data are geographic entities including conceptual ones such as districts, real world objects such as roads and rivers, and observations. (An observation is an act that results in the estimation of the value of a feature property, and involves application of a specified procedure, such as a sensor, instrument, algorithm or process chain. A temperature at a given geographic location provided by a sensor is an example of an observation.) For vector feature data, there is one additional required table: gpkg_geometry_columns. Features are stored in the user-defined data tables identified by the table_name values in gpkg_contents (one table per row).
basemaps/styles at main · protomaps/basemaps
Basemap PMTiles generation and cartographic styles for OpenStreetMap data and more - protomaps/basemaps
protomaps/basemaps: Basemap PMTiles generation and cartographic styles for OpenStreetMap data and more
Basemap PMTiles generation and cartographic styles for OpenStreetMap data and more - protomaps/basemaps
Inspecting Files - geoparquet-io
Fast I/O and transformation tools for GeoParquet files
Bloom filters enable efficient point lookups on low-cardinality columns. DuckDB 1.5+ automatically writes bloom filters when creating Parquet files.
AGENTS.md, {admiral}, and the AI-Assisted Programmer – pharmaverse blog
AI coding agents are increasingly part of how we develop clinical R code — but they don’t automatically understand CDISC standards, ADaM conventions, or the pharmaverse ecosystem. AGENTS.md changes that.
Reading large spatial data
Reading in large spatial data can sometimes be a pain. Here are some simple things to consider when working in spatial data in R and tips for breaking large data sets into more manageable chunks.
Windows WSL Interop
ViewParquet
Lightning fast Parquet file viewer powered by DuckDB
GDAL: GDAL
Pricing | Tigris
Tigris is globally distributed, S3-compatible object storage with zero egress fees. Scales infinitely, works across any cloud, and provides instant access to your data worldwide.
Command Line Client
Installation To use the DuckDB CLI client, visit the CLI installation page. The latest stable version of the DuckDB command line client is {{ site.current_duckdb_version }}. Installation The DuckDB CLI (Command Line Interface) is a single, dependency-free executable. It is precompiled for Windows, Mac and Linux for both the stable version and for nightly builds produced by GitHub Actions. Please see the installation page under the CLI tab for download links. The DuckDB CLI is based on the SQLite command line shell, so CLI-client-specific functionality is similar to what is described in the SQLite documentation (although DuckDB's SQL syntax follows…
Harness engineering: leveraging Codex in an agent-first world
By Ryan Lopopolo, Member of the Technical Staff
Deterministic, Zero-Copy Parallel Execution for R
Provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. shard enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking.
Better Git diff with difftastic · Maëlle's R Blog
An Interactive and Feature-Rich Data Viewer
Provides an interactive viewer for data.frame, tibble and data.table objects using shiny and DT . It supports complex filtering, column selection, and automatic generation of reproducible dplyr code for data manipulation. The package is designed for ease of use in data exploration and reporting workflows.