Everything you already do. Dramatically faster.
Serious GIS. Entirely in your browser. Open massive files. Run advanced spatial analysis. Keep your data on your device. No installs. No uploads. No pay-per-query.
rgSpatial brings serious spatial computing directly to the browser. Load, analyze, and explore massive geospatial datasets interactively in real time, without server round-trips, data uploads, or per-query compute costs.
A simple CSV file dropped into an ordinary browser tab — parsed, indexed, and on the map in the time it takes to blink. No server doing the work behind the scenes.
Real screen recording, unedited: a 1,000,000-point CSV file dragged into the browser and drawn as one-pixel dots.
Nearly every GIS product — open-source or commercial — is assembled on the same handful of spatial engines and data-conversion libraries developed decades ago. They’ve served the industry well, but they were designed for a different era of computing.
rgSpatial was engineered end-to-end for modern hardware and modern data. High-performance I/O gets geospatial data in and out fast. Apache Arrow keeps it in an efficient columnar format. Our ground-up Rust engine handles the spatial computation in parallel.
One high-performance stack powers rgSpatial everywhere: browser, Python, R, and server.
Same operations. Same data. Same machine. Every result verified for correctness before it was timed. rgSpatial didn’t just edge out the competition — it was 10× to 40× faster.
Not just rendering. Not just filtering. Real geoprocessing. Buffers, unions, intersections, dissolves, spatial joins, predicates, and more — all running directly on your machine.
Watch rgSpatial chain multiple operations across nationwide datasets, live.
Real screen recording, unedited: a point-in-polygon workflow to find all FAA wind-turbine applications within an 8 km radius of US natural-gas pipelines.
The agent turns a sentence into a spatial analysis — and shows you its plan, step by step, before anything runs. Inspect it, then hit Run.
Real screen recording, unedited: FAA wind-turbine permits semi-joined to US congressional districts and colored by party. The agent plans the join and the styling, shows both steps, and runs them on your machine.
Shapefile, FileGDB, GeoParquet, GeoPackage, KML, GeoJSON, CSV, Excel, and more. Drag it in and start working.
Buffer, dissolve, intersect, union, clip, spatial join, predicates, and more — across millions of features, directly on your machine.
Style by attributes, classify data, add labels and legends, and move seamlessly between light, dark, satellite, 3D, and globe views.
Query, filter, aggregate, and run spatial operations with rgSQL — a full spatial query engine for advanced workflows.
Describe what you want in plain English — “find parcels within 500 m of a school” — and rgSpatial turns it into an analysis you can inspect and run.
Date-aware layers automatically become interactive timelines. Scrub through time and watch spatial patterns change.
Thousands of coordinate reference systems are recognized and transformed automatically. Bring layers together without wrestling with projections.
Save datasets and analysis results into the formats your colleagues, clients, and existing tools already use.
Keep the workflows you know. Replace the engine underneath. rg brings its high-performance Rust I/O and spatial engine to Python and R — Arrow-native, parallel by design, and built for notebooks, pipelines, and production.
If you know GeoPandas or sf, rg will feel familiar.
40× median vs GeoPandas · 37× median vs sf — benchmarks →
import rg parcels = rg.read_file("parcels.parquet") zones = rg.read_file("flood_zones.gpkg") joined = parcels.sjoin(zones, predicate="within")
library(rg) parcels <- rg_read("parcels.parquet") zones <- rg_read("flood_zones.gpkg") joined <- rg_join(parcels, zones, join = "within")
Try it on your own data. A 30-minute walkthrough with the engineers who built it is there if you want one.
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