Getting Started

A lightweight, dependency-free geospatial database for .NET that combines SQLite R*Tree spatial indexing with custom C# geometry algorithms for high-performance spatial queries. No SpatiaLite, no NetTopologySuite — just SQLite and math.

Need more than a local SQLite file?

Shiny.Spatial is deliberately small — an on-device, file-based spatial store with distance, envelope, and intersection queries. If your needs are bigger, use Shiny.DocumentDb’s spatial support instead. It gives you the full topological predicate family (GeoIntersects, GeoContains, GeoTouches, GeoCrosses, GeoOverlaps, …), NearestNeighbors, LINQ composition, and distance ordering — over the documents you’re already storing, and against whichever database you’re already using: SQLite on device plus PostgreSQL, MySQL, SQL Server, Oracle, DuckDB, CosmosDB, and MongoDB on the server.

GitHub
Downloads

Frameworks

.NET

Operating Systems

Android

iOS

Windows

Features

• Two-pass query pipeline — R*Tree bounding box filter (SQL, O(log n)) followed by C# geometry refinement for exact results
• Dual coordinate systems — WGS84 (GPS/geographic) with Haversine distance and Cartesian with Euclidean distance
• Full geometry type support — Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, GeometryCollection
• WKB serialization — Well-Known Binary encoding/decoding for all geometry types
• Fluent query builder — Chain spatial filters, property filters, sorting, and paging
• Bulk insert — Transaction-wrapped batch inserts, ~20% faster than individual inserts
• Pre-built databases — US states, US cities, Canadian provinces, and Canadian cities included
• AOT-compatible and trimmable — works with ahead-of-time compilation on all .NET platforms
• Zero geospatial dependencies — only depends on Microsoft.Data.Sqlite

Setup

1. Install the NuGet package

   

   dotnet add package Shiny.Spatial

2. Create a database and spatial table

   using Shiny.Spatial.Database;
   using Shiny.Spatial.Geometry;
   
   using var db = new SpatialDatabase("mydata.db");
   
   var table = db.CreateTable(
       "cities",
       CoordinateSystem.Wgs84,
       new PropertyDefinition("name", PropertyType.Text),
       new PropertyDefinition("population", PropertyType.Integer)
   );

3. Insert features

   var feature = new SpatialFeature(new Point(-104.99, 39.74))
   {
       Properties = { ["name"] = "Denver", ["population"] = 715000L }
   };
   long id = table.Insert(feature);

4. Query spatially

   // Find all cities within 150km of Denver
   var results = table.FindWithinDistance(
       new Coordinate(-104.99, 39.74),
       distanceMeters: 150_000
   );

Quick Examples

Bulk insert

var features = cities.Select(c => new SpatialFeature(new Point(c.Longitude, c.Latitude))
{
    Properties = { ["name"] = c.Name, ["population"] = c.Population }
}).ToList();

table.BulkInsert(features);

Polygon intersection

var colorado = new Polygon(new[]
{
    new Coordinate(-109.05, 37.0),
    new Coordinate(-102.05, 37.0),
    new Coordinate(-102.05, 41.0),
    new Coordinate(-109.05, 41.0),
    new Coordinate(-109.05, 37.0)
});

var citiesInColorado = table.FindIntersecting(colorado);

Fluent query builder

var center = new Coordinate(-104.99, 39.74);

var results = table.Query()
    .WithinDistance(center, 150_000)
    .WhereProperty("population", ">", 200000L)
    .OrderByDistance(center)
    .Limit(10)
    .ToList();

Using a pre-built database

using var db = new SpatialDatabase("databases/us-states.db");
var states = db.GetTable("states");

var denver = new Point(-104.99, 39.74);
var result = states.FindIntersecting(denver);
// result[0].Properties["name"] == "Colorado"

Architecture

Shiny.Spatial uses a two-pass query pipeline to deliver both speed and accuracy:

1. Pass 1 — R*Tree bounding box filter (SQL): SQLite’s R*Tree index rapidly eliminates candidates whose bounding boxes don’t overlap the query region. This runs entirely in SQL at O(log n).

2. Pass 2 — C# geometry refinement: Remaining candidates are tested with exact geometry algorithms (point-in-polygon, segment intersection, Haversine distance). This eliminates false positives from the bounding box approximation.

This approach eliminates 99%+ of candidates before expensive geometry checks, delivering sub-millisecond queries on datasets of 100K+ features.

SQLite Schema

Each spatial table creates an R*Tree virtual table with auxiliary columns:

CREATE VIRTUAL TABLE {name}_rtree USING rtree(
    id,
    min_x, max_x, min_y, max_y,
    +geometry BLOB,                -- WKB-encoded geometry
    +prop_name TEXT,               -- user-defined property columns
    +prop_population INTEGER
);

Metadata is tracked in two internal tables:

• __spatial_meta — table coordinate systems
• __spatial_columns — property definitions

Supported Platforms

Framework	Support
.NET 10+	Full support with AOT/trimming
.NET Standard 2.0	Full support (Xamarin, .NET Framework, etc.)
iOS / Android	Full support via Microsoft.Data.Sqlite
Windows / macOS / Linux	Full support

AI Coding Assistant

Claude CodeGitHub Copilot

Step 1 — Add the marketplace:

claude plugin marketplace add shinyorg/skills

Step 2 — Install the plugin:

claude plugin install shiny@shiny

One plugin installs all 35 Shiny skills. Your agent loads only the skill relevant to what you're building, so there's no cost to having them all available.

Step 1 — Add the marketplace:

copilot plugin marketplace add https://github.com/shinyorg/skills

Step 2 — Install the plugin:

copilot plugin install shiny@shiny

One plugin installs all 35 Shiny skills. Your agent loads only the skill relevant to what you're building, so there's no cost to having them all available.

View shiny-spatial Skill