foodshare-geo

High-performance geospatial utilities for distance calculations and PostGIS parsing.

Features

• Haversine Distance - Accurate great-circle distance calculations
• PostGIS Parsing - Parse coordinates from GeoJSON and WKT formats
• Batch Processing - Calculate distances for thousands of points efficiently
• Parallel Processing - Optional rayon support for multi-threaded batch operations
• WASM Support - Compile to WebAssembly for browser usage

Installation

Add to your Cargo.toml:

[dependencies]
foodshare-geo = "1.3"

Feature Flags

Usage

Distance Calculation

use foodshare_geo::{Coordinate, haversine_distance};

let berlin = Coordinate::new(52.5200, 13.4050);
let paris = Coordinate::new(48.8566, 2.3522);

let distance_km = haversine_distance(&berlin, &paris);
println!("Berlin to Paris: {:.1} km", distance_km); // ~878 km

PostGIS Point Parsing

Parse coordinates from Supabase/PostGIS location data:

use foodshare_geo::parse_postgis_point;
use serde_json::json;

// GeoJSON format (from PostGIS)
let geojson = json!({
    "type": "Point",
    "coordinates": [13.4050, 52.5200]  // [lng, lat]
});
let coord = parse_postgis_point(&geojson).unwrap();

// WKT format
let wkt = json!("POINT(13.4050 52.5200)");
let coord = parse_postgis_point(&wkt).unwrap();

Batch Distance Calculation

Calculate distances from a user to multiple products:

use foodshare_geo::{Coordinate, batch::calculate_distances};

let user_location = Coordinate::new(52.5200, 13.4050);
let products = vec![
    ("product_1".to_string(), Coordinate::new(52.5100, 13.4000)),
    ("product_2".to_string(), Coordinate::new(52.4800, 13.3900)),
    ("product_3".to_string(), Coordinate::new(52.5300, 13.4200)),
];

let results = calculate_distances(&user_location, &products);
for result in results {
    println!("{}: {:.2} km", result.id, result.distance_km);
}

Filtering by Radius

use foodshare_geo::{Coordinate, batch::calculate_distances};

let user = Coordinate::new(52.5200, 13.4050);
let products = vec![/* ... */];

let nearby: Vec<_> = calculate_distances(&user, &products)
    .into_iter()
    .filter(|r| r.distance_km <= 5.0)  // Within 5 km
    .collect();

WASM Usage

For browser usage, see the @foodshare/geo-wasm npm package.

import init, { distance, calculate_product_distances } from '@foodshare/geo-wasm';

await init();

// Single distance
const km = distance(52.52, 13.405, 48.8566, 2.3522);

// Batch calculation
const products = JSON.stringify([
  { id: "1", location: { type: "Point", coordinates: [13.4, 52.51] } },
  { id: "2", location: { type: "Point", coordinates: [13.39, 52.48] } },
]);
const results = JSON.parse(calculate_product_distances(52.52, 13.405, products));

Performance

Benchmarks on Apple M1:

The batch operation processes 10,000 products in under 1 millisecond, making it 50-400x faster than equivalent JavaScript implementations.

Accuracy

The Haversine formula provides accuracy within 0.5% for most use cases. For very long distances (>10,000 km), consider using Vincenty's formula for sub-meter accuracy.

License

MIT License - see LICENSE for details.