Implement real GIS data import and PostGIS spatial queries

This commit is contained in:
(jenkins)
2026-03-16 20:32:22 +00:00
parent e764efb189
commit ad07a70125
4 changed files with 312 additions and 56 deletions
+90 -50
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@@ -1,66 +1,106 @@
const express = require('express');
const cors = require('cors');
const { Pool } = require('pg');
require('dotenv').config();
const app = express();
const PORT = process.env.PORT || 3001;
// Database connection pool
const pool = new Pool({
connectionString: process.env.DATABASE_URL || 'postgresql://line_of_sight:line_of_sight_pass@postgres:5432/line_of_sight'
});
app.use(cors());
app.use(express.json());
// Mock conurbation data for MVP
const MOCK_CONURBATIONS = [
{ id: 1, name: "London", population: 9000000, distance_km: 0, lat: 51.5074, lon: -0.1278 },
{ id: 2, name: "Paris", population: 2161000, distance_km: 344, lat: 48.8566, lon: 2.3522 },
{ id: 3, name: "Berlin", population: 3644000, distance_km: 878, lat: 52.5200, lon: 13.4050 },
{ id: 4, name: "Warsaw", population: 1793000, distance_km: 1200, lat: 52.2297, lon: 21.0122 },
{ id: 5, name: "Moscow", population: 12506000, distance_km: 2063, lat: 55.7558, lon: 37.6173 },
{ id: 6, name: "Kazan", population: 1257000, distance_km: 2850, lat: 55.7897, lon: 49.1219 },
{ id: 7, name: "Almaty", population: 2000000, distance_km: 3900, lat: 43.2220, lon: 76.8512 },
{ id: 8, name: "Urumqi", population: 3500000, distance_km: 4500, lat: 43.8256, lon: 87.6168 },
{ id: 9, name: "Lahore", population: 11126000, distance_km: 5400, lat: 31.5204, lon: 74.3587 },
{ id: 10, name: "New Delhi", population: 29399000, distance_km: 5800, lat: 28.6139, lon: 77.2090 },
{ id: 11, name: "Dhaka", population: 21006000, distance_km: 6200, lat: 23.8103, lon: 90.4125 },
{ id: 12, name: "Chennai", population: 10971000, distance_km: 6500, lat: 13.0827, lon: 80.2707 },
{ id: 13, name: "Bangkok", population: 10539000, distance_km: 7200, lat: 13.7563, lon: 100.5018 },
{ id: 14, name: "Jakarta", population: 10562000, distance_km: 8100, lat: -6.2088, lon: 106.8456 },
{ id: 15, name: "Singapore", population: 5686000, distance_km: 8300, lat: 1.3521, lon: 103.8198 },
{ id: 16, name: "Manila", population: 17801000, distance_km: 8700, lat: 14.5995, lon: 120.9842 },
{ id: 17, name: "Tokyo", population: 37400000, distance_km: 9500, lat: 35.6762, lon: 139.6503 },
{ id: 18, name: "Seoul", population: 9720000, distance_km: 9200, lat: 37.5665, lon: 126.9780 },
{ id: 19, name: "Beijing", population: 21540000, distance_km: 8900, lat: 39.9042, lon: 116.4074 },
{ id: 20, name: "Shanghai", population: 27058000, distance_km: 9000, lat: 31.2304, lon: 121.4737 }
];
// Helper to calculate destination point given start, bearing, and distance (km)
const calculateDestination = (lat, lon, bearing, distance) => {
const R = 6371;
const brng = (bearing * Math.PI) / 180;
const φ1 = (lat * Math.PI) / 180;
const λ1 = (lon * Math.PI) / 180;
const δ = distance / R;
// Mock API endpoint - returns dummy conurbations based on input coordinates
app.get('/api/line-of-sight', (req, res) => {
const φ2 = Math.asin(
Math.sin(φ1) * Math.cos(δ) +
Math.cos(φ1) * Math.sin(δ) * Math.cos(brng)
);
const λ2 =
λ1 +
Math.atan2(
Math.sin(brng) * Math.sin(δ) * Math.cos(φ1),
Math.cos(δ) - Math.sin(φ1) * Math.sin(φ2)
);
return {
lat: (φ2 * 180) / Math.PI,
lon: (((λ2 * 180) / Math.PI + 540) % 360) - 180
};
};
// Real API endpoint - uses PostGIS for spatial queries
app.get('/api/line-of-sight', async (req, res) => {
const { lat, lon, direction, tolerance } = req.query;
console.log(`Received request: lat=${lat}, lon=${lon}, direction=${direction}, tolerance=${tolerance}`);
// Return mock data for MVP
res.json({
success: true,
data: {
start_point: { lat: parseFloat(lat) || 51.5074, lon: parseFloat(lon) || -0.1278 },
direction: parseInt(direction) || 45,
tolerance_km: parseInt(tolerance) || 50,
conurbations: MOCK_CONURBATIONS.slice(0, 20),
line_coordinates: [
{ lat: 51.5074, lon: -0.1278 },
{ lat: 48.8566, lon: 2.3522 },
{ lat: 52.5200, lon: 13.4050 },
{ lat: 55.7558, lon: 37.6173 },
{ lat: 43.2220, lon: 76.8512 },
{ lat: 28.6139, lon: 77.2090 },
{ lat: 13.7563, lon: 100.5018 },
{ lat: -6.2088, lon: 106.8456 },
{ lat: 35.6762, lon: 139.6503 },
{ lat: 51.5074, lon: -0.1278 } // Complete the circle
]
},
message: "Mock data returned for MVP - Real geospatial calculations coming soon"
});
const startLat = parseFloat(lat) || 51.5074;
const startLon = parseFloat(lon) || -0.1278;
const bearing = parseInt(direction) || 0;
const toleranceKm = parseInt(tolerance) || 50;
console.log(`Processing real request: lat=${startLat}, lon=${startLon}, bearing=${bearing}, tolerance=${toleranceKm}`);
try {
// Generate path points for visualization and spatial query
const pathPoints = [];
const totalDistance = 10000;
const steps = 20;
for (let i = 0; i <= steps; i++) {
const dist = (totalDistance * i) / steps;
pathPoints.push(calculateDestination(startLat, startLon, bearing, dist));
}
const lineWKT = `LINESTRING(${pathPoints.map(p => `${p.lon} ${p.lat}`).join(',')})`;
const query = `
WITH path AS (
SELECT ST_GeogFromText($1) as route
)
SELECT
id,
name,
population,
country,
ST_Y(geom::geometry) as lat,
ST_X(geom::geometry) as lon,
ST_Distance(geom, (SELECT route FROM path)) / 1000 as distance_to_line_km,
ST_LineLocatePoint((SELECT route FROM path)::geometry, geom::geometry) as pos_on_line
FROM cities
WHERE ST_DWithin(geom, (SELECT route FROM path), $2 * 1000)
ORDER BY pos_on_line ASC
LIMIT 20;
`;
const result = await pool.query(query, [lineWKT, toleranceKm]);
res.json({
success: true,
data: {
start_point: { lat: startLat, lon: startLon },
direction: bearing,
tolerance_km: toleranceKm,
conurbations: result.rows.map(row => ({
...row,
distance_km: Math.round(row.distance_to_line_km)
})),
line_coordinates: pathPoints
}
});
} catch (err) {
console.error('Database query error:', err);
res.status(500).json({ success: false, error: 'Database query failed' });
}
});
// Health check endpoint
+132
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@@ -8,6 +8,7 @@
"name": "line-of-sight-backend",
"version": "1.0.0",
"dependencies": {
"axios": "^1.13.6",
"cors": "^2.8.6",
"dotenv": "^17.3.1",
"express": "^5.2.1",
@@ -1426,6 +1427,23 @@
"sprintf-js": "~1.0.2"
}
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"license": "MIT"
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@@ -1891,6 +1909,18 @@
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@@ -2008,6 +2038,15 @@
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@@ -2128,6 +2167,21 @@
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@@ -2329,6 +2383,26 @@
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@@ -2345,6 +2419,43 @@
"url": "https://github.com/sponsors/isaacs"
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@@ -2533,6 +2644,21 @@
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@@ -4158,6 +4284,12 @@
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+8 -6
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@@ -6,16 +6,18 @@
"scripts": {
"start": "node app/server.js",
"dev": "nodemon app/server.js",
"test": "jest"
"test": "jest",
"seed-data": "node scripts/import_cities.js"
},
"dependencies": {
"express": "^5.2.1",
"axios": "^1.13.6",
"cors": "^2.8.6",
"pg": "^8.20.0",
"dotenv": "^17.3.1"
"dotenv": "^17.3.1",
"express": "^5.2.1",
"pg": "^8.20.0"
},
"devDependencies": {
"nodemon": "^3.1.14",
"jest": "^30.3.0"
"jest": "^30.3.0",
"nodemon": "^3.1.14"
}
}
+82
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@@ -0,0 +1,82 @@
const { Client } = require('pg');
const axios = require('axios');
require('dotenv').config();
const DATA_URL = 'https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_10m_populated_places_simple.geojson';
async function importCities() {
const client = new Client({
connectionString: process.env.DATABASE_URL || 'postgresql://line_of_sight:line_of_sight_pass@localhost:5432/line_of_sight'
});
try {
console.log('Connecting to database...');
await client.connect();
console.log('Downloading Natural Earth data (GeoJSON)...');
const response = await axios.get(DATA_URL);
const data = response.data;
console.log(`Downloaded ${data.features.length} features. Preparing database...`);
// Ensure table exists and is clean
await client.query('TRUNCATE TABLE cities');
let count = 0;
const batchSize = 100;
for (let i = 0; i < data.features.length; i += batchSize) {
const batch = data.features.slice(i, i + batchSize);
const values = [];
const queryParts = [];
batch.forEach((feature, index) => {
const props = feature.properties;
const coords = feature.geometry.coordinates; // [lon, lat]
const name = props.NAME || 'Unknown';
const population = props.POP_MAX || 0;
const country = props.ADM0NAME || 'Unknown';
const lon = coords[0];
const lat = coords[1];
const baseIndex = index * 4;
queryParts.push(`($${baseIndex + 1}, $${baseIndex + 2}, $${baseIndex + 3}, ST_SetSRID(ST_MakePoint($${baseIndex + 4}, $${baseIndex + 1}), 4326)::geography)`);
values.push(lat, name, population, country, lon); // Note: ST_MakePoint takes lon, lat
});
// Simple positional mapping for query (lat, name, pop, country, lon)
// Actually let's refine the query to be clearer
const refinedQueryParts = [];
const refinedValues = [];
batch.forEach((feature, index) => {
const p = feature.properties;
const c = feature.geometry.coordinates;
const base = index * 5;
refinedQueryParts.push(`($${base + 1}, $${base + 2}, $${base + 3}, ST_SetSRID(ST_MakePoint($${base + 4}, $${base + 5}), 4326)::geography)`);
refinedValues.push(p.NAME || 'Unknown', p.POP_MAX || 0, p.ADM0NAME || 'Unknown', c[0], c[1]);
});
await client.query(
`INSERT INTO cities (name, population, country, geom) VALUES ${refinedQueryParts.join(',')}`,
refinedValues
);
count += batch.length;
if (count % 1000 === 0 || count === data.features.length) {
console.log(`Imported ${count}/${data.features.length} cities...`);
}
}
console.log('SUCCESS: Natural Earth data import complete.');
} catch (err) {
console.error('ERROR during import:', err);
} finally {
await client.end();
}
}
importCities();