2f:T7f1,function applyLloydsAlgorithm(data, maxIter = 20, rows = 10000, cols = 10000) { // Extract centroid coordinates let centroids = data.features.map( (feature) => turf.centroid(feature).geometry.coordinates ); // Initialize positions (this could be random or based on an initial configuration) let positions = centroids; // Here we just use the original centroids let bbox = turf.bbox(turf.points(positions)); // Apply Lloyd's algorithm for (let iter = 0; iter < maxIter; iter++) { // Construct Voronoi cells var options = { bbox: bbox }; let voronoi = turf.voronoi(turf.points(positions), options); // Compute Voronoi centroids and move points let newPositions = voronoi.features.map( (feature) => turf.centroid(feature).geometry.coordinates ); // Check convergence (here we use a simple position change threshold) let maxPosChange = d3.max(newPositions, (pos, i) => turf.distance(pos, positions[i], { units: "meters" }) ); if (maxPosChange < 1e-6) break; positions = newPositions; } // Define the number of rows and columns for your grid let xExtent = d3.extent(positions, (d) => d[0]); let yExtent = d3.extent(positions, (d) => d[1]); // Define the bin size let xBinSize = (xExtent[1] - xExtent[0]) / cols; let yBinSize = (yExtent[1] - yExtent[0]) / rows; // Update positions based on binning positions = positions.map((pos) => { let i = Math.floor((pos[0] - xExtent[0]) / xBinSize); let j = Math.floor((pos[1] - yExtent[0]) / yBinSize); return [ xExtent[0] + (i + 0.5) * xBinSize, yExtent[0] + (j + 0.5) * yBinSize ]; }); // Save the updated positions as a new GeoJSON file let updatedData = { type: "FeatureCollection", features: positions.map((pos, i) => ({ type: "Feature", geometry: { type: "Point", coordinates: pos }, properties: data.features[i].properties // Preserve the original properties })) }; return updatedData; }30:T6e8,function applyLloydsAlgorithmWithForce(data, maxIter = 10, forceStrength = 1) { // Extract centroid coordinates let centroids = data.features.map( (feature) => turf.centroid(feature).geometry.coordinates ); // Initialize positions (this could be random or based on an initial configuration) let positions = centroids; // Here we just use the original centroids let bbox = turf.bbox(turf.points(positions)); // Initialize force simulation let simulation = d3 .forceSimulation(positions) .force("center", d3.forceCenter()) .force("charge", d3.forceManyBody().strength(forceStrength)) .stop(); // Apply Lloyd's algorithm for (let iter = 0; iter < maxIter; iter++) { // Construct Voronoi cells var options = { bbox: bbox }; let voronoi = turf.voronoi(turf.points(positions), options); // Compute Voronoi centroids and move points let newPositions = voronoi.features.map( (feature) => turf.centroid(feature).geometry.coordinates ); // Apply force simulation simulation.nodes(newPositions); for (var i = 0; i < 300; ++i) simulation.tick(); // Check convergence (here we use a simple position change threshold) let maxPosChange = d3.max(newPositions, (pos, i) => turf.distance(pos, positions[i], { units: "meters" }) ); if (maxPosChange < 1e-6) break; positions = newPositions; } // Save the updated positions as a new GeoJSON file let updatedData = { type: "FeatureCollection", features: positions.map((pos, i) => ({ type: "Feature", geometry: { type: "Point", coordinates: pos }, properties: data.features[i].properties // Preserve the original properties })) }; return updatedData; }31:Tde9,function applyLloydsAlgorithm2( data, maxIter = 20, gridRadius = 50, forceStrength = 0.1 ) { // Extract polygon vertices and create positions let positions = data.features .map((feature) => { // Assuming first ring contains vertices let ring = feature.geometry.coordinates[0]; // Create points from each vertex, ensuring each object has an array for coordinates return ring.map((coord) => ({ id: feature.properties.fid, coordinates: [coord[0], coord[1]] // Nest coordinates in an array })); }) .flatMap((ring) => ring); // Define hexagonal grid parameters const gridWidth = Math.sqrt(3) * gridRadius; const gridHeight = 2 * gridRadius; // Apply Lloyd's algorithm with adaptive grid placement for (let iter = 0; iter < maxIter; iter++) { // Construct Voronoi cells using extracted points let voronoi = turf.voronoi(turf.points(positions)); let newPositions = voronoi.features.map( (feature) => turf.centroid(feature).geometry.coordinates ); // Create adaptive hexagonal grid based on current positions let grid = {}; for (let pos of positions) { let col = Math.floor(pos.x / gridWidth); let row = Math.floor(pos.y / gridHeight); let cellKey = `${col}-${row}`; if (!grid[cellKey]) { grid[cellKey] = []; } grid[cellKey].push(pos); } // Update positions using force simulation with grid constraints let simulation = d3 .forceSimulation(positions) .force("collide", d3.forceCollide().radius(gridRadius * 0.8)) .force( "link", d3 .forceLink(positions) .id((d) => d.id) // Use 'id' property .strength((d, i) => { // Stronger attraction for closer points let neighbors = grid[ `${Math.floor(d.x / gridWidth)}-${Math.floor(d.y / gridHeight)}` ]; let minDist = Infinity; for (let neighbor of neighbors) { if (neighbor !== d) { minDist = Math.min( minDist, turf.distance([d.x, d.y], [neighbor.x, neighbor.y], { units: "meters" }) ); } } return 1 / (minDist * minDist); }) ) .force( "x", d3 .forceX((d) => { let col = Math.floor(d.x / gridWidth); return col * gridWidth + gridWidth / 2; }) .strength(forceStrength) ) .force( "y", d3 .forceY((d) => { let row = Math.floor(d.y / gridHeight); return row * gridHeight + gridHeight / 2; }) .strength(forceStrength) ) .alpha(0.5) // Adjust alpha for desired simulation intensity .tick(10); // Adjust tick count for simulation duration positions = simulation.nodes(); // Check convergence let maxPosChange = d3.max(newPositions, (pos, i) => turf.distance([pos[0], pos[1]], [positions[i].x, positions[i].y], { units: "meters" }) ); if (maxPosChange < 1e-6) break; } // Create output GeoJSON with original properties let updatedData = { type: "FeatureCollection", features: positions.map((pos, i) => ({ type: "Feature", geometry: { type: "Point", coordinates: [pos.x, pos.y] }, properties: data.features[i].properties })) }; return updatedData; }32:[{"id":"68b8daab90317e8b3020d5d3218867672fb05504c3f4aaee64689cd6aa89de6bb935444cdc66e28edc44496b010ffb095239f4d3cf86d1ae0152b21026da4d17","href":"https://static.observableusercontent.com/files/68b8daab90317e8b3020d5d3218867672fb05504c3f4aaee64689cd6aa89de6bb935444cdc66e28edc44496b010ffb095239f4d3cf86d1ae0152b21026da4d17","name":"kabupaten (3).json","type":"application/json","lastModified":1708360507401.257,"size":4078260},{"id":"dd9dd6a32fa68c07797114641dc6d198e795732c97aad8b2f662da40f3e1e34a5065f6ca6a00d274eba477b2f35de3bc3dbf22938a0a15240a29b865553b987b","href":"https://static.observableusercontent.com/files/dd9dd6a32fa68c07797114641dc6d198e795732c97aad8b2f662da40f3e1e34a5065f6ca6a00d274eba477b2f35de3bc3dbf22938a0a15240a29b865553b987b","name":"kabupaten (2).json","type":"application/json","lastModified":1708360516495.595,"size":3503931},{"id":"78c478fd5ea7dc6d608e2bbad57b352695374ab65e37a03e636679bb49bf5922671fa8ac769a6f1a629bd68a0a2a244cfe7ce221b1e84f0cbbb7bbb87b003a74","href":"https://static.observableusercontent.com/files/78c478fd5ea7dc6d608e2bbad57b352695374ab65e37a03e636679bb49bf5922671fa8ac769a6f1a629bd68a0a2a244cfe7ce221b1e84f0cbbb7bbb87b003a74","name":"cartogram_indo2.geojson","type":"application/geo+json","lastModified":1708362255855.122,"size":4010578},{"id":"3d5ede7ffa659a865a68efc9074a690a45b366ee72e78ba79b9d5d564e8b80e63952c17ee075cc918b53629c1e551439c801f820a128b4fcb8a2e4ce3adb7a83","href":"https://static.observableusercontent.com/files/3d5ede7ffa659a865a68efc9074a690a45b366ee72e78ba79b9d5d564e8b80e63952c17ee075cc918b53629c1e551439c801f820a128b4fcb8a2e4ce3adb7a83","name":"sleman.geojson","type":"application/geo+json","lastModified":1694840815274.295,"size":3608000}] 2d:{"title":"Lloyd's Algorithm","cells":[{"id":0,"value":"# Lloyd's Algorithm","pinned":false,"hidden":false,"mode":"md"},{"id":21,"value":"data = applyLloydsAlgorithm(sleman)","pinned":true,"hidden":false,"mode":"ojs"},{"id":56,"value":"{\n const container = yield htl.html`

`;\n const map = L.map(container);\n const layer = L.geoJSON(data).addTo(map);\n map.fitBounds(layer.getBounds(), {maxZoom: 9});\n L.tileLayer(\"https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\", {\n attribution: \"© OpenStreetMap contributors\"\n }).addTo(map);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":57,"value":"{\n const container = yield htl.html`

`;\n const map = L.map(container);\n const layer = L.geoJSON(sleman).addTo(map);\n map.fitBounds(layer.getBounds(), {maxZoom: 9});\n L.tileLayer(\"https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\", {\n attribution: \"© OpenStreetMap contributors\"\n }).addTo(map);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"sleman = FileAttachment(\"sleman.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3,"value":"$2f","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"turf = require(\"@turf/turf\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":30,"value":"import { quickmap } from \"@danylaksono/quickmap\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":69,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":71,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":77,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":61,"value":"kabupaten3 = FileAttachment(\"kabupaten (3).json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":83,"value":"kabupaten3.features.forEach((feature, index) => {\n let ring = feature.geometry.coordinates[0];\n ring.forEach((coord, i) => {\n if (!Array.isArray(coord) || coord.length !== 2) {\n console.log(`Invalid coordinates at feature ${index}, ring ${i}:`, coord);\n }\n });\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":63,"value":"data2 = applyLloydsAlgorithmWithForce(kabupaten3)","pinned":true,"hidden":false,"mode":"ojs"},{"id":97,"value":"{\n const container = yield htl.html`

`;\n const map = L.map(container);\n const layer = L.geoJSON(data2).addTo(map);\n map.fitBounds(layer.getBounds(), {maxZoom: 9});\n L.tileLayer(\"https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\", {\n attribution: \"© OpenStreetMap contributors\"\n }).addTo(map);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":62,"value":"kabupaten2 = FileAttachment(\"kabupaten (2).json\").json()","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}