all these changes

frontend/node_modules/@turf/clusters-dbscan/LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2017 TurfJS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

frontend/node_modules/@turf/clusters-dbscan/README.md

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+# @turf/clusters-dbscan
+
+<!-- Generated by documentation.js. Update this documentation by updating the source code. -->
+
+## clustersDbscan
+
+Takes a set of [points][1] and partition them into clusters according to [https://en.wikipedia.org/wiki/DBSCAN][2] data clustering algorithm.
+
+**Parameters**
+
+-   `points` **[FeatureCollection][3]&lt;[Point][4]>** to be clustered
+-   `maxDistance` **[number][5]** Maximum Distance between any point of the cluster to generate the clusters (kilometers only)
+-   `options` **[Object][6]** Optional parameters (optional, default `{}`)
+    -   `options.units` **[string][7]** in which `maxDistance` is expressed, can be degrees, radians, miles, or kilometers (optional, default `"kilometers"`)
+    -   `options.mutate` **[boolean][8]** Allows GeoJSON input to be mutated (optional, default `false`)
+    -   `options.minPoints` **[number][5]** Minimum number of points to generate a single cluster,
+        points which do not meet this requirement will be classified as an 'edge' or 'noise'. (optional, default `3`)
+
+**Examples**
+
+```javascript
+// create random points with random z-values in their properties
+var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
+var maxDistance = 100;
+var clustered = turf.clustersDbscan(points, maxDistance);
+
+//addToMap
+var addToMap = [clustered];
+```
+
+Returns **[FeatureCollection][3]&lt;[Point][4]>** Clustered Points with an additional two properties associated to each Feature:-   {number} cluster - the associated clusterId
+-   {string} dbscan - type of point it has been classified as ('core'|'edge'|'noise')
+
+[1]: https://tools.ietf.org/html/rfc7946#section-3.1.2
+
+[2]: DBSCAN's
+
+[3]: https://tools.ietf.org/html/rfc7946#section-3.3
+
+[4]: https://tools.ietf.org/html/rfc7946#section-3.1.2
+
+[5]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number
+
+[6]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object
+
+[7]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String
+
+[8]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean
+
+<!-- This file is automatically generated. Please don't edit it directly:
+if you find an error, edit the source file (likely index.js), and re-run
+./scripts/generate-readmes in the turf project. -->
+
+---
+
+This module is part of the [Turfjs project](http://turfjs.org/), an open source
+module collection dedicated to geographic algorithms. It is maintained in the
+[Turfjs/turf](https://github.com/Turfjs/turf) repository, where you can create
+PRs and issues.
+
+### Installation
+
+Install this module individually:
+
+```sh
+$ npm install @turf/clusters-dbscan
+```
+
+Or install the Turf module that includes it as a function:
+
+```sh
+$ npm install @turf/turf
+```

frontend/node_modules/@turf/clusters-dbscan/dist/es/index.js

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+import clone from "@turf/clone";
+import distance from "@turf/distance";
+import { coordAll } from "@turf/meta";
+import { convertLength, } from "@turf/helpers";
+import clustering from "density-clustering";
+/**
+ * Takes a set of {@link Point|points} and partition them into clusters according to {@link DBSCAN's|https://en.wikipedia.org/wiki/DBSCAN} data clustering algorithm.
+ *
+ * @name clustersDbscan
+ * @param {FeatureCollection<Point>} points to be clustered
+ * @param {number} maxDistance Maximum Distance between any point of the cluster to generate the clusters (kilometers only)
+ * @param {Object} [options={}] Optional parameters
+ * @param {string} [options.units="kilometers"] in which `maxDistance` is expressed, can be degrees, radians, miles, or kilometers
+ * @param {boolean} [options.mutate=false] Allows GeoJSON input to be mutated
+ * @param {number} [options.minPoints=3] Minimum number of points to generate a single cluster,
+ * points which do not meet this requirement will be classified as an 'edge' or 'noise'.
+ * @returns {FeatureCollection<Point>} Clustered Points with an additional two properties associated to each Feature:
+ * - {number} cluster - the associated clusterId
+ * - {string} dbscan - type of point it has been classified as ('core'|'edge'|'noise')
+ * @example
+ * // create random points with random z-values in their properties
+ * var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
+ * var maxDistance = 100;
+ * var clustered = turf.clustersDbscan(points, maxDistance);
+ *
+ * //addToMap
+ * var addToMap = [clustered];
+ */
+function clustersDbscan(points, maxDistance, options) {
+    // Input validation being handled by Typescript
+    // collectionOf(points, 'Point', 'points must consist of a FeatureCollection of only Points');
+    // if (maxDistance === null || maxDistance === undefined) throw new Error('maxDistance is required');
+    // if (!(Math.sign(maxDistance) > 0)) throw new Error('maxDistance is invalid');
+    // if (!(minPoints === undefined || minPoints === null || Math.sign(minPoints) > 0)) throw new Error('options.minPoints is invalid');
+    if (options === void 0) { options = {}; }
+    // Clone points to prevent any mutations
+    if (options.mutate !== true)
+        points = clone(points);
+    // Defaults
+    options.minPoints = options.minPoints || 3;
+    // create clustered ids
+    var dbscan = new clustering.DBSCAN();
+    var clusteredIds = dbscan.run(coordAll(points), convertLength(maxDistance, options.units), options.minPoints, distance);
+    // Tag points to Clusters ID
+    var clusterId = -1;
+    clusteredIds.forEach(function (clusterIds) {
+        clusterId++;
+        // assign cluster ids to input points
+        clusterIds.forEach(function (idx) {
+            var clusterPoint = points.features[idx];
+            if (!clusterPoint.properties)
+                clusterPoint.properties = {};
+            clusterPoint.properties.cluster = clusterId;
+            clusterPoint.properties.dbscan = "core";
+        });
+    });
+    // handle noise points, if any
+    // edges points are tagged by DBSCAN as both 'noise' and 'cluster' as they can "reach" less than 'minPoints' number of points
+    dbscan.noise.forEach(function (noiseId) {
+        var noisePoint = points.features[noiseId];
+        if (!noisePoint.properties)
+            noisePoint.properties = {};
+        if (noisePoint.properties.cluster)
+            noisePoint.properties.dbscan = "edge";
+        else
+            noisePoint.properties.dbscan = "noise";
+    });
+    return points;
+}
+export default clustersDbscan;

frontend/node_modules/@turf/clusters-dbscan/dist/es/package.json

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+{"type":"module"}

frontend/node_modules/@turf/clusters-dbscan/dist/js/index.d.ts

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+import { Properties, Units, FeatureCollection, Point } from "@turf/helpers";
+export declare type Dbscan = "core" | "edge" | "noise";
+export declare type DbscanProps = Properties & {
+    dbscan?: Dbscan;
+    cluster?: number;
+};
+/**
+ * Takes a set of {@link Point|points} and partition them into clusters according to {@link DBSCAN's|https://en.wikipedia.org/wiki/DBSCAN} data clustering algorithm.
+ *
+ * @name clustersDbscan
+ * @param {FeatureCollection<Point>} points to be clustered
+ * @param {number} maxDistance Maximum Distance between any point of the cluster to generate the clusters (kilometers only)
+ * @param {Object} [options={}] Optional parameters
+ * @param {string} [options.units="kilometers"] in which `maxDistance` is expressed, can be degrees, radians, miles, or kilometers
+ * @param {boolean} [options.mutate=false] Allows GeoJSON input to be mutated
+ * @param {number} [options.minPoints=3] Minimum number of points to generate a single cluster,
+ * points which do not meet this requirement will be classified as an 'edge' or 'noise'.
+ * @returns {FeatureCollection<Point>} Clustered Points with an additional two properties associated to each Feature:
+ * - {number} cluster - the associated clusterId
+ * - {string} dbscan - type of point it has been classified as ('core'|'edge'|'noise')
+ * @example
+ * // create random points with random z-values in their properties
+ * var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
+ * var maxDistance = 100;
+ * var clustered = turf.clustersDbscan(points, maxDistance);
+ *
+ * //addToMap
+ * var addToMap = [clustered];
+ */
+declare function clustersDbscan(points: FeatureCollection<Point>, maxDistance: number, options?: {
+    units?: Units;
+    minPoints?: number;
+    mutate?: boolean;
+}): FeatureCollection<Point, DbscanProps>;
+export default clustersDbscan;

frontend/node_modules/@turf/clusters-dbscan/dist/js/index.js

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+"use strict";
+var __importDefault = (this && this.__importDefault) || function (mod) {
+    return (mod && mod.__esModule) ? mod : { "default": mod };
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var clone_1 = __importDefault(require("@turf/clone"));
+var distance_1 = __importDefault(require("@turf/distance"));
+var meta_1 = require("@turf/meta");
+var helpers_1 = require("@turf/helpers");
+var density_clustering_1 = __importDefault(require("density-clustering"));
+/**
+ * Takes a set of {@link Point|points} and partition them into clusters according to {@link DBSCAN's|https://en.wikipedia.org/wiki/DBSCAN} data clustering algorithm.
+ *
+ * @name clustersDbscan
+ * @param {FeatureCollection<Point>} points to be clustered
+ * @param {number} maxDistance Maximum Distance between any point of the cluster to generate the clusters (kilometers only)
+ * @param {Object} [options={}] Optional parameters
+ * @param {string} [options.units="kilometers"] in which `maxDistance` is expressed, can be degrees, radians, miles, or kilometers
+ * @param {boolean} [options.mutate=false] Allows GeoJSON input to be mutated
+ * @param {number} [options.minPoints=3] Minimum number of points to generate a single cluster,
+ * points which do not meet this requirement will be classified as an 'edge' or 'noise'.
+ * @returns {FeatureCollection<Point>} Clustered Points with an additional two properties associated to each Feature:
+ * - {number} cluster - the associated clusterId
+ * - {string} dbscan - type of point it has been classified as ('core'|'edge'|'noise')
+ * @example
+ * // create random points with random z-values in their properties
+ * var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
+ * var maxDistance = 100;
+ * var clustered = turf.clustersDbscan(points, maxDistance);
+ *
+ * //addToMap
+ * var addToMap = [clustered];
+ */
+function clustersDbscan(points, maxDistance, options) {
+    // Input validation being handled by Typescript
+    // collectionOf(points, 'Point', 'points must consist of a FeatureCollection of only Points');
+    // if (maxDistance === null || maxDistance === undefined) throw new Error('maxDistance is required');
+    // if (!(Math.sign(maxDistance) > 0)) throw new Error('maxDistance is invalid');
+    // if (!(minPoints === undefined || minPoints === null || Math.sign(minPoints) > 0)) throw new Error('options.minPoints is invalid');
+    if (options === void 0) { options = {}; }
+    // Clone points to prevent any mutations
+    if (options.mutate !== true)
+        points = clone_1.default(points);
+    // Defaults
+    options.minPoints = options.minPoints || 3;
+    // create clustered ids
+    var dbscan = new density_clustering_1.default.DBSCAN();
+    var clusteredIds = dbscan.run(meta_1.coordAll(points), helpers_1.convertLength(maxDistance, options.units), options.minPoints, distance_1.default);
+    // Tag points to Clusters ID
+    var clusterId = -1;
+    clusteredIds.forEach(function (clusterIds) {
+        clusterId++;
+        // assign cluster ids to input points
+        clusterIds.forEach(function (idx) {
+            var clusterPoint = points.features[idx];
+            if (!clusterPoint.properties)
+                clusterPoint.properties = {};
+            clusterPoint.properties.cluster = clusterId;
+            clusterPoint.properties.dbscan = "core";
+        });
+    });
+    // handle noise points, if any
+    // edges points are tagged by DBSCAN as both 'noise' and 'cluster' as they can "reach" less than 'minPoints' number of points
+    dbscan.noise.forEach(function (noiseId) {
+        var noisePoint = points.features[noiseId];
+        if (!noisePoint.properties)
+            noisePoint.properties = {};
+        if (noisePoint.properties.cluster)
+            noisePoint.properties.dbscan = "edge";
+        else
+            noisePoint.properties.dbscan = "noise";
+    });
+    return points;
+}
+exports.default = clustersDbscan;

frontend/node_modules/@turf/clusters-dbscan/package.json

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+{
+  "name": "@turf/clusters-dbscan",
+  "version": "6.5.0",
+  "description": "turf clusters-dbscan module",
+  "author": "Turf Authors",
+  "contributors": [
+    "Lukasz <@uhho>",
+    "Denis Carriere <@DenisCarriere>",
+    "Stefano Borghi <@stebogit>"
+  ],
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/Turfjs/turf/issues"
+  },
+  "homepage": "https://github.com/Turfjs/turf",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/Turfjs/turf.git"
+  },
+  "funding": "https://opencollective.com/turf",
+  "publishConfig": {
+    "access": "public"
+  },
+  "keywords": [
+    "turf",
+    "geojson",
+    "cluster",
+    "clusters",
+    "clustering",
+    "density",
+    "dbscan"
+  ],
+  "main": "dist/js/index.js",
+  "module": "dist/es/index.js",
+  "exports": {
+    "./package.json": "./package.json",
+    ".": {
+      "import": "./dist/es/index.js",
+      "require": "./dist/js/index.js"
+    }
+  },
+  "types": "dist/js/index.d.ts",
+  "sideEffects": false,
+  "files": [
+    "dist"
+  ],
+  "scripts": {
+    "bench": "ts-node bench.js",
+    "build": "npm-run-all build:*",
+    "build:es": "tsc --outDir dist/es --module esnext --declaration false && echo '{\"type\":\"module\"}' > dist/es/package.json",
+    "build:js": "tsc",
+    "docs": "node ../../scripts/generate-readmes",
+    "test": "npm-run-all test:*",
+    "test:tape": "ts-node -r esm test.js",
+    "test:types": "tsc --esModuleInterop --noEmit types.ts"
+  },
+  "devDependencies": {
+    "@turf/centroid": "^6.5.0",
+    "@turf/clusters": "^6.5.0",
+    "@types/density-clustering": "^1.3.0",
+    "@types/tape": "*",
+    "benchmark": "*",
+    "chromatism": "*",
+    "concaveman": "*",
+    "load-json-file": "*",
+    "npm-run-all": "*",
+    "tape": "*",
+    "ts-node": "*",
+    "tslint": "*",
+    "typescript": "*",
+    "write-json-file": "*"
+  },
+  "dependencies": {
+    "@turf/clone": "^6.5.0",
+    "@turf/distance": "^6.5.0",
+    "@turf/helpers": "^6.5.0",
+    "@turf/meta": "^6.5.0",
+    "density-clustering": "1.3.0"
+  },
+  "gitHead": "5375941072b90d489389db22b43bfe809d5e451e"
+}