all these changes

frontend/node_modules/@turf/moran-index/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/moran-index/README.md

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+# @turf/moran-index
+
+<!-- Generated by documentation.js. Update this documentation by updating the source code. -->
+
+## moranIndex
+
+Moran's I measures patterns of attribute values associated with features.
+The method reveal whether similar values tend to occur near each other,
+or whether high or low values are interspersed.
+
+Moran's I > 0 means a clusterd pattern.
+Moran's I &lt; 0 means a dispersed pattern.
+Moran's I = 0 means a random pattern.
+
+In order to test the significance of the result. The z score is calculated.
+A positive enough z-score (ex. >1.96) indicates clustering,
+while a negative enough z-score (ex. &lt;-1.96) indicates a dispersed pattern.
+
+the z-score can be calculated based on a normal or random assumption.
+
+**Bibliography\***
+
+1.  [Moran's I](https://en.wikipedia.org/wiki/Moran%27s_I)
+
+2.  [pysal](http://pysal.readthedocs.io/en/latest/index.html)
+
+3.  Andy Mitchell, The ESRI Guide to GIS Analysis Volume 2: Spatial Measurements & Statistics.
+
+**Parameters**
+
+-   `fc` **[FeatureCollection](https://tools.ietf.org/html/rfc7946#section-3.3)&lt;any>** 
+-   `options` **[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)** 
+    -   `options.inputField` **[string](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String)** the property name, must contain numeric values
+    -   `options.threshold` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the distance threshold (optional, default `100000`)
+    -   `options.p` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the Minkowski p-norm distance parameter (optional, default `2`)
+    -   `options.binary` **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)** whether transfrom the distance to binary (optional, default `false`)
+    -   `options.alpha` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the distance decay parameter (optional, default `-1`)
+    -   `options.standardization` **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)** wheter row standardization the distance (optional, default `true`)
+
+**Examples**
+
+```javascript
+const bbox = [-65, 40, -63, 42];
+const dataset = turf.randomPoint(100, { bbox: bbox });
+
+const result = moranIndex(pts, {
+  inputField: 'CRIME',
+});
+```
+
+Returns **[MoranIndex](#moranindex)** 
+
+## mean
+
+get mean of a list
+
+**Parameters**
+
+-   `y` **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)&lt;[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)>** 
+
+Returns **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** 
+
+## variance
+
+get variance of a list
+
+**Parameters**
+
+-   `y` **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)&lt;[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)>** 
+
+Returns **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** 
+
+## MoranIndex
+
+Type: [Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)
+
+**Properties**
+
+-   `moranIndex` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the moran's Index of the observed feature set
+-   `expectedMoranIndex` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the moran's Index of the random distribution
+-   `stdNorm` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the standard devitaion of the random distribution
+-   `zNorm` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** the z-score of the observe samples with regard to the random distribution
+
+<!-- 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/moran-index
+```
+
+Or install the Turf module that includes it as a function:
+
+```sh
+$ npm install @turf/turf
+```

frontend/node_modules/@turf/moran-index/dist/es/index.js

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+import spatialWeight from "@turf/distance-weight";
+import { featureEach } from "@turf/meta";
+/**
+ * Moran's I measures patterns of attribute values associated with features.
+ * The method reveal whether similar values tend to occur near each other,
+ * or whether high or low values are interspersed.
+ *
+ * Moran's I > 0 means a clusterd pattern.
+ * Moran's I < 0 means a dispersed pattern.
+ * Moran's I = 0 means a random pattern.
+ *
+ * In order to test the significance of the result. The z score is calculated.
+ * A positive enough z-score (ex. >1.96) indicates clustering,
+ * while a negative enough z-score (ex. <-1.96) indicates a dispersed pattern.
+ *
+ * the z-score can be calculated based on a normal or random assumption.
+ *
+ * **Bibliography***
+ *
+ * 1. [Moran's I](https://en.wikipedia.org/wiki/Moran%27s_I)
+ *
+ * 2. [pysal](http://pysal.readthedocs.io/en/latest/index.html)
+ *
+ * 3. Andy Mitchell, The ESRI Guide to GIS Analysis Volume 2: Spatial Measurements & Statistics.
+ *
+ * @name moranIndex
+ * @param {FeatureCollection<any>} fc
+ * @param {Object} options
+ * @param {string} options.inputField the property name, must contain numeric values
+ * @param {number} [options.threshold=100000] the distance threshold
+ * @param {number} [options.p=2] the Minkowski p-norm distance parameter
+ * @param {boolean} [options.binary=false] whether transfrom the distance to binary
+ * @param {number} [options.alpha=-1] the distance decay parameter
+ * @param {boolean} [options.standardization=true] wheter row standardization the distance
+ * @returns {MoranIndex}
+ * @example
+ *
+ * const bbox = [-65, 40, -63, 42];
+ * const dataset = turf.randomPoint(100, { bbox: bbox });
+ *
+ * const result = turf.moranIndex(dataset, {
+ *   inputField: 'CRIME',
+ * });
+ */
+export default function (fc, options) {
+    var inputField = options.inputField;
+    var threshold = options.threshold || 100000;
+    var p = options.p || 2;
+    var binary = options.binary || false;
+    var alpha = options.alpha || -1;
+    var standardization = options.standardization || true;
+    var weight = spatialWeight(fc, {
+        alpha: alpha,
+        binary: binary,
+        p: p,
+        standardization: standardization,
+        threshold: threshold,
+    });
+    var y = [];
+    featureEach(fc, function (feature) {
+        var feaProperties = feature.properties || {};
+        // validate inputField exists
+        y.push(feaProperties[inputField]);
+    });
+    var yMean = mean(y);
+    var yVar = variance(y);
+    var weightSum = 0;
+    var s0 = 0;
+    var s1 = 0;
+    var s2 = 0;
+    var n = weight.length;
+    // validate y.length is the same as weight.length
+    for (var i = 0; i < n; i++) {
+        var subS2 = 0;
+        for (var j = 0; j < n; j++) {
+            weightSum += weight[i][j] * (y[i] - yMean) * (y[j] - yMean);
+            s0 += weight[i][j];
+            s1 += Math.pow(weight[i][j] + weight[j][i], 2);
+            subS2 += weight[i][j] + weight[j][i];
+        }
+        s2 += Math.pow(subS2, 2);
+    }
+    s1 = 0.5 * s1;
+    var moranIndex = weightSum / s0 / yVar;
+    var expectedMoranIndex = -1 / (n - 1);
+    var vNum = n * n * s1 - n * s2 + 3 * (s0 * s0);
+    var vDen = (n - 1) * (n + 1) * (s0 * s0);
+    var vNorm = vNum / vDen - expectedMoranIndex * expectedMoranIndex;
+    var stdNorm = Math.sqrt(vNorm);
+    var zNorm = (moranIndex - expectedMoranIndex) / stdNorm;
+    return {
+        expectedMoranIndex: expectedMoranIndex,
+        moranIndex: moranIndex,
+        stdNorm: stdNorm,
+        zNorm: zNorm,
+    };
+}
+/**
+ * get mean of a list
+ * @param {number[]} y
+ * @returns {number}
+ *
+ */
+function mean(y) {
+    var sum = 0;
+    for (var _i = 0, y_1 = y; _i < y_1.length; _i++) {
+        var item = y_1[_i];
+        sum += item;
+    }
+    return sum / y.length;
+}
+/**
+ * get variance of a list
+ * @param {number[]} y
+ * @returns {number}
+ *
+ */
+function variance(y) {
+    var yMean = mean(y);
+    var sum = 0;
+    for (var _i = 0, y_2 = y; _i < y_2.length; _i++) {
+        var item = y_2[_i];
+        sum += Math.pow(item - yMean, 2);
+    }
+    return sum / y.length;
+}
+/**
+ * @typedef {Object} MoranIndex
+ * @property {number} moranIndex the moran's Index of the observed feature set
+ * @property {number} expectedMoranIndex the moran's Index of the random distribution
+ * @property {number} stdNorm the standard devitaion of the random distribution
+ * @property {number} zNorm the z-score of the observe samples with regard to the random distribution
+ */

frontend/node_modules/@turf/moran-index/dist/es/package.json

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+{"type":"module"}

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

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+import { FeatureCollection } from "@turf/helpers";
+/**
+ * Moran's I measures patterns of attribute values associated with features.
+ * The method reveal whether similar values tend to occur near each other,
+ * or whether high or low values are interspersed.
+ *
+ * Moran's I > 0 means a clusterd pattern.
+ * Moran's I < 0 means a dispersed pattern.
+ * Moran's I = 0 means a random pattern.
+ *
+ * In order to test the significance of the result. The z score is calculated.
+ * A positive enough z-score (ex. >1.96) indicates clustering,
+ * while a negative enough z-score (ex. <-1.96) indicates a dispersed pattern.
+ *
+ * the z-score can be calculated based on a normal or random assumption.
+ *
+ * **Bibliography***
+ *
+ * 1. [Moran's I](https://en.wikipedia.org/wiki/Moran%27s_I)
+ *
+ * 2. [pysal](http://pysal.readthedocs.io/en/latest/index.html)
+ *
+ * 3. Andy Mitchell, The ESRI Guide to GIS Analysis Volume 2: Spatial Measurements & Statistics.
+ *
+ * @name moranIndex
+ * @param {FeatureCollection<any>} fc
+ * @param {Object} options
+ * @param {string} options.inputField the property name, must contain numeric values
+ * @param {number} [options.threshold=100000] the distance threshold
+ * @param {number} [options.p=2] the Minkowski p-norm distance parameter
+ * @param {boolean} [options.binary=false] whether transfrom the distance to binary
+ * @param {number} [options.alpha=-1] the distance decay parameter
+ * @param {boolean} [options.standardization=true] wheter row standardization the distance
+ * @returns {MoranIndex}
+ * @example
+ *
+ * const bbox = [-65, 40, -63, 42];
+ * const dataset = turf.randomPoint(100, { bbox: bbox });
+ *
+ * const result = turf.moranIndex(dataset, {
+ *   inputField: 'CRIME',
+ * });
+ */
+export default function (fc: FeatureCollection<any>, options: {
+    inputField: string;
+    threshold?: number;
+    p?: number;
+    binary?: boolean;
+    alpha?: number;
+    standardization?: boolean;
+}): {
+    moranIndex: number;
+    expectedMoranIndex: number;
+    stdNorm: number;
+    zNorm: number;
+};
+/**
+ * @typedef {Object} MoranIndex
+ * @property {number} moranIndex the moran's Index of the observed feature set
+ * @property {number} expectedMoranIndex the moran's Index of the random distribution
+ * @property {number} stdNorm the standard devitaion of the random distribution
+ * @property {number} zNorm the z-score of the observe samples with regard to the random distribution
+ */

frontend/node_modules/@turf/moran-index/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 distance_weight_1 = __importDefault(require("@turf/distance-weight"));
+var meta_1 = require("@turf/meta");
+/**
+ * Moran's I measures patterns of attribute values associated with features.
+ * The method reveal whether similar values tend to occur near each other,
+ * or whether high or low values are interspersed.
+ *
+ * Moran's I > 0 means a clusterd pattern.
+ * Moran's I < 0 means a dispersed pattern.
+ * Moran's I = 0 means a random pattern.
+ *
+ * In order to test the significance of the result. The z score is calculated.
+ * A positive enough z-score (ex. >1.96) indicates clustering,
+ * while a negative enough z-score (ex. <-1.96) indicates a dispersed pattern.
+ *
+ * the z-score can be calculated based on a normal or random assumption.
+ *
+ * **Bibliography***
+ *
+ * 1. [Moran's I](https://en.wikipedia.org/wiki/Moran%27s_I)
+ *
+ * 2. [pysal](http://pysal.readthedocs.io/en/latest/index.html)
+ *
+ * 3. Andy Mitchell, The ESRI Guide to GIS Analysis Volume 2: Spatial Measurements & Statistics.
+ *
+ * @name moranIndex
+ * @param {FeatureCollection<any>} fc
+ * @param {Object} options
+ * @param {string} options.inputField the property name, must contain numeric values
+ * @param {number} [options.threshold=100000] the distance threshold
+ * @param {number} [options.p=2] the Minkowski p-norm distance parameter
+ * @param {boolean} [options.binary=false] whether transfrom the distance to binary
+ * @param {number} [options.alpha=-1] the distance decay parameter
+ * @param {boolean} [options.standardization=true] wheter row standardization the distance
+ * @returns {MoranIndex}
+ * @example
+ *
+ * const bbox = [-65, 40, -63, 42];
+ * const dataset = turf.randomPoint(100, { bbox: bbox });
+ *
+ * const result = turf.moranIndex(dataset, {
+ *   inputField: 'CRIME',
+ * });
+ */
+function default_1(fc, options) {
+    var inputField = options.inputField;
+    var threshold = options.threshold || 100000;
+    var p = options.p || 2;
+    var binary = options.binary || false;
+    var alpha = options.alpha || -1;
+    var standardization = options.standardization || true;
+    var weight = distance_weight_1.default(fc, {
+        alpha: alpha,
+        binary: binary,
+        p: p,
+        standardization: standardization,
+        threshold: threshold,
+    });
+    var y = [];
+    meta_1.featureEach(fc, function (feature) {
+        var feaProperties = feature.properties || {};
+        // validate inputField exists
+        y.push(feaProperties[inputField]);
+    });
+    var yMean = mean(y);
+    var yVar = variance(y);
+    var weightSum = 0;
+    var s0 = 0;
+    var s1 = 0;
+    var s2 = 0;
+    var n = weight.length;
+    // validate y.length is the same as weight.length
+    for (var i = 0; i < n; i++) {
+        var subS2 = 0;
+        for (var j = 0; j < n; j++) {
+            weightSum += weight[i][j] * (y[i] - yMean) * (y[j] - yMean);
+            s0 += weight[i][j];
+            s1 += Math.pow(weight[i][j] + weight[j][i], 2);
+            subS2 += weight[i][j] + weight[j][i];
+        }
+        s2 += Math.pow(subS2, 2);
+    }
+    s1 = 0.5 * s1;
+    var moranIndex = weightSum / s0 / yVar;
+    var expectedMoranIndex = -1 / (n - 1);
+    var vNum = n * n * s1 - n * s2 + 3 * (s0 * s0);
+    var vDen = (n - 1) * (n + 1) * (s0 * s0);
+    var vNorm = vNum / vDen - expectedMoranIndex * expectedMoranIndex;
+    var stdNorm = Math.sqrt(vNorm);
+    var zNorm = (moranIndex - expectedMoranIndex) / stdNorm;
+    return {
+        expectedMoranIndex: expectedMoranIndex,
+        moranIndex: moranIndex,
+        stdNorm: stdNorm,
+        zNorm: zNorm,
+    };
+}
+exports.default = default_1;
+/**
+ * get mean of a list
+ * @param {number[]} y
+ * @returns {number}
+ *
+ */
+function mean(y) {
+    var sum = 0;
+    for (var _i = 0, y_1 = y; _i < y_1.length; _i++) {
+        var item = y_1[_i];
+        sum += item;
+    }
+    return sum / y.length;
+}
+/**
+ * get variance of a list
+ * @param {number[]} y
+ * @returns {number}
+ *
+ */
+function variance(y) {
+    var yMean = mean(y);
+    var sum = 0;
+    for (var _i = 0, y_2 = y; _i < y_2.length; _i++) {
+        var item = y_2[_i];
+        sum += Math.pow(item - yMean, 2);
+    }
+    return sum / y.length;
+}
+/**
+ * @typedef {Object} MoranIndex
+ * @property {number} moranIndex the moran's Index of the observed feature set
+ * @property {number} expectedMoranIndex the moran's Index of the random distribution
+ * @property {number} stdNorm the standard devitaion of the random distribution
+ * @property {number} zNorm the z-score of the observe samples with regard to the random distribution
+ */

frontend/node_modules/@turf/moran-index/package.json

@@ -0,0 +1,66 @@
+{
+  "name": "@turf/moran-index",
+  "version": "6.5.0",
+  "description": "turf moran-index module",
+  "author": "Turf Authors",
+  "contributors": [
+    "Haoming Zhuang <@zhuang-hao-ming>"
+  ],
+  "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",
+    "moran-index"
+  ],
+  "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"
+  },
+  "devDependencies": {
+    "@types/tape": "*",
+    "benchmark": "*",
+    "load-json-file": "*",
+    "npm-run-all": "*",
+    "tape": "*",
+    "ts-node": "*",
+    "tslint": "*",
+    "typescript": "*",
+    "write-json-file": "*"
+  },
+  "dependencies": {
+    "@turf/distance-weight": "^6.5.0",
+    "@turf/helpers": "^6.5.0",
+    "@turf/meta": "^6.5.0"
+  },
+  "gitHead": "5375941072b90d489389db22b43bfe809d5e451e"
+}