node-red-contrib-tak-registration
Version:
A Node-RED node to register to TAK and to help wrap files as datapackages to send to TAK
140 lines (139 loc) • 4.74 kB
JavaScript
"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
*/