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
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TypeScript
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
*/