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Version: 7.0.0



Quadrat analysis lays a set of equal-size areas(quadrat) over the study area and counts the number of features in each quadrat and creates a frequency table. The table lists the number of quadrats containing no features, the number containing one feature, two features, and so on, all the way up to the quadrat containing the most features. The method then creates the frequency table for the random distribution, usually based on a Poisson distribution. The method uses the distribution to calculate the probability for 0 feature occuring, 1 feature occuring, 2 features, and so on, and lists these probabilities in the frequency table. By comparing the two frequency tables, you can see whether the features create a pattern. If the table for the observed distribution has more quadrats containing many features than the table for the random distribution dose, then the features create a clustered pattern.

It is hard to judge the frequency tables are similar or different just by looking at them. So, we can use serval statistical tests to find out how much the frequency tables differ. We use Kolmogorov-Smirnov test.This method calculates cumulative probabilities for both distributions, and then compares the cumulative probabilities at each class level and selects the largest absolute difference D. Then, the test compares D to the critical value for a confidence level you specify. If D is greater than the critical value, the difference between the observed distribution and the random distribution is significant. The greater the value the bigger the difference.

Traditionally, squares are used for the shape of the quadrats, in a regular grid(square-grid). Some researchers suggest that the quadrat size equal twice the size of mean area per feature, which is simply the area of the study area divided by the number of features.


pointFeatureSetFeatureCollection<Point>point set to study
options?Objectoptional parameters (default {})
options.studyBbox?bboxbbox representing the study area
options.confidenceLevel?numbera confidence level. The unit is percentage . 5 means 95%, value must be in {@link K*TABLE} *(default 20)_


    Object result QuadratAnalysisResult


var bbox = [-65, 40, -63, 42];
var dataset = turf.randomPoint(100, { bbox: bbox });
var result = turf.quadratAnalysis(dataset);


$ npm install @turf/quadrat-analysis

import { quadratAnalysis } from "@turf/quadrat-analysis";
const result = quadratAnalysis(...);
$ npm install @turf/turf

import * as turf from "@turf/turf";
const result = turf.quadratAnalysis(...);