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The attached\n * [_z_-score](https://en.wikipedia.org/wiki/Standard_score) indicates how many\n * standard deviations above or below the expected mean distance the data's\n * observed mean distance is. The more negative, the more clustered. The more\n * positive, the more evenly dispersed. A _z_-score between -2 and 2 indicates\n * a seemingly random distribution. That is, within _p_ of less than 0.05, the\n * distribution appears statistically significantly neither clustered nor\n * dispersed.\n *\n * **Remarks**\n *\n * - Though the analysis will work on any {@link FeatureCollection} type, it\n * works best with {@link Point} collections.\n *\n * - This analysis is _very_ sensitive to the study area provided.\n * If no {@link Feature}<{@link Polygon}> is passed as the study area, the function draws a box\n * around the data, which may distort the findings. This analysis works best\n * with a bounded area of interest within with the data is either clustered,\n * dispersed, or randomly distributed. For example, a city's subway stops may\n * look extremely clustered if the study area is an entire state. On the other\n * hand, they may look rather evenly dispersed if the study area is limited to\n * the city's downtown.\n *\n * **Bibliography**\n *\n * Philip J. Clark and Francis C. Evans, “Distance to Nearest Neighbor as a\n * Measure of Spatial Relationships in Populations,” _Ecology_ 35, no. 4\n * (1954): 445–453, doi:[10.2307/1931034](http://doi.org/10.2307/1931034).\n *\n * @function\n * @param {FeatureCollection} dataset FeatureCollection (pref. of points) to study\n * @param {Object} [options={}] Optional parameters\n * @param {Feature} [options.studyArea] polygon representing the study area\n * @param {Units & AreaUnits} [options.units='kilometers'] unit of measurement for distances and, squared, area.\n * @param {GeoJsonProperties} [options.properties={}] properties\n * @returns {NearestNeighborStudyArea} A polygon of the study area or an approximation of one.\n * @example\n * var bbox = [-65, 40, -63, 42];\n * var dataset = turf.randomPoint(100, { bbox: bbox });\n * var nearestNeighborStudyArea = turf.nearestNeighborAnalysis(dataset);\n *\n * //addToMap\n * var addToMap = [dataset, nearestNeighborStudyArea];\n */\nfunction nearestNeighborAnalysis(\n dataset: FeatureCollection,\n options?: {\n studyArea?: Feature;\n units?: Units & AreaUnits;\n properties?: GeoJsonProperties;\n }\n): NearestNeighborStudyArea {\n // Optional params\n options = options || {};\n const studyArea = options.studyArea || bboxPolygon(bbox(dataset));\n const properties = options.properties || {};\n const units = options.units || \"kilometers\";\n\n const features: Array> = [];\n featureEach(dataset, (feature) => {\n features.push(centroid(feature));\n });\n const n = features.length;\n const observedMeanDistance =\n features\n .map((feature, index) => {\n const otherFeatures = featureCollection(\n features.filter((f, i) => {\n return i !== index;\n })\n );\n // Have to add the ! to make typescript validation pass\n // see https://stackoverflow.com/a/40350534/1979085\n return distance(\n feature,\n nearestPoint(feature, otherFeatures).geometry!.coordinates,\n { units }\n );\n })\n .reduce((sum, value) => {\n return sum + value;\n }, 0) / n;\n\n const populationDensity = n / convertArea(area(studyArea), \"meters\", units);\n const expectedMeanDistance = 1 / (2 * Math.sqrt(populationDensity));\n const variance = 0.26136 / Math.sqrt(n * populationDensity);\n properties.nearestNeighborAnalysis = {\n units: units,\n arealUnits: units + \"²\",\n observedMeanDistance: observedMeanDistance,\n expectedMeanDistance: expectedMeanDistance,\n nearestNeighborIndex: observedMeanDistance / expectedMeanDistance,\n numberOfPoints: n,\n zScore: (observedMeanDistance - expectedMeanDistance) / variance,\n };\n studyArea.properties = properties;\n\n return studyArea as NearestNeighborStudyArea;\n}\n\nexport {\n nearestNeighborAnalysis,\n NearestNeighborStatistics,\n NearestNeighborStudyArea,\n};\nexport default nearestNeighborAnalysis;\n"]}