Sample Data: Fin Pines

Locations of pine trees annotated with diameter (in centimeters) and height (in meters) marks

Details

Locations of pine trees in the observation region Rectangle[{-5,-8}, {5,2}] meters, annotated with diameter (in centimeters) and height (in meters) marks.

Examples

Basic Examples (1)

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Summary of the spatial point data:

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Visualizations (1)

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Visualize points with diameter annotations:

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Analysis (5)

Compute probability of finding a point within given radius of an existing point - NearestNeighborG is the CDF of the nearest neighbor distribution:

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NearestNeighborG as the CDF of nearest neighbor distribution can be used to compute the mean distance between a typical point and its nearest neighbor - the mean of a positive support distribution can be approximated via a Riemann sum of 1- CDF. To use Riemann approximation create the partition of the support interval from 0 to maxR into 100 parts and compute the value of the NearestNeighborG at the middle of each subinterval:

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Now compute the Riemann sum to find the mean distance between a typical point and its nearest neighbor:

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Test for complete spacial randomness:

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Fit a Poisson point process to data:

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Gosia Konwerska, "Sample Data: Fin Pines" from the Wolfram Data Repository (2022)