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

## Examples

### Basic Examples (1)

Summary of the spatial point data:

### Visualizations (1)

Visualize points with diameter annotations:

### Analysis (5)

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

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:

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

Test for complete spacial randomness:

Fit a Poisson point process to data:

## Bibliographic Citation

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

## Data Resource History

## Publisher Information