# Sample Data: NZ Trees

Locations of New Zealand trees without annotations

## Details

Locations of New Zealand trees in the observation region Rectangle[{0, 0}, {153, 95}] feet without annotations.

## Examples

### Basic Examples (1)

 In[1]:=
 Out[1]=

Summary of the spatial point data:

 In[2]:=
 Out[2]=

### Visualizations (2)

Plot the spatial point data:

 In[3]:=
 Out[3]=

Visualize smooth point density of the data:

 In[4]:=
 Out[4]=
 In[5]:=
 Out[5]=

### Analysis (6)

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

 In[6]:=
 Out[6]=
 In[7]:=
 Out[7]=
 In[8]:=
 Out[8]=

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:

 In[9]:=

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

 In[10]:=
 Out[10]=

Account for scale and units:

 In[11]:=
 Out[11]=

Test for complete spacial randomness:

 In[12]:=
 Out[12]=

Fit a Poisson point process to the data:

 In[13]:=
 Out[13]=

Gosia Konwerska, "Sample Data: NZ Trees" from the Wolfram Data Repository (2022)