Wolfram Research

Sample Data: Fin Pines

Source Notebook

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) 

In[1]:=
ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"]
Out[1]=

Summary of the spatial point data:

In[2]:=
ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"]["Summary"]
Out[2]=

Visualizations (1) 

In[3]:=
ListPlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"], Frame -> True, AspectRatio -> 1]
Out[3]=

Visualize points with diameter annotations:

In[4]:=
PointValuePlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"], {"Diameter" -> "Size"}, PlotLegends -> Automatic]
Out[4]=

Analysis (5) 

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

In[5]:=
nnG = NearestNeighborG[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"]]
Out[5]=
In[6]:=
maxR = nnG["MaxRadius"]
Out[6]=
In[7]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[7]=

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[8]:=
step = maxR/100;
middles = Subdivide[step/2, maxR - step/2, 99];
values = nnG[middles];

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

In[9]:=
Total[(1 - values)*step]*ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "RegionScale"]
Out[9]=

Test for complete spacial randomness:

In[10]:=
SpatialRandomnessTest[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
Out[10]=

Fit a Poisson point process to data:

In[11]:=
EstimatedPointProcess[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Fin Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Fin Pines-Input",
AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
Out[11]=

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

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