Wolfram Research

Sample Data: Swedish Pines

Source Notebook

Locations of pine tress

Details

Locations of pine trees in the observation region Rectangle[{0, 0}, {96, 100}]*0.1 meters without annotations.

Examples

Basic Examples (2) 

Retrieve the data:

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

Summary of the spatial point data:

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

Visualizations (2) 

Plot the spatial point data:

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

Visualize the smooth point density:

In[4]:=
density = SmoothPointDensity[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "Data"]]
Out[4]=
In[5]:=
Show[ContourPlot[density[{x, y}], {x, y} \[Element] ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "ObservationRegion"], ColorFunction -> "Rainbow"], ListPlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "Data"], PlotStyle -> Black]]
Out[5]=

Analysis (4) 

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

In[6]:=
nnG = NearestNeighborG[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "Data"]]
Out[6]=
In[7]:=
maxR = N@nnG["MaxRadius"]
Out[7]=
In[8]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[8]=

Mean distance between a typical point and its nearest neighbor (for positive support distribution can be approximated via a Riemann sum of 1-CDF):

In[9]:=
step = maxR/100;
partition = Table[{k, k + step}, {k, 0, maxR, step}];
values = nnG[Mean /@ partition];
In[10]:=
Total[(1 - values)*step]*ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "RegionScale"]
Out[10]=

Test for complete spacial randomness:

In[11]:=
SpatialRandomnessTest[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
Out[11]=

Fit a hardcore point process to data:

In[12]:=
EstimatedPointProcess[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Swedish Pines\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Swedish Pines-Input",
AutoDelete->True]\), "Data"], HardcorePointProcess[\[Mu], r, 2]]
Out[12]=

Gosia Konwerska, "Sample Data: Swedish Pines" from the Wolfram Data Repository (2021) 

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