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Sample Data: Waka Trees

Source Notebook

Locations of trees recorded at Waka National Park, Gabon, annotated with diameter (in centimeters) marks

Details

Locations of trees recorded at Waka National Park, Gabon, in the observation region Rectangle[{0., 0.}, {100., 100.}] meters, annotated with diameter (in centimeters) marks.

Examples

Basic Examples (2) 

Retrieve the data:

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

Summary of the spatial point data:

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

Visualizations (3) 

Plot the spatial point data:

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

Visualize the points with annotations:

In[4]:=
PointValuePlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "Data"], {1 -> "Color"}, PlotStyle -> PointSize[.015], PlotLegends -> Automatic]
Out[4]=

Visualize the smooth point density:

In[5]:=
density = SmoothPointDensity[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "Data"]]
Out[5]=
In[6]:=
Show[ContourPlot[density[{x, y}], {x, y} \[Element] ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "ObservationRegion"], ColorFunction -> "Rainbow"], ListPlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "Data"], PlotStyle -> Black]]
Out[6]=

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[7]:=
nnG = NearestNeighborG[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "Data"]]
Out[7]=
In[8]:=
maxR = nnG["MaxRadius"]
Out[8]=
In[9]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[9]=

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[10]:=
step = maxR/100;
partition = Table[{k, k + step}, {k, 0, maxR, step}];
values = nnG[Mean /@ partition];
In[11]:=
Total[(1 - values)*step]*ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "RegionScale"]
Out[11]=

Test for complete spacial randomness:

In[12]:=
SpatialRandomnessTest[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}] // Column
Out[12]=

Fit a Poisson point process to data:

In[13]:=
EstimatedPointProcess[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Waka Trees\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Waka Trees-Input",
AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
Out[13]=

Gosia Konwerska, "Sample Data: Waka Trees" from the Wolfram Data Repository (2021) 

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