Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Sample Data: Japanese Pines

Source Notebook

Locations of Japanese black pine saplings

Locations of Japanese black pine saplings in the observation region Rectangle[{0, 0}, {1, 1}]*5.7 meters.

Examples

Basic Examples (1) 

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ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"]
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Summary of the spatial point data:

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ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"]["Summary"]
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Plot the spatial point data:

In[3]:=
ListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"], AspectRatio -> 1]
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Visualize the smooth point density:

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density = SmoothPointDensity[ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"]]
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In[5]:=
Show[ContourPlot[density[{x, y}], {x, y} \[Element] ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "ObservationRegion"], ColorFunction -> "Rainbow"], ListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"], PlotStyle -> Black]]
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Compute probability of finding a point within given radius of an existing point - NearestNeighborG is the CDF of the nearest neighbor distribution:

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

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Total[(1 - values)*step]
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Account for scale and unit:

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%*ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "RegionScale"]
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Test for complete spacial randomness:

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SpatialRandomnessTest[ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
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Fit a Poisson point process to data:

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EstimatedPointProcess[ResourceData[\!\(\* TagBox["\"\<Sample Data: Japanese Pines\>\"", #& , BoxID -> "ResourceTag-Sample Data: Japanese Pines-Input", AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
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Gosia Konwerska, "Sample Data: Japanese Pines" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation: Diggle, P.J. (2003) Statistical Analysis of Spatial Point Patterns. Arnold Publishers.

Publisher Information

  • Prepared for the Wolfram Data Repository By: Gosia Konwerska
    (Wolfram Research, Inc.)
  • Publisher of Record: Gosia Konwerska