Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Sample Data: Lansing Woods

Source Notebook

Locations of trees in Lansing Woods annotated with species marks

Details

Locations of trees in Lansing Woods in the observation region Rectangle[{0, 0}, {1, 1}]*924 feet, annoated with species marks.

Examples

Basic Examples (1) 

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

Summary of the spatial point data:

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

Visualizations (3) 

Plot the spatial point data:

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

Visualize data with annotations:

In[4]:=
PointValuePlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "Data"], PlotLegends -> Automatic]
Out[4]=

Visualize smooth point density:

In[5]:=
density = SmoothPointDensity[ResourceData[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "Data"]]
Out[5]=
In[6]:=
Show[ContourPlot[density[{x, y}], {x, y} \[Element] ResourceData[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "ObservationRegion"], ColorFunction -> "Rainbow"], ListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "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[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "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]
Out[11]=

Account for scale and units:

In[12]:=
%*ResourceData[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "RegionScale"]
Out[12]=

Test for complete spacial randomness:

In[13]:=
SpatialRandomnessTest[ResourceData[\!\(\* TagBox["\"\<Sample Data: Lansing Woods\>\"", #& , BoxID -> "ResourceTag-Sample Data: Lansing Woods-Input", AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
Out[13]=

Gosia Konwerska, "Sample Data: Lansing Woods" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation:
    • Gerrard, D.J. (1969) Competition quotient: a new measure of the competition affecting individual forest trees. Research Bulletin 20, Agricultural Experiment Station, Michigan State University.

      Cox, T.F. (1979) A method for mapping the dense and sparse regions of a forest stand. Applied Statistics 28, 14--19.

Data Downloads

Publisher Information

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