Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Sample Data: Eurasian Collared Dove

Source Notebook

Observation locations of the Eurasian Collared Dove for the years 1986 to 2008 annotated with total counts

Details

Observation locations of the Eurasian Collared Dove for the years 1986 to 2008 in the observation region which is the convex hull of the observation locations, annotated with total counts.

Examples

Basic Examples (1) 

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

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

Plot the spatial point data:

In[3]:=
GeoListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Eurasian Collared Dove\>\"", #& , BoxID -> "ResourceTag-Sample Data: Eurasian Collared Dove-Input", AutoDelete->True]\), "Data"]]
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Visualize points with annotations:

In[4]:=
PointValuePlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Eurasian Collared Dove\>\"", #& , BoxID -> "ResourceTag-Sample Data: Eurasian Collared Dove-Input", AutoDelete->True]\), "Data"]]
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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[5]:=
nnG = NearestNeighborG[ResourceData[\!\(\* TagBox["\"\<Sample Data: Eurasian Collared Dove\>\"", #& , BoxID -> "ResourceTag-Sample Data: Eurasian Collared Dove-Input", AutoDelete->True]\), "Data"]]
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In[6]:=
maxR = nnG["MaxRadius"]
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In[7]:=
res = Table[nnG[r], {r, range = Range[maxR/100, maxR, maxR/100]}]; ListPlot[Transpose[{range, res}], 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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Test for complete spacial randomness:

In[11]:=
SpatialRandomnessTest[ResourceData[\!\(\* TagBox["\"\<Sample Data: Eurasian Collared Dove\>\"", #& , BoxID -> "ResourceTag-Sample Data: Eurasian Collared Dove-Input", AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
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Gosia Konwerska, "Sample Data: Eurasian Collared Dove" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation:
    • Cressie, N. and Wikle, C. (2011). Statistics for Spatio-temporal Data. John Wiley & Sons, New York.[ix, 14, 156, 174, 193, 361, 365]

Publisher Information

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