Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Sample Data: Bunyaruguru Crater Field

Source Notebook

Locations of craters in Bunyaruguru volcanic field in Uganda

Details

Eastings and Northings for locations of craters in Bunyaruguru volcanic field in West Uganda.

Examples

Basic Examples (2) 

Retrieve the data as a SpatialPointData object:

In[1]:=
ResourceData[\!\(\* TagBox["\"\<Sample Data: Bunyaruguru Crater Field\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bunyaruguru Crater Field-Input", AutoDelete->True]\)]
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Summary of the spatial point data:

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

Plot the spatial point data:

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

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

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[\!\(\* TagBox["\"\<Sample Data: Bunyaruguru Crater Field\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bunyaruguru Crater Field-Input", AutoDelete->True]\), "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, 100]; values = nnG[middles];

Now compute the Riemann sum to find the mean distance between a typical point and its nearest neighbor:

In[10]:=
Total[(1 - values)*step]
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Test for complete spatial randomness:

In[11]:=
SpatialRandomnessTest[ResourceData[\!\(\* TagBox["\"\<Sample Data: Bunyaruguru Crater Field\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bunyaruguru Crater Field-Input", AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}] // Column
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Fit a hardcore point process to data:

In[12]:=
Clear[\[Mu], r]; EstimatedPointProcess[ResourceData[\!\(\* TagBox["\"\<Sample Data: Bunyaruguru Crater Field\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bunyaruguru Crater Field-Input", AutoDelete->True]\), "Data"], HardcorePointProcess[\[Mu], r, 2]]
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Gosia Konwerska, "Sample Data: Bunyaruguru Crater Field" from the Wolfram Data Repository (2026)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation: Bailey and Gatrell (1995) Interactive Spatial Data Analysis. Prentice Hall.

Publisher Information

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