Sample Data: Murchison Gold Deposits

Source Notebook

Locations of gold deposits in the Murchison area of Western Australia

Details

Locations of gold deposits in Murchison area of Western Australia, in the observation region Rectangle[{352783., 6699742}, {682590., 7101484}] meters, without annotations.

Examples

Basic Examples (1) 

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

Summary of the spatial point data:

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

Visualizations (2) 

Plot the spatial point data:

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

Visualize smooth point density:

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

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[\!\(\*
TagBox["\"\<Sample Data: Murchison Gold Deposits\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Murchison Gold Deposits-Input",
AutoDelete->True]\), "Data"]]
Out[6]=
In[7]:=
maxR = nnG["MaxRadius"]
Out[7]=
In[8]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[8]=

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:

In[10]:=
Total[(1 - values)*step]
Out[10]=

Account for scale and units:

In[11]:=
 %*ResourceData[\!\(\*
TagBox["\"\<Sample Data: Murchison Gold Deposits\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Murchison Gold Deposits-Input",
AutoDelete->True]\), "RegionScale"]
Out[11]=

Test for complete spacial randomness:

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

Gosia Konwerska, "Sample Data: Murchison Gold Deposits" from the Wolfram Data Repository (2022)  

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