Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Sample Data: France Bastides

Source Notebook

Locations of bastides (walled cities) in France annotated with name and foundation year

Details

Locations of medieval bastides (walled cities) in the observation region in the South-West France, annotated with name and foundation year.

Examples

Basic Examples (1) 

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

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

Plot the spatial point data:

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GeoListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: France Bastides\>\"", #& , BoxID -> "ResourceTag-Sample Data: France Bastides-Input", AutoDelete->True]\), "Data"]]
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Visualize the varying intensity:

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GeoSmoothHistogram[ResourceData[\!\(\* TagBox["\"\<Sample Data: France Bastides\>\"", #& , BoxID -> "ResourceTag-Sample Data: France Bastides-Input", AutoDelete->True]\), "Data"]["Points"]]
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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[\!\(\* TagBox["\"\<Sample Data: France Bastides\>\"", #& , BoxID -> "ResourceTag-Sample Data: France Bastides-Input", AutoDelete->True]\), "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:

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SpatialRandomnessTest[ResourceData[\!\(\* TagBox["\"\<Sample Data: France Bastides\>\"", #& , BoxID -> "ResourceTag-Sample Data: France Bastides-Input", AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}] // Column
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Gosia Konwerska, "Sample Data: France Bastides" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

Publisher Information

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