Wolfram Data Repository
Immediate Computable Access to Curated Contributed Data
Locations of cancer incidence annotated with larynx/lung marks
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Summary of the spatial point data:
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Plot the spatial point data:
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Visualize the data with the annotations:
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Visualize the smooth point density:
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Compute probability of finding a point within given radius of an existing point - NearestNeighborG is the CDF of the nearest neighbor distribution:
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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:
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Now compute the Riemann sum to find the mean distance between a typical point and its nearest neighbor:
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Account for scale and units:
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Test for complete spatial randomness:
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Gosia Konwerska, "Sample Data: Cancer Incidence" from the Wolfram Data Repository (2022)