Sample Data: Bramble Canes

Locations of bramble canes annotated with three classes of age marks

Details

Locations of bramble canes in the 9-meter square observation region scaled to Rectangle[{0, 0}, {1, 1}], annotated with three classes of age marks: newly emergent (denoted by 0), one (1) or two (2) years old. Bramble canes are long and arching offshoots of bramble bushes. Bramble canes do not flower or set fruit until their second year of growth.

Examples

Basic Examples (1)

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$ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"]$

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Summary of the spatial point data:

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$ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"]["Summary"]$

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Visualizations (3)

Plot the spatial point data:

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$ListPlot[ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"], Frame -> True, AspectRatio -> 1]$

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Plot the locations annotated by age:

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$PointValuePlot[ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"], PlotLegends -> Automatic]$

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Visualize smooth point density:

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$density = SmoothPointDensity[ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"]]$

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$Show[ContourPlot[density[{x, y}], {x, y} \[Element] ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "ObservationRegion"], ColorFunction -> "Rainbow"], ListPlot[ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "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:

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$nnG = NearestNeighborG[ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"]]$

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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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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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Account for scale and units:

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$%*ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "RegionScale"]$

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Test for complete spatial randomness:

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$SpatialRandomnessTest[ResourceData[\!$\* TagBox["\"\<Sample Data: Bramble Canes\>\"", #& , BoxID -> "ResourceTag-Sample Data: Bramble Canes-Input", AutoDelete->True]$, "Data"], {"PValue", "TestConclusion"}] // Column$

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Bibliographic Citation

Gosia Konwerska, "Sample Data: Bramble Canes" from the Wolfram Data Repository (2022)

Data Resource History

Date Created: 7 October 2021

Source Metadata

Citation: Diggle P. (1983). Statistical Analysis of Spatial Point Patterns. Academic Press, London.

Publisher Information

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