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The method is not wholly objective because two investigators may produce somewhat different maps whenever interpolation between data points is necessary for construction of the contours.
Tables are excellent for presenting specific data and making exact comparisons between data points.
It produces static images that neglect the relationships between data points in the time course.
Wearable sensors generate multi-dimensional, nonlinear, dynamic data streams with weak correlation between data points.
The curves are interpolated between data points using piecewise cubic interpolation.
When data are truly independent, the correlation between data points is zero.
Using these data, interpolating between data points where necessary, a rectangular Cartesian mesh is generated.
For each property, anisotropic variograms were used to adequately capture the spatial correlation between data points.
The Euclidean metric is commonly used to compute the distance between data points and centroids.
Specifically, it is the weighted square distance between data points and the fitting curve.
In this method, the sub-watersheds are grouped into successively larger clusters based on distance or similarities between data points.
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