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Per Wikipedia on to quote:

Spatial variability occurs when a quantity that is measured at different spatial locations exhibits values that differ across the locations. Spatial variability can be assessed using spatial descriptive statistics...

In accord with the above, a sampling scheme that may assist is as follows:

1. Draw a horizontal diameter (an x-axis) for each circle.

2. Fit a simple linear (two-parameter) regression model (Least-Squares or perhaps a robust Least-Absolute Deviations model) to predict the x-values (dependent variable) versus the # of the point in the sample (independent variable) as they occur and happen to fall on the diameter.

3. Select one of the usual goodness-of-fit metrics for the regression model.

4. Rank the circles based on the chosen comparative statistic.

I would argue a valid spatial variability analysis based on the definition provided above, as to quote, "a quantity that is measured at different spatial locations exhibits values that differ across the locations". Also, I believe my extension from the recommended mean model (citing the employment of the distance formula) to a linear model is more informative here and is executed with a sampling, that is, a data-reduction technique.

Per Wikipedia on to quote:

Spatial variability occurs when a quantity that is measured at different spatial locations exhibits values that differ across the locations. Spatial variability can be assessed using spatial descriptive statistics...

In accord with the above, a sampling scheme that may assist is as follows:

1. Draw a horizontal diameter (an x-axis) for each circle.

2. Fit a simple linear (two-parameter) regression model (Least-Squares or perhaps a robust Least-Absolute Deviations model) to predict the x-values (dependent variable) versus the # of the point in the sample (independent variable) as they occur and happen to fall on the diameter.

3. Select one of the usual goodness-of-fit metrics for the regression model.

4. Rank the circles based on the chosen comparative statistic.

Arguably, a valid spatial variability analysis concurrent with the definition provided above, as to quote, "a quantity that is measured at different spatial locations exhibits values that differ across the locations". Note, clearly an extension from the recommended mean model (which recommended the employment of the distance formula) as compared to a linear model. Likely, in my opinion, more informative than a mean model and executed with a sampling design that is a de facto data-reduction technique (often necessary).

Per Wikipedia on to quote:

Spatial variability occurs when a quantity that is measured at different spatial locations exhibits values that differ across the locations. Spatial variability can be assessed using spatial descriptive statistics such as the range.

In accord with the above, a sampling scheme that may assist is as follows:

1. Draw a horizontal diameter (an x-axis) for each circle.

2. Fit a simple linear (two-parameter) regression model (Least-Squares of Least-Absolute Deviations) to predict the x-values (dependent variable) versus the # of the point in the sample (independent variable) as they occur and happen to fall on the diameter.

3. Select one of the usual goodness-of-fit metrics for the regression model.

4. Rank the circles based on the chosen comparative statistic.

I would argue a valid spatial variability analysis based on the definition provided above, as to quote, "a quantity that is measured at different spatial locations exhibits values that differ across the locations".

Per Wikipedia on to quote:

Spatial variability occurs when a quantity that is measured at different spatial locations exhibits values that differ across the locations. Spatial variability can be assessed using spatial descriptive statistics...

In accord with the above, a sampling scheme that may assist is as follows:

1. Draw a horizontal diameter (an x-axis) for each circle.

2. Fit a simple linear (two-parameter) regression model (Least-Squares or perhaps a robust Least-Absolute Deviations model) to predict the x-values (dependent variable) versus the # of the point in the sample (independent variable) as they occur and happen to fall on the diameter.

3. Select one of the usual goodness-of-fit metrics for the regression model.

4. Rank the circles based on the chosen comparative statistic.

I would argue a valid spatial variability analysis based on the definition provided above, as to quote, "a quantity that is measured at different spatial locations exhibits values that differ across the locations". Also, I believe my extension from the recommended mean model (citing the employment of the distance formula) to a linear model is more informative here and is executed with a sampling, that is, a data-reduction technique.

Per Wikipedia on to quote:

Spatial variability occurs when a quantity that is measured at different spatial locations exhibits values that differ across the locations. Spatial variability can be assessed using spatial descriptive statistics such as the range.

In accord with the above, a sampling scheme that may assist is as follows: Draw a horizontal diameter (an x-axis) for each circle. Then, fit a simple linear (two-parameter) regression model to predict the x-values (dependent variable) versus the # of the point in the sample (independent variable) as they occur and happen to fall on the diameter.

I would argue that differences in the usually cited goodness-of-fit metrics for the regression models for each circle should provide one such comparative measure.

This follows, per the provided spatial variability definition above, as to quote, "a quantity that is measured at different spatial locations exhibits values that differ across the locations".

Per Wikipedia on to quote:

Spatial variability occurs when a quantity that is measured at different spatial locations exhibits values that differ across the locations. Spatial variability can be assessed using spatial descriptive statistics such as the range.

In accord with the above, a sampling scheme that may assist is as follows:

1. Draw a horizontal diameter (an x-axis) for each circle.

2. Fit a simple linear (two-parameter) regression model (Least-Squares of Least-Absolute Deviations) to predict the x-values (dependent variable) versus the # of the point in the sample (independent variable) as they occur and happen to fall on the diameter.

3. Select one of the usual goodness-of-fit metrics for the regression model.

4. Rank the circles based on the chosen comparative statistic.

I would argue a valid spatial variability analysis based on the definition provided above, as to quote, "a quantity that is measured at different spatial locations exhibits values that differ across the locations".