Hi,

I'm trying to get an understanding of global autocorrelation statistics by programatically implementing them.

I'm providing 10 rows of data given here http://www.ats.ucla.edu/stat/r/faq/morans_i.htm, to ArcGIS (just considered Av8Top,Lat and Lon columns treating them as attribute value (which would be my input field for Moran's I computation), X and Y columns respectively) and then performed the Spatial Autocorrelation (spatial statistics toolbox).

Upon examining the results, the values generated in Arc (expected, observed, p-value and z-score) are not matching with those given on that page which are computed using R language. I have also written a simple java program computing Moran's I using the Global Moran's I explanation given in ArcGIS desktop help. They don't match them either.

It would be extremely helpful if some one can tell me if I'm missing any steps in arriving at the final values that are reported by the Spatial Autocorrelation tool and point me in the right direction.

Also it would be very helpful to have some sample datasets with precomputed autocorrelation statistic values so that I can tally my results with them.

Thanks in advance

I'm trying to get an understanding of global autocorrelation statistics by programatically implementing them.

I'm providing 10 rows of data given here http://www.ats.ucla.edu/stat/r/faq/morans_i.htm, to ArcGIS (just considered Av8Top,Lat and Lon columns treating them as attribute value (which would be my input field for Moran's I computation), X and Y columns respectively) and then performed the Spatial Autocorrelation (spatial statistics toolbox).

Upon examining the results, the values generated in Arc (expected, observed, p-value and z-score) are not matching with those given on that page which are computed using R language. I have also written a simple java program computing Moran's I using the Global Moran's I explanation given in ArcGIS desktop help. They don't match them either.

It would be extremely helpful if some one can tell me if I'm missing any steps in arriving at the final values that are reported by the Spatial Autocorrelation tool and point me in the right direction.

Also it would be very helpful to have some sample datasets with precomputed autocorrelation statistic values so that I can tally my results with them.

Thanks in advance

Yes, if the weights are different, then the results are going to be different. This is particularly evident with Inv Distance, as we apply a hybrid to avoid weights greater than 1... I.e. if dist < 1, then w = 1, else, w = 1/d^{exponent}

We also use the randomization assumption for the variance, but there is no "Monte Carlo".... That would be a "permutation" approach, which we do not use due to the extreme computational cost.

We also apply a "two-sided" alternative hypothesis... so, if you get the same weights into R in the form of a listw:

1. Make sure you honor the row standardization approach in both products

2. Try and use "Fixed Distance" to assure that our alternative IDW doesnt cause the issue

3. Make sure the alternative hypothesis is two.sided

I am attaching a zip file that contains CA counties and the SWM/GAL file necessary to compare. Just run Moran inside ArcGIS using the caQueen.swm, and then run the R script to use spdep with caQueen.gal. You will note that they are the same. The image is in the zip file as well as below if you want to just take my word for it... but, the R script should show you how to call moran.test in a manner consistent with ArcGIS.

Thanks much,

MJ

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