Chandan Roy

The main difference between Moran I in ArcMap 10.4 and GeoDa is the way that they calculate the spatial lag. In ArcMap 10.4, the spatial lag is calculated using a queen contiguity matrix, while in GeoDa, the spatial lag can be calculated using a variety of different matrices, including queen contiguity, rook contiguity, and k nearest neighbors.

The queen contiguity matrix is a binary matrix that indicates whether or not two cells are adjacent to each other. The rook contiguity matrix is also a binary matrix, but it only indicates whether or not two cells are horizontally or vertically adjacent to each other. The k nearest neighbors matrix is a weighted matrix that indicates the distance between each cell and its k nearest neighbors.

The choice of spatial lag matrix can affect the results of the Moran I test. For example, if the spatial lag is calculated using a queen contiguity matrix, then the Moran I test will be more sensitive to spatial autocorrelation that is present in the data. However, if the spatial lag is calculated using a rook contiguity matrix, then the Moran I test will be less sensitive to spatial autocorrelation that is present in the data.

In addition to the choice of spatial lag matrix, the results of the Moran I test can also be affected by the size of the spatial window. The spatial window is the area around each cell that is used to calculate the spatial lag. The larger the spatial window, the more likely it is that the Moran I test will detect spatial autocorrelation. However, the larger the spatial window, the more likely it is that the Moran I test will detect spurious spatial autocorrelation.

It is important to choose the spatial lag matrix and spatial window carefully when conducting a Moran I test. The choice of these parameters can have a significant impact on the results of the test.

Here are some additional tips for conducting a Moran I test:

• Use a variety of different spatial lag matrices and spatial windows to see how the results change.
• Use a robust Moran I test to reduce the impact of outliers.
• Use a Monte Carlo simulation to assess the significance of the Moran I test.

I hope this helps! . Please recommend my reply if you find it useful .Thanks

Thank you very much for your detail explanation.

I will make emphasis on: Differences in spacial lag can lead to differences in conclusions about Moran´s I.