| 1. | Spatial autocorrelation can also occur geographic areas are likely to have similar errors.
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| 2. | Negative values indicate negative spatial autocorrelation and positive values indicate positive spatial autocorrelation.
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| 3. | Negative values indicate negative spatial autocorrelation and positive values indicate positive spatial autocorrelation.
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| 4. | Like spatial autocorrelation, this can be a useful tool for spatial prediction.
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| 5. | Classic spatial autocorrelation statistics include Getis's G and the standard deviational ellipse.
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| 6. | The latter is a well-known statistic that accounts for the Global spatial autocorrelation.
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| 7. | Spatial autocorrelation statistics measure and analyze the degree of dependency among observations in a geographic space.
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| 8. | Classic spatial autocorrelation statistics compare the spatial weights to the covariance relationship at pairs of locations.
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| 9. | There may be spatial trends and spatial autocorrelation in the variables that violate statistical assumptions of regression.
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| 10. | Spatial autocorrelation is more complex than autocorrelation because the correlation is multi-dimensional and bi-directional.
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