The results of local Moran's I index can be standardized, so its significance
level can be tested. When using local Moran's I index to analyze in Geoda software the results were affected by the definition of weight function, data transformation and existence of extreme values (Zhang et al., 2008). Its values are from −1 to 1.When I N 0 indicates positive spatial
autocorrelation, while I b 0 suggests negative spatial autocorrelation.
When there is positive local spatial autocorrelation, LISA has two kinds of spatial clusters: high-high cluster (high values in a high value neighborhood) and low-low clusters (low values in a low value neighborhood). Meanwhile, a high negative local Moran's I value implies a potential spatial outlier, which may be high-low (a high value in a low value neighborhood) or low-high (a low value in a high value neighborhood)outlier (Lalor and Zhang, 2001; Fu et al., 2016).