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Spatial autocorrelation (Global Moran’s I) analysis

BH Bayuh Asmamaw Hailu
GB Getahun Gebre Bogale
JB Joseph Beyene
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Statistically significant clusters are defined as geographic areas in which the magnitude of stunting and severe stunting is disproportionately higher compared to neighboring areas. Global Moran’s I tests detect the existence of at least one cluster with the specific location of the study setting30. To detect the presence of clustering in the study setting, Global Moran’s I test was performed in Geographic Information System (GIS). Global Moran’s I statistics were calculated, a value close to − 1 indicates dispersion and Moran’s I value close to + 1 indicates clustering, whereas Moran’s I value of zero indicates the random distribution31. In this study, spatial analysis was performed using the spatial statistics tools in GIS (Getis-OrdGi* statistics, Anselin Local Moran’s I) which have been widely used to detect clusters in a variety of health-related fields29.

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