Analyzing geostatistical data typically involves several steps:
Data Cleaning: Removing errors and inconsistencies from the data to ensure its accuracy. Data Visualization: Using maps and other visual tools to explore the spatial distribution of health events. Spatial Analysis: Applying statistical methods to identify patterns and relationships in the data. Common techniques include spatial autocorrelation, kriging, and cluster analysis. Modeling: Developing predictive models to forecast disease spread and assess the impact of interventions.
