Description: Zonal sampling is a sampling technique that focuses on specific zones within a dataset. This methodology allows researchers and analysts to concentrate their efforts on areas of particular interest, which can result in greater efficiency and effectiveness in data collection. Instead of conducting random sampling across the entire dataset, zonal sampling segments the data space into different zones or regions, each of which can be analyzed independently. This technique is especially useful in situations where data characteristics are expected to vary significantly between different zones, allowing for a better understanding of local dynamics. Additionally, zonal sampling can be applied in various disciplines, from ecology to economics, and is particularly relevant in fields like data science and machine learning, where the goal is to optimize data usage to train more accurate and representative models. By focusing on specific zones, patterns and trends can be identified that might otherwise go unnoticed in a more generalized analysis.
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