![]() ![]() Zonal statistics are useful for comparison between zones–and sometimes between different cells in a zone. ![]() This means that an individual cell in a zone is assigned the characteristics of the zonal area. Therefore, cells falling within a common zone have the same raster cell values. The outputs of zonal statistics are attached to all the cells corresponding to a particular zone. In short, zonal statistics give us an idea of the statistical characteristics of a particular zone, e.g., average temperature by land-use/land-cover type. On a side note: raster cells refer to a grid-like method of representing an area, and will be explained in more detail later in this article.Įxamples of summary data obtained through zonal statistics include mean, median, count, standard deviation, and sum. Zonal statistics summarize the values of a particular group of cells. Zonal statistics are statistics calculated from the values of raster cells falling within zones defined by another raster or vector dataset. What are zonal statistics and why are they important? What are their application areas and which tools can you use for zonal statistics? In this article, we answer these questions. They make the Earth's diverse surface more understandable through spatial summaries, i.e., zonal statistics. Zones (also known as classes or regions) generalize the Earth's surface. These similarities enable us to identify and define zones, which are characterized by internal uniformity within a zone but with significant differences between zones. However, based on certain attributes, there are spatial similarities: areas of the same land use, soil type, vegetation type, elevation, climate, economic development, and so on. ![]()
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