From the course: Hands-On PostgreSQL Project: Spatial Data Science

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Introduction to raster data for spatial analysis

Introduction to raster data for spatial analysis

From the course: Hands-On PostgreSQL Project: Spatial Data Science

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Introduction to raster data for spatial analysis

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- [Instructor] While vector data is great for representing discrete features, like buildings and roads, raster data is used to represent continuous surfaces, things that change gradually over space. Raster data is essentially a grid of cells or pixels that cover an area of the Earth. Each cell in this grid contains a value representing information about that location, and this grid can be made up of thousands, or even millions of cells. Raster data is structured as a matrix of rows and columns. Each cell has a value that represent a specific attribute, and the size of that cell, called the resolution, determines the level of detail. A higher resolution means smaller cells and more detailed data, while a lower resolution means larger cells and less detail. Let's say you are analyzing forest cover. In a high resolution raster dataset, each cell might cover a very small area, allowing you to see individual trees, but in a lower resolution dataset, each cell might cover a much larger…

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