December 2025
Intermediate to advanced
338 pages
8h 38m
English
Content preview from Cloud Native Geospatial Analytics with Apache Sedona
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Chapter 5. Raster Data Analysis
In many modern OLAP and OLTP databases and data processing engines, you can use some of the geospatial vector functions, but raster processing is, in most cases, missing. Combining them in one library is a rare thing. Apache Sedona provides the capability to analyze and process both vector and raster data types. Raster data is semi-structured data collected by aerial and satellite imagery. To get the information from it, we need sophisticated functions, which Apache Sedona provides for you. In this chapter, we will explain the raster data model used in Apache Sedona and then proceed to discuss loading and writing to raster data. Processing raster data includes operations on pixels in one band and multiple bands. We will cover different raster spatial functions, as well as the zonal statistics and map algebra operations. Spatial join in Apache Sedona is optimized not only for vector data but also for raster data. We will conclude the chapter with an insurance risk modeling use case that combines vector and raster data.
The Raster Data Model
A raster is a spatial data model that defines space as an array of cells called pixels arranged into rows and columns. It is a discrete representation of the space. The simplest raster consists of a single 2D array with values (Figure 5-1). Each raster consists of rows and cells, and the numbering starts from the top left corner and ends at the bottom right. Each coordinate refers to the centroid of the pixel. ...
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