As described in Chapter 2, Learning Geospatial Data, geospatial datasets are typically large, complex, and varied. This challenge makes libraries that efficiently read, and in some cases write, this data essential to geospatial analysis. Without access to data, geospatial analysis cannot begin.
Furthermore, accuracy and precision are key factors in geospatial analysis. An image library that resamples data without permission, or a computational geometry library that rounds a coordinate by even a couple of decimal places, can adversely affect the quality of the analysis. Also, these libraries must manage memory efficiently. A complex geospatial process can last for hours, or even days.
If a data access library has a memory fault, it can delay an entire project or even an entire workflow, involving dozens of people who are dependent on the output of that...