Python for Geospatial Data Analysis

Chapter 2. Essential Facilities for Spatial Analysis

Chapter 1 covered the complexity of mapping a three-dimensional (3D) globe to a two-dimensional (2D) coordinate system. This often requires an understanding of how to select an appropriate map projection depending on the area you are interested in viewing or analyzing. You learned how 3D coordinates on the surface of the Earth can be converted to 2D coordinates. For that, the concepts of geoid and ellipsoid were introduced. You also saw a variety of map projections in Chapter 1, and I discussed how to choose an appropriate one for a given area—for example, selecting a projection that minimizes distortion over the area you are viewing.

What happens to topographical features that are below or above the Earth’s surface? Geospatial tools are also able to analyze these features spatially as well as time-series data, which is often described as a fourth dimension. 

You may have noticed the term GIS doing a lot of the heavy lifting when learning about spatial literacy, but in reality there are multiple integrated concepts contributing at the systems level. I have discussed the spatial data framework, where spatial locations are identified on the Earth’s surface. This is fundamental for building a comprehensive (and reliable) reference system for geocoding and mapping data.

In this chapter, you will continue to build on your spatial literacy skills with three brief sample projects in QGIS. First, you will download and customize ...