What this book covers
Chapter 1, Learning Geospatial Analysis with Python, introduces geospatial analysis as a way of answering questions about our world. The differences between GIS and remote sensing are explained. Common geospatial analysis processes are illustrated and a code for a simple geographic information system in Python is introduced.
Chapter 2, Geospatial Data, discusses geospatial data, and explains the forms geospatial data comes in. The most challenging part of geospatial analysis is acquiring the data you need and preparing it for analysis. This chapter explains the two major categories of data as well as several newer formats that are becoming more and more common. Familiarity with these data types is essential to understand geospatial analysis.
Chapter 3, The Geospatial Technology Landscape, covers the geospatial technology ecosystem that consists of thousands of software libraries and packages. This vast array of choices is overwhelming for newcomers to geospatial analysis. The secret to learning geospatial analysis quickly is to understand the handful of libraries and packages that really matter. Most other software is derived from these critical packages. Understanding the hierarchy of geospatial software and how it's used allows you to quickly comprehend and evaluate any geospatial tool.
Chapter 4, Geospatial Python Toolbox, explains the software and libraries introduced which forms the basis of the book and are used throughout. In this chapter, Python's role within the geospatial industry is elaborated: GIS scripting language, mash-up glue language, and full-blown programming language. Code examples are used to teach data editing concepts, and many of the basic geospatial concepts in Chapter 1, Learning Geospatial Analysis with Python, are also demonstrated in Python.
Chapter 5, Python and Geographic Information Systems, teaches the simple yet practical python GIS geospatial products using processes which can be applied to a variety of problems.
Chapter 6, Python and Remote Sensing, shows readers how to work with remote sensing geospatial data. Remote sensing includes some of the most complex and least documented geospatial operations. This chapter will build a solid core for the reader and demystify remote sensing using Python.
Chapter 7, Python and Elevation Data, demonstrates the most common uses of elevation data, which can be contained in almost any geospatial format but is used quite differently from other types of geospatial data, and will show you how to work with its unique properties.
Chapter 8, Advanced Geospatial Python Modeling, discusses how geospatial data editing and processing help us understand the world as it is. But the true power of geospatial analysis is modeling. Geospatial models help us predict the future, narrow vast fields of choices down to the best options, and visualize concepts which cannot be directly observed in the natural world. This chapter uses Python to teach the reader the true power of geospatial technology.
Chapter 9, Real-Time Data, introduces real-time data and examines a modern phenomenon. A wise geospatial analyst once said, "As soon as a map is created it is obsolete." Until recently, by the time you collected data about the earth, processed it, and created a geospatial product, the world it represented had already changed. But modern geospatial data shatters this notion. Data sets are available over the Internet which are up to the minute or even the second. These data sets fundamentally change the way we perform geospatial analysis.
Chapter 10, Putting It All Together, combines the skills from previous chapters step-by-step to build a simple, automated geospatial analysis system which produces a report.
