In this chapter, we briefly introduced the Python programming language and the main concepts behind geospatial development. We saw that Python is a very high-level language and that the availability of third-party libraries for working with geospatial data makes it eminently suited to the task of geospatial development. We learned that the term geospatial data refers to finding information that is located on the earth's surface using coordinates, and the term "geospatial development" refers to the process of writing computer programs that can access, manipulate, and display geospatial data.

We then looked at the types of questions that can be answered by analyzing geospatial data, saw how geospatial data can be used for visualization, and learned about geospatial mash-ups, which combine data (often geospatial data) in useful and interesting ways.

Next, we learned how Google Maps, Google Earth, and the development of cheap and portable GPS units have "democratized" geospatial development. We saw how the open source software movement has produced a number of high-quality, freely available tools for geospatial development and looked at how various standards organizations have defined formats and protocols for sharing and storing geospatial data.

Finally, we saw how geolocation is being used to capture and work with geospatial data in surprising and useful ways.

In the next chapter, we will look in more detail at traditional geographic information systems including a number of important concepts that you need to understand in order to work with geospatial data. Different geospatial formats will be examined, and we will finish by using Python to perform various calculations using geospatial data.