In this chapter, we briefly introduced the Python programming language and the main concepts behind geo-spatial development. We have seen:
- That Python is a very high-level language eminently suited to the task of geo-spatial development.
- That there are a number of libraries that can be downloaded to make it easier to perform geo-spatial development work in Python.
- That the term "geo-spatial data" refers to information that is located on the Earth's surface using coordinates.
- That the term "geo-spatial development" refers to the process of writing computer programs that can access, manipulate, and display geo-spatial data.
- That the process of accessing geo-spatial data is non-trivial, thanks to differing file formats and data standards.
- What types of questions can be answered by analyzing geo-spatial data.
- How geo-spatial data can be used for visualization.
- How mash-ups can be used to combine data (often geo-spatial data) in useful and interesting ways.
- How Google Maps, Google Earth, and the development of cheap and portable GPS units have "democratized" geo-spatial development.
- The influence the open source software movement has had on the availability of high quality, freely-available tools for geo-spatial development.
- How various standards organizations have defined formats and protocols for sharing and storing geo-spatial data.
- The increasing use of geolocation to capture and work with geo-spatial data in surprising and useful ways.
In the next chapter, we will look in more detail at traditional Geographic Information Systems (GIS), including a number of important concepts that you need to understand in order to work with geo-spatial data. Different geo-spatial formats will be examined, and we will finish by using Python to perform various calculations using geo-spatial data.
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