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