小结#

Summary

在本章中，我们简要介绍了Python编程语言以及地理空间开发的主要概念。我们已经了解了：

  • Python是一种非常高层次的语言，非常适合进行地理空间开发任务。

  • 有许多可以下载的库，可以使在Python中进行地理空间开发变得更加容易。

  • “地理空间数据”是指通过坐标表示的位于地球表面上的信息。

  • “地理空间开发”是指编写计算机程序，以便访问、操作和显示地理空间数据的过程。

  • 由于不同的文件格式和数据标准，访问地理空间数据的过程并非易事。

  • 通过分析地理空间数据可以回答哪些类型的问题。

  • 如何将地理空间数据用于可视化。

  • 如何使用“混搭”方式将数据（通常是地理空间数据）以有用和有趣的方式结合起来。

  • Google Maps、Google Earth以及廉价便携GPS设备的开发如何使地理空间开发“民主化”。

  • 开源软件运动对高质量、免费可用的地理空间开发工具的可用性产生的影响。

  • 各种标准组织如何定义共享和存储地理空间数据的格式和协议。

  • 地理定位技术的日益普及，如何以令人惊讶和有用的方式捕捉和处理地理空间数据。

在下一章中，我们将更详细地探讨传统的GIS，包括一些重要的概念，您需要理解这些概念才能有效地处理地理空间数据。我们将审视不同的地理空间格式，并通过使用Python进行各种地理空间数据计算来结束本章。

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.