小结#

Summary

在本章中，我们回顾了多种可自由获取的地理空间数据来源。我们了解了以下内容：

  • OpenStreetMap 是一个协作网站，用户可以在全球范围内创建和编辑矢量地图。

  • TIGER 是美国地质调查局提供的服务，提供有关街道、铁路、河流、湖泊、地理边界以及诸如学区和城市区域等法律和统计实体的地理空间数据。

  • Natural Earth Data 是一个出色的源，提供矢量格式的物理和文化边界数据，以及地球的各种栅格格式可视化数据。

  • GSHHS 是一个高分辨率的海岸线数据库，包含了全球海岸线、湖泊和河流的详细矢量数据。

  • World Borders Dataset 是一个简单的矢量数据源，包含全球国家边界及相关数据，打包成一个便捷的文件。

  • Landsat 提供了全球所有陆地的详细栅格卫星影像。

  • GLOBE 提供了全球范围的中分辨率数字高程（DEM）数据。

  • National Elevation Dataset 包含了美国大陆、阿拉斯加、夏威夷及其他美国领土的高分辨率数字高程（DEM）数据。

  • GEOnet Names Server 提供了除美国和南极洲以外的每个国家的官方地点名称信息。

  • GNIS 提供了美国的官方地点名称。

在下一章中，我们将使用 第3章，地理空间开发的Python库 中描述的Python工具包，以有趣且实用的方式处理一些地理空间数据。

In this chapter, we have surveyed a number of sources of freely-available geospatial data. We have learned that:

  • OpenStreetMap is a collaborative website where people can create and edit vector maps worldwide.

  • TIGER is a service of the US Geological Survey providing geospatial data on streets, railways, rivers, lakes, geographic boundaries, and legal and statistical entities such as school districts and urban regions.

  • Natural Earth Data is an excellent source for physical and cultural boundaries in vector format, as well as various raster-format visualizations of the Earth.

  • GSHHS is a high-resolution shoreline database containing detailed vector data for shorelines, lakes, and rivers worldwide.

  • The World Borders Dataset is a simple vector data source containing country borders and related data for the entire world bundled into one convenient package.

  • Landsat provides detailed raster satellite imagery of all land masses on

the Earth. - GLOBE provides medium-resolution digital elevation (DEM) data for the entire world. - The National Elevation Dataset includes high-resolution digital elevation (DEM) data for the Continental United States, Alaska, Hawaii, and other US territories. - The GEOnet Names Server provides information on official place names for every country other than the US and Antarctica. - GNIS provides official place names for the United States.

In the next chapter, we will use the Python toolkits described in Chapter 3, Python Libraries for Geospatial Development, to work with some of this geospatial data in interesting and useful ways.