空间数据库#

Spatially-enabled databases

从某种意义上说，几乎任何数据库都可以用来存储地理空间数据：只需将几何对象转换为 WKT 格式，并将结果存储在文本列中。但尽管这样可以将地理空间数据存储在数据库中，它却无法让你以任何有用的方式查询这些数据。你唯一能做的就是逐条记录地检索原始的 WKT 文本，并将其转换回几何对象。

另一方面，空间支持数据库能够理解“空间”的概念，允许你直接操作空间对象和概念。特别是，空间支持数据库允许你执行以下操作：

  • 将 空间数据类型 （点、线、面等）直接存储在数据库中，以几何列的形式。

  • 在数据上执行 空间查询 。例如：

    select all landmarks within 10 km of the city named "San Francisco"

  • 在数据上执行 空间连接 。例如：

    select all cities and their associated countries by joining cities
and countries on (city inside country).

  • 使用各种 空间函数 创建新的空间对象。例如：

    set "danger_zone" to the intersection of the "flooded_area" and
"urban_area" polygons.

如你所想，空间支持数据库是处理地理空间数据的极其强大的工具。通过使用**空间索引**和其他优化，空间数据库能够快速执行这些类型的操作，并且能够扩展以支持庞大的数据量，这在其他数据存储方案中是无法实现的。

In a sense, almost any database can be used to store geospatial data: simply convert a geometry to WKT format and store the results in a text column. But while this would allow you to store geospatial data in a database, it wouldn’t let you query it in any useful way. All you could do is retrieve the raw WKT text and convert it back to a geometry object, one record at a time.

A spatially-enabled database, on the other hand, is aware of the notion of space, and allows you to work with spatial objects and concepts directly. In particular, a spatially-enabled database allows you to do the following:

  • Store spatial datatypes (points, lines, polygons, and so on) directly in the database, in the form of a geometry column.

  • Perform spatial queries on your data. For example:

select all landmarks within 10 km of the city named "San Francisco"

  • Perform spatial joins on your data. For example:

select all cities and their associated countries by joining cities
and countries on (city inside country).

  • Create new spatial objects using various spatial functions. For example:

set "danger_zone" to the intersection of the "flooded_area" and
"urban_area" polygons.

As you can imagine, a spatially-enabled database is an extremely powerful tool for working with geospatial data. By using spatial indexes and other optimizations, spatial databases can quickly perform these types of operations, and can scale to support vast amounts of data simply not feasible using other data-storage schemes.