Geospatial Data Analytics on AWS by Scott Bateman & Janahan Gnanachandran & Jeff DeMuth

Author:Scott Bateman & Janahan Gnanachandran & Jeff DeMuth
Language: eng
Format: epub
Publisher: Packt
Published: 2023-11-15T00:00:00+00:00

Spatial query structure

Performing any kind of query on geospatial data that interrogates the location information of the geometry can be referred to as a spatial query. Spatial queries unlock new opportunities and threats by adding the context of where the data exists. Not only can you determine where a particular feature sits on Earth but you can also find out what that feature is close to, contained within, or related to.

Spatial queries follow the similar SQL format of attribute queries but gain location awareness. When working with geospatial data, it is important to always know the coordinate reference system of your data. The coordinate system can be specified as an argument when converting latitude and longitude into a point geometry. Knowing the correct reference system will ensure that your location accuracy is maintained throughout conversions and transformations.

A common example of where spatial queries can provide unique insights can be seen with the ST_Contains spatial function. This built-in function allows you to find out whether or not one geometry is contained within the boundaries of another. This function will return true if the second argument is completely contained within the boundary of the first argument. This functionality can be combined with a join of two spatial datasets, as shown here, to determine which earthquakes occurred in Kern County, California:

SELECT counties.name, earthquakes.earthquake_date, earthquakes.magnitude, earthquakes.latitude, earthquakes.longitude FROM counties CROSS JOIN earthquakes WHERE ST_CONTAINS (ST_GeomFromLegacyBinary(counties.boundaryshape), ST_POINT(earthquakes.longitude, earthquakes.latitude)) AND counties.name = 'Kern'

This results in the following output:

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