Working with Geospatial Features in MySQL

Data is abstract. Geospatial design management is how engineers across various disciplines make sense of complex data to make more informed decisions and better understand the spatial relationships in the world around us. In this blog post, I explore how complex data and geographic features can be represented in MySQL.

Geospatial data, often referred to in technical documentation as geodata , includes information related to locations on the Earth's surface. In MySQL, geographic features represent anything in the real world with a location and are defined as either Entities or Space.

MySQL spatial data types

In the paragraph above, we covered what Spaces and Entities are. How exactly are these geographic features represented in MySQL and other relational databases? These real-world objects and areas can be modeled within the database by utilizing specific data types and spatial functions. MySQL's capabilities revolve around three core geospatial object types: points, paths, and polygons.

In MySQL, spatial data types store geometry and geography values in the table column. Both single-geometry and multi-geometry types are supported. Single-geometry values include GEOMETRY , POINT , LINESTRING , POLYGON . Multi-geometry types that represent multiple objects of the same type include MULTIPOINT , MULTILINESTRING , MULTIPOLYGON , and GEOMETRYCOLLECTION .

Supported spatial data formats

MySQL supports several spatial data formats for storing and manipulating geospatial data within its database. Here are the three primary formats:

• Well-Known Text (WKT) format : Which is a human-readable text format for representing geometric objects
  • Uses keywords like POINT , LINESTRING , POLYGON followed by coordinates and optional metadata.

• Well-Known Binary (WKB) format : Which is a compact binary format for representing geometric objects. It’s not human-readable but it’s easily parsed by software and tends to have more efficient storage and transmission than WKT.
• Internal Format : MySQL stores spatial data internally in a format similar to WKB but with an additional 4 bytes for storing the Spatial Reference Identifier (SRID). SRID defines the coordinate system of the geometry, ensuring accurate interpretation.

Working with geospatial features in MySQL

Geospatial objects are just another data type in MySQL and can be used right alongside numbers, strings, and JSON.

Creating a geospatial table

Use the CREATE TABLE statement to create a table with a spatial column. Here we have a table named geom that has a column named g that can store values of any geometry type. We also defined the column with a spatial data type to have an SRID attribute and explicitly indicated the spatial reference system (SRS) for values stored in the column:

CREATE TABLE
    `locations` (
        `id` int NOT NULL,
        `city` varchar(255) NOT NULL,
        `city_ascii` varchar(255) NOT NULL,
        `country` varchar(255) NOT NULL,
        `iso3` varchar(3) NOT NULL,
        `admin_name` varchar(255) NOT NULL,
        `capital` varchar(255) NOT NULL,
        `population` int NOT NULL,
        `g` geometry NOT NULL SRID 4326,
        PRIMARY KEY (`id`),
        SPATIAL KEY `g` (`g`),
        FULLTEXT KEY `city_ascii` (`city_ascii`)

Use the ALTER TABLE statement to add or drop a spatial column to or from an existing table:

ALTER TABLE geom ADD pt POINT;





    
ALTER TABLE geom DROP pt;

Querying geospatial data

Understanding geographic features and their representation in MySQL is crucial for working with spatial data effectively. This allows you to store, retrieve, analyze, and visualize geographic information efficiently within your database.

MySQL provides various spatial functions for manipulating and analyzing geographic data. Let's cover some of the common spatial functions.

Location functions

Location functions are used to extract coordinates.

ST_GeomFromText(wkt_string) -- to convert WKT to a geometry object

ST_X(geom), ST_Y(geom) -- to extract coordinates.

Distance calculations

Distance calculations are used to measure the distance between features.

ST_Distance(geom1, geom2)

Area and perimeter calculations

Area and perimeter calculations are used to determine the area and perimeter of polygons.

ST_Area(geom) -- calculate the area of a polygon

Intersection and containment

Intersecton and containment are used to find features that overlap or are contained within others.

ST_Contains(geom1, geom2) -- to check if one feature contains another

ST_Intersects(geom1, geom2) -- to check if features intersect.

Buffering

Buffering is used to create zones around features based on a specified distance.

ST_Buffer(geom, distance) -- to create a zone around a feature with a specified distance.

Analysis functions

Analysis functions can be used to combine or diff geometries.

ST_Union(geom1, geom2) -- to combine geometries

ST_Difference(geom1, geom2) -- to obtain the difference between geometries.

Relationship functions

Relationship functions are used to detect relationships between features.

ST_Touches(geom1, geom2), ST_Crosses(geom1, geom2)

ST_Overlaps(geom1, geom2) -- to determine various spatial relationships between features

Examples using these spatial functions

Remember, the specific functions and queries you use will depend on your specific data and analysis goals. Let's look through some common examples of spatial functions.

Distance between two cities

select st_distance_sphere( (
            select g
            from locations
            where city_ascii = 'Santos'
        ), (
            select g
            from locations
            where
                city_ascii = 'Sao Paulo'
select st_distance_sphere( (
            select g
            from locations
            where
                city_ascii = 'New York'
        ), (
            select g
            from locations
            where city_ascii = 'Blauvelt'

Find cities in a radius

select
    city,
    st_astext(g),
    st_distance_sphere(
        g, (
            select g
            from locations
            where city_ascii = 'New York'
from locations
where st_distance_sphere(
        g, (
            select g
            from locations
            where
                city_ascii = 'New York'
    ) <= 15000
order by 3 desc;
select
    city,
    st_astext(g),
    st_distance_sphere(
        g, (
            select g
            from locations
            where city_ascii = 'Santos'
from locations
where st_distance_sphere(
        g, (
            select g
            from locations
            where
                city_ascii = 'Santos'
    ) <= 15000
order by 3 desc;