SQL supports spatial data processing and is suitable for map applications, urban planning and other scenarios. Common spatial data types include POINT (point), LINESTRING (line), POLYGON (region). Different database systems such as PostGIS, MySQL, etc. have slightly different support; ST_Distance can be used to calculate the distance between two points and sort the closest users; ST_Contains can be used to determine whether the points are in the region; ST_Intersects can be used to achieve the statistics of spatial grouping; when using it, you need to pay attention to the coordinate system 1. Establish a spatial index to improve performance, control the calculation accuracy, and verify the results with map tools.

Spatial data is very common in many scenarios, such as map application, urban planning, logistics scheduling, etc. As a widely used query language, SQL actually supports the processing of spatial data. If you need to analyze geographical location information, calculate the distance between two points, or determine whether a point is in a region, you can do it with SQL's spatial functions.

Below are some common usage scenarios and operation methods, suitable for students who are new to spatial data.

Space data types and storage methods

Before you begin your analysis, you need to understand how the database stores spatial data. Common spatial data types include:

• POINT : represents a point, such as latitude and longitude
• LINESTRING : represents a line, such as a road trajectory
• POLYGON : Represents a region, such as administrative division boundaries
• GEOMETRY / GEOGRAPHY : Different databases may be called differently. The former is usually used for plane coordinates, and the latter is used for spherical geographic coordinates (such as WGS84)

Different database systems (such as PostgreSQL PostGIS extension, MySQL, SQL Server, BigQuery) support slightly different space types, but the basic concepts are the same.

For example, in PostGIS you can create a table like this:

 CREATE TABLE locations (
    id serial PRIMARY KEY,
    name varchar(100),
    geom geometry(Point, 4326) -- represents a point under the WGS84 coordinate system);

Common spatial analysis operations

Calculate the distance between two points

This is one of the most commonly used actions, such as if you have a user location list and want to find the 5 users closest to a certain location.

In PostGIS, you can use the ST_Distance function:

 SELECT name, ST_Distance(geom, ST_SetSRID(ST_MakePoint(-73.99, 40.75), 4326)) AS distance
FROM locations
ORDER BY distance
LIMIT 5;

In this example, we create a New York center point (longitude -73.99, latitude 40.75), then calculate the distance from each location to that point and sort by distance.

Note: If the data volume is large, remember to add a spatial index to geom field, otherwise the query will be very slow.

Determine whether a point is in a certain area

For example, if you want to know which users are located in the administrative area of ??a certain city, you can use space to include functions.

Suppose you have a polygon representing the boundary of a city, the field is boundary , and the user's location field is user_location , then you can write it like this:

 SELECT u.name
FROM users u
JOIN city_boundaries c ON ST_Contains(c.boundary, u.user_location);

This statement returns all users that fall within the city boundary.

Aggregation and spatial grouping

Sometimes you need to count the number of points in certain areas, such as how many stores there are in each business district.

You can first label the location of each store as the business district, or group it directly through spatial intersection:

 SELECT c.district_name, COUNT(*) AS shop_count
FROM shops
JOIN districts d ON ST_Intersects(s.geom, d.boundary)
GROUP BY c.district_name;

This code will count the number of stores in each administrative district.

Some precautions in actual use

• Coordinate system problem : Make sure your spatial data are all in the same coordinate system, otherwise the calculation result may be errors.
• Performance optimization : Spatial queries can easily slow down, especially when scanning the full table. It is recommended to establish a spatial index to reduce unnecessary calculations.
• Precision control : Some functions have "simplified" versions, such as ST_DWithin which can be used instead of precise distance comparisons, is more efficient.
• Visual Assistance : Although SQL is good at analytics, it is best to verify the results with map tools such as QGIS or GeoPandas when viewing spatial relationships.

Basically that's it. By mastering these key functions and techniques, you can easily deal with most spatial data analysis tasks in your daily work.

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