Finding the Closest Geographic Points Between Two Tables in BigQuery Using Haversine Formula

Introduction to Geographic Point Distance Calculation in BigQuery

BigQuery is a powerful data warehousing and analytics platform that offers a range of features for analyzing and processing large datasets. One common use case in BigQuery involves calculating distances between geographic points, which can be useful in various applications such as location-based services, route optimization, and spatial analysis.

In this article, we will explore how to find the closest geographic points between two tables in BigQuery using the Standard SQL language.

Background on Geographic Point Distance Calculation

The distance between two geographic points can be calculated using various algorithms, including the Haversine formula, Vincenty’s formula, and others. For simplicity, we will use the Haversine formula to calculate distances between geographic points.

The Haversine formula is based on the spherical geometry of the Earth and calculates the shortest distance between two points on a sphere (such as the Earth) given their longitudes and latitudes.

Setting Up the Problem

We have two tables: Table A with latitude and longitude columns, and Table B with latitude, longitude, parameter1, and parameter2 columns. We want to create a new table (Table C) that contains all rows from Table A, along with the information from Table B based on finding the closest point in Table B to each row in Table A.

Data Structures and Data Types

To understand this problem better, let’s first define some data structures and data types:

Arrays: In BigQuery, arrays are a collection of values of the same type. For example, parameter1 is an array of integers.
Structures: A structure is a combination of fields with specific data types. We will use structures to represent rows in our tables.

Creating the Sample Tables

To demonstrate the solution, let’s create sample tables using BigQuery Standard SQL:

WITH `project.dataset.tableA` AS (
  SELECT 1 id, 39.79 latitude, 86.03 longitude, '123 Vine St' address UNION ALL
  SELECT 2, 39.89, 84.01, '123 Oak St' 
),
`project.dataset.tableB` AS (
  SELECT 39.74 latitude, 86.33 longitude, [1, 2, 3] parameter1, [.1, .2, .3] parameter2 UNION ALL
  SELECT 39.81, 83.90, [4, 5, 6], [.4, .5, .6] 
)

Calculating the Closest Geographic Points

To calculate the closest geographic points between Table A and Table B, we use the Haversine formula to find the distance between each point in Table A and each point in Table B.

Here is the BigQuery Standard SQL code to calculate the distances:

SELECT AS VALUE 
  ARRAY_AGG(STRUCT(id_A, address_A, parameter1_B, parameter2_B) ORDER BY ST_DISTANCE(a.point, b.point) LIMIT 1)[OFFSET(0)]
FROM (SELECT *, ST_GEOGPOINT(longitude, latitude) point FROM `project.dataset.tableA`) a,
(SELECT *, ST_GEOGPOINT(longitude, latitude) point FROM `project.dataset.tableB`) b
GROUP BY id_A

Explanation of the Code

Let’s break down the code:

We use a Common Table Expression (CTE) to define two tables: tableA and tableB.
For each row in tableA, we calculate the distance between the point in tableA and all points in tableB using the Haversine formula.
We group the results by the ID in tableA to get the closest point in tableB for each row in tableA.
Finally, we use the ARRAY_AGG function to aggregate the results into a single structure containing all columns from both tables.

Conclusion

In this article, we demonstrated how to find the closest geographic points between two tables in BigQuery using the Standard SQL language. We covered data structures and data types, created sample tables, calculated distances using the Haversine formula, and explained the code step by step.

By following these steps, you can extend this solution to your own use cases involving geographic point distance calculation in BigQuery.

Additional Considerations

When working with large datasets in BigQuery, keep the following considerations in mind:

Data Types: Make sure to understand the data types used in your query and optimize them according to your needs.
Indexing: Use indexing strategies to improve performance when querying large datasets.
Aggregation: When aggregating results using functions like ARRAY_AGG, ensure that you handle null values correctly.

By being aware of these considerations, you can write efficient and effective queries in BigQuery to tackle complex data analysis tasks.

Last modified on 2025-02-04