How to Reduce GPS Data Set by Distance Using Python and Pandas

Discover how to efficiently reduce your GPS data set in Python using the Haversine formula and Pandas, ensuring locations are approximately one metre apart.
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How to Reduce GPS Data Set by Distance Using Python and Pandas

GPS data collection is pivotal, especially in projects involving vehicles, drones, or smart devices. When polling GPS coordinates at high frequencies, like 10 Hz, you can end up with a flood of data—sometimes more than you need. If your goal is to simplify this data by focusing only on significant geographical changes—perhaps, to ensure points are roughly one metre apart—this guide will guide you through a solution in Python, utilizing the Pandas library and the Haversine formula.

Understanding the Problem

Consider this scenario: You have a Raspberry Pi mounted on your car, recording GPS data while you drive at varying speeds. The data is recorded into an SQL database, which means when you check your database later, it might contain a lot of closely spaced coordinates, including many that are very similar, especially when you stop to allow other cars to pass.

Your goal is to filter the GPS coordinates so that there is approximately one metre between each recorded point, reducing redundancy while retaining the essential movements.

Solution Overview

To tackle this problem, we'll use the mpu library to compute the distance between two GPS coordinates using the Haversine formula. The process involves:

Querying the database for the GPS coordinates.

Iterating through the coordinates and calculating the distance between each new point and the last recorded point.

Recording the new coordinate only if it's more than one metre away from the last recorded point.

Step-by-Step Breakdown

1. Import Libraries

Make sure you have the mpu library installed. You can install it using pip if you don’t have it yet:

[[See Video to Reveal this Text or Code Snippet]]

Now, let's import the required libraries in our Python script:

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2. Connect to Your Database

You'll need to connect to your SQL database to fetch the stored GPS data. Here is how to establish the connection:

[[See Video to Reveal this Text or Code Snippet]]

3. Code the Main Function

The main function will query the GPS data and process it. Here’s how it looks:

[[See Video to Reveal this Text or Code Snippet]]

4. Running the Code

Finally, call the function with the appropriate batch ID that corresponds to the specific data set you wish to process:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By filtering GPS data in Python using the Pandas and mpu library, you can efficiently reduce your data set while ensuring significant location changes are maintained. Whether for logistics, tracking, or other applications, a clean and concise dataset improves analysis and usability.

If you have any questions or need further enhancements to this process, feel free to ask or share your suggestions below!