Introduction

Efficient route optimization is crucial for businesses in the logistics and delivery sector. In this tutorial, we will explore how to optimize delivery routes in London using Python and visualize the results with Kepler.gl. We'll leverage GeoJSON to represent our routes, offering a clear and interactive map experience.

Prerequisites

Before we start, ensure you have the following: >Python 3.x installed on your system. >Basic understanding of Python programming and geospatial data. >An IDE or text editor like VSCode for writing code.

Step 1: Set Up Your Environment

Install the necessary Python libraries using pip: These libraries will help us handle data, perform geospatial operations, and create visualizations.

Step 2: Prepare Your Data

We'll use a CSV file with restaurant data in London. The file should contain columns for longitude and latitude. If you don’t have a dataset, you can create one using the following code:

Step 3: Load and Visualize Data

Next, we’ll load the restaurant data and visualize it using Kepler.gl. Create a Python script and add the following code: This code will create a map displaying restaurant locations.

Step 4: Optimize Routes with OSRM

To calculate optimal routes between restaurants, we use the OSRM (Open Source Routing Machine) API. Update your script with the following code to fetch and decode the route:

Conclusion

In this tutorial, we've learned how to optimize delivery routes in London using Python and Kepler.gl. By using GeoJSON, we can effectively visualize routes and make data-driven decisions to enhance delivery operations.

Feel free to adapt this approach to other cities or datasets, and explore further to refine your route optimization strategies.

May your code compile flawlessly. Until then!