Legal and Structured Extraction of Location Data from Google Maps Using Python and the Google Places API

Abstract

The exponential growth of location-based services has made geographic and business-related data crucial for applications including market analysis, travel planning, and service optimisation. Google Maps contains a vast repository of real-time, location-specific data such as business names, ratings, addresses, and user reviews. This project focuses on programmatically accessing such data using Python— not through web scraping, which violates Google’s Terms of Service, but by leveraging the official and authorised Google Places API. Through the API, structured data about places such as restaurants, hospitals, and tourist spots can be retrieved legally and efficiently using simple HTTP requests. The project integrates the API with Python to send queries, parse JSON responses, and extract relevant fields such as name, location, rating, and user reviews; the retrieved data can then be stored in files or databases and visualised for further analysis. By adhering to legal and ethical data-usage practices, the project highlights responsible data access while demonstrating practical skills in Python programming, RESTful APIs, and JSON handling. Pagination and error handling allow large datasets to be collected reliably, and alternative open data sources such as OpenStreetMap and Foursquare are discussed for free access to similar geographic datasets. Unit and functional testing confirmed correct behaviour with no defects, demonstrating a stable, scalable, and legally compliant alternative to traditional scraping.