Network Analysis in Python: Shortest Path Routing for Small Towns

Navigating physical spaces efficiently is a fundamental challenge in logistics, urban planning, and software development. Understanding how to represent a town as a network of connected points is the first step to solving complex routing problems. This text-based course guides you through the process of building, analyzing, and solving spatial routing problems using Python. You will transition from understanding basic graph theory to writing clean code that calculates the absolute shortest path between any two locations in a simulated town. What you'll learn: Understand foundational graph theory concepts, including nodes, edges, weights, and directed versus undirected networks; Build spatial network models using the NetworkX library in Python with modern type hints for clean, readable code; Apply classic routing algorithms, such as Dijkstra's algorithm, to find the most efficient paths between locations; Represent real-world constraints like distance and street limits using edge weights; Analyze network properties to identify critical intersections and bottlenecks in a town map. The course starts with essential terminology and structural definitions before moving into hands-on coding exercises. You will read clear explanations, study structured code examples, and practice modeling a small town's street grid from scratch. This course is designed for beginner Python programmers, aspiring data analysts, and GIS enthusiasts. No prior experience with graph theory or network analysis is required. Start reading today to master the fundamentals of network routing and spatial analysis.