Network analysis in GIS: How to model movement across the real-world

The closest point on a map isn’t always the fastest to reach. While a straight line can make two places look near each other, practical movement depends on the network between them. In a geographic information system (GIS), a hospital might seem close to a neighborhood at first glance, but travel time depends on transit stops, traffic, and turn restrictions.

Network-based thinking helps you move past straight line assumptions and model how connected systems work. Instead of asking only where places are, you can find out how reachable they are and which paths make the most sense.

In this article, we’ll explain what network analysis in GIS means, its main types, and the ways it supports practical decisions.

What’s network analysis in GIS?

Network analysis refers to using spatial data to understand how movement and access work across connected systems. Instead of measuring only straight line distance between two points, it models the paths that people and vehicles can follow through specific types of networks.

Think of it as a more advanced version of digital GPS. A navigation app can suggest the fastest route based on roads and traffic. But GIS network analysis applies that same logic to larger operations, where spatial datasets represent connected features like rivers, pipes, and service routes.

The basic building blocks of network analysis include:

• Nodes: Connection points in the network, like intersections, stations, and junctions
• Edges: The lines that connect those points, like road networks, rail lines, and pipelines
• Topology: The rules that define how network features connect and where movement can and can’t pass through based on physical geography
• Cost factors: The values used to choose or recommend one path over another, typically constraints that affect movement like travel time, distance, and road restrictions

These connected datasets contextualize network analysis. They help you avoid decisions based on map proximity and account for limitations that affect real-world access.

Common types of network analysis in GIS

Different analysis methods answer different questions about access and connectivity. Below, we’ll discuss different types of network analysis, explain the output, and connect it to a practical example.

Routing and shortest path analysis

This analysis answers: What’s the best route between two or more locations?

The output is usually a path through the network, calculated by distance, travel time, or cost. “Shortest” doesn’t always mean the fewest miles or briefest duration. Instead, a route that avoids sharp turns or high-traffic areas may be a better option, reducing mileage and overall time.

Service area analysis

Service area analysis answers: What locations can you reach from a starting point within a set time, distance, or cost?

The output is a set of polygons that show reachable areas across a network. For example, a 10-minute service area around a depot shows where crews can realistically arrive within that window. Because the calculation follows the network, the shape usually looks very different from a simple circle drawn around the same point. It may be quicker to reach northern, eastern, and western points, leading to a triangle-shaped polygon.

Closest facility analysis

This analysis answers: Which available facility can reach a location fastest, or which facility is easiest to reach from a given point?

The output is a ranked set of facilities with routes and travel costs. This might look simple on the surface, but the network influences the answer. 

For example, the nearest fire station by distance might not be the fastest option if a bridge or one-way road gets in the way. So an emergency team may compare stations near an incident to quickly grab supplies or use a decontamination shower.

Origin-destination cost matrix

An origin-destination cost matrix answers: What’s the travel cost between many starting points and many destinations?

Instead of producing one route, it creates a table of travel times, distances, and costs between multiple origins and destinations. A GIS solver (a network analysis tool) calculates those pairings across a network, letting you compare many possible movements simultaneously.

Location-allocation analysis

This analysis method answers: Where should you place facilities, services, and resources to serve demand most effectively?

The output finds the best potential locations and how much demand weight they carry. It combines site selection with task allocation, answering “Where should this go?” and “Who should it serve?” together. 

For example, a city planning committee can compare service hubs like charging stations or clinics and decide which locations cover the most demand with the least travel cost.

Vehicle routing and fleet optimization

Vehicle routing and fleet optimization answers: How should multiple vehicles complete multiple stops while meeting operational constraints?

The output is a list of optimized routes with stop sequences, vehicle and supply chain assignments, and workload estimates to measure travel times. While shortest-path analysis finds the best route for a specific trip, vehicle routing coordinates multiple trips across a fleet.

Network analysis applications in GIS workflows

Let’s see where those outputs become useful. Here’s how a network analyst would apply the above methods in various industries.

Transportation planning

Transportation companies use network analysis in GIS to understand whether a system connects people to the places they need to go. A planned transit line might look convenient on a regional map, but network analysis shows how well it links to existing routes and where neighborhoods gain or lose access due to various constraints.

Felt’s Future Transit Map visualizes proposed transit connections, while the Transportation Safety Assessment reveals safety gaps and infrastructure priorities through traffic volume, crash corridors, and planned bike lanes.

Bike and pedestrian networks

Bike and pedestrian road network analysis prioritizes comfort and continuity for movement, not just distance. A short route can still fail if it sends riders across awkward crossings through high-speed roads.

A map like the 2030 Network for the Bike-Curious demonstrates how city planners look at local mobility networks as connected systems. It lets you compare which streets improve access to biking-heavy areas and where service areas expand after adding safer links.

Emergency response and public services

Network analysis can help emergency responders and dispatchers understand how to reach incidents and hospitals as fast as possible. The best route isn’t obvious at a glance — road connectivity, traffic, and access restrictions add time to the map’s shortest path.

A healthcare facilities map, like Felt’s hospitals and clinics in the US, support this planning by providing facility-level datasets with details like addresses, operator information, and bed capacity.

Utilities and infrastructure

Utilities operate across networks where location, exposure, and connectivity all affect risk and public services. A single asset might look isolated on a map, but its influence depends on what it connects to and what depends on it.

A map like Fire Risk & Utility Infrastructure visualizes assets alongside environmental risk. Here, network analysis helps utility companies understand which areas need attention.

Turn network analysis into live GIS workflows with Felt

Network analysis lets teams quickly understand the world around them and make smart, informed decisions. But not all analysis is equal. While legacy GIS tools help you run network analysis, the output is usually a static map or a specialist-owned workflow that’s hard for non-technical teammates to use.

Felt turns analysis outputs into live, shared maps that support practical, enterprise-level decisions. Different departments can bring diverse data sources into one collaborative mapping workspace. With cloud sources, all your maps stay connected to data from Snowflake, BigQuery, Databricks, Redshift, Postgres, Amazon S3, Azure Blob Storage, and Google Cloud Storage.

You can also ask spatial questions in plain language prompts to Felt AI, generate SQL-based analysis, and turn connected data from cloud sources into map layers with less manual input.

Sign up for Felt, and keep outputs current, governed, and simple to act on.