Urban Networks
Dec 1, 2025
By Kennon Stewart
Networks and Complexity
While working as a Data Engineer II at Amazon, I worked with some incredible teams. I worked with logistics and shipping teams, business teams that were trying to make a buck, and operations teams who made the magic happen.
The biggest disconnect between corporate and operations teams wasn’t the office location or job role. It was how well one could understand the complexity that comes with shipping a package.
Business teams think in numbers. We need data and metrics to make sense of operations, otherwise we’d be lost. Ops managers frequently knew their processes inside-out. They had a skill of take a complex system like a fulfillment center and making it simple enough to address the problem at hand.
They could estimate their daily delivery speeds based on the weather and the number of staff on hand. They planned for workforce numbers by reducing the problem to the target volume set by the business and the average speed at which they know their team can work. Most of the decisions made in this environment relied on getting rid of the unnecessary information to get the data you need to attack a problem.
Cities are Just as Complex
Cities are just as complex, if not more. They’re hotbeds of human innovation and art. Businesses and governments alike need to make sense of these complex environments.
Modeling the city as a network (a collection of nodes and edges) doesn’t do justice to the city’s real state, and that’s the goal. It strips away the complexity of road networks, traffic delays, and infrastructure information to solve the problem at hand. It’s easy to get pessimistic about the idea of intentionally throwing out information, but this is actually the key to big data.
Why Network Models Still Help
A network model is a lens, not the whole city. When you abstract a street grid to nodes and edges, you aren’t denying reality—you’re choosing the right level of detail for a decision.
- For routing, you care about connectivity and costs (travel time), not every tree on the block.
- For resilience, you care about centrality—which intersections matter most if traffic or power fails.
- For planning, you care about flows—how people, goods, and information move.
If the model helps you decide faster and better, it’s working.
Practical Patterns
Delivery Under Uncertainty
Think like an ops manager: define the network, instrument the few variables that make or break the day (weather, staffing, peak windows), and forecast feasible throughput. The model’s job is not perfection—it’s actionability.
City Resilience
Use betweenness centrality and cut sets to identify fragile corridors. Plan detours and micro-infrastructure (signals, curb space) where small changes unlock big gains.
Public Services
Map clinics, schools, and transit stops to understand service deserts. Simple network reachability at rush hour can reveal inequities faster than complex microsimulations.
Key Takeaways
Good models strip detail so decisions become clear.
Cities are processes
optimize connectivity and movement.
Track the variables that drive outcomes day
to-day. Push everything else to the side.
Start simple; add richness only to break ties.
The Bigger Picture
Urban systems resemble the fulfillment centers I’ve seen: complex, alive, and navigable when you respect the hierarchy of what matters. Network thinking gives city leaders, researchers, and engineers a shared language for making sense without drowning in detail.
If you’re working on Detroit’s infrastructure—or any city’s—start with flows, instrument the levers, and let the right abstraction guide the next step.