The Programming Language and Algorithms of SimCity and How Land Use Planners can Learn from It

Crafted by Gabe Bailer, PP, AICP

May 16, 2025

If you're an urban planner—or as I now often refer to myself, a land use planner, since many of the municipalities I work with aren't technically cities—chances are you’ve played SimCity. Created by Will Wright, SimCity was a groundbreaking simulation game first released in 1989 for early computer systems such as the Commodore 64 and Macintosh (what we now know as Apple).

The purpose of SimCity is to simulate the experience of building, managing, and growing a city. But how exactly does that work? Players are given a budget and can zone land by use—residential, commercial, or industrial—placing these zones on a grid-based landscape divided into individual tiles. To make the city functional, players must then lay out essential infrastructure such as electricity, water, and roads.

In later versions of SimCity, the game introduced more detailed land use classifications. For example, residential zones could be designated for single-family homes, multi-family units, or apartment buildings; commercial zones could include strip malls, shopping centers, or hotels; and industrial zones could accommodate power plants or factories. Players were also responsible for providing critical public services such as police, fire protection, and sanitation.

What truly set SimCity apart, however, was its simulation engine. The city didn't simply grow because zones were placed—it evolved through algorithm-driven simulation. As residential areas expanded, so did the population. As commercial areas developed, city revenue increased. Infrastructure like roads influenced traffic flow and urban growth.

But how did the game actually simulate that growth? What programming language and algorithms powered this system? And most importantly, what can today’s land use planners learn from it? Let’s take a closer look.

Programming languages form the foundation of computer games, but it's the algorithms embedded within the code that bring the game to life. These algorithms define the rules, behaviors, and logic that drive gameplay. The early versions of SimCity were developed using the C programming language, a procedural language created in the 1970s that has become one of the most widely used languages for building computer games, operating systems, websites, and virtually any logic-based electronic system.

C later gave rise to more advanced languages like C++ and Java, but it remains the backbone of modern programming. In the case of SimCity, while C provided the structure, it was the algorithms written into the code that powered the simulation—turning zoning decisions, infrastructure placement, and budget management into a dynamic city-building experience.

So what exactly were those algorithms that made SimCity function as a planning simulation? Let’s take a deeper dive into the logic that drove the game.

Zoning and Land Use Based Algorithms

For us land use planners, zoning is the foundation of our work. It dictates how land can be used, as well as the permitted dimensions, height, design, and density of what can be built. SimCity effectively translated these real-world planning principles into a simulation environment using an algorithmic concept known as cellular automata.

So, what does that mean? SimCity operates on a grid-based playing field composed of individual tiles, each of which can be assigned a land use, roadway, or utility. The growth or decline of each tile is influenced by a range of factors including access to power, road connectivity, proximity to municipal services, and levels of pollution and crime. These conditions determine the land value of a tile, which in turn influences the type and intensity of development that occurs there.

This system mirrors real-world land use dynamics. As planners—and real estate developers—know, land becomes more valuable when it is close to public transit, commercial activity, low-crime areas, walkable environments, and diverse land uses. In SimCity, these factors are quantified in the simulation, forming the algorithmic basis for economic value across the grid. This value drives how neighborhoods evolve, making the simulation a compelling model for understanding urban development patterns.

Another core algorithm in SimCity is its traffic and pathfinding system. While it may seem straightforward—you build roads, and development follows—the underlying mechanics are more complex and play a crucial role in shaping the city.

Vehicle movement in SimCity operates on a continuous loop, driven by algorithms that simulate commuting patterns. These pathfinding algorithms are designed to identify the shortest or most efficient routes between residential areas and job centers. As roads become congested, the simulation redirects traffic along alternate routes, mimicking real-world traffic dynamics.

Additionally, the game employs zone connectivity algorithms, which tie land development directly to infrastructure. Simply put, constructing roads doesn’t just move traffic—it also unlocks development potential, as zoning only becomes active when connected to the road network. Together, these systems model how transportation access drives growth and how congestion can reshape land use patterns over time.

SimCity incorporates an algorithm that simulates economic activity and development demand, with taxation serving as a central variable. In the game, tax rates directly influence the desirability of your city. Lower tax rates tend to attract residents and businesses, stimulating development and population growth. In contrast, higher tax rates can suppress demand, leading to reduced development, business closures, job losses, and a declining population. This dynamic models the economic balancing act that real-world planners and policymakers must navigate to maintain a healthy, thriving community.

The nature of crime impacts your tax revenue and population satisfaction. But how is crime determined in SimCity. Each playing tile has a crime evaluation algorithm based on how dense an area with the algorithm projecting denser areas with high crime rates, the value of the land, with the algorithm determining areas with low land value have higher crime, the proximity to police stations, with areas closer to police areas being safer and those located further away having higher crime rates, and the number of police stations covering the playing grid, with more police stations resulting in reduce crime rate.

These core algorithms form the foundation and driving force of SimCity, creating a dynamic looped simulation where your city evolves in response to your decisions. As you place more residential zones, your population increases. Expanding commercial and industrial zones generates jobs and stimulates economic activity. Roads are essential for enabling growth and connectivity, while utilities—such as power and water—are necessary to support development. Together, these interdependent systems bring the simulated city to life, reflecting the complexities and challenges of real-world urban planning.

What can land use planners learn from SimCity? The game offers a powerful introduction to systems thinking, demonstrating how cities are complex, interconnected networks. Every decision—whether it's adding a road, adjusting tax rates, or modifying zoning—can trigger ripple effects across housing, traffic, land value, pollution, and the broader economy.

SimCity allows planners to observe firsthand how zoning decisions influence land use and development patterns. Thoughtful, coordinated zoning leads to a more cohesive and functional urban network. The game also illustrates how transportation planning affects mobility; a well-connected road network helps reduce congestion and improve traffic flow.

Land value in SimCity is directly impacted by surrounding uses. For example, high-density residential areas adjacent to commercial zones—or near parks and green spaces—tend to have higher land values. This reflects real-world urban planning principles, where mixed-use development and access to amenities enhance community desirability.

At its core, SimCity captures the essence of public satisfaction. A well-planned city—with smart zoning, integrated transportation, adequate infrastructure, and accessible open space—results in a happier, more prosperous population. For planners, it’s a reminder that successful urban development is not just about growth, but about creating places where people want to live, work, and thrive.

SimCity is a foundational urban planning simulation that introduced many, including land use planners like myself, to the dynamic and interconnected systems that shape a city. Through player decisions and the game's embedded algorithms, SimCity demonstrates how each action influences the growth, function, and evolution of an urban environment.