Location theory is a branch of economics and geography that explains why economic activities settle where they do. It asks a deceptively simple question: what makes one spot more profitable, efficient, or desirable than another for a farm, factory, store, or city? The answers, developed over nearly two centuries, form a family of models that share a common logic. Distance costs money, and the interplay between transportation costs, market access, and competition shapes the geographic pattern of human activity.
Where It Started: Von Thünen’s Agricultural Rings
The earliest formal location theory came from a German landowner named Johann Heinrich von Thünen in 1826. He imagined an isolated city surrounded by farmland and asked how different crops would arrange themselves around the central market. His key insight was that transport costs eat into a farmer’s profit, so the crops that are most expensive or difficult to ship (perishable dairy, fresh vegetables) would be grown closest to the city. Crops that are cheaper to transport or less perishable would occupy land farther out.
The result is a pattern of concentric rings. Intensive farming sits nearest the market, followed by timber (bulky and heavy to move), then progressively less intensive agriculture, with livestock ranching on the outermost ring where land is cheapest. The model assumes flat, uniform terrain and a single market, which obviously doesn’t match reality. But its core principle holds: the relative cost of moving goods to buyers is a powerful force shaping how land gets used, and the most productive activities outbid less productive ones for the most accessible locations.
Weber’s Least Cost Theory for Industry
Alfred Weber extended location thinking to manufacturing in 1909. His question was different from von Thünen’s: where should a factory sit to minimize its total costs? Weber identified three factors that pull a factory toward different spots.
- Transportation costs are the most important. A factory needs to move raw materials in and finished products out, so the ideal site minimizes the combined cost of both. When raw materials lose significant weight during production (think of smelting ore into metal), the factory is pulled toward the raw material source. When the finished product is heavier or bulkier, the factory is pulled toward the market.
- Labor costs can override transportation logic. A factory might locate farther from both materials and markets if sufficiently cheap labor is available at that site.
- Agglomeration economies describe the benefits of clustering near other businesses. When many firms locate in the same area, they share infrastructure, skilled workers, and specialized services, which lowers costs for everyone.
Weber’s framework explains patterns you can still observe. Steel mills historically sat near coal mines because coal lost most of its weight when burned. Bottling plants sit near cities because water (the heaviest ingredient) is available everywhere, but shipping heavy bottles is expensive. The model is simplified, but the cost-minimizing logic remains a foundation of industrial geography.
Central Place Theory and City Hierarchies
Walter Christaller tackled a different scale in the 1930s: why are some cities large and far apart while small towns are numerous and closely spaced? His central place theory explains this through two concepts.
The first is threshold, the minimum number of customers needed for a business to survive. A gas station needs a small population to stay open; a heart surgery center needs a very large one. The second is range, the maximum distance consumers will travel to buy something. People will drive across a region for specialized medical care but won’t cross town for a loaf of bread.
These two forces create a hierarchy. Small settlements offer low-order goods (groceries, gas) that people buy frequently and won’t travel far for. Larger cities offer all of those plus high-order goods (universities, specialist hospitals, luxury retail) that require bigger populations and draw customers from wider areas. Higher-order places are fewer in number and more widely spaced. Lower-order places are numerous and close together. On a map, this produces a nested pattern: many small towns surrounding fewer medium cities, which in turn surround a few large metropolitan areas.
Spatial Competition: Why Businesses Cluster
In 1929, economist Harold Hotelling posed a famous thought experiment. Imagine two ice cream vendors on a beach. Where should each one stand? If they’re competing only on location (not price), both vendors maximize their customer base by moving to the exact center of the beach, right next to each other. This is called the principle of minimum differentiation. It explains why you see competing gas stations on the same intersection or four coffee shops on the same block.
When price competition enters the picture, the logic flips. If two vendors can undercut each other, clustering together triggers a price war that hurts both. In that scenario, they’re better off spreading apart to create some geographic loyalty among nearby customers. This is the principle of maximum differentiation. Real markets usually fall somewhere between these two extremes, which is why you see both clustering (fast food strips, auto dealerships) and spacing (supermarkets serving distinct neighborhoods).
Agglomeration: Localization vs. Urbanization
Agglomeration economies, the benefits of geographic clustering, show up in two distinct forms. Localization economies are industry-specific. When tech firms concentrate in one area, they create a deep pool of specialized talent, attract venture capital, and generate knowledge spillovers between companies. These benefits grow disproportionately as the cluster gets bigger, showing increasing returns to scale. Silicon Valley and Hollywood are textbook examples.
Urbanization economies are broader. They come from city size itself: better transportation infrastructure, larger consumer markets, more diverse services. Research published in Royal Society Open Science found an important distinction between the two. Localization economies, especially in knowledge-intensive industries, tend to show increasing returns, meaning doubling the cluster more than doubles the benefit. Urbanization economies, by contrast, tend to show constant returns. A city twice as large doesn’t necessarily make every business twice as productive. Individual industries within a city may thrive with increasing returns, but the city as a whole operates closer to constant returns when all sectors are combined.
The Behavioral Critique
Classical location theories assume that decision-makers have perfect information and always choose the most profitable site. Geographer Allan Pred challenged this in the 1960s with his behavioral matrix. He argued that real entrepreneurs operate with incomplete information and limited ability to process what they do know, a concept economists call bounded rationality. Instead of finding the single best location, most business owners settle for one that seems “good enough.” They are satisficers, not optimizers.
Pred’s matrix places decision-makers along two axes: how much information they have and how well they can use it. A new entrepreneur with little market knowledge and limited analytical resources will land in a very different part of the matrix than a multinational corporation with dedicated site-selection teams. This framework doesn’t predict specific locations the way classical models do, but it explains why real-world patterns are messier than any geometric model suggests. Firms end up in suboptimal locations all the time, and some succeed anyway through adaptation or luck.
Gravity Models in Retail
One of the most practically useful descendants of location theory is the Huff gravity model, developed in 1964 and still widely used in retail site selection. It borrows from physics: just as gravitational pull depends on mass and distance, the probability that a shopper visits a particular store depends on two things. The first is the store’s attractiveness (its size, product range, or reputation). The second is the distance the shopper must travel to reach it.
A large, appealing store pulls customers from farther away, while distance pushes them toward closer alternatives. The model calculates the probability that any given customer visits a specific store by comparing its attractiveness-to-distance ratio against every competing store. Retailers and shopping center developers use this framework to estimate trade areas, forecast revenue at potential new sites, and evaluate the impact of a competitor opening nearby.
Modern Applications With GIS
Location theory today lives inside geographic information systems (GIS) and computational models that would have been unimaginable to von Thünen. Analysts use location-allocation models to solve practical siting problems: where to place fire stations, hospitals, warehouses, or electric vehicle charging stations to serve a population most efficiently.
Three classic model types dominate this work. Set covering models find the minimum number of facilities needed so that every demand point falls within a specified distance. Maximal covering models place a fixed number of facilities to reach as many people as possible. P-median models minimize the total (or average) distance between facilities and the people they serve. A recent study comparing these approaches for EV charging station placement in Beijing found that the p-median model consistently placed stations closer to communities with the highest demand, giving the majority of users more convenient access than either covering model achieved.
These tools combine classical location theory principles with real-world data on road networks, population density, competitor locations, and consumer behavior. The underlying question, though, is the same one von Thünen asked in 1826: given the friction of distance, where is the best place to be?