Urban Hierarchy and City Systems: Size, Functions, and Influence

Cities as Interconnected Systems

Cities rarely function in isolation. They form city systems: networks of settlements linked by flows of people (commuting, migration), goods (supply chains), services (health care, education, finance), information (business decisions, media), and infrastructure (roads, rail, ports, airports, broadband). In a city system, a small town may depend on a nearby city for specialized hospitals, while that city depends on a larger metropolis for corporate headquarters, international flights, and advanced research.

Two ideas help you analyze these relationships:

• Connectivity: how easily places interact (travel time, cost, frequency of transport, digital access).
• Complementarity: how places “fit” together (one produces what another needs; one offers services another lacks).

When connectivity is high, people can reach higher-level services farther away, and the “pull” of large cities increases. When connectivity is low, smaller places must provide more locally, or residents face unmet needs.

Urban Hierarchy: Levels of Services and Influence

An urban hierarchy ranks settlements by the order (complexity and rarity) of goods and services they provide and the size of the area they serve. Lower-order places provide everyday needs; higher-order places provide specialized services used less frequently.

Typical hierarchy (illustrative)

Hierarchy is not only about population size. It is about functions and market reach. A smaller capital city can have higher-order political functions than a larger industrial city.

Continue in our app.

• Listen to the audio with the screen off.
• Earn a certificate upon completion.
• Over 5000 courses for you to explore!

Or continue reading below...

Download the app

Central Place Ideas: Threshold and Range

Central place ideas explain why certain services cluster in particular settlements and how far people travel for them. Two core concepts are threshold and range.

Threshold

The threshold is the minimum number of customers (or spending power) needed to support a service.

• A coffee kiosk has a low threshold (few customers needed).
• A specialized children’s hospital has a very high threshold (needs a large population base and referrals).

Range

The range is the maximum distance (or travel time/cost) people are willing to travel for a service.

• Range is small for everyday goods (bread, fuel).
• Range is larger for rare, high-value services (major surgery, international flights).

In general: higher-order services have higher thresholds and larger ranges. This is why they concentrate in fewer, larger, better-connected places.

Practical method: estimating whether a service “fits” a place

You can make a quick, testable prediction using three steps:

1. Estimate the threshold market: How many potential users are needed per year? (Use rough assumptions.)
2. Estimate the accessible population within a realistic travel time (the range): Who can reach the place within, say, 30, 60, or 90 minutes?
3. Compare: If accessible population × likely usage rate meets or exceeds the threshold, the service is plausible.

Example: Suppose a cinema needs about 80,000 ticket sales/year to survive. If the region averages 2 visits/person/year, then the threshold population is roughly 80,000 / 2 = 40,000 people. If Town A has 15,000 residents but can draw 30,000 more within 45 minutes, it might support a cinema; if Town B is isolated and only reaches 20,000 total within 45 minutes, it likely cannot.

Functional Specialization: What a City “Does” in the System

Many settlements specialize because of location advantages, institutions, or accumulated infrastructure. Specialization shapes employment, land use, migration, and influence within the city system.

Common specialized city types

• Port city: Focused on shipping, logistics, warehousing, customs, ship repair, and trade services. Often has strong transport corridors inland (rail/highway) and clusters of freight-related jobs.
• Capital city: Concentrates government institutions, embassies, national museums, regulatory agencies, and related professional services (law, consulting). Influence can exceed its population size.
• Industrial hub: Dominated by manufacturing and supplier networks. Often located near raw materials, energy, transport nodes, or large labor pools. May have freight yards, industrial parks, and technical training centers.
• Resort / tourism center: Employment concentrated in hospitality, recreation, seasonal services, and second homes. Demand fluctuates by season; housing affordability and service-worker commuting are common issues.
• University town: Anchored by higher education and research. Has a large student population, knowledge-based jobs, cultural venues, and spin-off firms. Service mix includes rentals, cafés, bookstores, labs, and conference facilities.

How to identify specialization using simple indicators

Use observable clues and basic data:

• Employment structure: unusually high share in one sector (e.g., transport/logistics in a port city).
• Institutions: presence of parliament, major university, naval base, large resort complexes.
• Infrastructure: container terminals, intermodal rail yards, research parks, convention centers.
• Flow patterns: commuter inflows, tourist peaks, freight volumes, student term-time population.

Mini-activity: Choose a city you know and list (1) its top three employers or sectors, (2) the key infrastructure that supports them, and (3) the main “incoming flows” (commuters, tourists, freight, students). From this, write one sentence describing its role in the wider system.

Rank-Size Patterns and Primate City Tendencies

Urban systems can be described by how city sizes relate to each other. Two descriptive tools are commonly used: rank-size patterns and primate city tendencies. These do not “explain” everything by themselves, but they help you compare regions and ask better questions about concentration and balance.

Rank-size rule (as a pattern)

In a rank-size pattern, the population of the city with rank r is approximately:

P(r) ≈ P(1) / r

Where P(1) is the population of the largest city. If the pattern holds, the 2nd city is about half the size of the 1st, the 3rd about one-third, and so on. Real regions vary, but the pattern provides a baseline for comparison.

Primate city tendency

A primate city tendency occurs when the largest city is disproportionately large and dominant compared with the next cities. This often corresponds to strong concentration of political power, finance, media, or international connectivity in one place.

A simple diagnostic is the primacy ratio:

Primacy ratio = P(1) / P(2)

Higher values indicate stronger primacy (more dominance by the largest city).

Charting exercise 1: quick rank-size check

Goal: Compare actual city sizes to a rank-size expectation.

1. List the largest 6–10 settlements in a region and their populations.
2. Rank them from 1 (largest) to N.
3. Compute the expected size using P(1)/r.
4. Create a small table and compare.

Interpretation tips:

• If many mid-ranked cities are far below expected, the system may be more concentrated (a few big places, many small ones).
• If several mid-ranked cities are close to expected, the system may be more balanced.
• If the largest city is far above what the rest suggest, primacy may be strong.

Charting exercise 2: log plot (optional but powerful)

Goal: See whether the size distribution approximates a straight line on a log scale.

1. Compute log10(population) for each city.
2. Compute log10(rank) for each rank.
3. Plot log10(pop) (y-axis) against log10(rank) (x-axis).

If points roughly form a straight downward line, the distribution resembles a rank-size relationship. Curves or sharp breaks suggest unusual concentration, missing middle cities, or multiple sub-systems.

Spatial Reasoning Activity: Inferring Service Areas from a Regional Map

This activity trains you to infer likely service areas (who goes where for services) and predict which settlements host higher-order services using only a map with towns and roads.

Setup (what you need)

• A simple regional map showing: settlement locations (at least 8–15), road types (highway vs local), and at least one major barrier (river, mountains) if possible.
• Optional: approximate populations for each settlement (even rough categories: small/medium/large).

Step-by-step method

1. Identify the network spine: Mark highways, rail lines, bridges, and major junctions. These reduce travel time and expand range.
2. Locate likely “nodes”: Settlements at intersections, river crossings, or central positions tend to attract higher-order services because they are accessible from multiple directions.
3. Estimate travel-time zones: Instead of straight-line distance, use road quality. For a quick approximation, treat: highway distance as “faster” and local roads as “slower.” Draw 30-, 60-, and 90-minute catchments around key nodes (even roughly).
4. Assign lower-order services first: Assume every settlement has basic convenience services. Then ask: where would people go for the next level (supermarket, high school)? Choose the nearest accessible node with enough surrounding population.
5. Build service areas (catchments): For each candidate service center, outline the area from which it is the most accessible option. Boundaries often fall along ridgelines, rivers without bridges, or midway points between competing centers along the road network.
6. Predict higher-order services: Place rare services (hospital, courthouse, major mall, university campus) in the settlement(s) that (a) sit on the network spine, (b) have the largest accessible population within 60–90 minutes, and (c) face the least competition from nearby large centers.
7. Check for specialization triggers: Add functional roles if the map shows clues: a port (coast/river terminal), a border crossing, a resort landscape (coast/mountains with access), or a campus/research park symbol.

Worked example (generic map logic)

Imagine a region with 12 towns. One town (M) sits at the intersection of two highways and near a bridge over a major river. Two medium towns (N and P) lie on separate highway branches. Several small villages lie on local roads.

• Likely hierarchy: M becomes the regional center (largest range, highest threshold support). N and P become sub-regional centers. Villages depend on the nearest of N/P/M depending on travel time.
• Service areas: The river limits cross-river travel except at the bridge near M, so M’s catchment extends strongly across both sides near the bridge, while other towns’ catchments are constrained.
• Higher-order services: A hospital and courthouse are most plausible in M. A community college might appear in N or P if they each have a sizable accessible population and are far enough from M to avoid direct competition.

Student task: infer service areas and service locations

Using your provided map, complete the following:

1. Circle the top 3 accessibility nodes (junctions/bridges/rail hubs) and justify each in one sentence.
2. Draw approximate catchments for: (a) supermarket/high school, (b) hospital, (c) major retail center.
3. Label which settlement most likely hosts each service and explain using threshold (market size) and range (travel willingness/time).
4. Identify one settlement that might be functionally specialized (port/resort/university/industrial) based on map cues, and list two expected impacts on commuting or land use.

Extension: competition and “shadowing”

Large centers can “shadow” nearby towns by capturing demand for higher-order services. To test shadowing:

• Pick a medium town near a large city.
• Compare travel time from surrounding villages to each.
• If the large city is only slightly farther but offers much more variety, higher-order services may not survive in the medium town (its threshold cannot be met).