Microsimulation

Microsimulation flips the modelling paradigm from aggregate to individual. Instead of working with zone-level averages (“Sheung Wan has 28,000 residents”), you synthesize a virtual population where each person has demographics, income, preferences, and location. Then you simulate their behaviour.

Why Microsimulation?

The aggregate models (Gravity, Huff, Regression) work with averages. But averages hide crucial variation:

• A zone with median income HK$35,000 contains both HK$15,000 and HK$80,000 households
• Age distribution matters: students, young professionals, and retirees eat differently
• Household composition affects spending: families vs. singles vs. couples

Microsimulation models individual heterogeneity — the fact that people in the same zone behave very differently.

“Microsimulation is a technique that focuses on the characteristics and behaviour of individuals, rather than the groups that are used by conventional spatial interaction models.” — Birkin & Clarke, Ch. 10

The Pipeline

1. Build a synthetic population — merge census demographics with market research data to create individual-level records
2. Assign product ownership / preferences — what does each synthetic person consume? Based on their demographics + location
3. Generate behaviour — where do they go? How often? How much do they spend?
4. Simulate — run the population through spatial interaction, accounting for accessibility, competition, and individual preferences

Technical Structure: EC-Sim Example

Birkin & Clarke describe a 4-step microsimulation for financial services (EC-Sim), which translates directly to retail:

Step 1: Build micro-population sharing census demographics → Census data (age, income, household type) × geographic zones

Step 2: Add consumption patterns → Merge with market research / survey data

Step 3: Generate behaviour preferences → Not just demographics — also accessibility to services, which varies by location

Step 4: Simulate channel usage → Include physical provision (store locations, opening hours), brand, demographics

Tip

The key insight from Birkin & Clarke: Step 3 is not just a merge. Consumer behaviour depends on demographics AND accessibility AND location. A high-income person in a food desert behaves very differently from a high-income person in Soho.

Applied to 14 Wa In Fong East

Synthetic Population for Sheung Wan TPU

Attribute	Source	Example Distribution
Age	Census 2021	25–34: 28%, 35–44: 22%, 65+: 15%
Household income	Census 2021	Median HK$35,000; range HK$10K–120K
Household size	Census 2021	1-person: 18%, 2-person: 25%, 3+: 57%
Employment status	Census 2021	Working: 62%, Student: 8%, Retired: 12%
Cuisine preference	Survey / proxy	Chinese: 55%, Western: 20%, Japanese: 15%, Other: 10%

Simulation Logic

For each synthetic individual i near 14 Wa In Fong East:

P(visit_restaurant_j) = f(
  distance(i, j),           // walking time from home/office
  cuisine_match(i, j),      // does j serve what i likes?
  price_match(i, j),        // is j in i's budget?
  attractiveness(j),        // size, reviews, brand
  time_of_day,              // lunch crowd vs. dinner
  competition_nearby(j)     // alternatives within 200m
)

Running this for 28,000 synthetic individuals across 208 restaurants in the district produces a predicted visit count and revenue for any restaurant at our address.

Data Requirements for HK

Data	Availability	Quality
Census demographics	✅ Census 2021, TPU level	Excellent
Household income distribution	✅ Census 2021	Good (banded)
Restaurant locations	✅ FEHD licenses	Complete
Consumer preferences	⚠️ No public data	Need survey or proxy
Actual spending patterns	⚠️ No public data	Need Octopus/credit card data

Caution

The hard part isn’t the simulation — it’s Step 2: consumption data. Hong Kong has excellent census data but limited public consumer behaviour data. In the UK, Birkin & Clarke used loyalty card data (Tesco Clubcard). In HK, Octopus card data would be the equivalent, but it’s not publicly available.

Microsimulation vs. Agent-Based Modelling

	Microsimulation	ABM
Unit	Synthetic individual	Autonomous agent
Behaviour	Rule-based from data	Emergent from interactions
Interactions	Individual → environment	Agent ↔ agent ↔ environment
Dynamics	Static snapshot or step-wise	Continuous time evolution
Data needs	Heavy (census + surveys)	Lighter (rules + parameters)
Best for	Demand estimation	Scenario testing

Our Agent Simulation uses LLM-powered agents (Claude Opus) that combine microsimulation’s individual-level detail with ABM’s emergent behaviour — each agent has a synthetic persona AND can reason about complex tradeoffs.

Computational Reality

“In the late 1980s, two of the present authors developed a microsimulation approach using a synthetic sample of 50,000 households… programs were run overnight in batch mode on a mainframe computer costing about £1.5 million. In Chapter 10, we reported on an application using a sample of one million households, and can be run in a few seconds real time on a personal computer costing around £1,000.” — Birkin & Clarke, Ch. 12

In 2026, we can run microsimulation for all of Hong Kong (~2.7M households) on a laptop in minutes. The bottleneck is data, not compute.

Source

📖 Birkin, M. & Clarke, G. (2023). Retail Geography. Chapter 10: Microsimulation — EC-Sim Channel Model. Chapter 12: Computational advances in microsimulation.