Synthetic Population

Imagine you’re part of a team tasked with creating a new, improved public transport system for your city. Unless you live in a very small city, efficiently collecting data from every person who lives there might sound impossible due to time limitations, cost, privacy concerns, and people's willingness to participate. Also, you should probably consider people who live on the outskirts of such a city, but travel inward for work. Sounds hard, right? This is where creating a synthetic population comes in. 

The idea is that this synthetic population statistically simulates the real population you need to inform and create your new transport system. Taking this approach helps researchers and planners model, analyze, and predict outcomes and patterns without surveying actual people.

So, you might be asking yourself: how do you create this population so it accurately reflects real people? 

After identifying the purpose of their synthetic population—meaning what they aim to uncover with the data—researchers begin to collect data from reliable sources, such as national censuses, surveys, and other relevant databases. This data is then used to generate synthetic households or groups of individuals that mirror the attributes found in real-world distributions, such as income and education.

Next, these households or groups are assigned to specific geographical locations to reflect the actual distribution of people within a given area. The model is then validated and calibrated statistically to ensure it accurately represents the real population. Finally, researchers can use this synthetic population to simulate scenarios and analyze outcomes.¹

The idea is that by following these steps—or something similar, as each institution typically has its own procedure—we can better understand and deal with complex societal issues. This can be quite useful for policymakers, researchers, and analysts who seek to study and solve issues in a regulated and systematic way.