Where young adults live in a city, and why it matters to your business

Young adults, roughly those aged 20 to 29, tend to live in a few kinds of place: near universities, in central rented flats, around new apartment blocks near transport, and in older inner districts where rents are lower. You can find them by mapping the share of residents aged 20 to 29 in each part of a city, not the total number of people. The share matters because it tells you where a business aimed at them will find the most of its customers per street, and where a business aimed at families or retirees will find the fewest.

This is for businesses whose customers are mostly in their twenties (co-living and student housing, gyms and climbing walls, quick-service food, bars, phone and bike shops, entry-level recruiters) and for planners deciding where youth services belong. It covers how to find these areas, a worked example in Paris, what can go wrong, and an AI employee that maps it for you.

Why the share beats the total

A large suburb may hold as many people in their twenties as a small inner district, simply because it holds more people of every age. But they are spread across family homes, and a shop there serves mostly families. In the inner district, a much larger share of everyone on the street is in their twenties. For a business that serves them, the share is what decides whether a site has enough of the right people walking past.

Look at both: the share to find the character of an area, and the count to make sure it is big enough.

What drives where they live

• Study. Universities pull students into the surrounding streets, and many stay nearby after graduating.
• Renting. Young adults rent more than they own, so areas with many rented flats and small units have more of them.
• Transport and nightlife. Being close to a metro line, bars and work matters more than space at this age.
• New building. New apartment blocks near transport often fill first with young professionals.

None of these is in the population figures directly. They are reasons to expect a pattern, which the age groups then confirm or contradict.

A worked example in Paris

The figures below are made up. A co-living operator is choosing between three Paris districts for its next building. Its residents are mostly aged 22 to 30. The analyst measures 1 km around a candidate address in each district.

Invented figures, for illustration only.

The university area has the highest share, but the operator knows students there mostly live in halls or with family and move on each June. The inner-east district has nearly as many people in their twenties and a more settled renting crowd. The west district is out despite having more residents than the first.

The operator then checks what is around each address: gyms, cafes and transport, which its residents expect within a few minutes' walk.

What the figures cannot tell you

• Students may be counted elsewhere. Depending on the country, students can be counted at their family home rather than where they study. Areas around campuses may show fewer young adults than live there in term time.
• Income and working status. A 25-year-old student and a 25-year-old engineer look the same in the age groups.
• Recent change. A district that young people have moved into over the last couple of years may not show it yet.
• Countries without figures. Population figures exist in some countries only, among them France, Canada, India, Thailand, Egypt and Australia, and not the United Kingdom or the United States.

Mistakes to avoid

• Mixing 20 to 29 with 20 to 39. The thirties are when many people start families and move out. A business aimed at people in their twenties should not lump the two groups.
• Ranking by count alone. The biggest district will always win on count. Rank by share, then check the count is large enough.
• Ignoring the calendar. Areas around universities empty in summer. A business there should plan for quiet months.

Mapping it with an AI employee

1. Schedule once, or each year when plans are reviewed.
2. Area of interest on the city, or on the districts you are considering, drawn as shapes.
3. Demographics set to Every hexagon. Each hexagon comes back with its residents and age_20_29.
4. A Calculated field with young_share = round(age_20_29 / max(population, 1) * 100), and a Filter that keeps hexagons with at least 50 residents, so near-empty cells do not show extreme shares.
5. A second line in the same Calculated field puts each hexagon in a band, such as category = if(young_share >= 25, "25% or more", if(young_share >= 15, "15 to 24%", "Under 15%")).
6. Show on a map, coloured by category, so each band is its own layer you can switch on and off.

What you get is a saved map of the city where each band of the share of residents in their twenties has its own colour, cell by cell. For a ranked list of candidate addresses instead, run Demographics on your own sites with My data, add Nearby places counting cafes and gyms around each, and have Email send the sites sorted by the share.