Men and women in an area: when the split matters, and when it does not

The split between men and women matters only when two things are true at once: what you sell is used very differently by men and women, and you are looking at a small area, a few streets rather than a whole city. Across a city the two numbers are usually close to equal, so the split adds nothing. In a single block of the map it can lean hard one way, and that is where it earns a place in a decision.

This article is for owners and planners in businesses where it can make a difference: women-only gyms and studios, barbers and beauty salons, maternity and women's health clinics, sports shops, and agencies placing local advertising. It explains what the figures are, how to read them without fooling yourself, a worked example, and how to have an AI employee add them to every site you look at.

What the figures are

The Demographics block reads xMap's population grid, small hexagons of about 0.1 km². For any circle, city or drawn area it gives the residents, then population_male and population_female, and male_pct, the share of men in percent. The same hexagons also carry nine age groups, so you can ask a sharper question than the split alone: not just how many women live here, but how many women aged 20 to 39.

Two limits come with it. These are residents, the people who sleep in the area, not the people who work or shop there. And population figures exist only in some countries: among them France, Canada, India, Thailand, Egypt and Australia, but not the United Kingdom or the United States. If your sites are in a country without data, the block says so and returns nothing for them.

When the split is worth reading

• Your offer serves one group. A women-only fitness studio, a barber, a breast screening clinic. Here the count of the group you serve is the real catchment, and the other half of the population is close to irrelevant.
• The area is small. Within 500 m or 1 km of a site, local quirks show: student halls, a large hospital with staff housing, a district of new one-bedroom flats, a neighbourhood where many working men live without families. A city-wide average flattens all of that.
• The lean combines with age. A patch with many young women suggests different services from one with many women over 60. Read the two together.
• You are buying local advertising. When an agency places posters or local ads for a product bought mostly by one group, the split per hexagon helps choose the streets.

For a supermarket, a pharmacy, a cafe or a general clinic, the split rarely changes anything. People of every kind use them. Checking it anyway is harmless, but do not let a few points of difference sway a choice that should rest on the number of people and the competition.

Reading it without fooling yourself

A share in percent hides the size behind it. An area with 58% women sounds attractive, but if only 1,200 people live there, that is under 700 women. A neighbour at 50% with 9,000 residents has 4,500. Always read the count of the group next to its share, and rank by the count.

Small areas also make small numbers jumpy. A single hexagon can hold a few hundred people; one building of student rooms tips it one way. Use circles of 500 m to 1 km for a shop people walk to, and do not draw conclusions from one hexagon on a heat map. Look for a run of hexagons leaning the same way.

A worked example

Here is a made-up example. A small chain of women-only fitness studios in Pune has two candidate units and asks for the residents within 1 km of each, by sex and by age. The figures below are invented to show the reading, not real counts.

Made-up figures, for illustration only.

Unit A has more people around it, and a first look at residents alone would favour it. But it leans towards men, and the number of women is the same as Unit B's. Narrow to the ages the studio serves and Unit B pulls ahead by about 800 women. The studio would then check what else is near each unit: how many gyms already offer women-only classes, and whether Unit B's street has the shops and schools that bring its residents past.

If the brand had been a barber's, the same two rows would have read the other way. That is the point of the split: it changes the answer only for businesses that serve one group.

Mistakes to avoid

• Ranking areas by the share of women or men instead of the number of the people you serve.
• Reading a single hexagon. One building can tip it; look at the area you actually draw customers from.
• Treating residents as customers. A women-only studio next to a large office may fill its lunchtime classes with people who live elsewhere; residents do not show that.
• Assuming a product is used by one group because of habit or packaging. Check your own customer list first; if it is close to half and half, ignore the split.
• Comparing a country with population data against one without, as if the second had no people.

How an AI employee keeps it next to your sites

You do not need to look this up by hand for each candidate. An AI employee can add the right counts to every site on your list and email you the ranking.

1. Start with My data: upload the candidate units, or your existing studios, as a table with their addresses or positions.
2. Add Demographics set to Each incoming place, within 1 km. Each unit gets the residents, men and women, and the nine age groups.
3. Add a Calculated field that adds up the women in the ages you serve, for example women aged 20 to 49 from the age groups and the women's share.
4. Add Nearby places set to give each unit a count of gyms within 1 km, so the list shows the competition beside the people.
5. Finish with Sort and limit on your new field, and Email or a sheet.

What lands in your inbox is one row per unit: residents, the group you serve, its share, and the gyms around it, best first. Run it again whenever the property agent sends new units. For advertising, the Best neighbourhoods to target ads around a store template works area by area instead of site by site, and Demographic profile of each city gives the whole-city backdrop.