Population data for clinic and pharmacy planning
How health providers use residents and age groups to size a clinic's catchment, place a pharmacy and choose services, and where the figures stop being enough.
For clinics and pharmacies, population data answers three planning questions: how many people live within reach of a site, how old they are, and how many other providers already serve them. Residents within a walk or a short drive give the size of a catchment; the age bands point to the services it needs; the count of nearby clinics or pharmacies shows how much of that demand is already met. Together they turn a hunch about a site into a number you can defend.
This is for healthcare operators, pharmacy groups, public health teams and the consultants who advise them. It covers the figures that matter for health services, a worked example, the limits a health planner must respect, and how to keep a network's figures up to date.
The figures a health planner uses
- Residents in the catchment: population from the Demographics block, within a circle or a Travel area around each site.
- Young children: age_0_9, for paediatric care, vaccination days and child health clinics.
- Older residents: age_60_69 up to age_80_plus, or seniors_60_plus and its share, for chronic care, repeat prescriptions, falls and memory services.
- Density: people_per_km2, which changes how far patients will travel and whether walk-in or appointment-led works better.
- Existing providers: Nearby places counting clinics, pharmacies, dentists or hospitals around each site, with the distance to the nearest.
What the supply side looks like
Before working out demand, see how crowded the market already is. xMap's places database lists 297 medical clinics within 10 km of the centre of Sydney (November 2026). Only 17 belong to a chain; 280 are independent. Of the 166 with a rating, the average is 4.1, and just one clinic with an opening date opened in the twelve months before.
A market of mostly independent clinics with few new openings behaves differently from one where chains are expanding fast. Dentistry in the same circle is busier still: 768 dentists listed, and of the 465 nearest the centre only 18 belong to a chain, rated 4.7 on average.
A worked example: a clinic in Mumbai
A made-up case to show the reasoning. A clinic group is choosing between two sites in Mumbai for a family practice with a strong child health service. For each, it asks for the residents within a 10-minute walk and the clinics within 1 km. Suppose the answers are these (invented for the example):
| Site 1 | Site 2 | |
|---|---|---|
| Residents within a 10-minute walk | 58,000 | 41,000 |
| Children 0-9 | 9% | 15% |
| Over-60s | 14% | 8% |
| Clinics within 1 km | 11 | 3 |
| Nearest clinic | 120 m | 650 m |
Invented figures, to show the method; not real figures for Mumbai.
Site 1 has more people but a crowded field of clinics close by. Site 2 has fewer residents, a much younger profile and the nearest clinic a fair walk away. For a practice built around children, Site 2 is the stronger case, and the numbers say why in a way a board can check.
Placing a pharmacy
Pharmacies are a daily need, so the catchment is small and walking distance matters most. A useful routine:
- Draw the districts you would consider, or bring in candidate addresses.
- Count residents within 500 m or a 10-minute walk of each.
- Count pharmacies around each and note the nearest one's distance.
- Keep the candidates with many residents and no pharmacy within a short walk.
- Check the share of over-60s: they fill most repeat prescriptions and value a short walk most.
For public services the same routine answers a different question: which districts have no pharmacy close by at night, or no clinic within reach of many older people. The template Districts with no pharmacy close by does exactly that.
Where population data stops
- It does not say who is ill. Disease rates, insurance cover and income are not in the grid.
- It does not count patients who travel in from elsewhere, such as commuters who see a doctor near work.
- It covers about 100 countries, among them India, Egypt, Thailand, France, Canada and Australia, but not the United Kingdom or the United States. A site outside coverage comes back as 0 and the step says so.
- It changes slowly. Refresh it a few times a year; refresh the count of providers more often, because clinics open and close faster than people move.
An AI employee for a health network
For a network of sites, set up one AI employee that checks every site each quarter:
- A schedule and My data with your clinics or pharmacies.
- Demographics around each, Within 1 km or after a Travel area.
- Nearby places counting other providers, passing on each site with counts.
- xMap LLM asked to flag sites whose neighbourhood looks older or younger than the services they offer.
- An Email to the planning team, or a PDF report for the board.
Other templates that start from the same figures: Where to open a dialysis centre, Where to open sample collection points and Pharmacies near schools for a vaccination drive.
Questions
How many residents does a clinic need?
It depends on the service and the country's health system, so there is no one figure. Compare candidate sites on the same measure and against your existing sites that work.
Can I see how many women of childbearing age live near a site?
Not exactly. The data gives women in total and age groups in total, but not women of each age.
Should a pharmacy use a circle or a walking area?
A walking area of about 10 minutes is closer to how people choose a pharmacy, especially near rivers, rail lines or main roads.
Does it include patients who commute in?
No. The figures count residents. People who work or shop nearby are not in them.
Templates that do this
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