Methodology
Estimated daily scripts, explained.
How much dispensing demand could this location attract? DealMap uses population and location data to estimate local prescription demand, then considers how that demand may be shared with nearby pharmacies. The result helps you compare locations and decide what to investigate next.
The measure behind the estimate is called Geographic Dispensing Potential (GDP) — a model of dispensing demand, not a measure of the economy and not a guarantee of trade.
Modelled demand, not actual dispensing. Check the assumptions and verify a pharmacy’s figures during due diligence.
How the estimate is built.
- 1
Estimate local demand
DealMap starts with population. Each Victorian statistical area carries a population figure and a governed prescription-demand rate. Where the enabled model supports a factor — demographics or health context — it is applied there. Factors that are not enabled in the model do not quietly add demand.
- 2
Share that demand between reachable pharmacies
Nearby pharmacies compete for a share of each area's demand. The model weights each pharmacy by distance, so a pharmacy closer to the population takes a larger share than one further away. Distance is straight-line proximity in the model — not driving time, and not an observed patient journey.
- 3
Express annual demand per trading day
The allocated annual demand is divided by the pharmacy's trading days. Where opening hours are unknown, the model assumes 312 trading days a year (six days a week), reduces confidence and flags the output. The headline figure is scripts per trading day, not per calendar day.
- 4
Show the uncertainty
Every estimate carries a range that widens as evidence thins. Confidence describes how complete the supporting data is — it is not a verified probability that the number is correct. Treat the range as the honest answer and the centre as a starting point.
A simple worked example
The final conversion, in one line.
Allocated annual prescription demand ÷ trading days = estimated scripts per trading day.
This line shows only the final conversion. The allocation of demand between pharmacies is the model’s core work and happens before this step. The numbers below are illustrative — they are not an estimate for any named pharmacy.
Illustrative example
62,400 ÷ 312 = 200. Demonstrates the final conversion only.
Context, not multipliers.
Accessibility, retail anchors, health services and population growth provide context. Not every signal on the map directly increases script estimates.
The model does use
- Population of surrounding areas (ABS ERP, SA2 level)
- A governed statewide prescription-demand rate
- Proportional inverse-distance weighting between pharmacies
- Trading days, with 312 days/year flagged wherever hours are unknown
- A governed statewide calibration pool (FY 2023–24 PBS statistics)
It does not assume
- That a nearby hospital dispenses through the subject pharmacy
- That a medical centre proves prescription capture
- That an aged-care facility guarantees the supply contract
- That schools, stations or libraries create scripts
- That future population scenarios are current demand
Distance in the model is straight-line proximity with distance weighting. It is not driving time and not an observed patient journey. Future population scenarios, where shown, are separate from current-demand estimates. The model is calibrated to aggregate Victorian data; there are no individual-pharmacy calibration observations, and aggregate calibration is not the same as proven predictive accuracy.
Common questions.
Are estimated scripts actual dispensing figures?
No. Estimated daily scripts are modelled from population, distance and calibration data. They are not observed dispensing, not PBS claims data for the individual pharmacy, and not a measure of revenue.
Does a nearby hospital or medical centre increase the estimate?
Not automatically. Health services are context. A hospital may dispense internally; a medical centre is a prescribing signal, not a captured prescription; an aged-care facility does not prove the pharmacy holds the supply contract. The model only applies factors actually enabled, and it prevents double-counting.
What does the 0–100 number mean? Is it a score out of 100?
The index, the confidence score and the scripts-per-day estimate are three different things. Confidence describes evidence completeness. Demand and competition indices are relative measures used for comparison and map colour. None of them is a percentage of revenue or a ranking of business quality.
Why does the model assume 312 trading days?
Where a pharmacy's opening hours are unknown, the model uses a documented default of six trading days a week (312 days a year). This assumption is flagged wherever it is used, because the same annual demand spread over fewer days gives a higher daily figure.
Is a gap between actual and modelled dispensing 'underperformance'?
No. A difference between a modelled estimate and observed dispensing has many possible causes: the model's assumptions, catchment boundaries, the pharmacy's own operating pattern. DealMap never labels that gap underperformance and never claims the gap is recoverable scripts.
Does a high estimate mean the pharmacy will be profitable?
No. Location potential does not determine revenue, profit, business value or operational performance. Lease terms, staffing, rent, retail mix and management all sit outside the model. Use the estimate to shape questions for due diligence, not as a substitute for it.
Preliminary decision-support tool only. Results are based on available data, user inputs and automated measurements. They are not legal, surveying, financial or regulatory advice and do not determine whether ACPA, the Department of Health, Disability and Ageing or the Victorian Pharmacy Authority will approve an application. Verify all requirements against current legislation, official guidance and appropriately qualified advisers.