Know your ground: what a location tells you before you commit to it
Every business address sits inside a story that is already written down somewhere, just not in one place. Who lives within walking distance, whether the housing stock nearby is turning over or staying put, and who else is competing for the same customers on the same street: all of that is public information, scattered across a building register here and a statistics office there. A business owner sizing up a location, or checking on one they already have, ends up either guessing or spending an afternoon stitching together sources that were never meant to be read side by side.
What it answers
The questions a business asks before, and after, committing to a location.
Who actually lives and works around this address?
Households, population, income, age structure, and homeownership at a fine-grained area level, drawn at walking and driving distance from the location itself, not an arbitrary radius.
Who else is competing for the same customers here?
Nearby businesses in the same or a related category, identified as a competitor set rather than a generic list of "nearby companies."
Where the data comes from
Four public and internal layers, joined at the location level.
Building and address data
So a location's own footprint, and the buildings actually around it, can be established precisely rather than approximated. See the full addresses and premises product.
Neighbourhood statistics
Households, population, income, age structure, and homeownership at a fine-grained area level. See the full neighbourhood key figures product.
Business location and category data
So nearby businesses in the same or a related category can be identified as a competitor set, not a generic list of "nearby companies." See the full business locations and branches product.
Recent home-sale records
The same layer used in the Welkomstbrief case, here used to show turnover in the immediate area. See the full housing market transactions product.
Method, in plain language
Two rings, every relevant signal pulled inside them.
Draw the catchment, pull in every signal it holds
We take a business's location and draw two rings around it, a short walking distance and a wider driving distance, and pull every relevant signal inside those rings: household and population counts, an income and housing-tenure profile, and the businesses nearby that compete for the same custom. The result renders as a dashboard, not a static report: a real map with the catchment rings, an area profile in plain figures, and a competitor benchmark. Because the same rings and the same joins can be re-run on a schedule, the dashboard is a living product, not a one-time snapshot, refreshed as the underlying registers update rather than rebuilt from scratch each time.
What this doesn't show
Honest limits, stated plainly.
The area, not the business itself
It describes the area, not the business's own performance inside it. A strong neighbourhood profile is a favourable setting, not a guarantee of footfall or revenue.
Competitors by category, not by quality
Competitor identification is based on category and proximity, not on an assessment of quality, pricing, or how good a competitor actually is. Two businesses in the same category are treated as comparable even when they serve different customers in practice.
Not every layer refreshes at the same pace
Some underlying registers publish yearly, not daily. Where a figure is a once-a-year snapshot, an income or demographic profile for instance, rather than a daily-refreshed one, a recent sale or a new competitor appearing, the dashboard says so, and the two kinds of data should not be read as equally current.
Not every tile is available everywhere
A newer or thinly-covered area may show fewer live signals than a well-covered city centre, and any tile without enough underlying data is shown as locked rather than filled in with a guess.