Location intelligence · evidence, not scenery
Turn any place on the map into readable evidence
Location intelligence is the practice of turning place data into measurable evidence around a specific point. A general-purpose map shows where something is; location intelligence measures how much, how many, how far, and how current. LocateRight assembles that evidence from eight open and public geodata sources — Overture Places, OpenStreetMap, WorldPop, ESA WorldCover, Microsoft and Google open building footprints, Natural Earth and GeoNames — and labels every measurement with its source, dataset version and retrieval date. It publishes no score, no ranking and no recommendation.
Definition, kept short
What location intelligence actually is
The phrase is used loosely, so here is the working definition this product follows. A map is a picture of places. Location intelligence is what you can measure and reason about from those places — density, mix, proximity, reach, and the physical and demographic character of an area — assembled around a specific question you are trying to answer.
The difference matters in practice. Two sites can look identical on a map and be entirely different businesses to run: one sits inside a dense residential grid a short walk from transit, the other on a commuter road with nothing adjacent. Reading that difference — as data, not as a guess — is the work of location intelligence, and it is the reason the evidence around a place is worth more than a screenshot of it.
Why the geographic layer changes answers
Most business questions have a location half
Ask whether a market is worth entering, where a delivery radius should end, which territory a competitor is quietly winning, or whether an unfamiliar neighborhood fits your format — and each question stops being answerable the moment it touches a map. The geographic evidence is not decoration behind the real analysis; for these questions it is the analysis.
The difficulty has never been that the data does not exist. Public and open geodata — places, population, land cover, transit, boundaries — is more available than it has ever been. The difficulty is that it is scattered, differently licensed, differently current, and rarely labeled well enough to trust in a decision. Location intelligence software earns its name by assembling those scattered signals into one legible, source-tagged view of a place — one a reader can check rather than take on trust.
The evidence stack
What you can examine about a place
LocateRight groups its layers around business questions rather than a list of data providers, and this is the actual evidence surface the analyzer assembles around a chosen point or drawn area. Eight open and public geodata sources sit underneath those layers: Overture Places and OpenStreetMap for business and place records, WorldPop for population grids, ESA WorldCover for land cover, Microsoft and Google open building footprints for built form, Natural Earth for boundaries and GeoNames for nearest-city context. Each layer is measured within the radius or area you set, and each figure arrives labelled with its source, its dataset version and the date it was retrieved. The layers are grouped as what is here, how reachable, and what surrounds it — so a question maps onto a measurement rather than onto a provider.
What is here
Places & category density
Place density is the number of business and community records inside a stated radius, divided by the area that radius covers — so the figure always travels with the radius that produced it. Records are first gathered from Overture Places and OpenStreetMap POI sources and de-duplicated by record, meaning the same place is never counted twice across the two stores. Density is reported as places per square kilometre at the stated radius, and the radius used is published alongside the result rather than left for the reader to assume. Records are split into category buckets — food, hotels, healthcare — so a place reads as a mix rather than a scatter of pins. The figure inherits the completeness limits of the underlying open data: a region mapped more thoroughly returns more records regardless of how many businesses actually operate there. Overture Places is published under CDLA Permissive 2.0; OpenStreetMap is ODbL, and its share-alike obligation is the one licence that travels with your export.
How reachable
Access & transit
Reachability is the share of sampled points around a place that can be reached within set minute bands, reported as a distribution rather than as a smooth circle that would look more authoritative than the data is. Origins are sampled across the area rather than taken from a single point. The probe returns the average, fastest and longest travel times, the share of routable points within 10, 15 and 20 minutes, a histogram of the distribution, and the actual number of points behind each figure — with the bands reported together with the coverage statement attached. The extent of the analysis is stated: drive-time areas are built out to 1500 metres, and that limit is what defines where the measurement stops. Unroutable sample points are counted and excluded rather than dropped silently, so the denominator behind the share is stated rather than assumed. Finally, a 15-minute ring drawn as a perfect circle asserts a symmetry that roads never have; the sampled shape keeps the figure checkable.
Who is around
Population
Population within reach is the sum of stored WorldPop cells falling inside the analysis radius, expressed as a count of residents rather than as a customer base. Cells are roughly 100 metres across, and the radius used is stated alongside the result so it can be re-derived by anyone holding the same inputs. The figure is a modelled gridded estimate derived from census and survey inputs — not a headcount — and the page that explains it says so plainly: it is not a customer base and not a forecast. Additionally, the nearest real settlement is shown from GeoNames city context, which carries a source and a retrieval date of its own. A radius of one kilometre around a dense residential grid and the same radius around a commuter road return different counts from the same dataset, at the same resolution, with the same method — which is exactly why the radius is published with the result instead of being assumed.
What it is made of
Land & built form
Land cover is a classification of what physically covers the ground at a place, and land use is a separate question the classification does not answer. LocateRight uses ESA WorldCover at 10 metre resolution: the stored tile covering the point is identified, classes are counted inside the radius, and each class is reported as a percentage share of the total with the dominant class named. Moreover, ESA WorldCover is released on an annual cadence, so the retrieval date on the block is what tells you how current the classification is — a class recorded last year may not describe the site today. Where footprints have been published, building counts come from the Microsoft and Google open building stores with the dataset version recorded. A count of zero is reported as a coverage gap, not as a measurement: footprints exist only where a publisher has released them, so an empty result is a legitimate answer about data availability rather than a finding about the site.
A wider catalog of datasets exists beyond what a single analysis requests — the full, honest inventory of what feeds the numbers and what is catalog-only is on the data-sources page.
How the product applies it
Location intelligence, shown with its receipts
LocateRight is location intelligence software built on a raw-geodata platform, and it takes one stance that shapes everything else: the evidence is shown, the judgment is yours. Where a scoring product collapses a place into a number and hides the reasoning, this one keeps the measurements visible and labeled — the source, the dataset version, and the date it was retrieved sit under every block, so an area that reads thin can be checked rather than trusted blindly.
The workflow follows how you would actually interrogate a place. Fix the point — search it, paste coordinates, drop a pin, or draw the boundary yourself. Choose a radius and a business category. The map assembles its layers, and the evidence sheet underneath gives you the counts and listings behind what you are seeing. Toggle the access and context layers on and off to separate signal from scenery. The usage guide explains what each block means and which layer answers which question. Screening a whole list of places at once works too: bulk analysis runs a CSV of addresses and returns one evidence table. When a location becomes a shortlist decision between specific candidates, that is a different job — site selection — and LocateRight handles it as the next step, not by pretending one map can do both. Before candidates even exist, the same evidence can be pointed at the narrower question of what one address says about your specific business location.
Location intelligence in practice
Decisions the evidence layer supports
Read a market
Market evaluation
Market evaluation is the reading of a place before a business commits to it, and the reading is assembled from three measurements that each carry their own stated limits. How dense the surrounding places are, taken from Overture Places and OpenStreetMap, what the category mix looks like, and how much population sits within reach. Density inherits the mapping completeness of Overture Places and OpenStreetMap; population is a modelled gridded estimate at roughly 100 metre resolution rather than a headcount; land cover is a classification at 10 metre resolution with an annual release cadence. Every one of those three figures is published with its source, dataset version and retrieval date, so a market read today can be re-checked later and the comparison will hold. The evidence layer supports the reading and stops there. What it does not do is tell you whether the market is worth entering — that depends on criteria only the business holds, which is why no score, ranking or recommendation is published.
Frame a network
Territory & expansion context
Territory framing is the comparison of candidate zones rather than a single address, assembled so that the zones stay comparable with one another. Each zone is measured with the same radius, the same category buckets and the same sources, because a comparison between zones measured differently is not a comparison at all. Because every measurement carries its source, dataset version and retrieval date, a zone read today can be re-checked later and the comparison holds across time as well as across space. Campaigns additionally keep dated snapshots, so a change between two readings is a diff between two dated observations rather than an argument about which number was right. The limits travel with the figures too: a zone whose open-data coverage is thinner returns fewer place records regardless of how many businesses operate there, and that gap is stated rather than smoothed.
An unfamiliar place
Area familiarization
Area familiarization is the assembly of what surrounds a place the reader does not already know, and it deliberately stops short of a verdict. The layer gathers, first, the businesses and community points inside the radius split by category, the transit stops, parking and walk/drive rings that describe how the point is reached, the WorldPop population figure within the radius at roughly 100 metre resolution, and the ESA WorldCover classification at 10 metre resolution with the dominant class named. Each of those figures arrives with its source, its dataset version and the date it was retrieved. What familiarization produces is not a rating of the area and not a recommendation about it. The point is that the reader ends up knowing what the data says and where it stops — not that someone else has already decided for them.
Attach the facts
Evidence for a proposal
Traceable evidence is a figure a partner can re-derive rather than take on trust, and that is the standard a proposal built on this layer has to meet. Each measurement carries, first, its source, its dataset version and the date it was retrieved, so any number in the output can be followed back to where it came from. The dataset inventory is published with publisher and licence for every source, and whether that licence has been reviewed and approved for commercial use. That matters for exports specifically: OpenStreetMap data is ODbL, and its share-alike obligation is the one licence that travels with the output, while Overture Places is CDLA Permissive 2.0. A count of zero building footprints is reported as a coverage gap, not a measurement — which means a thin figure in a proposal is an honest statement about data availability rather than an error to be explained away.
Where it fits
Location intelligence is not just a map
General-purpose maps are excellent at one thing: showing where things are. They are not built to answer the analytical questions a business decision actually turns on, and there is no fault in that — it is simply a different tool. Location intelligence software sits on top of the map and adds the reasoning layer. The table is about that difference in role, not about any particular product being good or bad.
| What you need from a place | A general-purpose map | Location intelligence (LocateRight) |
|---|---|---|
| Where something is | Its core strength | Also shown, on the same canvas |
| How many of a category sit here | Not the job | Counts and density by bucket |
| How much population is in range | Not shown | Gridded population within your radius |
| How current a fact is | Not exposed to you | Retrieval date on every measurement |
| Whether a number is trustworthy | Assumed | Labeled with its source and version |
None of this replaces a map — LocateRight uses one as its canvas. It is what you add when where has already been answered and you have moved on to so what.
Common questions
Location intelligence FAQ
What is location intelligence?
How is location intelligence different from a regular map?
What kind of data does LocateRight use for location intelligence?
Does location intelligence here produce a score?
What business decisions can location intelligence support?
How do I start using LocateRight?
Read the place, not just the pin
Point LocateRight at any location and see the evidence it can assemble around it — with every source named.
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