Combining a map and a report, the safe way
A map shows where people are. A report shows who they are. Together they give you a genuinely useful picture — and it's what Kanon Maps is built to do. The care goes into how, because the risk isn't the combination itself; it's precise mapping and loose data handling.
Combining a map and a report is what makes community data genuinely useful — and it's exactly what Kanon Maps is built to do. The care goes into how it's done, because two ordinary habits are what actually create privacy risk: mapping people at pin-point precision, and handling the raw data loosely.
Pin-point precision means plotting an exact postcode or address, so a dot marks a doorstep. Loose handling means the underlying list living in shared spreadsheets, forwarded emails, or paper survey forms — anywhere it can be picked up and read row by row. Neither is about the map or the report on its own; it's these habits that let someone work back to a real person. Avoid both and the combined picture is safe to share.
A typical use case
A community health charity wants to plan outreach. They map where the people they support set off from, then layer in ward boundaries and an aggregate measure of deprivation to see which communities are under-served and hard to reach by public transport. That is genuinely useful — it directs limited resources to the people who need them most, and it is the kind of combined mapping-and-reporting picture funders increasingly ask for.
Where the risk creeps in
Precision plus loose data
Picture the same charity working the old way: exact postcodes dropped on a map, and the raw spreadsheet — plus a stack of paper survey forms — passed around the office. Break the numbers down by ward and by an attribute, then look at a rural ward with only a handful of people in it, and the "aggregate" figure quietly becomes a description of a few identifiable households.
The map and the report aren't the problem. The problem is precise location plus a reported attribute plus a small group — and raw data that's easy to pass on. Coarsen the location, wait until the group is large enough, and keep the raw data out of circulation, and there's simply nothing to work back from. That's what k-anonymity is designed to guarantee.
Doing it safely
Kanon Maps is built so the combination stays safe by design:
- Postcodes are never stored. A postcode becomes an approximate point a short distance away, then is discarded.
- Locations are coarsened before they are ever shown, so a dot never marks a doorstep.
- Nothing unlocks until a group is large enough. Maps and reports stay hidden until enough people have joined that no single person can be picked out.
- Reporting is aggregate-only. You see counts and proportions across an area, never a row that describes one person.
The data we layer in
The value of combined reporting grows with each context layer — but every layer is applied as aggregate context over an area, never as an attribute pinned to an individual. Today we use electoral ward boundaries, social deprivation indices and NHS region. We're adding:
- School catchment
- Proximity to public transport
- Travel mode (optional question) — to understand how people actually get to you.
Each new layer makes the report richer without making any individual more visible. That is the whole point: a clearer picture of the communities you serve, and never a database of the people in them.