I work in public health and I will be displaying positive infection cluster data to the public. For the safety of the public and those infected, I want to somehow conceal their precise location. I have thought of a couple possibilities.

Is there some way I can distort the streets' shapes in my shapefile? Is there a basemap that's entirely vague? Pro does not have one vague enough for what I need. How can I move the basemap so that it isn't representing my actual points' locations?

Any other techniques or practices?

  • 4
    Do the opposite: move or aggregate the points, not the basemap. It is too easy to replace the transformed basemap by a standard one; or to simply fetch the displayed points XY (then put it in google or else and you can see where it is). Also don't simply add the same offset to all points, a determined user could find the offset and revert it (like someone that knows he is in the dataset knows his true XY and "just" has to find his point in your data)
    – JGH
    Aug 26, 2020 at 18:50

2 Answers 2


Group your data geographically, mapping total positive cases by ZIP code, county, state, province, or whatever area. Start with a small unit of area (maybe a city block) and if you think that's too specific, move up to a bigger unit like a ZIP code until you're comfortable with the level of anonymity. For example, if you use a city block as the area, and somewhere in your city there is a block with only one house on it, then the data isn't anonymized enough.

Depending on where you live, HIPAA or another law like it may have something to say on how much anonymization your data needs to go through before it can be released.


I agree with @Dan C. Adding ambiguity via some form of aggregation to your point data is a much better strategy than trying to modify the basemap or other reference layers.

To count points within your administrative boundary of choice, use the Summarize Within tool, using the administrative boundaries as the Input Polygons and your point data as the Input Summary Feature.

You could also try using some form of density calculation to add ambiguity to the data and display clusters with tools like Kernel Density or Calculate Density. The latter may be particularly useful in your case, as you can choose an arbitrary bin shape (square or hexagon) and size to use.

  • 1
    For more background: I am mapping infections within zip code / Sewage Plants. I ended up displaying Block Groups within these areas using the summarize within tool to properly visualize my desired values. These two suggestions, @Dan C, worked together beautifully. Regarding density calculations, I have worked with hot spot optimizations to represent this data but it is not one my peers agree with. Thanks a ton!!
    – Jeremy
    Aug 26, 2020 at 20:40
  • Glad that worked for you @Jeremy.
    – lambertj
    Aug 26, 2020 at 20:44
  • 1
    @Jeremy Please consider accepting one of these answers if it resolved your question.
    – Aaron
    Aug 27, 2020 at 20:03

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