I am working on the geocoding of patient addresses for a public health organization.
We use the Normatim (OpenStreetMap) and Google APIs. With the Google service there is no problem so far, but it seems that Normatim, sometimes, has errors in the geolocation of some addresses, particularly with the numbers of the streets.
As you can see in the list, the street numbers are very different (from 2500 to 4300) and should be distributed along the entire street, but Normatim locates them at the same block (image below).
I realize that these are errors in Normatim's data, and I do not intend a solution to this. I am looking for a way to identify these cases (when there is a 'suspicious' grouping of points) to re-process them through Google.
My first approach is to make a heat map, which allows me to visually identify these suspicious clusters and select cases manually.
But I'm looking for a way to avoid this manual selection, see if there is any way to perform this analysis in a more systematic way. Any ideas or advice?
Note: we must use the Normatim service since it is free and we do not have enough funds (yet) to pay Google (we can only use the 2000 free queries per day that Google offers, but our databases are larger, and growing).