Non-spatial researchers are trying to map and intersect 2 national phenomenon relevant to their professional society. The spatial metadata that they have is low-quality textual data that was input free-form into unstructured fields by volunteers.
They've tried to manually search for and correct errors in the data and use free geocoding services to map them, but the results appear to contain persistent errors and the geocoding services produce different results. They are aware of false positive errors that persist and are unsure how to test for false negatives.
Non-spatial researchers are trying to map and intersect 2 national phenomenon relevant to their professional society. The spatial metadata that they have is low-quality textual data that was input free-form into unstructured fields by volunteers.
They've tried to manually search for and correct errors in the data and use free geocoding services to map them, but the results appear to contain persistent errors and the geocoding services produce different results. They are aware of false positive errors that persist and are unsure how to test for false negatives.