Several factors may cause imprecision in datasets. As example of such sources, we should list: "age of data", "formatting", "scale", the technique employed to create the data and others. Each of them disturbs the precision in a different way and magnitude.
For instance, the scale in which the data are produced cause a well known imprecision pattern and it also has standards like that proposed by the United States Geological Survey:
"requirements for meeting horizontal accuracy as 90 per cent of all measurable points must be within 1/30th of an inch for maps at a scale of 1:20,000 or larger, and 1/50th of an inch for maps at scales smaller than 1:20,000."
Accuracy Standards for Various Scale Maps
1:1,200 ± 3.33 feet 1:2,400 ± 6.67 feet
1:4,800 ± 13.33 feet
1:10,000 ± 27.78 feet
1:12,000 ± 33.33 feet
1:24,000 ± 40.00 feet
1:63,360 ± 105.60 feet
1:100,000 ± 166.67 feet
However, in my researches I have found no other indicators of the magnitude of errors associated with the other factors and sometimes it seems to me that nobody has done this yet. My goal is to build a probabilistic model for GIS positional errors and use it to create probabilistic versions of spatial operations like spatial joins, for example.
Do somebody know some works that have quantified GIS erros such that it can help me achieve that goal?