Maps are dead. Long live maps
Data-Centric Map Making
Implications for Map Conversion Projects
That the craft of map automation has become more like the science of database construction is
probably not a surprise to anyone closely involved in geospatial data projects. Yet, there are
implications for the planning, management and control of geospatial data conversion projects
that are still emerging, and probably require time to reach a level of maturity comparable to the
rapid advances in the integrated database targets. At the top of the list is the recognition that
accuracy standards for digitizing maps are completely inadequate as quality standards for a
geospatial database. The bar has been raised for data quality precisely because the level of
interdependence between previously disparate elements has just gone through the roof. An error
in the coding of one feature, for example, can have an affect on the behavior of adjacent features
and prevent the execution of certain methods that permit connectivity tracing, symbology
assignments and map image rendering. In short, almost any invalid or unexpected piece of data
can have a chain reaction of inoperability throughout the entire geospatial database. The benefit
of eliminating redundancies in an optimally normalized database is offset by the vulnerability the
entire system experiences when even a tiny percentage of the product is defective. That is why
conversion projects completed successfully with a 98% accuracy requirement can still result in
an inoperable system. 2% error is too much.
Radical Idea #1: Geospatial databases cannot operate with 2% error.
The task of building an operable geospatial database, therefore, goes beyond the careful
digitizing of existing maps for two reasons:
- The accuracies achieved from a single operator™s interpretation of map information is too
low, even after a 100% quality review inspection, and
- Even a perfect conversion of existing maps is not likely to produce data of appropriate
normalization because the constraints on the existing maps (even digital ones) are too
loose.
Figure 3 ΠMethods of Map Automation
The Downfall of Sample Inspection
The reason map conversion vendors have traditionally had difficulty reaching accuracy levels
above 95-98% is the concentration of human judgment that occurs in the simultaneous
interpretation of spatial and non-spatial attributes. A pipe exists from fitting iiali to fitting iibli
with a certain radius, and it also has a certain diameter, material and date. That™s a lot of
information to get right in one pass, and that is why most conversion vendors will institute a
quality sample inspection step. The logic is, that if human interpretation is defective just 10% of
the time in one pass, then a second independent pass, when reconciled against the first, should
allow just 10% of 10% (or just 1%) of the defects to get through.
The problem with this logic, when it comes to digitizing maps, is that the second pass is never
completely independent. A quality inspector has to look at an existing graphic object to determine if it was captured correctly, and the fact that he/she even looked at the first operator™s
product before determining its correctness means that his/her judgment is already biased. Unlike
the quality inspection of other tangible products like bullets or radios, the major portion of
human judgment being tested for map conversion has to do with the very existence and position
of an object. Every map object is different, so the inspector cannot work from a fixed
imagination of what is correct. Therefore, he/she must imagine the topological properties of the
correct object before seeing each object being tested. Although this may sound like an absurd
requirement, it is unlikely that the statistical accuracy levels being calculated by conventional
mistake-finding techniques are truly meaningful. Looking for mistakes is not the same thing as
duplicating and comparing judgment. It is more like trying to find needles in a haystack. Not
only is the process painful, but you never really know when you are done.
Radical Idea #2: The true quality level of geospatial databases created by
conventional map digitizing methods is not really known by mistake-catching
techniques, and is probably lower than what is currently reported.
