Utility Owners’ Approaches to Conversion Quality Control
On the positive side, high position quality of both types reduces surveying needs. And when
survey and GPS measurements are taken, they integrate well into the digital graphics.
Engineering designs are more accurate, reducing expensive field changes and construction
change orders. Quality positional data enables logical consistency to be better maintained,
particularly when different utilities subjects from different sources are overlaid together in digital
format. This is important for studies such as risk assessment analyses of underground gas and
electric utility adjacencies in seismic zones.
It also bears emphasizing that “low accuracy” has a different meaning than “positional errors”. A
low accuracy map may contain zero out-of-specification errors. In absolute positional accuracy,
higher accuracy costs more. It costs significantly more to do planimetric mapping at high
accuracy scales (approx. 0.5 to 2.0 ft. rms) than lower accuracy scales; however, advancing
photogrammetric technology is dropping the price of high accuracy planimetrics.
Relative accuracy, however, can be achieved simply with careful and intelligent digitizing
techniques. You are paying for the digitizing anyway; you might as well have it done in a
manner that preserves relative accuracy. “
A phenomenon that I have observed is that utility features generally have good relative positional
accuracy with respect to curbs or property lines on source documents. This is probably because
when the drafter placed the line originally on the manual drawing, s/he probably did so given
fairly precise measurements between the curb and the pipe. Thus the positional accuracy of the
utility relative to the curb or property line may be better than the overall inherent accuracy of the
map according to a USGS- or ASPRS-style specification.
So when utilities are being digitized from more than one source, it is important to register maps
using the same methodology for each set. Recall the gas and electric utility scenario introduced
at the beginning of this paper. To maintain relative accuracy during digitizing, the gas maps are
registered to the curbs on a curb-face by curb-face basis, and the electric maps are registered to
the property lines on a block-face by block-face basis. In this manner, the data sets, when viewed
together, have a better chance of mimicking the true topology in the field, and maintaining
logical consistency. Registering the map once per page is not enough because of discrepancies
between the curb and property lines on the map and those same lines as they appear in the
planimetric or COGO data.
To check for absolute positional accuracy, use the QC check for photogrammetrically captured
features described in the completeness section. Make sure the final feature locations match the
ASCII coordinate positions. To check for relative position errors, lay a plot of the digital data
over the source document on a light table. Using the same registration procedure as the digitizer
did, use a red pencil to mark any features that do not overlay correctly for later adjustment. If
some features have been moved to photogrammetricall y-captured locations, these features should
be shown differently than digitized features on this particular plot. For example, the captured
features could be shown as filled symbols and the digitized features as hollow, by using the
source attributes (digitized or photogrammetric) of the features to create a “thematic” map. This
way, the QC checker can determine if discrepancies exist for a legitimate reason. This light table
check can be competently performed by an intern or junior technician.
Clearly, high positional accuracy is most beneficial to engineering-oriented multiple utility
owners, and when underground work is common. However, if you have a single above ground
utility such as an overhead electrical distribution utility or an above-ground gas transmission
line, spatial accuracy isn’t nearly as important, because people can clearly see components and
they tend not to get paved over!
Attribute Value
Unless attribute value errors are quite frequent and widespread, they generally have the potential
to cause relatively minor information and decision problems. You could end up with slightly
misleading results to database queries. Of course, these results could impact operations and
maintenance decisions to varying degrees. More widespread and frequent errors have the ability
to impact network modeling results in which, for example, attribute values of pipe diameter and
material may be important.
Attribute errors are commonly used to measure conversion accuracy. However, this maybe
because they are easy to count rather than a really good measure of overall conversion process
performance !
To check for attribute errors, programmatically read the attributes for each feature and create a
text block containing the values. Place each text block on or near (with a leader line) the feature
it describes. Compare the plot (done on a subject by subject basis) with the source document to
identify discrepancies. This check can be performed by technicians and bright, careful interns.
This check can be complicated by using more than one source document for attribute data.
Deriving information from more than one graphic source document is prone to human error. The
digital data that results is also more difficult to check. For example, a water utility might have its
detailed 1” =40 block maps, and a 1” = 1000’ distribution map, both containing diameter and
material attributes. I might simply convert both sources and then do an automated comparison of
the attribute discrepancies.
Data Structure
Data structure errors are often software platform-dependent. The ability to perform application
development is affected strongly by data structure errors. Some types of data structure errors can
be detected or even corrected automatically with software; others require more expensive manual
intervention. Network connectivity is not software platform-dependent and is an especially
important aspect of data structure integrity.
Cartographic Representation
Cartographic representation errors impact the legibility of output products and their initial
acceptability to people accustomed to working with the source documents but not with GIS data
and maps. (This can be a very large percentage of a utility organization.) From an application
developer’s point of view, such errors are of negligible importance, but they will certainly
influence the perceived success of the conversion project on the part of people who are not
intimate with it. Undeniably, these opinions can influence the future course of the AM/FM/GIS
project as a whole.
Conformance to the look of the source documents is the ideal standard of performance in this
area. The graphics of the digital map should match the look of the source map. Assuming
conformance to the source document was specified, errors exist when source document
conventions for annotation placement, line patterns and thicknesses, etc. are not followed in the
creation of the digital representation. Naturally, some accommodation should be made for the
reduced ability to take “artistic license” in digital graphics vs. manual graphics. In addition, it
would probably not be cost effective to require the conversion vendor to avoid graphic conflicts
(especially regarding annotation) between utilities graphics derived from different source
documents.
Project Management considerations and Conclusions
In the above discussion, I have attempted to demonstrate how approaches to quality control
activities are dependent upon the anticipated uses of the AM/FM/GIS, and how sensible project
specifications are central to achieving a level of data quality that is suitable to those uses. The
better your conversion project specifications match the anticipated uses of your AMIFM/GIS, the
better your chances of a successful implementation.
I have suggested that different types of errors can have different consequences for your utility
organization. When developing a conversion specification with your conversion vendor,
consider specifying different acceptable error rates for each type of error, depending on the
importance of quality data in each of these areas to your conversion goals and to your
organization as a whole.
Several examples showed that it is easy to make a conversion project more difficult without
getting a reasonable pay-back for that extra work. Keep conversion methods and quality control
procedures simple, understandable and consistent. Perform a pilot conversion on a small area to
test that the results from your specifications and the conversion process will meet your
organization’s expectations before contracting out the bulk of the work.
Finally, budget sufficient permanent and temporary staff resources to do a thorough job of
quality control checking. Not only will this ensure that the utility owner gets what it pays for,
but it will also build acceptance and confidence in the digital data in-house which in turn will
build enthusiasm and smooth the transition to use of the new data on a day-to-day basis.
Acknowlegements
The author gratefully acknowledges Chip Eitzel of Geodesy in San Francisco, CA for his
intellectual and practical contributions to some of the ideas and methods expressed in this paper.
Bibliography
- Fergusson, K. J., and C. Eitzel, “Quality Control Methodology for Infrastructure Data
Conversion: A Municipal Approach, ”proceedings of the Urban and Regional Information
Systems Association (URISA) conference, San Antonio, TX, July 16-20, 1995.
- McLure, G. W., “Quality Control Data Checking, ”proceedings of the AM/FM International
conference X, Snowmass, CO, July 20-23, 1987.
Montgomery, G. E., and H. C. Schuch, GIS Conversion Handbook, GIS World Inc., Fort Collins,
co, 1993.
- Stefanini, J. J., “QA/QC: Keeping Your Data Accurate, ”proceedings of the AM/FM International
conference XIII, Baltimore, MD, April 23-26, 1990.
- Thorpe, A., “Keys to Successful Water Conversion, ”proceedings of the Urban and Regional
Information Systems Association (URISA) conference, San Antonio, TX, July 16-20, 1995.
