Utility Owners’ Approaches to Conversion Quality Control
Data Structure
Data structure errors concern how data are stored and how data elements relate to each other.
Pipe connectivity, pipe directionality, and logical associations between graphics and annotation
are some of the areas where data structure errors occur.
Cartographic Representation
These errors are present when graphics standards have not been followed. Ideally, the digital
graphics should match the look of the source map. In this case, errors exist when conventions
used in the source document for annotation placement, line patterns and thicknesses, etc. are not
followed in the creation of the digital representation.
Error Consequences, QC Methods and QC Costs
This section analyzes the data error types by discussing their potential consequences, and makes
some observations regarding the methods, benefits, and staffing levels and costs associated with
identifying them.
Completeness (Sufficiency)
The completeness errors have the potential to cause the most serious consequences from a safety
and risk point of view. Cutting into a gas main or service in the field because it wasn’t on the
map can be a life threatening accident if a spark ignites at the break. Anything we can do to
reduce the number of line breaks in the gas industry is worth it. USA and careful work by well
trained equipment operators provide some safeguards, but complete maps and engineering plans
can also help prevent these accidents.
As another example, a valve feature that is missing from a map can make a big difference in
emergency response. Valves frequently get paved over in the field. When a line break does
occur, the difference between a quick, fast response in which few customers are affected, and a
slow, poor response in which many customers are affected can be the ability to quickly locate
that paved-over valve.
Quality control checks for completeness depend on the method used to generate the feature in
digital form. For photogrammetrically (“stereo-captured”) features, the check consists of
comparing the x, y, and z coordinate points from an ASCII file generated from the stereo-capture
process with the features in the final delivered digital data. This check is recommended
particularly if all or part of the data has gone through one or more digital translations where
features are prone to being dropped. This check aids in positional accuracy checking as well. It
is a fairly quick check, but may require an expert user to perform it. The possible extra step of
comparing the coordinate points with a high resolution digital orthophoto is not worth the effort
unless the points file itself is suspect.
To check digitized features for completeness, plots of the delivered digital data are compared to a
copy of the source document to determine omissions. Ideally, the bounda~ extents of the plots,
if possible, should match the extents of the source document in order to eliminate the confusion
inherent in matching a north-south oriented rectilinear grid with the street pattern grid of the
source document set. By highlighting features on the copy of the source document as the
corresponding features are found on the digital map, errors of omission are easily identified as
being the un-highlighted features left over when the check is complete.
Some industry experts might recommend that a utility owner only digitize selected features from
the source documents rather than all the features, but I disagree with this approach for four
reasons. First, the process of capturing only certain features is prone to generating completeness
errors. If some features are supposed to be digitized while others are not, invariably digitizers or
QC checkers will omit features or add features that do not belong. Secondly, capturing
everything on the source document makes digitizing a clear and well-defined task, and it makes
quality control checking for completeness a clear and well-defined task. Digitizers don’t have to
spend valuable time deciding what to digitize and what not to digitize. QC checkers have a much
easier time identifying omissions. Thirdly, information on the source documents is valuable, or
it wouldn’t have been put there in the first place. This is your one and only chance to capture
information on the source documents. It doesn’t cost much more (if any) to digitize all of the
information rather than just some of it. The fourth and final reason is cost. As serious as the
consequences for completeness errors can be, if the project specification calls for all features on a
source document to be converted, completeness checking of digitized data can be performed by
less experienced and lower-paid staff.
Utility owners that are converting a single utility subject should be concerned primarily with
ensuring that their data is complete. For utility owners of multiple utilities, errors of
classification, discussed next, and positional accuracy also may have significant consequences.
Classification
Classification errors can have consequences as serious as those of completeness errors, as well as
less serious implications. When two or more utility subjects are being converted from the same
source document, errors occur when digitizers or document preparation personnel classify
features in the wrong subject. Such an error is similar to a completeness error when the subjects
are plotted separately, but could be less serious if the subjects are always plotted together on the
same output document. For example, consider a source document containing both water and
sewer utility graphics. If a 4“ water fire service is incorrectly classified as a 4“ sewer lateral, fire
department personnel later using the maps could lose precious seconds or minutes locating the
water source in the field. Gross errors, such as the sewer layer containing all the fire hydrants,
can occur through errors in a translation process from one digital system to another.
Subject classification errors can be detected by plotting each utility subject in a different color.
Experienced utility personnel will be able to quickly detect if any features are misclassified,
because the color establishes the subject categorization for the brain, and looking at each subject,
the QC checker can quickly distinguish if something does not belong.
The above example in which the fire service was confused with a sewer lateral can occur either
through misinterpretation or because the source documents occasionally are ambiguous. If this is
the case, determining the true field configuration may require a field check. However, field
checks are extremely expensive compared to other quality control tasks. Save field check work
98.for only the most risk-inherent situations. Otherwise, I suggest keeping an “Items for Field
Checking” log book consisting of photocopies of the ambiguous source document graphics . So
long as you keep track of these ambiguous situations, it is advisable to defer them because you
need to focus all your staff time on the most important and cost-effective checks. Field check
items can be checked as time and budget permit well after successful completion of the
conversion project.
Classification errors also occur within a single subject. In practice, this seems to happen most
frequently with text features. For example, service description text might erroneously be
categorized as main pipe text. Similarly, attribute classification errors may also occur, whereby
an abandoned date is confused with’s installation date, or a diameter with a distance
measurement. These types of classification errors can impact the smooth development of
applications that are intended to help you manage and keep track of your utilities. Application
developers are limited in what they can do when there are classification errors. They have to
build in more routines to handle different exceptions, and more error trapping. Such code is
more expensive to write, less elegant in design, and more expensive to maintain.
Certain classification errors, such as the use of an illegal name for a feature class, can be detected
quickly, automatically, and inexpensively by programmatically comparing the feature names in
the digital data with the list of legal feature names in the project’s data dictionary. Although a
specialist or an expert user will need to write the program, it can be executed by a beginning or
intermediate user.
Classification is one of the most underrated and difficult tasks in conversion. The classification
step generates many errors because (through map interpretation) this step must add the
“intelligence” we expect from an AM/FM/GIS. In addition to the subjects-by-color and
automated checks listed above, there are two other techniques that I have found helpful in
reducing classification errors.
The first is an additional QC check. In this computer-assisted “visualization” check, all features
in one subject are displayed on the screen. Using a menu pick, the QC checker selects one
feature class at a time to display in a bright yellow color. The QC checker determines whether
all the highlighted features truly fall into the selected category. For example, if fire hydrant
valves are the selected feature, only fire hydrant valves should appear in yellow on the screen. If
a regular main valve shows as highlighted, the checker flags it as an error. The personnel
performing this check must be thoroughly versed in the utility system they are checking because
the brain relies on contextual cues in this check. Therefore, utility technicians or engineers are
recommended for this check rather than less experienced interns.
The final technique is to develop a source document interpretation guide at the project’s
specifications development stage. In conjunction with designing the project’s data dictionary,
examples of every type of feature and every type of attribute information are explicitly identified
on copies of the source documents. This guide becomes an invaluable reference to the vendor’s
digitizing team to aid in classification of features and attributes. As a supplement to this
approach, consider defining and including an “unknown” feature class for each of lines, map
symbols, and annotation text. These “unknown” feature classes will ensure that features that
cannot otherwise be classified are at least captured, thus meeting our goal of completeness.
Position
There are several consequences of position errors. In general, they reduce the usefulness of the
digital basemap for engineering purposes such as design. This is a consideration for utilities that
do a significant amount of in-house design. Absolute position error (or a too-loose absolute
accuracy specification) makes it difficult to integrate GPS points with the utility graphics.
Logical consistency suffers most with relative position errors. When the basemaps don’t make
sense, people loose confidence in the GIS in general.
