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CHAPTER FOUR
QUALITY MANAGEMENT PRACTICES
This chapter focuses on the specific quality management source of variability that can be controlled, and take the neces-
principles and techniques currently being followed by trans- sary production adjustments to minimize the "controllable"
portation agencies for pavement condition data collection. It variability. It is also important that the quality control process
discusses the various approaches and tools used for quality detect problems soon, before large quantities of data have
control, quality acceptance, and independent assurance. to be re-collected. The sources of variability for the various
pavement condition indicators were discussed in detail in
The creation and implementation of a comprehensive data chapter three.
collection quality management program is a very important
step to ensure that quality data are collected. However, as
reported in the previous sections, a significant percentage Contents of a Quality Control Plan
of highway agencies (approximately half ) still do not have
such procedures in place. With respect to how these plans Based on the examples reviewed, it becomes apparent that a
are developed, the survey showed that most agencies pre- comprehensive quality control plan typically includes the
pare their own quality acceptance plans, whereas the quality following elements:
control plans are developed either by the agency, the data
collection service provider, or as a collaborative effort. Only · Clear delineation of the responsibilities;
one agency reported having used a third-party contractor for · Documented (and available) manuals and procedures;
developing the pavement data collection quality manage- · Training of survey personnel;
ment plan. · Equipment calibration, certification, and inspection
procedures;
The following sections discuss the main techniques and · Equipment and/or process quality verification procedures
procedures used for quality control, acceptance, and inde- (e.g., testing of control sections) before starting and
pendent assurance. As discussed in chapter three, the dis- during production testing; and
tinction between quality control and acceptance activities · Checks for data reasonableness, consistency, and com-
depends on how the activities are incorporated into the man- pleteness.
agement plan, rather than the activities themselves. Many
of the tools used for quality control and quality assurance
are the same. To avoid duplication, this chapter discussed The survey revealed that a large percentage of the respon-
the various activities and tools organized in the most common dents (64%) have a formal data collection quality control plan
configuration. or require the data collection service providers to develop such
a plan (Figure 14). It is noted that some of the agencies that
checked the "other" option indicated they have procedures
QUALITY CONTROL
for only some of the pavement condition indicators or that
such a plan was under development.
Common quality control activities include personnel training
and certification, equipment calibration, certification and ver- Approximately half of the data collection quality control
ification, production quality data verification, and on-vehicle plans were developed by the service provider collecting the
and office data checks. These are covered in detail in the data. Although it was not asked specifically, it is hypothesized
following sections after a brief discussion on pavement con- that this corresponds to all or most of the services being
dition data variability and quality control planning for control- contracted. It is also interesting to note that a shorter survey
ling this variability. sent to pavement data collection service providers showed
that all the service providers have a formal data collection
quality control plan; however, none of them provided a
Quality Control and Variability copy and several indicated that the plan was project-specific
or proprietary. This last result is an indication of the impor-
The purpose of quality control is to quantify the variability in tance data collection companies place on the quality control
the process, maintain it within acceptable limits, identify the procedures.
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Prepared by data
No Response 10.7% 32.1%
collection contractor
Not Sure 5.4% Developed by Agency 23.2%
Prepared by independent
No 19.6% 1.8%
third party
Yes 64.3% Other 7.1%
0% 20% 40% 60% 80% 0% 20% 40% 60%
FIGURE 14 Percentage of highway agencies having a formal quality control plan.
(1) review of completeness, (2) review of section-level data,
From the available quality management tools and meth- and (3) review of data management. The first step simply ver-
ods, the most common methods/tools used for quality ifies that all fields are processed. The section-level review is
control are the following (in order of decreasing fre- conducted by examining approximately 50% of the sections.
quency, the percentage of agencies citing each method/ The operator also verifies that the data have been saved and
tool is provided within brackets): inputs a subjective evaluation of the crack detection process
(good, fair, and poor). Further details on this approach are
1. Calibration of equipment and/or analysis criteria
provided in a case study presented in chapter five.
before the data collection [94%],
2. Testing of known control segments before data
collection [94%],
3. Periodic testing of known control segments during Personnel Training and Certification
production [81%],
4. Software routines that check if the data are within It is very important that the personnel operating the equipment
the expected ranges [57%], and or conducting the visual surveys are continuously trained.
5. Software routines that check for missing road seg- This is particularly critical given the current environment in
ments or data elements [55%]. the transportation profession, where the work force is highly
mobile, and technicians and engineers change positions and
employers quite frequently. Training is even more impor-
tant for the distress surveys because the classification of the
distresses is somehow subjective.
All the data collection service providers responded that they
use calibration of equipment and/or analysis criteria before
the data collection, and software routines that check if the data Personnel Training
are within the expected ranges for missing road segments or
data elements, and for inconsistencies in the data. At least Personnel training is necessary for obtaining repeatable and
half of the service providers also indicated using the other reproducible pavement condition data. It affects manual, semi-
techniques. automated, and automated practices. Adequate training helps
improve the consistency and accuracy of visual surveys and
An illustrative example of a pavement data collection proper operation of the equipment using automated or semi-
quality control plan is presented in Figure 15. The figure automated procedures.
presents examples of the activities that are typically conducted
before, during, and after the production process, rather than According to the survey of practice, pavement evaluation
a comprehensive list of all available tools and processes. personnel are trained mostly through on-the-job training
from experienced staff and in-house training programs. The
For example, the Maryland State Highway Administration pavement condition data collection is primarily composed of
(MDSHA) is one of the agencies that reported having a formal experienced technicians (69% have more than 6 years and 26%
quality control process for its in-house pavement data col- more than 10 years of experience). They hold associate degrees
lection activities. The agency developed a detailed quality (44%) or high-school diplomas (39%), with only a small per-
management program when transitioning from windshield centage having bachelor's and graduate degrees. The pavement
distress data collection to an automated system to measure data collection staff for the pavement data collection service
smoothness, rutting, and cracking (66). In the case of the providers appears to be a little less experienced (57% have
automated crack detection, the quality control plan includes more than 6 years and 14% more than 10 years) and have a
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PRODUCTION
BEFORE
PRODUCTION
DURING
PRODUCTION
AFTER
FIGURE 15 Example of quality control plan.
higher average level of education, with 43% having graduate of the workshops organized by the Ontario Ministry of Trans-
degrees (MS/PhD). It can be noted that the sample of pavement portation every two years to certify pavement raters. As part
data collection service providers that responded to the survey of the workshop, the raters from five regions evaluated a cir-
was much smaller than the number of highway agencies. Only cuit of nine sections and the results were statistically analyzed.
one service provider requires certification for the pavement Reference values for each section were obtained by a panel of
evaluation staff. four experts. The analysis compared the accuracy and precision
of the raters, established province-wide, within-region, and
between-region variability, investigated the effect of reducing
Personnel Certification Practices
the number of severity and density levels, and identified the
Only a small percentage of highway agencies (15%) currently distress types particularly hard to evaluate. The investigation
require "certification" of the pavement distress raters for the showed that there were significant differences among raters,
agency, service provider, or both. However, most agencies but no regional bias. The study also found that reducing the
indicated that they only use experienced personnel to rate number of severity and density levels would help to reduce
pavements and that they undergo extensive training before the variability in the pavement condition index.
collecting data. The accreditation workshops developed by
LTPP provide a good example of a certification practice. These Equipment and Method Calibration,
workshops were intended for experienced technicians who had Certification, and Verification
completed high school, had experience with data collection,
had received formal training on the Distress Identification The verification that the equipment is functioning according
Manual, and had experience in assisting an accredited rater to expectations and that the collection and analysis methods are
in distress data collection or data interpretation for both being followed is key for ensuring the quality of the collected
asphalt and concrete pavement. The accreditation includes a data. This is typically done before the initiation of the data
written test and a two-part film-interpretation examination. collection activities and periodically after that. Equipment
The results are compared with reference values provided by or process verification and validation is typically assessed
experienced raters. To remain accredited, it is important that by determining their repeatability and reproducibility (16).
a rater regularly perform a minimum number of interpretations Repeatability is the variation in measurements taken by a
per year (67, 68). single piece of equipment on the same road segment(s) and
under the same conditions over a short period of time. It is
Ponniah et al. (69) present another example of a rater generally evaluated based on the standard deviation of repeated
certification program. The paper presents the results of one values from different measurements. Repeatability measures
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the ability of the equipment/technology/raters to produce calculated to determine the requirements for equipment and
the same values on repeated measurements. Reproducibil- personnel criteria calibration (18). For example, a 95% con-
ity is the ability of a technology or equipment to accurately fidence interval with respect to the reference rating has been
reproduce or replicate measurements. The reproducibility of used for evaluating distress data collection equipment (71).
pavement condition measurements is typically measured as Calibration of rutting measurements is typically conducted
the standard deviation of measurements taken with different at the profiler calibration centers, such as the one at the
equipment or using different technologies. It is a measure Texas Transportation Institute (72).
of how well two different devices/methods/raters are able
to measure the same pavement condition value on the same The Iowa DOT uses eight control sites (four asphalt con-
road segment(s). crete and four PCC sections) for the initial verification of
the data collection equipment and methodology. The service
provider tests these sections before starting the production data
Equipment Calibration
collection. The sections have a variety of distress conditions
Equipment calibration is critical for the collection of accurate and serve as a sample of the state and local roads in the state.
pavement data, especially for sensor-based measurements. The reference distress measurements are determined by expe-
Calibration is a systematic process to validate the data collec- rienced staff and the DOT equipment is used to collect ride and
tion methodology and/or equipment by comparing the mea- rutting information. The service provider measures the site
surements with a standard reference value or ground truth three times and the data are compared with the benchmark data
that is considered correct. Adjustment to the equipment or tech- collected by the Iowa DOT. The final data delivery require-
nique may be required to match the "correct" measurement. ments are set based on this comparison. The control sites are
also measured monthly by the service provider during produc-
The LTPP pioneered the development of detailed and tion or whenever there is a change in equipment or subsystems
well-documented calibration procedures for pavement con- on that same equipment (73).
dition measurement equipment. For example, the LTPP defined
a clear distress rating methodology and established rigor- Smoothness Proper calibration of smoothness equipment
ous calibration procedures for the high-quality photographic requires an accurate and repeatable reference measurement.
images used for distress identification and quantification (67). Early approaches to determine this reference value included
Equipment calibration and harmonization of the measurements the use of rod and level to determine the actual road profile.
is the subject of several current national and international Another common method for determining the reference value
research efforts. The basic principles are discussed later in is to perform a continuous, close-looped test in accordance with
this section. Additional sources of information about equip- ASTM E950 using a static inclinometer. Slow-speed profile
ment calibration and verification can be found in the litera- measuring devices, often called "walking" profilers, have also
ture references provided. been developed. Standard methods for evaluating profiler accu-
racy are provided in ASTM E950 Measuring the Longitudinal
Distress For pavement distress, calibration is usually done Profile of Traveled Surfaces with an Accelerometer Established
by evaluating control sites where the pavement condition is Inertial Profiling Reference and AASHTO PP49 (74). These
closely monitored by a group of experts (70). These experts methods are generally considered appropriate for network-
determine the condition of the control site, usually through level pavement data collection (see Table 2); however, recent
careful evaluation and consensus ratings before equipment research has suggested that the method used in ASTM E950
calibration, or in the case of manual data collection, personnel does not ensure that two calibrated profilers can measure
training. The expert ratings are considered the reference ratings the same value of IRI within an acceptable tolerance for
of the control site. Statistical confidence intervals are often construction quality control (a project-level function) (75).
TABLE 2
TOLERANCES FOR THE VARIOUS TYPES OF PROFILERS
ACCORDING TO ASTM E950
Longitudinal Vertical Precision Bias
Class Sample (LS) Measurement Requirement Requirement
(mm) Resolution (VR) (SD, mm) (mm)
(mm)
Class 1 LS 25 VR 0.1 0.38 1.25
Class 2 25 < LS 150 0.1 < VR 0.2 0.76 2.50
Class 3 150 < LS 300 0.2 < VR 0.5 2.5 6.25
Class 4 LS > 300 VR > 0.5 -- --
Source: ASTM E950, Measuring the Longitudinal Profile of Traveled Surfaces with an
Accelerometer Established Inertial Profiling Reference.
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Additional information on calibration of longitudinal and experiments to compare and harmonize texture and skid
transverse profiles can be obtained from the Transportation resistance measurements (83), the Transportation Pooled Fund
Pooled Fund Project 5(063), Improving the Quality of Profiler Project 5(141) Pavement Surface Properties Consortium (84),
Measurement (55); University of Michigan Road Roughness and the AASHTO Guide for Pavement Friction (57).
Home Page (76), PIARC evenness harmonization studies (56),
and the Road Profiler User Group meetings and equipment
comparison studies (77). The aforementioned pooled fund Equipment and Method Certification
(55) objectives are to assist states with the implementation of
AASHTO provisional standards; establish a level of integrity There is an increasing trend toward establishing formal certi-
to the measurements; deliver sample procurement specifica- fication procedures for the pavement condition data collection
tions, maintenance guidelines, and profile analysis software; equipment and methods. These procedures typically require
establish criteria for verification centers and assist with the the use of a "certification center" that verifies the correct
development of these centers; develop and deploy a traceable functioning of the various components of the equipment and
verification process; and provide technical review of related the training and skills of the operators and provides an official
software. This research program is currently working on select- certificate of compliance with a specific standard. Certification
ing equipment and technologies for measuring the reference typically implies that the equipment and/or operator have
profile (ground truth) and devising a practical approach for pro- successfully passed formal verification testing. The granted
filer calibration and certification, which probably will involve certificate attests that the measurements meet some minimum
the establishment of regional certification centers. accuracy and precision requirements.
Structural Capacity For structural capacity (FWD) equip- Examples include the Texas DOT inertial profiler operator
ment, this phase typically includes sending the equipment for and equipment certification program (72) and the LTPP dis-
calibration and/or certification to a regional calibration center. tress rater accreditation program (67,68). In the Texas DOT
These centers are operated by the equipment manufacturers procedure, the profiler operators are required to pass written
and/or independent agencies; typically, university research and and practical tests to be certified to receive an operator iden-
engineering centers. Regional FWD calibration centers were tification card that specifies the type or brand of inertial pro-
first established in four states (Colorado, Texas, Minnesota, filer they are certified to operate. The equipment certification
and Pennsylvania) for supporting the LTPP data collection. includes the collection of profile data on two test sections
The hardware and software in these centers have recently been in accordance with Test Method Tex-1001-S (85) in 3 hours.
updated through a pooled-fund study (78, 79). A new protocol The results are evaluated for repeatability and accuracy and
for testing the load cell and deflection sensors has been pre- a profiler must meet all of the requirements to pass certification
pared; this protocol determines gain factors or dynamic cali- and receive a decal that is placed on the profiler as evidence
bration factors that are entered into the FWD software as of certification.
multipliers (79). It is important that the mass and drop height
(load) levels produce loads within ±10% of three pre-selected
Equipment and Method Verification
target loads and that the sensor gain factors agree within a
standard deviation of 0.003. The protocol also includes a pro- Verification tests are periodic checks that control the accuracy
cedure for conducting field-based relative calibration using a of pavement condition data collection by examining the data
stand provided by the FWD manufacturer. and/or comparing it with known reference measurements. It
is important that the data collection process is verified both
The Transportation Pooled Fund Project 5(039) FWD Cali- before and during the data collection process. This is necessary
bration Center and Operational Improvements (78,79) and to ensure that the actual pavement condition measurements
the LTPP Manual for Falling Weight Deflectometer Measure- meet the quality requirements and are adequate for support-
ments (80) provide additional information on FWD calibration. ing the decision processes that will be using the data. In
addition, this process could verify that the measurements
Friction Properties Friction measuring equipment is also are consistent with the historical records to ensure year-to-
sent for calibration and/or certification at regional calibration year consistency.
centers. The regional calibration sites for the locked-wheel
testers include the East Coast use of the Field Test and Eval-
uation Center for Eastern States in East Liberty, Ohio (81) and Data Verification Procedures
the Central and Western Field Test Center in College Station,
Texas (82). The principal features of the friction calibration During the data collection process, a variety of methods are
centers include water calibration and evaluation, force mea- available for ensuring the continued collection of satisfactory
surement calibration, and dynamic correlation. quality data. The purpose of verifying collected data in real
time or near real time is to avoid production of large quantities
Additional information on calibration and harmonization of unsatisfactory data. This benefits both the data collection
of friction properties measurements can be found in the PIARC teams and pavement managers by allowing quick detection
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and correction of errors and minimizing the delays and costs because it is unlikely that the same random error will occur
associated with poor data quality collection. For this reason, multiple times over the same pavement segment. If more than
it is recommended that the data be verified frequently during one evaluation team or type of equipment is available, cross-
production. For pavement distress data collection, the verifi- measurements by different teams or equipment could be used
cation of the distress ratings can be done for individual distress to overcome this limitation.
quantities, individual distress indices, multiple distress indices,
or overall condition indices.
Sampling and Independent
Quality management techniques that can be used to ver- Reanalyzing or Resurveying
ify the data collection process include periodic retesting of
"control" pavement segments, oversampling, and reanalyz- Another data verification method consists of reanalyzing or
ing or resurveying a sample of the sections measured by an resurveying a sample of the sections measured by an indepen-
independent evaluator. If the verification process identifies dent evaluator. The analysis of the sampled data is typically
deficiencies, the equipment will be checked and possibly different depending on the level of pavement management
recalibrated if automated or semi-automated procedures are considered.
used, or the raters' criteria will be normalized so that all rating
work remains within the acceptable limits of variation. This Network-Level Data Checks These checks often include
may include additional rater training (45). All of the substan- statistical testing of the differences between the mean values
dard data need to be reevaluated and the corrective actions (of the parameter being evaluated) for the quality control
be recorded and documented to support long-term quality or acceptance samples and the production surveys for the
improvement goals. same sections. The analyses typically include paired t-tests to
assess the potential bias of the collected data and provides an
indication of whether the pavement condition is consistently
Testing of Control Sections under- or overestimated as a result of the automated data
collection process. The differences between individual mea-
The testing of control pavement segments is used to determine:
surements from the verification sample and the production field
(1) the accuracy of the procedure if the results are compared
survey are computed for each sampling unit and the mean
with those obtained against reference measurements deter-
difference is tested against the null value using a pre-selected
mined using the best available practical technique for that
level of confidence (typically 95%). A two-sided t-test is used
particular pavement condition indicator; or (2) its repeata-
to determine if there is a significant difference. If there is a
bility and reproducibility if the results are compared with
results obtained with the same equipment or method. The significant difference, then a one-sided t-test can be used to
locations of these segments can be known or "blind" for estimate how far off the mean difference was by evaluating
data collection teams. Typically, the testing of known con- the achievable tolerance levels (12).
trol sections is used for quality control and testing of blind
controls for quality acceptance. In both blind and known Project-Level Data Checks If the collected data are to be
segment testing a second team of raters reevaluates a segment used for making project-level decisions, the mean comparison
of pavement for comparison testing. If the data collection may not be applicable because some individual differences
team's ratings are outside the established reevaluation team's can exceed the acceptable range at the project level owing to
confidence interval, the equipment and/or procedure is re- limitations and the production data collection and processing
checked and the data collected since the last satisfactory technology (88). Statistical tests based on individual measure-
evaluation is either closely examined for accuracy or rejected ment rating may be more appropriate in these cases. These
entirely (86). tests involve selecting a sample from a dataset, rating each
individual observation within this sample using established
passfail criteria for minimum acceptable quality, and con-
Oversampling cluding whether the whole dataset satisfies criteria for mini-
mum acceptable quality based on the number of "failed"
Another method of data verification during data collection observations in the sample (88).
is oversampling. In this procedure the data collection team
samples the same segment(s) multiple times. This is similar
to verification of blind or known pavement segments, because Comparison of Manual and Automated
data from the retest are used for comparison purposes (87). Distress Surveys
However, this method of verification is generally considered
less rigorous than verification testing by another team of raters, There have been several studies to verify AASHTO provisional
because if the source of error is systematic it most likely will protocol PP-44-01, which offers interesting approaches for
not be detected by retesting undertaken by the same data comparing the results of different pavement condition col-
collection team and equipment. This method of quality man- lection methods; for example, manual versus automated
agement is considered effective for assessing random errors surveys. These analyses provide good examples of tools and
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methods that can be used to compare the production and con- able degree of confidence that the sample is representative of
trol measurements. the entire process and sufficient to verify the required accuracy
in the measurements. For pavement data collection, the per-
Groeger et al. (89) used the cumulative cracking length to centage of data that is checked in the quality control process
compare two automated cracking detection procedures for typically ranges between 2% and 10%. However, it may be
a network, including approximately 2,000 data points. The noted that the sample size is also dependent on the scope of
results of the automated process were then compared with the quality control task in hand. For example, computer-based
the average of three experienced evaluators that classified the checks can be applied to all the data, whereas cross-testing of
section using a five-level condition scale (very good, good, fair, control sections in general is often limited to a small sample of
poor, and very poor). The data were evaluated as a function the network. The pavement data collection service providers
of the percentage of points that fall within one, two, and three surveyed indicated that they typically review 2% to 5% of the
deviations in the five-level scale. For example, if a pavement data (29%), 6% to 10% (29%), or more than 10% (42%) as
was classified as poor by one method and very poor by another, part of their regular quality control practices.
the deviation is one. The study found that the automated pro-
cedure produced good results for longitudinal and transverse The selection of the number of segments to verify for qual-
cracks, with 94% of the data falling within one deviation of ity control (and/or quality acceptance) purposes is often set
the visual assessment. at a "rational" number based on previous experience. How-
ever, there are a series of statistical techniques that allow the
Raman et al. (90) used statistical analysis to compare the calculation of the required sample size based on the desired
severity and extent of the transverse crack by various proce- accuracy and degree of risk that the agency is willing to take.
dures. The researchers used analysis of variance in the cases Procedures similar to the one developed by the National Parks
where data were normally distributed and nonparametric test Service (88) and that are discussed in the quality acceptance
(KruskarWallis) in the remaining cases. Statistical compar- section can be used for determining the most appropriate qual-
ison of sample and full-section image data showed that a 5% ity control sample.
sampling rate was enough to evaluate transverse cracks with
the precision desired for network-level pavement management Software Data Checks
in Kansas.
Many agencies use software routines that check the data
Wang et al. (91) compared the use of an automated cracking for inconsistencies for both quality control and acceptance,
survey system with manual evaluations in Arkansas using the although these checks are slightly more prevalent for qual-
provisional AASHTO protocol. The evaluators reviewed and ity acceptance than for quality control. There is some vari-
analyzed 5% of the images for each comparison section. The ation in verification methods used for quality control: 55%
study found some differences between the manual and auto- of the agencies surveyed perform checks for detecting miss-
mated process, especially for Level 1 and 3 cracks; however, ing segments or data elements, 57% check for ratings that are
it also suggested that these discrepancies may be the result of out of expected ranges, and 38% use statistical analysis to
the low repeatability of the manual surveys. check for data inconsistencies.
The Ontario Ministry of Transportation compared auto- The checks may include on-vehicle data checks, data
mated and semi-automated pavement distress collection tech- and video checks when the data are received in the office,
niques from three service providers with in-house manual condition rating data checks, and/or final database checks
surveys (92). The study included sections with surface-treated, after it has been entered into the relevant pavement/asset
hot-mix asphalt, composite, and PCC pavement structures. An management databases. On-vehicle data checks are conducted
overall pavement condition index, the distress manifestation in real time as the data are being collected and/or periodi-
index, was used for the comparisons. The investigation con- cally (e.g., at the end of the day). Real-time checks typically
cluded that, in general, automated results are comparable with include visual displays of certain data that alert crews if
manual surveys. However, the authors emphasized the need of anything is malfunctioning and/or data that are out of range.
supplementing the automated collection with manual surveys, Periodic diagnostics/data checks are typically scheduled at
especially for project-level analysis, because some of the pave- fixed intervals during breaks of the data collection to verify
ment distresses were difficult to identify with the automated the correct functioning of the equipment. These diagnosis
methods. checks are important to avoid collecting large amount of
deficient data. Final database checks are conducted to verify
Determination of Sample Size that data have been formatted properly and all the different
data have been entered in the final database. These later checks
One important element of the quality control process is the include tests for completeness and format, time-history com-
determination of how big a sample must be to have an accept- parisons, plots on GIS, etc.
