Capstone Document
Getting on the Map:
Underground Utility Location
And Municipalities
MGIS
Penn State University
Dr. Doug Miller
Academic Advisor
Dr. Sunil Sinha
Technical Advisor
Prepared By
Michael L. Gill, P.E.
Table of Contents
Introduction ......................................................................................................................... 1
Statement of the Problem .................................................................................................... 2
Literature Review................................................................................................................ 4
GIS Defined .................................................................................................................... 4
Extent of the Problem ..................................................................................................... 5
Developing a Municipal GIS .......................................................................................... 6
Data ................................................................................................................................. 9
Data Models .................................................................................................................. 13
Data Accuracy............................................................................................................... 14
Success Factors ............................................................................................................. 15
The Big Picture ............................................................................................................. 16
Research ............................................................................................................................ 19
Research Approach ....................................................................................................... 19
Methodology ................................................................................................................. 20
Data Analysis and Results ............................................................................................ 22
Conclusions ................................................................................................................... 27
Recommendations and Best Practices .......................................................................... 27
Summary ........................................................................................................................... 29
Abstract
This paper explores the problem of unknown underground utility location. The
problem has significant consequences for time delays and cost overruns for all types of
construction and maintenance projects. In addition, the unknown utility location problem
has lead to many construction accidents and deaths. While the unknown utility location
problem extends to all types of utilities, this paper specifically looks at how this problem
applies to municipalities.
This paper examines the literature on topics that impact the problem and its
solution. Topics include a statement of the problem, a Definition of GIS, Extent of the
problem, developing a municipal GIS, source of data, data models, data accuracy, GIS
development success factors, how these topics impact the overall operations of
municipality, research methodology and data, and recommendations. Finally, a summary
is presented. The summary shows that a GIS can be used to help solve this problem and
that municipalities are beginning to recognize the problem and look for ways to improve
there services.
Introduction
Visualizing the space under any modern city, would produce a picture of a maze
of underground utilities such as water, sanitary sewers, gas, storm sewer, electric, and
communications lines. As society has developed and grown, so have the demands for the
essential utility systems on which modern life depends. In fact, some of these utilities,
such as electric power or telecommunications, have the capacity to paralyze national
commerce and economic life if they fail (Mulaku, 2004). These facilities range in age
from the original installations of the late 1700’s early 1800’s and 1900’s to state of art
facilities currently being constructed. For perspective, the first municipal water system in
the country, the Philadelphia Waterworks, began operation in 1799. (About.com, 2005).
Utilities are challenged to evolve to meet the demands of today’s competitive market
place (Hemakumara, 2003).
The location of utility facilities has historically been based on information
maintained by the utilities themselves on two-dimensional media (NETTWORK, 2002).
The earliest forms of two-dimensional media were hand drafted on paper or sepia. Later,
Computer Aided Drafting and Design (CADD) were utilized for the preparation of utility
location maps. However, these maps have not been well maintained or mathematically
tied together (Mulaku, 2004). These maps were the basis for the design, construction,
operation and ultimately re-planning of utility facilities. This mapping paradigm is error
prone (Camateros & Oosteron, 2006).
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Statement of the Problem
“As transportation, communication, and utility networks continue to grow in
complexity and size, the likelihood of two or more networks occupying a common rightof-way or intersecting each other also continues to increase. Conflicts arise when one
network or another decides to perform construction or maintenance on their facilities.”
(Ellis, 2003, p. 5). Mulaku phrases this issue in other words by stating “there will be
intense completion for the finite space that utility facilities must occupy on road and other
reserves and hence precise location will become even more important” (Mulaku, 2004, p.
30). Each year departments of transpiration spend millions of dollars to deal with
problems that arise due to utility conflicts. As these conflicts arise, it is vital that the
owners of the various utilities be able to accurately locate their facilities in threedimensional space. Accurate location is the beginning of conflict detection, avoidance,
and resolution.
Mulaku documents that over 80 percent of all utility operations are spatial in
nature (Mulaku, 2004). This information was collaborated by Hemakumara in a paper
entitled Geographic Information Systems in Utilities and Utility Management.
Hemakumara determined that 80 to 85 percent of a utility’s information needs is location
or spatially based (Hemakumara, 2003). Utilities, needs are dependent upon spatial
information for their operations, engineering, and management. The information must not
only be available, it must be precise (Mulaku, 2004).
In 2003, Ellis completed a study for the Florida Department of Transportation
(FDOT) entitled Development of Improved Strategies for Avoiding Utility Related Delay
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During FDOT Highway Construction Projects. Ellis determined that utility relocation
delays were one of the top five causes of construction delays on FDOT projects. These
delays in turn caused project time delays and additional project costs. Ellis further
determined that one of the major factors accounting for the delays was the fact that actual
locations and types of utilities shown on the plans did accurately portray actual field
conditions (Ellis, 2003). Ellis accurately describes the fundamental problem by stating
“one of the fundamental problems is that there is usually no accurate data on the exact
location, or sometimes even the existence of these buried features (2003, p. 7). Allred
substantiated this view, in a paper entitled Underground Facilities: The Need for
Accurate Records in an Expanding Society. Allred documents that there is a need for
accurate as-built mapping of all underground facilities (Allred, 2004).
All types of utilities contribute to the complexity of locating underground utilities.
Kelly and Nawarynsky assert that “Excavation is one the most dangerous activities in the
construction industry (Kelly, Nawarynsky, 1996, p.1). Excavation is the single largest
cause of gas and hazardous liquid pipeline accidents in the United States. During the
1988 to 1993 time period, 33 percent of all gas pipeline incidents were cause by
excavation damage by persons other than the facilities owner or owner’s contractors. In
addition, 35 deaths and 151 injuries were attributed to theses incidents as well as $42.5
million in property damage (Kelly, Nawarynsky, 1996).
Given the extent of the problem, the construction industry can gain substantial
benefit from the accurate mapping of underground utilities. A Geographic Information
System (GIS) is especially well adapted to provide information on utility location
(Hemakumara, 2003). This view is shared by NETTWORK, 2002. They state that GIS
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provides the necessary computational, graphical, and information handling technology
needed to record all necessary information on all buried utilities in a user friendly,
accurate, three dimensional manner. While the accurate mapping of new installations is
well defined in the literature, this paper is intended to investigate methods and procedures
that can be used to capture the legacy utility location information in a modern GIS
format.
Literature Review
GIS Defined
Geographic Information System (GIS) is not easy to define and describe. Environmental
Systems Research Institute (ESRI) on their web site defines GIS as “GIS is a computer
technology that uses a geographic information system as an analytic framework for
managing and integrating data; solving a problem; or understanding a past, present, or
future situation.” (ESRI web site). Another definition of GIS is a computer based
information system used to digitally represent and analyze the geographic features
present on the earth's surface and the attributes associated with the feature.
(GISdevelopment.net, 2-18-2006).
Encompassed in the definitions of GIS is the concept that every object present on,
or under the earth’s surface can be geo-referenced. Geo-referencing is a fundamental key
to linking a database to GIS. The term database is a collection of information about things
and their relationship to each other and 'geo-referencing' refers to the location of a layer
or coverage in space defined by the co-ordinate referencing system.
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In the context of local governments, which include underground utility location,
Dr. O’Looney in Beyond Maps: GIS and Decision Making in Local Governments
describes GIS in these terms. “The term “geographic information system” is applied
loosely to a large group of interrelated technologies. For local government, GIS is “a
computer technology that combines geographic data (the locations of man-made and
natural features on the earth’s surface) and other type of information (names,
classifications, addresses, and much more) to generate visual maps and reports.” A GIS
system uses geographic location to relate otherwise disparate data and provides a
systemic way to collect and mange location-based information crucial to local
government” (Beyond Map, 2000, p. 5).
The term GIS is used as both a noun and a verb. It can refer to the whole system,
the software, a database, personnel using the system, the data, of the development of the
system.
The first work on GIS began in late 1950s. However, the first GIS software came
only in late 1970s from ESRI, the developer of ArcView software. Canada was the
pioneer in the development of GIS as a result of innovations dating back to early 1960s.
Much of the credit for the early development of GIS goes to Roger Tomilson. Evolution
of GIS has transformed and revolutionized the ways in which planners, engineers,
managers, scientists and many others practice their professions (GISdevelopment.net, 218-2006).
Extent of the Problem
NETTWORK describes the problem of unknown location of underground utilities
as one that is currently very troublesome and one that will continue to grow unless
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preventive steps are taken now. Urban populations are predicted to grow. It is expected
that seven to nine billion people will live in urban areas by the year 2010 (NETTWORK,
2002). As the population grows, so will both the quantity and complexity of the buried
underground infrastructure. The predicted growth will lead to increased cost, project
delays, and in the worst cases loss of utility service and even loss of life. Ellis (2003) has
documented that in spite of past effort, utility conflicts remain the leading cause of
construction delays and cost over-runs. Construction sites are often located where many
utilities have existing underground facilities that are inadequately mapped. The lack of
accurate location data is a fundamental problem in the design and construction industry.
While most utilities are regulated by many and varied regulations, in the United Stated,
there has never been any regulations requiring underground utilities to be accurately
mapped. Public utilities are allowed to occupy state and local rights-of-way by statutory
authority (Quiroga, Ellis, Shin, & Pina, 2002). Quiroga, et. al. further state that knowing
the location and current operating status of utilities with various rights-of-way is critical
for the planning and executing of transportation improvements.
Developing a Municipal GIS
The integration of GIS within municipalities is mixed at the current time. Most
large municipalities have some type of GIS systems, while many smaller cities and towns
do not have any type of GIS systems. The development of a municipal GIS system is a
complex task that requires comprehensive understanding of many factors. A properly
developed GIS system has the potential to serve municipalities by providing a tool set to
design and support important governmental decisions and to provide mapping services
for the various city departments as well as administrative personnel (Cardenas, 1998).
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The design and development of a GIS should be driven by what the use and application
will be. Different applications require different kinds of data. A GIS should be thought of
as a form of municipal infrastructure. A GIS is a virtual infrastructure that needs design,
implementation, and maintenance just like a physical infrastructure (MassGIS, 2002).
Developing a municipal GIS can be a challenging process for many
municipalities. The skill set required to design, develop and use a GIS is often completely
foreign to the skill sets of municipal employees (Cardenas, 1998). In fact, the skill level
of current municipal employees can dramatically affect the success rate of GIS
implementation. In addition, the data formats and manipulation techniques of a GIS are
often very different from the municipality’s legacy data. It is also vitally important that
municipal officials understand that a GIS system is not static. It must be continually
updated and expanded. This fact must be reflected in the budgets and time allocated for
personnel to maintain the system.
In the initial stage of GIS development and implementation, the GIS system is
often viewed as a mapping unit which responds to request and produces maps only.
Someone in a municipal department requests a map and the GIS section produces the
map. While this is a legitimate purpose of GIS, this single use will only deny the
municipality of many of the more advanced benefits of its GIS system. One way to assist
in the overall acceptance and utilization of a GIS is to develop simplified and
customizable applications that can be used by personnel from all departments with little
training Cardenas, 1998). Once a diverse group of municipal employees begin to see the
benefits of the GIS system, and begin to be influenced by the advantages of the system
they will tend to utilize the GIS in more a sophisticated manner (Cardenas, 1998).
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A fully developed municipal GIS will need highly trained personnel in the GIS
section. These dedicated personnel will need the skills, equipment, and data to
accomplish the following tasks:
a. Maintain, update, and distribute the GIS databases.
b. Certify the integration and integrity of other data sets with the municipal database.
c. Design new databases, and analysis techniques.
d. Expand the GIS capabilities, and analysis functions (Carenas, 1998). .
Once these skills are implemented into the municipal GIS system, the city will reap many
benefits in operation, analysis capabilities, and efficiency in governance (Carenas, 1998).
The first requirement for any GIS is a base map. Ultimately, the base map will
determine how functional the GIS will be. The base map is the one constant in a GIS that
provides a consistent visual reference for the ever-changing data displays of the many
data layers in a GIS system. This constant visual reference allows for comparison of all
the other data layers (Zelinsky & Johnson, 2002). Base maps can be composed of either
cadastral or planimetric features or a combination of both types of features. Planimetric
features can be seen with the human eye. Some examples of planimetric features are
streets, buildings, ponds and other similar features. Cadastral features are not easily
detected by the human eye. Cadastral features include items such as property lines,
zoning boundaries, municipal boundaries and similar types of data. Cadastral data often
must be established by survey techniques (Zelinsky & Johnson, 2002). The accuracy of
the base map is important. It should be remembered that it is easy to build a less accurate
GIS on a more accurate base map, but it is virtually impossible to build a more accurate
map on a less accurate base map ((Zelinsky & Johnson, 2002).
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The adoption of a GIS system within a municipality doe not happen by chance.
The adoption of a municipal GIS is dependent upon technical choices, the ability,
capacity, and willingness of the municipality to integrate and use new forms and
quantities of information. Adoption usually follows the S-shaped curve of adoption.
Initially, adoption occurs slowly, followed by rapid acceptance, with ultimate overall
acceptance of the technology. Ventura has documented that the use of a GIS typically
follows a three-step pattern of acceptance (Ventura, 2002). When the system is first in
place, it is mainly used for mapping and inventory applications. Next, the system evolves
to provide support for simple queries related to location and feature conditions. Finally,
in a mature system analysis, the GIS is used for decision support, modeling, simulation,
and forecasting (Ventura, 2002). It is not uncommon for each step to take five or more
years. Full utilization of advanced applications will be long term process for most
municipalities.
Data
Once it has been decided to develop a GIS, and an accurate base map has been
acquired, the next step is to gather the need data sets. Data requirements will be driven by
the expected uses of the GIS. Data layers could include parcel, streets, water line, sanitary
sewer lines, storm sewer lines, signs, zoning districts, municipal boundaries, taxing
districts, fire protection districts, and other similar types of data. Data acquisition is the
process of obtaining data in a form that a GIS is capable of utilizing, including all feature
attribute information (Segantine & Ramos, 2001).
Data as used in a GIS is composed of two components. The first component is
the spatial location of the feature. The second component is the attribute data of the
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feature. Attributes are data that describe the mapped features (Segantine & Ramos, 2001).
Attributes may include size, area, model, ID number, length, material, cost and other
similar types of data. Data gathering is a time consuming an expensive undertaking. In
fact, the collection of data and transfer of the data into a database is usually the most
expensive, complex, and important part of the development of a GIS (Segantine &
Ramos, 2001). Ventura states that digital data capture and conversions are estimated to be
60 to 80 percent of the implementation costs (Ventura, 2002).
Data exists in many different locations, formats, types, and quality levels.
Data can be broadly classified into two general categories. The two categories are
primary data, and secondary data. Primary data is obtained directly from field surveys.
Secondary sources describe data that is derived from documents, data sets, maps, charts
and other similar types of sources.
Historically, GIS data has been acquired by digitizing hard copy mapped data,
rectified aerial photography, and satellite imagery. Only during the past few years has it
become practical to acquire GIS data directly in the field by using Global Positioning
Systems (GPS) (Korte, 2006). In the book entitled ArcGIS and the Digital City, Huxhold
and associates describe five methods that are available to municipalities to covert legacy
data into a GIS format. The first method is compiling, which is basically using GPS to
field capture the data. Next is digitizing, which uses an electronic puck or mouse attached
to a digitizing table and connected directly into the compute. In this method, the operator
traces the feature with the puck and the digitizing table translates the motion into a GIS
format. Scanning is another method to get legacy data into a GIS. Scanning is similar to
making a copy. The scanner captures the digital file and converts it to a raster image.
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There are some programs that can convert the scanned raster imagine into vector data.
However, clean up of the data is usually necessary (ArcGIS and the Digital City,
Huxhold, Fowler, & Parr, 2004).
Screen digitizing or head-up digitizing is also used to capture legacy data. In this
method, the operator traces point lines and areas from a base map directly into a GIS
format. The final method described is importing. This method converts digital maps such
as CADD drawing into a GIS format. It is basically an electronic file transformation
(ArcGIS and the Digital City, Huxhold, Fowler, & Parr, 2004).
Once this information is captured, it must be translated into the coordinate system
used in the in GIS. In addition, none of the methods described capture any attribute data.
It will be necessary for the GIS personnel to add the attribute data once the location data
is captured by one of the methods described.
Some cities have electronic data bases that may contain some of the attribute
information needed for an effective GIS. It may be possible to link the existing database
data to the newly created GIS.
Another source of data that this author has observed on many occasions is
“mental data”. Mental data is information concerning municipal features that resides only
in the mind of humans, usually a municipal employee. The employee working on the
infrastructure itself usually acquires this information. The employee may remember the
location of water valve they help to install or locate at some point in time. The employee
may also remember installing a particular type of pipe, or it size. Collectively, all the
municipal employees’ knowledge has been described as institutional knowledge. This
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knowledge is very fragile and transitory. It is subject to loss at any time. If an employee
retirees, dies, or quits, the information is lost with little possibility of capture.
It is important to understand that combining data from different sources can lead
to problems. Gotway and Young state that properly combining different data sets into one
data set is one of the most challenging and fascinating areas of spatial data collection
(Gotway & Yung, 2002). The variety of data sources, storage locations, types of data, and
advances in data gathering techniques and equipment have all contributed to this
problem. Some of the terms used to describe these issues include polygonal overlay
problem, areal interpolation, inference with spatially misaligned data, contour
reaggregation, multiscale and multiresolution modeling, change of support problem,
scaling problem, and incompatibility zonal systems. Each of these problems constitutes a
study in and of themselves. The users of any GIS are frequently faced with these types of
problems, and must learn to how best to integrate all such information (Gotway & Yung,
2002). This issue is especially prominent when a municipality first begins to develop a
GIS system. The data is usually gathered from a variety of sources and locations. All the
data must be made compatible for the system to become effective.
The US Corps of Engineers has developed a series of papers on the development
of GIS systems. In the section entitled Collection of Spatial Data Sources, the importance
of data is detailed. It is stated that data collection must be reliable and accurate or the
outcomes of the entire system may be useless. This is true no matter whether the GIS is
used for display, storing, analyzing or manipulating spatial data. (Corps of Engineers,
1995). The Corps then list twelve items must be considered when evaluation the quality
of data. These factors are:
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Positional & attribute accuracy
Geographic extent of data sets
Completeness and correctness of data
Scale
Projection & Coordinate information
Drafting inaccuracies
Mixing source documents
Classification
Sampling techniques
Boundary definitions
Cartographic interpretation
Data input
It is noted that all data contains some errors. The prudent designers and users of a GIS
understand and properly accounts for the variance between their data sets and the real
world (Corps of Engineers, 1995, p. 2).
Data Models
A data model is a set of standards that attempts to define how information is
formatted, saved, and presented. The online webopedia, Wilipedia, defines a data model
as follows: “A data model is a model that describes in an abstract way how data is
represented in a business organization, an information system or a database management
system. This term is ambiguously defined to mean:
1. how data generally is organized, e.g. as described in Database management
system. This is sometimes also called "database model"
2. or how data of a specific business function is organized logically (e.g. the data
model of some business) (wikipedia, 2006).
Two major data models for utilities are currently available. The first data model
was developed by the Federal Geographic Data Committee. This data model is located at
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http://www.fgdc.gov/standards/standards_publications/. The second utility data model
has been developed by ESRI. ESRI has several data models that may apply to
municipalities. Some of these models are homeland security, local government,
transportation, water utilities, and pipelines among others. These data models are
available at http://support.esri.com/index.cfm?fa=downloads.dataModels.gateway.
The use of any specific data model is not required. However, use of and/or
reference to established data models may provide many benefits to the development of a
municipal GIS. Following the standard data models will allow the data to fit with other
GIS data sets and can reduce the cost and time of development.
Data Accuracy
Data accuracy is an important issue that both the designers and users of a GIS
must know and deal with effectively. The accuracy of the data generated from a GIS can
be no more accurate than the input data accuracy.
The input accuracy of data can be widely variable. If GPS techniques are utilized,
the equipment specifications will quantify the expected results. Other forms of data input
accuracy are less explored and will be the subject of a portion of this research.
One common usage of the output location data is for “one-call” underground
utility locates. A utility locate is a process whereby the municipality marks the ground
where the underground is located below the surface. This is very common in all
municipalities. In this circumstance, the utility owner is required to accurately locate the
horizontal position of the utility by marking the ground in the area of the proposed
excavation prior to excavation by a third party. The horizontal marking location
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accuracies range from 12 inches to 24 inches on each side of the utility. It should be
noted that currently, there are no requirements to locate the elevation of the underground
utilities.
There are many legal issues related to the correct marking of utilities. Who pays
for what is dependent on the accuracy of utility locates. This structure is set up in state
one-call statures. If the input data is less accurate than the output requirements, the GIS is
ineffective for this use. This concept was stated differently by Tulloch and Hu when they
stated “Accurate asset management is heavily reliant on accurate information.
Determining the location of buried utilities is one source of information that constitutes
good asset management practices.” (Tulloch & Hu, 2005, p. 1). They also document the
problem of inaccurate underground utility location and the associated problems of
unnecessary costs, project delays, human injury, and loss of time. The authors propose a
two step solution to the problem of inaccurate underground utility location. First they
propose accurate base maps, followed by the GPS location of the facilities that will be
transferred into a GIS (Tulloch & Hu, 2005). This is perhaps an ideal solution that could
be implemented as new utilities are constructed or exposed for repairs or maintenance.
However, this solution for the accurate mapping of existing underground is impractical.
Other methods of data input as described in other sections of this paper may be necessary
to map exiting underground utility locations.
Success Factors
Successful development of a GIS is much more than developing the technical
aspects of a database and feature locations. The people involved must embrace, use and
implement its capabilities into their daily work flow. One of the most important aspects
15
of successful implementation of a GIS is to involve the end-users in the development of
the system (Tychon, n.d.). When the end-users are included in the design and
development of the GIS a sense of ownership is developed. The end-users are then much
more likely to adopt the technology into their work. Other factors that are important in
the development of a GIS includes administration commitment and ongoing funding,
staff training, acquisition of adequate hardware and software, long range goals, an
implementation plan, and appropriate work related tasks. (Tychon, n.d.). One technique
that eases the transition into a GIS is use of pilots and prototypes. This system allows the
staff to see the capabilities of a GIS, have input into its development and allow time for
the adjustment of work routines.
The Big Picture
Society today is demanding more accountability from all forms of government.
This trend is obvious as voters routinely fail to approve tax increases and bonding
initiatives for school, roads, sewer and water facilities (Lemer, 2000). Strong opposition
is often encountered when funding for public programs is advanced. There is a growing
feeling that urban growth must be curtailed and that government must do more for less. In
addition, there is a growing awareness that undeveloped areas are needed to maintain an
overall healthy planet, and that infrastructure development puts great pressure on these
assets (Lemer, 2000).
In response to the community pressures at least two major initiatives have
developed. The first initiative is asset management as it applies to municipal
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infrastructure, and the second is Governmental Accounting Standards Board Statement
34 (GASB34). A GIS system is a component of each of these initiatives.
The term asset management originated in the real estate and financial markets and
was used as a tool to help measure and maximize financial return on investments. In the
arena of public works and utilities the term asset management has a different connotation.
Lemer provides several different definitions of the term asset management. Some of these
definitions are:

Asset management is a systematic process of maintaining, upgrading, and
operating physical assets cost-effectively.

Asset management is the combination of management, financial, economic,
engineering, and other practices applied to physical asses with the objective of
providing the required level of service in the most cost-effective manner.

Public-works asset management refers to the activates of deciding how to use
society’s resources to develop, operate, and maintain our infrastructure to achieve
the highest possible returns (Lemer, 2000).
A GIS is well suited to be one of the tools to assist in effectively applying asset
management to the vast investment society has made in infrastructure. A GIS cannot only
store utility location data, but condition data as well. In addition, querying, analysis,
modeling, and simulation techniques can be applied and developed. All of this
information is a part of utility asset management.
Another response to public pressures for efficient asset management is the
GASB34 requirement. GASB is a board that sets standards for governmental accounting
methods. While statement 34 has been in existence since 1979, it was significantly
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upgraded in 1999. The intent of the statement is to better reflect the way infrastructure is
accounted for, and making governmental financial reporting easier and more
comprehensive (ESRI, 2000). GASB34 aides the needs of public financial firms,
investors, and bond-rating agencies to assess in determining the credit worthiness of units
of government when they seek public financing options. The statement is intended to
allow units of government provide comprehensive cost information to the appropriate
entities. It allows creditors to make informed judgments about the ability of units of
government to repay their debts and support their service obligations (Bajadek, 2001).
This statement affects over 84,000 units of government.
The new GASB 34 statement allows for two methods of reporting the value of a
municipality’s infrastructure. The first method is to report the historic cost less
depreciation. The second method is called the modified approach. To utilize the modified
approach, the municipality must meet the following three requirements:
1. Have an up-to-date inventory of all infrastructure assets
2. Perform a condition assessment of all infrastructure assets every three years and
summarize the results with some measurable condition scale.
3. On an annual basis, estimate the cost to maintain and preserve the assets at their
reported level of functionality
Irregardless of the method a municipality chooses to use to comply with
GASB34, a GIS system can be an invaluable component in compliance efforts. A GIS is
capable of providing the inventory function, the categorization of the various assets for
valuation purposes, Cost tracking, and spreadsheet type accounting calculations. All of
these capabilities can assist municipalities in the GASB34 compliance efforts. The ability
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within a GIS to classify, categorize, segregate, and calculate each segment of the system
gives the GIS extraordinary power to accomplish this accounting function.
A municipality can obtain a double benefit at the time of developing a GIS. While
they are gathering the data to be placed in the GIS, a condition assessment can be made
an entered into the database simultaneously. The condition assessment can be another
attribute of the feature being captured into the GIS. This process can lead to a
considerable savings to the municipality. Once the data is captured, it can easily be
exported in a format that the accounts can utilize in their financial accounting, or, with
their assistance, all the calculation can be completed inside the GIS (Schutzberg, 2003).
Research
Research Approach
The goal of this research is to determine the length of a straight line (variance)
between two sets of coordinates for the same point. The “variance” is the hypotenuse of a
right triangle computed from the difference between the two X or easting values and the
two Y or northing values.
It is assumed that the GPS locations represent the actual or true location of the
points. The digitized locations represent the best knowledge the municipalities are able to
determine by institutional knowledge and the techniques required to transfer the
institutional knowledge into a GIS. The variance is the difference between these two
locations expressed in feet.
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ArcMap, a GIS system, was used to determine the “error”. The attributes of the
points were inputted into ArcMap. The calculations were made using the calculation
functions in the attribute table. Once the length of the variance was determined various
statistics were computed. Statistics were generated for the various variables. Finally, a
line was drawn and labeled with the variance distance, in ArcMap between symbols
depicting the location of the two sets of points.
Methodology
The first sets of data gathered were a combination of institutional knowledge and
heads up digitizing. Three different community operators is southern Illinois were
approached and agreed to assist in this project. The operators were fully informed of their
participation and how the data gathered would be used. No compensation was offered or
given for participation in this project.
In two of the communities, towns Albion and Grayville, the long employed
operators were given print outs of aerial photos of the test area at a representative fraction
of 1:1200. In both cases they were asked to mark as accurately as they could the location
of all fire hydrants and sanitary sewer manholes within the test area. Next, an experienced
CAD operator was instructed to digitize the marked locations as closely as possible. In
these two communities, two different sets of aerial photos were available. One set of
aerial photo had a 1-meter resolution, and the second set had a resolution of 6-inch. The
test area consisted of locations in both resolution areas.
Next, a professional survey crew was dispatched to GPS the location of the same
points. The survey crew used a Trimble 5700 GPS system, both with and without the
20
base station. Data capture with this equipment usually has a horizontal error of a few
hundredths of foot, depending upon occupation time, number of satellites available, and
lines of sight. These data points collected were “named” the same as the names assigned
in the digitizing portion of the research
Finally, both sets of points were inputted into Arc Map. The two tables were
joined, and the length of a line connecting the two sets of data representing the same
point was computed using the Pythagorean Theorem. Descriptive statistics of the
computed length were then calculated and are given in this paper. It is assumed that the
computed values would be similar to other underground location data that an operator
could develop using institutional knowledge.
Lawrenceville, data was derived in a similar but slightly different manner. The
data in Lawrenceville was gathered entirely by the cities long term operator using an
approach very similar to Albion and Grayville. In this test case, operator took a beginning
GIS class at the local community college and became very interested in developing a GIS
for his water system. Both as a part of his class, and his work, the operator digitized the
water system of Lawrenceville using heads up digitizing and his institutional knowledge
as input variables.
Later the water department purchased a Thales Mobile Mapper CE. In addition,
the city purchases a Mobile Mapper Beacon Pack. The Mobile Mapper Beacon Pack is
designed to improve the real-time positioning accuracy using broadcast DGPS
corrections of the data points acquired using the Mobile Mapper Beacon Pack.
The literature on the Mobile Mapper CE states that system is capable of real time
sub-meter accuracy and sub-foot accuracy when the data is post processed. While the
21
literature states that the Mobile Mapper Beacon pack improves real-time accuracy no data
is given on accuracy when the Beacon pack is used. A verbal communication with a
Thales represented revealed that Thales believes that using the Beacon pack will cut the
stand alone error in half to about one and one half feet.
Next, the operator GPS’ed 13 points, within the city, using the Thales Mobile
Mapper CE, both with and without the Mobile Mapper Beacon Pack. Finally, the operator
inputted all three sets of data into Arc Map and gave this author the data sets. This author
then made similar calculations on the data sets which are presented in this paper.
Data Analysis and Results
The variances were computed for several combinations of data. The data is
presented in table format.
The Albion data was GPS’ed with a Trimble 5700 system using both the base
station and rover. The stated accuracy of this unit is approximately five to ten
millimeters.
Fire Hydrants – 6-in Base Map
Manholes – 6-in Base Map
Albion
Albion
n
10
n
26
Min
1.79
Min
0.19
Max
23.18
Max
36.95
Range
Mean
Range
8.57
Mean
22
11.91
Std Dev
7.72
Std Dev
10.10
Fire Hydrants – 1m Base Map
Manholes – 1m Base Map
Albion
Albion
n
2
n
26
Min
7.21
Min
11.23
Max
8.64
Max
20.88
Range
Range
Mean
7.92
Mean
16.06
Std Dev
0.72
Std Dev
4.82
Fire Hydrants – Add Data
Manholes – All Data
Albion
Albion
n
12
n
28
Min
1.79
Min
0.19
Max
23.18
Max
36.95
Range
Range
Mean
8.46
Mean
12.20
Std Dev
7.06
Std Dev
9.88
23
Add Data
Albion
n
40
Min
0.19
Max
36.95
Range
Mean
11.08
Std Dev
9.28
The data in Grayville was gathered with a Trimble 5700 rover only GPS unit. The
stated accuracy with this configuration is one to three meters.
Fire Hydrants – 6-in Base
Manholes – 6-in Base
Grayville
Grayville
n
5
n
14
Min
2.21
Min
4.34
28.95
23.18
Max
68.17
Range
Range
Mean
5.79
Mean
23.75
Std Dev
2.58
Std Dev
18.99
24
Fire Hydrants – 1m Base
Manholes – 1m Base
Grayville
Grayville
n
16
n
24
Min
6.33
Min
2.45
Max
52.85
Max
89.09
Range
Range
Mean
24.87
Mean
24.92
Std Dev
13.60
Std Dev
17.51
Fire Hydrants – All Data
Manholes – All Data
Grayville
Grayville
n
21
n
38
Min
2.21
Min
2.45
Max
52.85
Max
89.09
Range
Range
Mean
20.33
Mean
24.49
Std Dev
14.44
Std Dev
18.08
25
All Data
Grayville
n
59
Min
2.21
Max
89.09
Range
Mean
23.01
Std Dev
17.00
The Lawrenceville data was gathered with a Thales Mobile Mapper CE with and
without the Beacon Pack attached.
Fire Hydrants – with Beacon Pack
Fire Hydrants – without Beacon Pack
Lawrenceville
Lawrenceville
n
13
n
13
Min
0.08
Min
3.83
Max
17.56
Max
37.69
Range
Range
Mean
5.82
Mean
10.21
Std Dev
5.34
Std Dev
8.61
26
Conclusions
Several conclusions can be derived from the data in spite of the high degree of
variability innate in the information. First, developing a GIS captures the institutional
information, currently available, before it is lost due to personnel retirement, or change of
employment. This is a vital first step.
The information captured by the study techniques can be used for general
mapping, inventory, a major component of GASBY 34 accounting requirements,
planning and hydraulic modeling. In addition, the data can be used to track maintenance
procedures, mapping work orders, and complaint monitoring. The information is not
accurate enough to be used of one-call system locates without significant accuracy
enhancements.
While not conclusive from the data, it is believed that the major contributor to the
inaccurate data is the lack of institutional knowledge. Inaccurate legacy data (CADD
maps), and digitizing errors are believed to be secondary.
Recommendations and Best Practices
The cycle of unknown utility locations can and should be broken. Municipalities
should adopt the following goals for their GIS system:

Get all underground utilities mapped into a modern GIS system

Have mapped data accuracies less than “one-call” system tolerances

Capture and map vertical data as well as horizontal data
27
Municipalities should implement the following procedures as current best practices to
develop, improve, and maintain their GIS system:

Municipalities should develop a modern GIS system

Municipalities should input utilities into the GIS system utilizing the best
available practices

GPS all valves, meters and other surface features (after digitizing)

Adjust previously digitized lines to match GPS valves, meters and other surface
features
o These surface features are directly above underground facilities and will
improve location accuracies

Municipalities should continually improve the accuracy of their existing utility
locations by:
o GPS both horizontal and vertical positions of all underground utilities, to
within one-call tolerances, when:


New construction takes place

During maintenance operations

Fixing breaks

Anytime facility is exposed

Use data to update and improve the accuracy of existing maps
When new urban construction takes place the as-build horizontal and vertical
locations should be mapped to sub-foot accuracies by:
o Capture both horizontal and vertical data to sub-foot location tolerances
o Capture location points at all fittings, valves, hydrants, meters, etc.
28
o Capture line locations at a maximum line spacing of 100 feet
o Keep GIS mapping up to date

If new construction is in a rural area use the following standards for as-built
location capture standards
o Capture both horizontal and vertical data to sub-foot location tolerances
o Capture location points at all fittings, valves, hydrants, meters, etc.
o Capture line locations at maximum spacing of 500 feet
o Keep GIS mapping up to date

Location capture standards could be made a part of the municipal and/or state
design and construction standards.

The municipality should develop a data distribution policy that balances the
public’s right to know with security precautions and issues
The information developed has applications well beyond the location of underground
utilities for construction and maintenance operations. This information can be the base
for hydraulic modeling, in situ condition assessment, inventory, and GASB 34 accounting
as well as a wide variety of GIS analysis functions.
Summary
The problem is huge, the implications are at best costly, and at worst life
threatening. No one knows where our underground utilities are located or their current
condition. We don’t know how long they will last or when many people will suffer
because the services they provide unavailable. This looming tragedy is occurring at a
time that society at large is demanding better and more considered use of our public
moneys as well as more efficient use of the natural resources available on our planet.
29
There is a light at the end of the tunnel however. Many researches have developed
asset management techniques that allow utility owners to provide more and better
services in a more cost effective and environmentally friendly manner. The asset
management techniques however depend on knowing the location and conditions of the
existing facilities. Knowing the exact location of underground utilities is not an easy task.
Getting this information into a usable format is a daunting challenge. GIS has developed
sufficiently to allow underground utility legacy data to be translated into a useable and
even desirable GIS format. This study will add to the literature on underground utility
location and its transfer into a modern GIS.
30
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