National Research Conseil national
Council Canada de recherches Canada
Municipal Infrastructure Investment Planning (MIIP)
MIIP Report: Geographic Information Systems (GIS) and
Interoperability of Software for Municipal Infrastructure Applications
City of Calgary
City of Edmonton
City of Hamilton
City of Ottawa
City of Prince George
Department of National Defence
Regional Municipality of Durham
Regional Municipality of Halton
Regional Municipality of Niagara
Municipal Infrastructure Investment Planning
D.J. Vanier, Ph.D., Project Manager
Y. Kleiner, Ph.D., P.Eng., Group Leader
Buried Utilities Group
D.A. Taylor, Ph.D., P.Eng., Director
Urban Infrastructure Rehabilitation
Report No: B-5123.4
Report Date: November 2004
Contract No: B-5123
Program: Urban Infrastructure Rehabilitation
iv + 63 pages
Copy No. 1 of 15 copies
Table of Contents
Table of Contents ............................................................................................................... i
List of Figures................................................................................................................... iv
Geographic Information Systems (GIS) and Interoperability of Software for
Municipal Infrastructure Applications........................................................................... 1
1. Overview of this Report.............................................................................................. 1
1.1. GIS and Municipal Infrastructure ............................................................... 1
1.2. Interoperability of Geographic Information Systems ................................. 1
Part 1 Geographic Information Systems (GIS) as an Integrated Decision Support
Tool for Municipal Infrastructure Asset Management................................................. 3
1. Introduction................................................................................................................. 5
2. Background Information............................................................................................. 5
3. Geospatial Technologies............................................................................................. 5
3.1. Global Positioning System (GPS)............................................................... 5
3.2. Personal Digital Assistant (PDA) ............................................................... 6
3.3. Mobile computing....................................................................................... 6
3.4. Automated Vehicle Location (AVL) .......................................................... 6
3.5. Road Weather Information Systems (RWIS) ............................................. 7
3.6. Remote Sensing .......................................................................................... 7
3.7. Summary of Geospatial Technologies ........................................................ 7
4. Associations and Networks......................................................................................... 7
5. GIS Literature related to managing Municipal Infrastructure .................................... 8
5.1. Surveys on GIS Usage ................................................................................ 8
5.2. Data Conversion Issues............................................................................. 10
5.3. Domain-Specific Applications.................................................................. 10
5.4. Advanced Implementations ...................................................................... 11
5.5. Systems Integration................................................................................... 12
5.6. Summary of GIS Literature ...................................................................... 13
6. Discussion................................................................................................................. 13
7. Conclusions and Recommendations ......................................................................... 14
8. Acknowledgement .................................................................................................... 15
9. References................................................................................................................. 15
Part 2 The Interoperability of Geographic Information Systems for Municipal Asset
Management Applications.............................................................................................. 19
1. Introduction............................................................................................................... 21
2. Main Issues in Developing Municipal Asset Management Systems ........................ 22
3. Role of Asset Management Systems in Municipalities ............................................ 25
4. Current State of Practice in Software Solutions for Municipal Asset Management 27
5. GIS Technology in Municipal Asset Management................................................... 28
6. Role of GIS in Municipal Asset Management Systems ........................................... 30
7. Recent Developments in GIS Data Collection, Access, and Modeling for Municipal
Asset Management.................................................................................................... 33
7.1. Spatial Data Collection ............................................................................. 33
7.2. Condition Data Collection ........................................................................ 33
7.3. Data Access............................................................................................... 34
7.4. Data Modeling .......................................................................................... 34
8. Data Requirements of Municipal Asset Management Systems................................ 35
9. Need for Interoperability and Spatial Data Standards .............................................. 37
10. Spatial Data Standards for GIS Interoperability ....................................................... 38
11. Federal Geographic Data Committee (FGDC) Standards ........................................ 40
11.1. Data Content Standards............................................................................. 40
11.2. Spatial Data Transfer Standards (SDTS) .................................................. 41
11.3. SDTS Base Specification.......................................................................... 42
11.4. SDTS Profiles ........................................................................................... 43
11.5. SDTS Implementation .............................................................................. 43
11.6. FGDC Spatial Metadata Standards ........................................................... 44
12. Spatial Data Standard for Facilities, Infrastructure, and Environmental Applications
(SDSFIE) .................................................................................................................. 45
13. The Open GIS Consortium (OGC) Standards .......................................................... 47
13.1. The OGC Abstract Specifications............................................................. 47
13.2. The OGC Implementation Specifications................................................. 49
14. ISO/TC 211 Geographic information/Geomatics Standards .................................... 49
14.1. International Standards ............................................................................. 50
14.2. Draft International Standards.................................................................... 50
15. Recommendations..................................................................................................... 51
15.1. Harmonization of AEC/FM Standard Data Models with Spatial Data
15.2. Adopting and Adapting Spatial Data Standards for Municipal Applications
15.3. A Model-Based Approach for Developing Municipal Asset Management
Applications .............................................................................................. 53
15.4. Developing and Deploying Sharable Municipal Spatial Data Repositories
15.5. Framework for Developing Integrated GIS-Based Municipal Asset
Management Systems ............................................................................... 54
15.6. GIS Interface Tier ..................................................................................... 56
15.7. Applications Tier ...................................................................................... 57
15.8. Common Asset Management Services Tier.............................................. 57
15.9. Data/Knowledge Repository Tier ............................................................. 58
16. Summary and Conclusion ......................................................................................... 58
17. Acknowledgement .................................................................................................... 60
18. References................................................................................................................. 60
List of Figures
Figure 1: Asset Management Systems as Integrators of Data and Workflow Processes.. 26
Figure 2: Relationship between SDTS base standard, profiles, and software products ... 42
Figure 3: SDSFIE/FMFIS Data Model Schema Browser................................................. 46
Figure 4: Relationship of OGC Abstract Specification Topics ........................................ 48
Figure 5: Applying a Model-Based Approach to Municipal Asset Management ............ 54
Figure 6: The Component-Based Framework for Integrated Asset Management Systems
Geographic Information Systems (GIS) and Interoperability of Software for
Municipal Infrastructure Applications
Dana J. Vanier, Ph.D. National Research Council Canada
1. Overview of this Report
This report on geographic information systems (GIS) and software interoperability consists of
two parts: Part 1 positions GIS as an integrated decision support tool for municipal infrastructure
asset management and Part 2 investigates the interoperability potential and opportunities of GIS
for municipal infrastructure asset management. As such, this report presents a comprehensive
evaluation of the state-of-practice of GIS in municipalities, and more specifically overviews and
details recent research, development, and implementation of this technology. In addition, both
parts of this report make reference to over 50 papers in related fields.
1.1. GIS and Municipal Infrastructure
GIS helps store, manage, analyze, manipulate and display data that are linked spatially. In
essence, GIS relates database records and their associated attribute data to a physical location in
"real" world coordinates, thereby creating a "smart map". Visualization of discrete parts of these
data on a GIS map is possible by layering the data into different "themes". GIS applications can
then display the intersection of various "themes". Typically, GIS applications in use today in
Canadian municipalities primarily assist administrative functions; however, municipalities are
recognizing the benefits of spatially related data to manage their municipal infrastructure assets.
Part 1 of this report provides an overview of the state-of-practice for using GIS to manage
municipal infrastructure. It outlines geospatial tools and technologies that augment GIS data
collection such as global positioning systems, personal digital assistants, mobile computing,
automated vehicle location, road weather information systems, and remote sensing. It describes
associations and networks that assist GIS education, research and technology transfer in the field.
It provides examples of GIS implementations for managing municipal infrastructure from the
body of GIS research and practitioner literature. Shortcomings of using GIS for managing
municipal infrastructure include the high costs of data conversion, the lack of strong 3D
capabilities, no ability to store "time-dependent" data, and the lack of object-oriented
representation. The major opportunity for GIS for an organization is to create an "Enterprise
GIS" solution where data, information and knowledge can be shared and flow freely throughout
the enterprise and potentially to the general public.
1.2. Interoperability of Geographic Information Systems
Municipalities are facing unprecedented challenges due to the increasing number of aging
infrastructure assets combined with relative declines in maintenance budgets. Leveraging the use
of information technology, in general, and of GIS and asset management systems, in particular,
to improve the efficiency and effectiveness of asset management work processes is considered as
a crucial strategy to address these challenges. Lack of interoperability and inefficient data
exchange between municipal asset management software has been a major impediment to the
efficient access and communication of asset information. Efficient data sharing and software
interoperability play a crucial role in supporting efficient data access and retrieval, which in turn
is important to support efficient operations and cost-effective decision-making processes at all
levels of municipal asset management. Much inefficiency has been attributed to the use of
inconsistent data models and formats across different software applications.
Part 2 of this report suggests that developing and adopting standardized data models can aid in
solving this problem. It also suggests that the use of standard data models can significantly
improve the availability and consistency of the asset data across different software systems and
platforms, can serve to integrate data across various disciplines, and can facilitate the flow and
exchange of information between various parties involved, resulting in removing or eliminating
deficiencies of information access and exchange.
Implementing and deploying integrated GIS-based asset management systems in municipalities
can be regarded as a long-term goal that can be realized through a number of incremental steps.
First, municipalities need to be actively participating in the development and adoption of
standard data models. Second, municipalities need to ensure the reusability of design and
construction information delivered at the end of construction or maintenance projects, and to
integrate this information back into the asset database to reflect the updated status of assets.
Third, municipalities should start adopting software solutions that support existing data standards
and encourage software vendors to implement these standards. And finally, municipalities need
to invest in training their technical staff to keep pace with the innovations in geospatial
technologies, and learn how to maximize the benefit of these technologies to aid various asset
Since this report is a deliverable of the Municipal Infrastructure Investment Planning (MIIP)
project and the project is focusing on the data model of sewer systems, the author recommends
the municipalities investigate ESRI’s water utility’s data model as an implementation model. The
data model (ESRI 2004) is fairly detailed and provides a large set of objects and feature classes
typically required to model sewer systems.
Geographic Information Systems (GIS) as an Integrated Decision Support
Tool for Municipal Infrastructure Asset Management
Dana J. Vanier, Ph.D.
Senior Research Officer
National Research Council Canada – Construction
Part 1: Geographic Information Systems (GIS) as an Integrated Decision
Support Tool for Municipal Infrastructure Asset Management
The three-year Municipal Infrastructure Investment Planning (MIIP) project, which started in
June 2002, is investigating investment planning and strategic asset management of municipal
infrastructure (www.nrc.ca/irc/uir/miip). The project proposes using geographic information
systems (GIS) as the framework for decision support tools for municipal infrastructure managers,
and more specifically, as a tool to assist them to prioritize infrastructure maintenance and capital
renewal. Part 1 of this report presents a global “state-of-practice”, with emphasis on North
America, of GIS for managing municipal infrastructure. The next four sections focus on:
• background information on GIS;
• geospatial technologies impinging on GIS; complementing GIS, or supporting GIS;
• support network for GIS to demonstrate the existence of a useful and active community
of users, researchers and vendors, and
• review of GIS literature related to managing municipal infrastructure.
More importantly, this review identifies opportunities for research and development and
demonstrates advantages as well as shortcomings of using the technology. The integration of GIS
(i.e. interoperability) with other applications in an organization (i.e. financial management
systems, enterprise databases, work orders) is outside the scope of this part of the report.
2. Background Information
A geographic information system (sometimes called geographical information systems) or GIS
helps store, manage, analyze, manipulate and display data (i.e. sewer centreline location) that are
linked spatially. In essence, GIS relates database records and their associated attribute data (i.e.
water main type, diameter and age) to a physical location, thereby creating a "smart map".
Visualization of discrete parts of these data on a GIS map is possible by layering the data into
different "themes" (e.g. roads, buildings, forests, rivers). GIS applications can then display the
intersection of various "themes", as well as the spatial relationships between various features (i.e.
pipe condition and soil type). Typically, GIS applications in use today in Canadian
municipalities assist administrative functions such as storing cadastral (i.e. survey) information
or obtaining area demographic data. However, many municipalities are recognizing the benefits
of spatially related data to manage their municipal infrastructure assets. This is later
demonstrated with examples from a number of municipalities.
3. Geospatial Technologies
The following technologies warrant a brief description and discussion because they impinge on
the selection of the most appropriate GIS implementation.
3.1. Global Positioning System (GPS)
GPS is a satellite-based positioning system operated by the US Department of Defense (Chivers
2003). The location of the GPS unit is determined by performing triangulation calculations on
the location of reference satellites at the time of the reading, and the GPS unit while considering
the time lag for the signal to reach the unit. The GPS unit must have an unobstructed view of the
sky, a clear line of site to the satellites, and limited cloud cover. Multipath interference, caused
by signals bouncing off neighbouring objects, alters signal travel time and reduces the accuracy
of calculations (Chivers 2003).
Prior to 2000, GPS accuracy was limited to ± 100 metres because of random timings errors
introduced by the US military to limit GPS misuse by their adversaries (Chivers 2003). Today,
relatively inexpensive hand-held GPS units (US$200 in 2003) are accurate to ± 10 metres
depending on the number of reference satellites used (minimum four) and their location
Unfortunately, these orders of accuracy are of little use to the majority of municipal
infrastructure applications. Differential GPS or DGPS relies on at least four satellites and a real
time connection to a known local reference point; this dramatically increases accuracy to ± 1
metre (Oliver 1996). Depending on the type of reference points used, the amount of post
processing, the duration of observation and the location of satellites, DGPS accuracy can be ±2
mm horizontally and triple that vertically (Oliver 1996).
3.2. Personal Digital Assistant (PDA)
The popularity of PDAs or Pocket PCs, as they are sometimes called, as business/personal
notepads and aide-memoires has pushed this tool into specialized data collection. These
handheld units greatly reduce the amount of paper needed in the field and eliminate potential
transcription errors from paper to the corporate database. The PDAs can directly interface to
GPS (to establish location); can download GIS maps (to georeference the user's location) and can
collect distance and azimuth data from LASER range finders (to sight and geolocate
neighbouring assets). PDAs can have upwards of 128 megabytes of RAM, a colour screen (320
by 320 pixels with 65,000 colours), and a 500 MHz processor.
3.3. Mobile computing
The term "mobile computing" is used to distinguish PDAs, Pocket PCs and similar devices from
field rugged, full-blown computers. Pen-based computers and wearable computers with heads-up
displays and voice recognition software provide faster processing speed, larger memory size and
higher resolution graphics display than PDAs (Hartle 2000). An example of an application is the
use of wearable computers to collect data for the Pennsylvania Department of Transport's Bridge
Management System (Hartle 2000). Mobile computers are more robust that PDAs; can use the
same Windows software and databases as the host database, and eliminate the need for the
Windows CE or Palm OS operating system or "dumbed down" versions for data entry. The
disadvantages of pen-based computers or wearable computers are the high costs (roughly $5000
US per unit in 2003).
3.4. Automated Vehicle Location (AVL)
AVL devices are specialized data collectors/transmitters/receivers mounted on moveable assets
such as emergency vehicles, snowplows, dump trucks, and the like. AVL devices can contain
integral GPS and/or multiplexors that access multiple sensors on the vehicle and collect data and
store or transmit these data to the vehicle operator or a central location. In most instances these
units contain cell phone or satellite phone capabilities.
The types of sensors that can be used include simple ones, such as toggles for the cab door
(open/closed) or plow blade position (up/down) to more sophisticated sensors that capture the
vehicle speed, the snow pressure on the plow blade, or the rate of salt dispersion. Data received
from the AVL (and other sources) can be analysed centrally to generate updated instructions to
transmit to the vehicle or operator. For example, road temperature data and meteorological
forecasts can be used to instruct the salt spreader to reduce the flow rate on a specific stretch of
3.5. Road Weather Information Systems (RWIS)
RWIS is the term used to describe an integrated system of weather sensors to record road surface
and environmental conditions at specific locations in a road network. RWIS can provide
invaluable information to GIS systems and associated municipal databases
(www.buckeyetraffic.org, www.nevadadot.com/traveler/rwis). For example, data that are
currently collected include wind speed and direction, humidity, air temperature, road surface
temperature, sub surface temperature, visibility, solar radiation, road surface status (dry, wet, icy)
and type and amount of precipitation (C-SHRP 2000).
3.6. Remote Sensing
Remote sensing is described as imaging without touching, and within the scope of this report
refers to the use of aircraft or satellites to collect geographic information. "A satellite image is
often the most practical way to acquire usable geographic information" (Huff and Johnson 2003).
Although this type of imagery will not help the identification of buried utilities, it is a good way
to identify roads and buildings and can assist in geolocating manholes, hydrants and catch basins
by providing reasonably accurate spatial data. Commercial services supply high resolution,
ortho-rectified images with a pixel resolution of 2.5 metres ($5000US in 2003) that are suitable
for superimposition on, and validation of, existing GIS maps (www.spot.com).
3.7. Summary of Geospatial Technologies
Geographic data collection and validation is expensive (this is described later in Part 1 of the
report) and the related technologies described above greatly assist these operations. For example,
inexpensive PDAs ($300US per unit in 2003) can be programmed so that trained operators can
enter asset data (e.g. signs, manholes, catch basins, trees) and attribute data (e.g. types, size, age,
condition, remaining service life) and can download and synchronize these data to the
enterprise's main databases (Stasik 2003). PDAs, pocket PCs, pen-based computers or wearable
computers can communicate between GPS and the central GIS databases and transmit spatial
coordinates alongside conventional attribute data. Remote sensing can be used to develop
preliminary asset maps and to validate existing engineering maps.
Although AVL technology appears to be peripherally related to GIS, the data collected by these
vehicles can augment the "Enterprise GIS" by identifying roads that have been swept or plowed;
by recording the surface temperature of a road at a given time, or by calculating its pavement
condition index. RWIS can supplement GIS data by recording the road subgrade temperatures
and environmental conditions that can be used for service life calculations.
4. Associations and Networks
GIS associations and networks exist to meet the needs of international, national or regional GIS
communities. This list of associations is not intended to be exhaustive.
The user communities of the two major vendors in the engineering field represent tens of
thousands of installations, and their conferences, meeting, and networks provide for the rapid
exchange of best practices and research experience. In addition, there are dozens of regional
"GIS Users Groups" in the USA and Canada that address the needs of the users in specific cities,
provinces, states, or national regions. The location, objectives, bylaws and activities of these can
be found on the Internet.
CIB W106 deals with "Geographical Information Systems" and was formed in 2000 to address
the needs of researchers in building and construction (W106 2000). The areas of interest to the
group include: (Task Group 1) GIS standards, (TG2) analysis and modelling of flow and
distribution of materials, (TG3) spatial dynamic modeling of the environment, and (TG4)
education and information sources. This CIB working commission activity is championed by the
Norwegian Building Institute and the Commonwealth Scientific and Industrial Research
Organization (Australia). A number of national “state-of-the-art” reports related to GIS have
already been published including contributions from Norway (cadastral data), France (waste
management), Italy (historical assets), Canada (GIS survey) and Japan (earthquake data) to name
a few (CIB W106 2003).
The Geospatial Information and Technology Association (GITA) provides education and training
to meet the needs of those individuals interested in geospatial information and technology
(www.gita.org). GITA has published a number of books and videos related to professional
applications (www.gita.org/book_store) including the results of recent surveys based on GIS
usage in domains related to municipal infrastructure: electric, gas, pipeline, telecom, water, and
public sector (www.gita.org/geo_report).
The Urban and Regional Information Systems Association (URISA) is a non-profit association
of GIS professionals that promotes the effective and ethical use of geospatial information and
information technologies. URISA also hosts conferences (www.urisa.org/annual.htm) and
publishes monograms (www.urisa.org/store.htm), and supports a peer-reviewed journal
5. GIS Literature related to managing Municipal Infrastructure
One of the first references to GIS in civil engineering is from the early 1950's relating to
developing quantitative methods in transportation studies (Miles 1999). GIS has also been a topic
of civil engineering research for approximately 50 years (Brodie 1984) through specialty civil
engineering conferences and papers, many are discussed in Part 1 of this report (Lior et al. 1991;
Wei et al. 1997; Zhao and Elbadrawi 1997; IWA 1999, DMinUCE 1998, 2000).
This section gives an overview of GIS technical literature related to civil engineering or
municipal infrastructure. The section investigates five main categories: surveys on GIS usage,
data conversion issues, domain-specific applications, advanced implementations, and systems
5.1. Surveys on GIS Usage
A comprehensive survey on GIS usage in civil engineering in Great Britain identifies that 80%
of local municipalities have purchased GIS and an additional 18% intend on purchasing
(McMahon 1997). It also mentions that 83% of public sector consultants (36% of private sector
consultants) in the field have already purchased GIS. The article also identifies that one software
product dominated the field at that time. The survey finds that GIS is used on projects related to
roads, transportation, environmental, drainage, pipelines and water supply. GIS’s actual use in
these projects relates to activities such as decision support, spatial analysis, information query, or
Better Roads (2000) published a recent survey on GIS usage by US counties (149 participants).
The survey identifies that 23% of those polled use GIS, whereas the remaining see the
advantages and would implement GIS if funding were available. Two software applications from
one vendor have a 57% market share whereas a half dozen other vendors make up the remaining
percentage. The majority (82%) uses the Windows O/S (www.microsoft.com). A total of 35% of
respondents indicate that they have seen costs savings, but one manager comments "We strive
more for accuracy of information and better decision making over cost savings". The article goes
on to quote another GIS manager: "… be prepared to continually fund the effort and embrace the
technology. Be prepared to let GIS change the way you think and conduct business".
A recent (and annual) survey conducted by GITA (2002) also confirms that one software vendor
is predominant in related engineering sectors: more specifically, 62% of the 55
water/waster/storm sector enterprises use software from one firm. The report also confirms that
Windows (81%) is the prominent operating system in this sector and most other engineering
sectors (electric, gas, pipeline and telecon). The survey also identifies the water/waste/storm
sector as the fastest growing sector in the survey for the second year.
The current eight members of the MIIP consortium recently completed a 20 question survey to
determine the extent of their GIS usage. The results of this survey indicate that:
• the majority use software from one specific firm (75%);
• half also use other software from other vendors;
• Windows O/S is predominant (100%);
• few of the respondents have data standards (38%);
• all respondents have completed at least 60% of their GIS data collection and conversion
(for both spatial and attribute data);
• all respondents have spent over $200,000 for GIS implementation, with three
municipalities spending $2 million;
• all respondents view GIS as a public works tool and the majority see that GIS is also an
administrative and data visualization tool;
• one half have integrated their asset management tools to GIS, and
• one half has data sharing agreements with their regional partners.
In order to determine how the municipalities in the consortium currently use GIS and how they
envisage using GIS, they selected responses from a list of 20 activities. The majority reported
they are currently using GIS to support activities in both categories, such as data storage,
mapping, data exchange, decision support, engineering analysis, and planning. Activities that are
also envisioned as desirable include work management, client information, document
management, and executive information. Few respondents see the need for using GIS for
maintenance monitoring, trouble calls, records management, or failure model analysis. Although
the results represent a small sample group, the consortium members are a cross-section of large
Canadian municipalities (+100,000 population).
5.2. Data Conversion Issues
Data conversion and data collection have been identified as major obstacles to the successful
implementation of GIS. Scanning drawings has been defined as tedious and time-consuming;
however, newer technologies such as drawing vectorization are improving the situation (Udo-
Sometimes it is the volume of existing data to be converted that is intimidating. A large
European water company in Belgium maintains over 300 A0 maps at various scales and 94,000
A4 maps on paper and Mylar. These maps were centrally located at company headquarters and
were difficult to access. The maps were scanned and stored centrally and georeferenced into an
overall framework. As a result, one dozen staff members can readily access the data (Reynaert
and Horemans 2002)
One problem encountered by a US water company is data alignment. This company serves
125,000 people and manages 1100 kilometres of pipe, 22 pumping stations, and 24 storage tanks.
When the digital tax parcel maps were overlaid with the newly scanned infrastructure drawings
they did not align (DeGironimo and Schoenberg 2002). Additionally, unanticipated work was
required to collect the correct data and to georeference these data to the existing GIS maps.
Retraining and re-education of staff is also an issue: in one case city staff were asked to abandon
an existing computer-aided design (CAD) tool and move to a GIS environment. Although staff
had initially resisted the change, they overwhelmingly accepted GIS as their tool, owing to the
integration opportunities provided (Lopez 2002).
The literature provides some insight as to the cost of data collection and conversion. Data
collection for a small town of 20,000 people and 11 square kilometres, took roughly 600 hours to
digitize paper maps, to do engineering take-offs, and to provide historical age and material
identification for water, sewer, and drainage services (Crafts 2002).
A major British water company manages 2 million water distribution features on more than
14,500 maps. The land base of 20,000 square kilometres contains one million water mains, 3.2
million property records, 50,000 kilometres of water mains and 50,000 kilometres of sewers. A
multi-year, multi-million dollar project was required to digitize the existing records. The GIS
system now functions as a technology platform for the enterprise, completely replacing the
largely paper-based systems (Coolidge 2002).
Even after scanning maps and entering attribute data, some enterprises find they cannot do large-
scale analysis of infrastructure systems (Fenner et al. 2000). Typically, the only data available
for sewer installations, for example, include the pipe material, sewer type, pipe size and depth,
and event history (e.g. breaks, leaks). However, Fenner et al. found that few enterprises
systematically save pipe age, soil type, pipe loading or cost of pipe repairs. These researchers
also state that few enterprises link their problem events properly to the asset location: more often
than not, linking the event to a property lot number and not the sewer lateral.
5.3. Domain-Specific Applications
The number of papers published in the research literature about GIS assisting in managing
municipal infrastructure is limited. Some relate to stormwater systems (Lior et al. 1991), sewer
systems (Przybla and Kiesler 1991; Halfawy et al. 2000), cost estimating and risk assessment
(Ashur et al. 1998), and water and sewer systems (Moutal and Bowen 1991). Other examples of
GIS applications in civil engineering relate to transportation noise, wetlands analysis, storm
water discharge, soil erosion, residential and business dislocation, and highway corridor analysis
(Amekudzi and Baffour 2002).
Recent work at the City of Saskatoon outlines opportunities for GIS to support economic
decision-making for sewer main rehabilitation projects (Clancy et al. 2002). The project selection
process considers the maintenance and cleaning records alongside sewer characteristics. The
process uses these characteristics along with the GIS spatial data to determine priorities for the
closed circuit TV (CCTV) scanning. Clancy et al. state that GIS helps to determine the sewer
CCTV priorities that, in turn, can improve on the economics of rehabilitation decisions. Other
work by the same team of authors positions GIS as a tool to assist the holistic management of the
municipal right-of-way (Higgins et al. 2002). In this research, GIS is proposed as a framework to
identify the spatial relationships between the assets in the right-of-way and to assist in producing
targeted work plans.
Recent research investigates the use of GIS to assist the study of pipe deterioration (Doyle and
Grabinsky 2003). These researchers found that many cities are now using their GIS systems to
monitor water main breaks and to identify areas at risk. Doyle and Grabinsky also found that the
existing municipal GIS data about soil type is not sufficiently detailed to identify high-risk areas
accurately. However, they do indicate, "GIS is a powerful tool for identifying the areas of the
city where future investigations should be focused". An added benefit to georeferencing water
main breaks in GIS is the potential elimination of duplicate occurrences of paper-based records,
and pin charts, for example.
5.4. Advanced Implementations
In this part of the report, an advanced implementation implies that other technologies such as
expert systems, artificial intelligence, or 3D visualization are used in conjunction with GIS.
There are limited research examples for managing municipal infrastructure.
Not all reports present favourable results from integrating GIS to other applications. Wei and
Gurdev (1997) identify the need for large relational databases and visualization techniques to do
rational, effective, and efficient bridge management. However, Feeney (1997) indicates that
2/3D spatial information in conjunction with a standard bridge inspection significantly augments
information display and exchange by combining photographs, CAD and video with location-
GIS has been used to improve the management of the architectural design, engineering and
construction building project process (Sun and Hasell 2002): a similar process that could apply to
the construction of water/sewage treatment facilities or utility networks. However, problems still
exist as to how GIS saves and displays 3D data. GIS "supplies a framework in which to model
spatially […] engineering phenomena" (Miles 1999); however, it is also poor at representing
alternatives, displaying hypothetical states, representing uncertainty of boundaries, or
representing interacting objects. In fact, GIS abstracts the real world to only a 2D environment
and 3D has not found its way to mainstream GIS applications or research (Miles 1999).
Similarly, attempts have been made to use GIS to model buildings, but experience has shown
that the 3D capabilities are not currently mature enough to model buildings or other types of
three-dimensional facilities from GIS data (Cote 2002).
Fenner et al. (2000) describes a GIS-based maintenance prioritization model to correlate problem
events to specific squares on a grid system superimposed on a municipal region. The author
states that the lack of digital data (i.e. pipe age, soil type, pipe loadings, repair costs) hampers
such implementations and restricts their utility.
The combination of GIS and expert systems has been used in a case study to plan snow removal
and bridge inspection (Salim et al. 2002). These opportunities indicate that spatial data in
combination with well-designed expert systems can increase staff efficiencies and improve
productivity at both the design and operations levels.
Other recent examples of GIS implementations in the energy sector demonstrate that well-
implemented "Enterprise GIS" increases business efficiency, reduce administrative overhead,
and help the corporate bottom line (Harder 2002). Examples include:
• a GIS-based "expert system" to locate suspect faulty transformers after lightning strikes,
based on spatial data of customers phoning about outages;
• a GIS system to manage leases on telephone poles (e.g. cable, pager, cell) and notify
lessors of problems, billings, and similar issues, and
• an application to spatially manage rights-of-way for transcontinental gas lines.
5.5. Systems Integration
A study of GIS integration to computerized maintenance management systems (CMMS)
identifies many intangible business benefits such as: providing maps of the utility with the work
orders; tracing water mains in the office prior to field work; reducing travel time of work crews,
and scheduling multiple repairs in one area (McKibben and Davis 2002).
"Combining GIS functionalities with a network solving package, a complete powerful tool is
created for giving information on the behaviour of the system under each water demand or
expansion scenario" (Tsakiris 1993). This type of implementation illustrates GIS potential when
integrated with other applications. Other examples of integrated GIS approaches in municipal
asset management include:
• managing irrigation usage in water zones under drought conditions (Cronin 2003);
• predicting ice build-up on roads for winter maintenance (Chapman and Thornes 2003);
• benchmarking "neighbourhood vitality" using key performance indicators such as road
condition (Langely 2003);
• modeling water main shutdown strategies prior to terminating services (Coate 2003), and
• coordinating street repair projects (Perry 2003).
Implementations of "Enterprise GIS" enable large enterprises to move from "pockets of GIS" to
centralized GIS and then to benefit from a common framework (Durham Region 2003).
Although not originally directed at public works or utilities, a coordinated enterprise effort can
provide GIS services throughout and can provide users with live access to data; in this case over
1,500 users have access to 60 thematic layers of data on the web-based service. Durham is
planning to extend this GIS service to public works applications and to the public.
5.6. Summary of GIS Literature
The body of literature from trade magazines and scientific journals indicates that there is limited
GIS research taking place at universities, centres of excellence and government labs in North
America. The review of research papers also shows that GIS is currently being used as a
visualization tool and not as an integral component of the engineering solution. The papers
discussed in this section however do illustrate the potential of GIS by providing specific
implementation examples. The papers also indicate where the technology is inadequate:
• a robust 3D capability is required that meets engineering requirements (Miles 1999);
• a time dimension is necessary to save versions of data during a project’s conceptual
phases, along the time line of a construction project, or during the service life of an asset.
GIS does not address temporal variations (Fang and Elbadrawi 1997), and
• object orientation modeling is required to represent the complex hierarchical structures
and attribute inheritance that is typical of municipal infrastructure assets (Miles 1999).
GIS is not a recent innovation; it has a long history of usage in civil engineering applications, but
there has been relatively little research. Primarily, the technical literature focuses on the
practitioner and describes site-specific implementations of GIS in municipalities. The review of
the GIS surveys in Part 1 of this report indicates that a large percentage of municipalities are
using GIS. This last statement is confirmed by a recent survey of 35 Canadian and USA
municipalities (Doyle and Grabinsky 2003).
A GIS shortcoming, identified by Miles (1999), is that there is no common forum in civil
engineering to discuss issues, disseminate ideas or review criticisms about GIS and its
applications. The literature on GIS and municipal infrastructure identified in this report is
scattered throughout a wide selection of journals, conferences, and association literature. As
researchers recognize the utility of GIS for managing municipal infrastructure, more papers and
discussions will appear in the scientific press.
Data conversion, collection and validation are expensive ventures. Perhaps the efficient usage of
the geospatial technologies described will help reduce the costs. The equipment required to
collect spatial data is rapidly reducing in price and a number of agencies are now using low-
priced labour such as students, untrained labour and even the general public to collect data about
asset location, asset condition, and asset defects. For example, providing GIS input at a "trouble
call" centre can help: record time of call, name of complainant, and type of problem; spatially
locate the problem (a pot hole or blocked drain), eliminate multiple copies of the same problem
from different sources; spatially coordinate multiple repairs in the area, and notify the
complainant when the complaint is resolved.
As mentioned earlier, the number of papers about GIS and civil engineering (in general) is
surprisingly small, compared to the volumes of research in CAD or IT in construction. However,
the examples cited in this report illustrate the many capabilities as well as the shortcomings of
GIS for managing municipal infrastructure. Miles (1999) summarized some of the latter: no
single package is sufficient; often in-house solutions are required; many existing tools are
inefficient and inflexible, and "engineering models are concerned with state, process and
relationships, whereas position-based GIS simply considers location". Three specific areas are
significantly devoid of solid research input on the needs of the municipal infrastructure
community, such as: (1) 3D capability that meets municipal engineering requirements; (2) a time
dimension to save versions of data in GIS, and (3) object orientation representation of municipal
infrastructure assets (complete with object classes, object hierarchies, and object inheritance).
7. Conclusions and Recommendations
Part 1 of this report provides an overview of the use of GIS to manage municipal infrastructure.
It is hoped that the knowledge, information and data provided in this report will assist others to
implement and maintain a GIS environment for managing, in part, municipal infrastructure.
AASHTO and FHWA have identified GIS and GPS as emerging technologies that "hold promise
for asset management" (AASHTO/FHWA 1997). Many large Canadian municipalities are
currently using GIS in closely related areas and are attempting to integrate this tool into
enterprise solutions to manage municipal infrastructure. "The organizations that have a well-
developed and deployed GIS appear to be benefiting from its use … Few enterprises completely
rely on a GIS-based system to store, maintain, and retrieve records, but the trend is heading in
that direction (Doyle and Grabinsky 2003).
Because of the high cost of data conversion, collection and validation for GIS, it is recommended
that the different types of geospatial technologies be investigated prior to the purchase of a GIS
system. A municipality's decision to implement any of the aforementioned geospatial
technologies can influence the choice of GIS tools, the GIS implementation strategy, or long-
term usefulness of GIS in any enterprise. The obverse also can be true. Interoperability with
other enterprise applications is essential, as it will increase the value of the GIS implementation.
It is advised that any group (municipality) venturing into the GIS field become familiar with
some of the associations identified, even if they are vendor-specific (provided the municipality
plans to use that product). It is recommended that a group such as CIB W106 be a mechanism to
encourage the discussion of research results in this field. Researchers should familiarize
themselves with this organization and participate in their meetings and workshops, as there are
few research exchange networks in the domain.
Geographic information systems can support decision-making in the management of municipal
infrastructure; however, GIS requirements cannot drive the research to make this possible. The
components that are lacking in current GIS applications and implementations, and cannot be
provided by the geographic or mapping community, are the engineering definitions and
engineering descriptions of the typical "features" on a GIS map (i.e. asphalt pavement,
stormwater ditch, bridge abutment and piers, transmission line). That is, these “features” on GIS
maps are not just road centrelines, water channels, bridges or overhead lines that are spatially
located on a map, but these are physical infrastructure assets consisting of complex data and
relationships such as hierarchical networks, object class structures, data attributes, 3D
components, and time-deterioration functions. These engineering attributes and relationships are
not currently accurately described on GIS systems. It is the responsibility of the civil
infrastructure community to research these notions, to identify potential solutions, and to develop
and supply the requisite structures to augment the geography component of GIS.
The core of Part 1 of the report was present at the CIB Triennial Congress in Toronto (Vanier
2004). The author would like to thank the MIIP consortium for support to this research.
Specifically, he would like to thank the Cities of Calgary, Edmonton, Hamilton, Ottawa, and
Prince George, the Regional Municipalities of Durham, Halton and Niagara, the Department of
National Defence, and the National Research Council Canada.
AASHTO/FHWA (1997) 21st Century asset management, American Association of State
Highway and Transportation Officials, and the Federal Highway Administration, Prepared
by Center for Infrastructure and Transportation Studies at Rensselaer Polytechnic Institute,
Albany, NY, 21p.
Amekudzi, A. and R. Baffour (2002) Remote sensing, image processing and geographic
information systems (GIS) techniques for transportation infrastructure and environmental
capital asset management, Applications of Advanced Technology in Transportation:
Proceedings of the Seventh International Conference on Applications of Advanced
Technology in Transportation, 5-7Aug, Boston, MA, pp. 362-368.
Ashur, S., M. H. Baaj, B. Crokett and K. Abaza (1998) GIS as a support tool for effective
decision-making in engineering management: two case studies from Arizona, First
International Conference on New Information Technologies for Decision Making in Civil
Engineering, Vol. 1, pp. 613-624, 11-13 Oct, Ecole de Technologie Superieure, Montreal,
Better Roads (2000) Are county agencies using GIS? Better Roads Magazine, Apr. Park Ridge,
IL, pp. 24-27, www.betterroads.com/articles/brapr00b.htm (29 Sep, 2003).
Brodie, W. G. (1984) CAD, CAM and GIS: Tools for facilities management and planning,
Advances in CAD/CAM: Case Studies, 5th Automation Technology Conference, Monterey
CA, pp. 59-69.
Chapman, L. and J.H. Thornes (2003) Geomatics inspire Winter Maintenance Revolution, APWA
Reporter, American Public Works Association, Kansas City, MO, Sep, pp. 22-23.
Chivers, M. (2003) Differential GPS explained, ArcUser, Jan-Mar, 2003, pp. 40-41.
CIB W106 (2003) Working Commission W106, Geographical Information Systems, International
Council for Research and Innovation in Building and Construction, <www.cibworld.nl> (29
Clancy, D., J.M. Gustafson and L. Higgins (2002) Economical sewer main rehabilitation
utilizing ArcGIS and dynamic segmentation, 22nd Annual ESRI International User
Conference, 8-12 Jul, San Diego, CA.<gis.esri.com/library/userconf/proc02/
pap1181/p1181.htm> (25 Sep, 2003).
Coate, J. (2003) Water main shutdown application prevents costly mistakes, ArcUser, Jan-Mar,
ESRI, Redlands, CA, pp. 20-21.
Coolidge, T. (2002) United Kingdom company vastly improves information retrieval and
management: Severn Trent water moves 14,500 paper maps to GIS, ArcNews, Fall 2002,
ESRI, <www.esri.com/news/arcnews/fall02articles/ tofc-fall02.html> (25 Sep, 2003).
Cote P.B. (2002) Real infrastructure for virtual cities: lessons learned modeling urban
environments at the Harvard Design School, 22nd Annual ESRI International User
Conference, 8-12 Jul, San Diego, CA.<gis.esri.com/library/userconf/proc02/
pap1325/p325.htm> (25 Sep, 2003).
Crafts, M. (2002) Methodology for Achieving GASB 34 Modified Approach Compliance Using
US Navy Smart Base Facility Management Practices, 114p, Masters Thesis, Massachusetts
Institute of Technology, Cambridge, MA.
Cronin, S.T. (2003) Using GIS to deal with drought, ArcUser, Apr-Jun, Redlands, CA, pp. 22-
C-SHPR (2000) Anti-Icing and RWIS Technology in Canada, Canadian Strategic Highway
Research Program (C-SHPR), Technical Brief # 20, Ottawa, Canada, 8p.
DeGironimo, E. and J. Schoenberg (2002) The Key to the Vault, Water Writes, Fall General
Edition, pp. 1-3, <www.esri.com/library/newsletters/waterwrites/waterfall02.pdf> (25 Sep,
DMinUCE (2000) Second International Conference on Decision Making in Urban and Civil
Engineering, 2 Vols., 20-22 Nov, Institut National des Sciences Appliquees de Lyon,
DMinUCE (1998) First International Conference on New Information Technologies for Decision
Making in Civil Engineering, 2 Vols., 11-13 Oct, Ecole de Technologie Superieure,
Doyle, G. and M. Grabinsky (2003) Applying GIS to a water main corrosion study, Journal
American Water Works Association, Vol. 95, No. 5 (May), pp. 90-104.
Durham Region (2003) Durham Region's successful enterprise GIS, ArcNorth News, Vol. 6. No.
2, ESRI Canada, Toronto, ON, pp. 6-7, 10.
Feeney, M.A. (1997) GIS for use in structural inspection, Computing in Civil Engineering:
Proceedings of the Fourth Congress held in Conjunction with A/E/C Systems '97,
Philadelphia, PA, pp. 559-565
Fenner R.A., L. Sweeting and M.J. Marriott (2000) A new approach for directing proactive
sewer maintenance, Journal - Proceedings of the Institution of Civil Engineers: Water,
Maritime and Energy, Vol. 142, No. 2, pp. 67-77.
GITA (2002) Geospatial Technology Report 2002: A Survey of Organizations Implementing
Geospatial Information Technologies, Geospatial Information and Technology Association,
Halfawy, M.R., D. Pyzoha, R. Young, M. Abdel-Latif, R. Miller, L. Windham and R. Wiegand
(2000) GIS-based sanitary sewer evaluation survey, 20th Annual ESRI International User
Conference, Jun., San Diego, CA, <gis.esri.com/library/userconf/
proc00/professional/papers/PAP158/p158.htm> (25 Sep, 2003).
Harder, C. (2002) Enterprise GIS for Energy Companies, ESRI, Redlands, CA, 107p.
Hartle, R.A. (2000) Zip through it easily, Roads and Bridges, Aug, pp. 46-48.
Higgins, L., D. Clancy and J.M. Gustafson (2002) Integrated infrastructure management
decision-making using ArcGIS, 22nd Annual ESRI International User Conference, 8-12 Jul,
San Diego, CA.<gis.esri.com/library/userconf/proc02/pap1180/p1180.htm> (25 Sep, 2003).
Huff, J. and R. Johnson (2003) Geomatics and satellite imagery in managing your infrastructure,
APWA Reporter, American Public Works Association, Kansas City, MO, Jun, pp. 12-13.
IWA (1999) Applying GIS Technologies in Water Utilities: A Challenge and a Necessity, Water
Supply: Review Journal of the International Water Association, Vol. 18, No. 4, Turin, Italy,
Langely, W. (2003) Neighborhood benchmarking - a GIS approach, APWA Reporter, Jun,
American Public Works Association, Kansas City, MO, pp. 16-17.
Lior, S.K., C.L. Chambers and J.F. Spollen (1991) Norfolk uses a GIS to satisfy its stormwater
management information needs, Proceedings: Conference on Civil Engineering
Applications of Remote Sensing and Geographic Information Systems, May, Washington,
DC, pp. 97-104.
Lopez, M. (2002) City of Anaheim: from CAD to GIS, Water Writes, Fall California Edition, pp.
1, 6-7, <www.esri.com/library/newsletters/waterwrites/water-ca-fall02.pdf> (25 Sep, 2003).
McKibben, J. and D. Davis (2002) Integrating GIS, computerized maintenance management
systems (CMMS) and asset management, 22nd Annual ESRI International User Conference,
8-12 Jul, San Diego, CA, <gis.esri.com/library/
userconf/proc02/pap0554/p0554.htm> (25 Sep, 2003).
McMahon, P. (1997) Geographic Information Systems in the Construction Industry - an
Underused Resource, Construction Papers, No. 82, The Chartered Institute of Building,
Ascot, UK, 8p.
Miles, S.B. (1999) Applications and issues of GIS as tool for civil engineering Modeling,
Journal of Computing in Civil Engineering, ASCE, Jul, pp. 144-162.
Moutal, H.P. and D.R. Bowen (1991) Updating New York City's sewer and water main
distribution systems: practical application of GIS, Proceedings of the Conference on Civil
Engineering Applications of Remote Sensing and Geographic Information Systems, May,
Washington, DC, pp. 155-164.
Oliver, M. (1996) GPS - a civil engineers guide, Proceedings of the Institution of Civil
Engineers-Civil Engineering, Vol. 114, No. 2 (May), pp. 100-101.
Perry, M. (2003) Coordinating street projects saves county money, ArcUser, Jan-Mar, ESRI,
Redlands, CA, pp. 16.
Przybla, J. and C.L. Kiesler (1991) Extending GIS capability for enhanced sewer systems
modelling, Proceedings of the Conference on Civil Engineering Applications of Remote
Sensing and Geographic Information Systems, May, Washington, DC, pp. 105-114.
Reynaert, B. and R. Horemans (2002) Successfully maintaining the water network, Water
Writes, Fall, pp. 8-9, <www.esri.com/library/newsletters/waterwrites/
waterfall02.pdf> (25 Sep, 2003).
Salim M.D., T. Strauss and M. Emch (2002) A GIS-integrated intelligent system for optimization
of asset management for maintenance of roads and bridges, International Conference on
Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 17-20
Jun, Cairns, pp. 628-637.
Stasik, M. (2003) ArcPad extension: enhances field productivity, ArcUser, Apr-Jun, ESRI, pp. 8-9.
Sun, W. and M.J. Hasell (2002) Exploring a GIS prototype to improve the management of the
architectural design, engineering and construction building project process, 22nd Annual
ESRI International User Conference, 8-12 Jul, San Diego, CA, 6p.
<gis.esri.com/library/userconf/proc02/pap0212/p0212.htm> (25 Sep, 2003).
Tsakiris, G. (1993) GIS technology for management of water distribution networks, Water
Supply Systems: State of the Art and Future Trends, Computational Mechanics Publications,
WIT Press, Southampton, UK, pp. 361-378.
Udo-Inyang, P.D. (1997) HICIMS: an Integrated GIS and DBMS application, Computing in
Civil Engineering: Proceedings of the Fourth Congress held in Conjunction with A/E/C
Systems '97, Philadelphia, PA, pp. 240-247.
W106 (2000) GIS and the Built Environment, CIB Publication 256, International Council for
Research and Innovation in Building and Construction, Rotterdam, 54p.
Wei, X. and G. Singh (1997) GIS model for concrete bridge maintenance database,
Infrastructure Condition Assessment: Art, Science, and Practice, Proceedings of the
Conference sponsored by the Facilities Management Committee of the Urban
Transportation Division of the American Society of Civil Engineers, 25-27Aug, Boston,
Massachusetts, pp. 267-276.
Vanier, D.J. (2004) Towards geographic information systems (GIS) as an integrated decision
support tool for municipal infrastructure asset management, CIB 2004 Triennial Congress
(Toronto, Ontario, 5/2/2004), pp. 1-11, May, (irc.nrc-cnrc.gc.ca/fulltext/nrcc46754> (May
Zhao, F. and H. Elbadrawi (1997) Time dimension in GIS, Computing in Civil Engineering:
Proceedings of the Fourth Congress held in Conjunction with A/E/C Systems '97,
Philadelphia, PA, pp. 57-64.
The Interoperability of Geographic Information Systems for Municipal Asset
Mahmoud M.R. Halfawy, Ph.D.
Post-Doctoral Research Fellow
Department of Civil Eng., University of British Columbia, Canada
Prepared Under a Collaborative Research Agreement Between
The National Research Council (NRC) and the University of British Columbia (UBC)
As Part of the Municipal Infrastructure Investment (MIIP) Project
Use of specific vendor or product name is for illustrative purposes only and is not an
endorsement or a criticism by the author. Recommendations and opinions expressed in this
document are those of the author and should not be considered as official positions of NRC,
UBC, or any other collaborating body or agency.
Part 2: The Interoperability of Geographic Information Systems for
Municipal Asset Management Applications
Municipalities are responsible for the planning, construction, operation and maintenance of an
inventory of land, properties, facilities, and infrastructure assets. Municipalities also provide a
wide range of public services related to these assets, such as inspection and permit issuing.
Municipal infrastructure assets normally include water distribution systems, sanitary and storm
water sewers, waste management, roads, bridges, and public facilities. Municipalities may also
be responsible for oversight and coordination with utility companies to manage assets such as
electric, gas, and telephone utilities. To meet these responsibilities, municipalities need to build
and maintain an extensive database of these assets. Moreover, municipal asset management
typically involves a wide range of technical, managerial, financial, environmental, social, and
political issues. Successful asset management strategies require addressing many of these issues
in an integrated manner.
The increasing number of aging municipal infrastructure assets combined with declining
maintenance budgets present unprecedented challenges to municipalities and public work
agencies. The gap between available funds and funds required to sustain acceptable performance
levels and the need to meet an ever increasing demand on municipal assets have prompted many
municipalities to adopt innovative techniques and methodologies to overcome this dilemma.
Moreover, the increasing sophistication and complexity of municipal assets has made the
decision-making process increasingly knowledge-intensive such that efficient access to a wide
range of accurate, detailed, and up-to-date asset information is mandatory. Efficient management
of asset information plays a key role in supporting efficient operations and cost-effective
decision-making at all levels of municipal asset management: operational, tactical, and strategic.
Municipal asset management is a multi-disciplinary area that involves many activities including
inspection and data collection, condition assessment, performance evaluation, prediction of
future performance, planning and prioritizing maintenance and repair operations, and evaluating
alternative technical and economic policies. The execution of these activities typically requires
the use of knowledge across several domains such as structural and construction engineering,
analysis and simulation, planning and operations management, economic analysis, and decision-
support models. Devising efficient and successful asset management approaches requires the
integration and application of a diverse body of knowledge that spans several of these domains.
Information technology (IT) has become an integral part of many of the asset management areas.
Various types of software tools are becoming an indispensable component of most asset
management processes. Generally, the success of any technological solution primarily depends
on the ability to support and integrate the various aspects of the asset management processes and
their associated information. Adopting an integrated approach to municipal asset management is
necessary to address the wide spectrum of requirements and constraints of these processes
(Lemer 1998, Halfawy et al. 2002).
Many initiatives and research projects have been launched with the aim of developing and
implementing new technologies that can assist in meeting the challenges of managing asset
information. There is an urgent and pressing need to improve the efficiency and cost-
effectiveness of managing municipal assets through improving the management of asset
information. In particular, there is an obvious need to adopt and adapt new information
technologies to improve operational efficiency and data management, as well as to support
decision-making and the efficient sharing of asset information.
There is no doubt that the use of asset management software in municipalities has drastically
improved the operational efficiency and maintainability of municipal assets. In recent years,
there has been an increasing realization of the useful role that Geographic Information Systems
(GIS) can play to support management of municipal assets (Halfawy et al. 2000, Vanier 2004).
As a result, there has been an increasing trend to augment existing asset management systems
with GIS functionality. Several asset management systems have already implemented GIS
functionality internally or supported links with other GIS systems.
The GIS technology has proved to be an ideal tool to enable more intuitive and efficient methods
to manage, query, explore, visualize, and analyze the municipal asset data in a spatial context.
The technology also improves the data access and management and integrates municipal spatial
data with non-spatial (or non-graphic) data. The GIS spatial analysis and visualization
capabilities potentially provide better support in various planning and decision-making
With recent advances in GIS and geospatial technologies in general, combined with the
availability of low cost and easy to use asset management and GIS software tools, many
municipalities across Canada and the world have started serious efforts to implement and deploy
these technologies, and to make them an integral part of municipal asset management processes.
However, with an increasing number of available GIS and asset management software systems, a
major challenge that needs to be addressed is how to integrate these systems in a way that can
support seamless interoperation and efficient sharing and exchange of asset spatial and non-
spatial data between different, possibly distributed and heterogeneous, software systems. To
address this challenge, there have been many efforts to standardize spatial data models so that
different systems can meaningfully exchange data in a manner consistent with the intended
semantics of the data (i.e. objects, relationships, attributes). Implementing interoperable GIS-
based asset management systems will require an industry wide adoption of these spatial data
This report addresses several issues to leverage the role and functionality of GIS technology in
enhancing the asset management processes and the efficiency of managing asset information.
The report highlights the data requirements of GIS-based asset management systems and
discusses some of the challenges of collecting and maintaining municipal asset data in a form
that enables efficient access, query, analysis, and retrieval. The importance of interoperability
and spatial data standards from the perspective of municipal asset management is also discussed.
Subsequently, the report presents an overview of the predominant spatial data standards, which
can serve as a model or a starting point to develop more comprehensive and integrated data
standards for representing spatial and non-spatial data of municipal assets. The report concludes
by presenting a number of recommendations that can lead the way towards developing fully
integrated and interoperable municipal GIS-based asset management systems.
2. Main Issues in Developing Municipal Asset Management Systems
Asset management decision-making is a complex process that encompasses a multitude of
issues. The main goal of asset management is to maintain the condition of the assets at
acceptable performance levels at minimum cost or within the budget constraints. A challenge is
to use performance and economic models as well as optimization techniques to determine
maintenance strategies that would minimize the cost while satisfying the performance
requirements and budget constraints.
Municipal asset management involves a wide range of processes such as inspection and data
collection, condition assessment, performance evaluation, prediction of future performance,
planning and prioritizing maintenance and repair operations, and evaluating alternative technical
and economic policies. Besides these technical processes, a wide range of managerial, financial,
social, and political issues are also involved. An integrative and multidisciplinary approach to
infrastructure management (Grigg 1999) is necessary to address the wide spectrum of
requirements and constraints of the diverse perspectives of different stakeholders.
According to Lemer (1998), the asset management process addresses two main sets of issues:
First, asset identification, appraisal, and valuation; and second, asset deployment, utilization,
exchange, and reinvestment. Vanier (1999) identified six main areas of asset management,
referred to as the six “Whats”. These areas are: asset identification, valuation, maintenance
backlog, condition, remaining service life, and maintenance prioritization. Developing and
deploying successful and cost-effective asset management processes will largely depend on the
ability to implement methods that can address each of these issues in a systematic and
quantifiable manner. The rest of this section highlights some of the most important issues.
Asset identification is the first issue that needs to be addressed for developing an asset inventory.
All other asset management procedures will depend on the accuracy and reliability of the data
stored in the asset inventory. An accurate and complete inventory of assets is the backbone of
asset management systems. Since most assets can be identified by their geographic location, the
use of a GIS-based asset inventory can potentially facilitate the storage and management of asset
data. Spatial data for assets are a primary component in the development of an accurate asset
The financial valuation of the municipal assets stored is an important issue that is needed to
perform life cycle cost analysis and to estimate asset replacement cost. This issue has become
increasingly important in the U.S. after the introduction of the new accounting reporting rules
known as the Government Accounting Standards Board (GASB) Statement 34 (www.gasb.org).
GASB requires treating infrastructure systems as financial assets that appear on financial
statements, and accounting for the costs to maintain these assets at acceptable performance
levels. Unfortunately, existing asset management systems rarely address this issue and offer little
assistance to asset managers in this regard.
Cost models are required for properly estimating the cost of maintenance work. These models
consider the current condition of the asset, the acceptable condition, and the resources and cost
of maintenance operations that are required to bridge the difference between the actual and the
acceptable levels. Cost models are used as a primary component of Life-Cycle Cost Analysis
Developing LCCA models to evaluate and predict the life cycle cost of infrastructure systems is
critical to making decisions regarding situations such as evaluating new projects or alternative
solutions, calculating the economic value of asset investment, and ensuring that the projects’
costs are justifiable. LCCA models consider all relevant direct and indirect costs of the facility or
infrastructure assets taking into consideration the time when these costs will be incurred. The
models typically itemize all costs that are expected to occur throughout the asset life cycle,
starting from conceptual design stages to the construction and maintenance phases, and ending
with the decommissioning of the asset. The models also include any relevant environmental or
social costs. Using LCCA, managers can also determine the implications of implementing
alternative maintenance strategies and/or deferring maintenance.
Maintenance decisions are dependent on many factors. More important are the current condition
and performance of the asset, the probability and consequences of failure (i.e. risk factors), the
priorities of the municipality, as well as on the current and future budget constraints. Developing
performance criteria, minimum performance requirements, and a quantitative performance
measure based on the condition data of the assets is another issue that needs to be addressed. A
performance measure is necessary for selecting and prioritizing maintenance work, which is key
to allocating financial resources.
Several methods have been implemented in asset management systems to address the
performance measure issue, most notable is the Condition Index (CI) method developed by the
US Army Corps of Engineers (Shahin 1992). The CI method was used in a series of
infrastructure management systems developed by the Construction Engineering Research Lab
(CERL) of the U.S. Corps of Engineers (www.cecer.army.mil). These systems address a number
of different assets such as pavement, roofs, sewers, railroads, and buildings. In these systems, the
CI is derived based on results of visual inspection according to predefined performance rating
criteria. Although many critics of this method object to the idea of deriving a single numeric
factor to describe the overall condition of a structural component, the CI undoubtedly provides
an insight to the overall condition where only qualitative and mostly subjective measures are
typically used. Moreover, the successful implementation of the method in a number of software
systems provides an additional support for the use of the method.
An important requirement for determining the maintenance priority and remaining service life of
an asset is the ability to predict the condition of the asset and its components at any time in the
future given the current condition of these components. Modeling municipal asset performance
deterioration is generally done using either deterministic or probabilistic methods (Vanier and
Rahman 2003). A deterministic approach assumes that asset components deteriorate at a
predictable rate. The probabilistic approach, which is more realistic and more complicated,
accounts for the uncertainties in the deterioration of infrastructure components. Examples
include regression models, Bayesian models, fuzzy set models, or a Markov chain model (Vanier
and Rahman 2003).
Markov chain models (Lounis and Vanier 1998) have been used in many infrastructure
management systems such as those used for bridge management, such as Pontis
(www.aashtoware.org), and roof management (Kyle et al. 2002). Developing Markov chain
models requires studying the changes in the asset condition reflected in the data collected over a
long period of time, to represent the sequence and rate of deterioration of the asset components.
These models can also incorporate the performance improvements that occur as a result of
Supporting analysis, simulation, and evaluation of municipal assets should be an integral part of
any asset management solution. Planning for maintenance work usually involves studying a
number of alternatives and evaluating the impact of each alternative on the overall performance.
Integrating asset management systems with simulation and analysis software tools can assist in
predicting condition changes and the resulting impact on asset performance. It can also enable
engineers and asset managers to perform “what if?” analysis in a cost-effective and efficient
manner by eliminating the need to manually marshal the data between these different systems,
which typically involve a tremendous amount of redundancy in data extraction, interpretation,
and re-entry. This process is known to be inefficient, time-consuming, and prone to
inconsistencies due to mapping and/or interpretation errors, and typically discourages asset
managers and engineers from performing in-depth investigation of various possible scenarios
due to the time and effort involved in the manual data exchange process.
Using asset management systems to support day-to-day operations of municipal assets is another
issue that needs to be addressed. These operations may include logging of performance problems
or customer complaints, estimating costs, issuance and tracking of work orders, and scheduling
maintenance activities, among others. Integrating spatial data into the operations and
maintenance procedures would enable more efficient and cost-effective operations. Enabling
access of site personnel to asset data using mobile devices would also improve the on-site
3. Role of Asset Management Systems in Municipalities
A municipal asset management system is used to store and manage asset information and to
support operational, tactical and strategic decision-making about assets operation, maintenance,
repair, rehabilitation, and replacement. Asset management systems comprise those software
solutions used for land and property management, facilities and infrastructure management, and
utility management. Danylo and Lemer (1998) defined asset management as “a methodology to
efficiently and equitably allocate resources amongst valid and competing goals and objectives.”
They envisioned the role of an asset management system as “an integrator, a system that can
interact with and interpret the output coming from many dissimilar systems.”
Generally, municipal asset management systems comprise two main components: a relational
database containing the asset inventory and condition data, and a set of analysis and decision-
support modules. The system allows entering data into the database through a number of data
entry forms, and also allows generating a number of standard textual and graphical reports. More
modern systems allow interaction with the database through a GIS interface. Analysis and
decision-support tools are used to support functions such as assessing current asset condition,
predicting future performance, analyzing costs and benefits, and identifying, prioritizing, and
selecting feasible maintenance plans.
Many new techniques and software solutions have been developed during the last decade in an
attempt to improve the asset management process in municipalities. Significant advances have
been made in developing asset management tools to support activities in various domains such as
pavement and bridge management, sanitary/storm water sewer management, and water supply
management (Vanier and Rahman 2003). Such tools have supported a wide spectrum of
functionality, such as inventory and condition data management and reporting, maintenance
management, and operations management. The tools have employed a wide range of advanced
algorithms for multi-objective optimization, probabilistic deterioration models for life cycle cost
analysis, and analytical and simulation techniques to accurately simulate and analyze systems
performance (e.g. Frangapol 1997, Lounis et al. 1998).
Maintenance & Planning
Inspection & Condition Policies & Priorities
Risk Management Budgeting & Finance
Life Cycle Cost
Figure 1: Asset Management Systems as Integrators of Data and Workflow Processes
A typical asset management system would have many potential uses to support the processes of
municipal asset management. Examples of these uses could include the following:
• Enabling efficient collection, storage, query, retrieval, management, analysis, and
reporting of asset information, which would support making informed decisions based on
accurate and reliable up-to-date information.
• Supporting more efficient and cost-effective decision-making processes by implementing
systematic methods and algorithms to analyze condition data, prioritize maintenance
operations, and to optimize the allocation of limited maintenance budgets according to
the required performance levels and the risks associated with deteriorating asset
• Integrating and managing various aspects of the municipal asset life cycle by integrating
different workflow processes and their associated datasets. This integration would
aid in supporting and improving the efficiency of the decision-making processes
• Enabling the sharing of data across a municipality and with other agencies (e.g. utility
• Increasing operational efficiency by aiding in the planning, execution, budgeting,
and coordination of maintenance operations, and tracking and managing the
information related to projects, work orders, inspections, and as-builts.
• Assisting municipalities and other government agencies in coordinating and
optimizing the allocation and distribution of maintenance budgets according to the
priority and risk associated with deteriorating components of the assets.
4. Current State of Practice in Software Solutions for Municipal Asset
Asset management software can be classified into two broad categories: general-purpose
software and asset-specific software. General-purpose asset management systems typically offer
generic functionality that can be customized and adapted for specific data and work processes.
Asset-specific software solutions focus primarily on supporting management processes for a
specific class of municipal assets (e.g. facilities, sewers, roads, bridges).
The main functionality provided by general-purpose software systems is the data management of
asset inventory and condition data using a Relational Database Management System (RDBMS).
Add-on modules of the underlying DBMS are developed to support a wide range of additional
asset management functionality such as data collection, analysis, reporting, scheduling, as well
as interfacing with other software (e.g. CAD systems).
Two software packages in this category are: RECAPP and MAXIMO. The RECAPP application
developed by Physical Planning Technologies, Inc. (www.recapp.com), includes a condition
monitoring module and the capability to set up automatic scheduling of inspection processes.
RECAPP also supports interfaces to a number of other software. MAXIMO, developed by MRO
Software, Inc. (www.mro.com), supports several general-purpose functions that can be adapted
to specific classes of infrastructure assets. The software supports the creation and management of
inventory data, condition data as well as the planning, budgeting, and management of inspection,
maintenance, and procurement operations. MAXIMO focuses primarily on plant and equipment
assets rather than on facilities and infrastructure assets.
General-purpose software systems are not widely used in municipalities mainly due to the large
installation and start-up costs, the need for specialized expertise to set up and customize these
systems to the processes of specific municipalities, and the high operational and maintenance
costs of these systems.
Unlike general-purpose asset management systems, the asset-specific software solutions
implement specific data and process management procedures that are required to support the
management of certain classes of infrastructure assets. A significant number of asset-specific
software systems have been under development during the past decade. The typical types of
systems are pavement management systems, bridge management systems, sewer management
systems, and facilities management systems. A number of asset-specific systems are also
available as Commercial-Off-The-Shelf (COTS) products. For example, Hansen Technology,
Inc. (www.hansen.com) offers a number of software solutions for municipalities for managing
buildings, water distribution systems, sanitary and storm water sewers, pavement, among others.
These applications use an Oracle DBMS to support the development and maintenance of the
asset inventory database. The applications also support interfacing with GIS software developed
by other vendors such as ESRI and Intergraph. However, many fundamental asset management
functions, such as performance modeling and maintenance prioritization, are not supported by
A group of asset-specific software solutions have been developed to extend the data management
functionality by implementing procedures for estimating and measuring the performance level of
a particular infrastructure asset based on the physical and condition data. This family, known as
Engineered Management Systems (EMS), enables asset engineers and managers, in addition to
managing the asset inventory data, to better evaluate the asset condition and to use pre-defined
performance criteria to asses the need for maintenance work and also to aid in the planning and
prioritization of the maintenance operations.
The US Army Construction Eng Research Laboratory (www.cecer.army.mil) has pioneered the
development of EMS software. The EMS software included applications for facilities
management (MicroBuilder), roof management (MicroRoofer), pavement management
(MicroPaver), among others. The EMS applications define a fairly detailed data model for the
infrastructure system. For example, BUILDER defines 12 sub-systems as part of a typical
facility, which includes roofing, site, specialities, structural, fire resistance, HVAC, interior
construction, exterior construction, plumbing, conveying, electrical, and exterior closure. Each of
the sub-systems is further divided into a number of components, which are, in turn, subdivided
into a number of sections. Besides the standard data management functionality, these systems
adopted a method of estimating and measure the performance level of infrastructure assets. The
method involves deriving a Condition Index (CI) from the condition data of the infrastructure
structural components. Based on predefined rating criteria, the CI will reflect the performance
level of the asset components. The CI is used as the basis for selecting and ranking appropriate
maintenance work and to allocate financial resources. The applications also supported interfacing
with GIS software; for example an interface between PAVER and ESRI’s ArcView
(PAVERGIS) was developed to enable the display of pavement information on GIS.
Despite the availability of several commercial asset management systems, many municipalities
and infrastructure management agencies, depending on their specific requirements and
circumstances, may find the functionality offered by these systems either too limiting or
inversely they may be too detailed. To overcome the limitations of generic solutions, many
municipalities have developed their own in-house customized asset management systems. Some
municipalities have developed these systems by customizing general purpose software tools to
their specific needs. For example, several commercial DBMS, CAD, and GIS applications have
been used as platforms to support the development of asset inventory databases. Many of these
systems evolved over the years to offer more complex and sophisticated functionality such as
condition assessment, maintenance planning, cost estimating, and life cycle cost analysis.
Examples of such systems include in-house pavement management systems that were developed
to manage and analyze data and support maintenance decisions about a pavement network. In
some cases, a consortium of local and federal government agencies have pulled their resources to
collectively develop a single asset management system. An example of this is the development
of the Pontis bridge management system from AASHTOWare (www.aashtoware.org).
5. GIS Technology in Municipal Asset Management
Municipal asset management systems are typically implemented on top of Relational Database
Management Systems, the main purpose to support storing, organizing, querying, managing,
analyzing, and reporting the asset data. A number of asset management add-on modules that
interface with the database are used to perform specific functions such as maintenance planning,
cost estimating, performance modeling, and life cycle cost analysis. The database records of each
asset are typically referenced by the asset’s unique identification number. The main drawback of
these systems is the lack of graphical representation of the assets, which would require end-users
to access the asset data through their unique IDs. Without graphical representation, the spatial
characteristics and inter-relationships of the assets are communicated to the end users in a textual
form, which may impede the ease of data access, navigation, and analysis.
Given the importance of representing and managing the spatial data about assets and for
supporting effective and efficient asset management processes, many asset management systems
have started to use CAD platforms to graphically represent the asset spatial data, define and
attach the non-graphic (attribute) data to the graphical objects, and use the DBMS to manage the
asset data. Several facility management systems have been implemented based on CAD
platforms. An example of this category is the AutoCAD-based Archibus software
(www.archibus.com). However, a major drawback of CAD-based asset management systems is
their limited data management functionality and the difficulty of updating and maintaining the
linkages between the data stored in the database and the drawing entities.
Also, CAD models are typically represented using primitive geometric objects such as lines,
curves, points, and polygons. These CAD models often lack the semantics needed to define the
assets or their inter-relationships. Using semantic-rich models that define high-level object
models of the assets and explicitly represent the inter-relationships between various assets is
necessary to perform the spatial analysis operations. Spatial analysis is an important tool that can
potentially support many asset management tasks. To overcome these limitations, some asset
management systems, e.g. Archibus (www.archibus.com), have supported the use of high-level
parametric objects that represent compositional assemblies of more primitive graphic objects and
define richer model semantics by explicitly modeling the inter-relationships between related
Unlike CAD, GIS technology has traditionally emphasized the definition and use of semantic
rich object models that are linked to Relational DBMS to store and manage the attribute data
associated with the spatial objects (or features) and topologies defined in maps and drawings. A
GIS-based asset management system typically stores the graphical data (e.g. maps, themes,
features) within the application itself while using the RDBMS to store and manage the attribute
(or non-graphical) data associated with the assets. The attribute data provide information about
the assets such as the type of entity, its properties, condition, and maintenance data. GIS data
identify objects as “features” which typically have more semantics than traditional CAD objects.
Also, spatial relationships among different features (e.g. topologies) are explicitly defined. The
data management functionality and the capability for linking attribute data and graphical objects
are readily available in GIS software. The GIS software maintains the link between the spatial
features and the attribute database records using a unique identification for each feature. The
software will also allow for the creation, storage, maintenance, retrieval, query, analysis, and
display of various features and their associated information. GIS software typically supports
importing files in the format of other CAD systems, which facilitates importing design and
inventory data directly into GIS.
Since a significant portion of municipal asset data is naturally indexed and referenced with
respect to their spatial characteristics, the use of GIS in asset management systems also seems to
be more suitable from a data modeling and management perspective. In an attempt to support
GIS-like functionality, many CAD platforms (e.g. AutoCAD and Microstation) have recently
introduced advanced capabilities for modeling semantic-rich data and for data management.
However, the ease of linking graphic and non-graphic data as well as the built-in spatial analysis
capabilities of GIS systems remain to be key features that are unparalleled by most CAD
platforms. Therefore, GIS platforms appear to be more suitable for asset management
applications than CAD platforms; a GIS platform can better support asset management
applications by its combined data management, visualization, query, and analysis capabilities.
Municipal assets are typically identified and referenced by their geographic location and spatial
relationships. Spatial data lends itself naturally to the domain of GIS technology. In recent years,
and due to a realization of the useful role that GIS can play to support many functions within
municipalities, there has been an increasing trend to extend asset management systems to support
GIS functionality. Many legacy asset management applications are now providing interfaces
with GIS software and implementing several spatial data management and analysis functionality.
The next generation of asset management systems will clearly be GIS-based and tend to exploit
the capabilities of spatial data technologies to support various aspects of municipal asset
management. The GIS technology is becoming an increasingly critical and integral component in
almost all modern municipal asset management systems.
Due to the recent availability of affordable and easy to use GIS hardware and software, many
municipalities have already implemented some form of a GIS. An important survey of “Current
GIS Practices in Municipal Infrastructure Management” (Vanier 2003) has indicated that many
municipalities across Canada have already started using GIS technology to support a wide range
of asset management processes.
The most common commercial GIS software programs currently in use by municipalities across
North America include ArcGIS (Environmental Systems Research Institute, ESRI), MGE and
GeoMedia (Intergraph), AutoCAD Map (Autodesk), MapInfo (MapInfo), and GeoGraphics
(Bentley). These GIS systems are mainly vector-based although some of them also support raster
GIS data. However, raster models are rarely used in municipalities and most of the municipal
spatial data are stored in vector format that are primarily created using CAD software.
GIS software typically supports interfacing with one or more RDBMS. Most notable of the
supported DBMS are Oracle, Microsoft SQL Server, Sybase, and Informix. These DBMS have
traditionally been widely used by municipalities, and have already been implemented in many
legacy asset management systems. Also, many GIS systems now provide an Application
Programming Interface (API) that can be used to customize and extend the core functionality of
the software. Various programming languages can be used for implementing these extensions.
This has enabled many third party specialized asset management solutions to be developed on
top of general-purpose GIS software.
However, despite its apparent potential, the GIS technology is still largely underutilized across
most municipalities, and its use needs to be further extended. In many cases, municipal GIS
systems are used primarily to support the storage and retrieval of maps. The key function and
value of a GIS system in supporting data management, query, and analysis remains largely
unused or underutilized. There is an obvious need to leverage the role and functionality of GIS to
enhance the asset management processes and to provide better decision-support and analysis
capabilities. A key challenge to achieving this objective is to enable the integration and
interoperability of asset management software through standardized representation of municipal
spatial and non-spatial data. The following sections present ways to address this challenge.
6. Role of GIS in Municipal Asset Management Systems
A significant portion of municipal asset information is typically related to or associated with
some spatial aspect of municipal assets. Spatial characteristics and inter-relationships of various
assets are key factors in most municipal decision-making processes. Municipal spatial data are
typically derived from sources such as maps, CAD drawings, surveying technologies, 3D laser
scanning devices, aerial orthophotos, GPS data, and remote sensing. These types of spatial data
can be easily accommodated by GIS technology. Therefore, using GIS to manage municipal data
can support the efficient indexing and referencing of various forms of information about
municipal assets based on the geographic location of these assets. This capability can play a
crucial role in increasing the efficiency of data presentation, access, management, integration,
query, and analysis. As a result, the asset management system can lead to improvements in
various processes of planning, engineering, operation, maintenance, and management of
Many of the services and asset management activities provided by municipalities can be
effectively supported using a GIS-based asset management system. A typical GIS-based asset
management system can support land parcel management, facilities management, and
infrastructure management. The system can also provide a variety of data management and
decision-support tools to aid in the planning, engineering, and management of municipal assets.
The system would typically build a spatially-based asset inventory which includes information
about the physical and structural characteristics of a municipal asset, such as geometry, material,
classification, as-built materials and thickness, surface types, and maintenance histories, among
others. Users can visualize the physical and spatial characteristics of the assets, and navigate
through the information by accessing various data cross-referenced from the spatial
representation of the physical components. The GIS functionality for data management, analysis,
and visualization provides better decision-support capabilities to asset managers for managing
various municipal assets.
There are mainly two forms for graphical representation of spatial features in GIS: vector and
raster. Vector features are generally defined using three primitives: points (nodes), lines (arcs), or
polygons (areas). Composite features or topologies are built from these three primitives. Point
features are used to represent node-type features, which can be identified as symbols (e.g. a
manhole). Line features are used to represent linear entities (e.g. sewers). Polygon features are
used to represent bounded areas (e.g. land parcels). On the other hand, raster features are
represented as an array of cells, referenced by row and column numbers, and are used to model
continuous entities or phenomena where attributes are attached to individual cells. Raster-based
GIS software is typically used for environmental and natural resource management applications
where features are continuous rather than discrete. Orthophotos and remote sensing images are
typical source of raster data.
GIS Technology provides the capability to store and correlate a variety of spatial and non-
spatial data on the basis of the geographic location. Documents in virtually any format (e.g.
textual, graphics, multimedia) can also be associated with assets to provide valuable information
to the user. The GIS can be used to enable easy and efficient access, retrieval, and query of
municipal data. The GIS enable users to visually access and query the database, and get the
results displayed on a map. This can also help to eliminate data inconsistency and redundancy,
and enable easy data update. Users could retrieve detailed information about various assets on
the map and generate a variety of reports about these assets. For example, to retrieve information
about a specific sewer line, an engineer could point to that sewer in the map and in response,
receive a detailed report of the physical and condition data and history of maintenance and repair
work along with any other related documents (e.g. CAD drawing, photos, CCTV files, etc) of
that sewer. Spatial relationships between different asset components can also be displayed,
analyzed, and queried.
Municipal asset data typically span many data sources across different disciplines. Several GIS
software program support interfacing with a number of DBMS through the use of standard
interfaces or API, e.g. Open Database Connectivity (ODBC). These GIS software programs can
integrate and manage data stored in multiple, potentially distributed, databases. The GIS
capability of integrating data from different sources will enable the development of integrated
asset management systems that can integrate asset data with other enterprise-wide data; that is,
those stored in Enterprise Resource Planning (ERP) systems. This will enable asset managers
and other stakeholders to efficiently share and exchange information seamlessly.
The GIS can also integrate with analysis software to simulate performance under various
conditions and to perform “what if” analysis scenarios. For example, a sewer management
system may interface with a hydraulic modeling application to simulate the flow in sewers and to
predict any flooding or surcharge that could occur as a result of heavy rain events. The GIS could
also interface with design tools for the planning and designing of new facilities, infrastructure, or
utilities. For example, several design software have GIS functionality to support design activities
for highways and sewers. The GIS could also interface with decision-support tools such as those
used for maintenance planning and prioritization, risk analysis, and life cycle cost analysis.
The user can interactively select a number of features on the map and query the database to
retrieve the attribute data of these features. The user can also select a set of features based on
their attributes and/or spatial relationships by querying the database. Query results can be
combined to create higher level queries by using a set of Boolean operators (union, intersection,
minus, and difference). Users can locate features based on attribute criteria or retrieve attributes
for specific features. Users can also query the database based on the spatial relationships between
map features such as proximity and enclosure relationships. After receiving the query result from
the DBMS including the unique identification of the features that meet the query criteria, the
system highlights these features on the map.
In addition to attribute data and spatial data queries, many GIS software programs can perform a
wide range of spatial analysis to discover spatial relationships between map features. This
capability, known as spatial analysis, involves analyzing the features’ data based on their
geographic location, and includes functionality such as network analysis and terrain
modeling/analysis. These capabilities are typically supported by add-on modules to the GIS
system. The spatial analysis capability can be used for a variety of purposes such as planning and
analysis of road and sewers networks and selection of suitable routing of utility lines. Network
analysis, for example, aids in modeling and analyzing domain areas such as water distribution,
sewer systems and road routes. The GIS network analysis functionality can also aid in
determining the optimum routes of utility lines. The 3D terrain modeling and analysis is another
spatial analysis functionality implemented in GIS. Terrain models are generally represented
using a Triangulated Irregular Network (TIN) or a grid surface mesh. The map can be overlaid
over the terrain model to provide a 3D view of the ground surface. The 3D terrain model can also
be used by hydrology software to automatically extract the land slopes and to perform runoff
One important function of an asset management system is to support the operations and
maintenance work in municipalities. This work involves scheduling inspection and routine
maintenance work, issuing, assigning, and tracking work orders, and surveying and logging the
conditions and performance of an asset. By supporting the integration of asset data with
workflow processes, the GIS system can significantly enhance the coordination and information
flow among various processes, which can lead to increasing the operational efficiency of
municipalities. A typical GIS-based asset inventory can facilitate the task of locating, repairing,
or replacing an asset. For example, an operations engineer could point to one or more assets on
the map, and schedule an inspection, issue a work order, or update the condition data based on
the locations of these assets. The map makes it easy to locate and identify the asset components
and to retrieve or update the data about these components. For example, if a resident complains
about a problem such as Water in Basement (WIB), it will be easy to determine the address on
the map, and to quickly find the sewers that may have caused the problem.
7. Recent Developments in GIS Data Collection, Access, and Modeling for
Municipal Asset Management
Efficient management of municipal assets requires collecting asset condition data in an efficient,
timely, and accurate manner. Collecting accurate spatial data about municipal assets and keeping
these data current and reliable have been a major challenge facing municipalities. Traditionally,
physical and condition data for an asset are collected through manual labour-intensive processes.
These processes are known to be slow and error prone.
Significant advances have been made in geospatial technologies, and in particular GIS
technology, in recent years, that can potentially improve the efficiency of asset management
processes. Vanier (2004) provided a survey of a number of geospatial technologies and their role
in supporting municipal asset management. The capabilities of GIS software and hardware have
been steadily increasing while their costs are decreasing. GIS software systems have also
increased in sophistication and functionality, while becoming more affordable and easier to use.
More significant are the developments related to data collection, access, and modeling. These
developments constitute important opportunities for municipalities to leverage their use of GIS
technology and to fully utilize this technology to improve operational efficiency and data
7.1. Spatial Data Collection
New technologies for spatial data collection have been introduced to enable more efficient and
timely data gathering (for more details, please refer to Part 1 of this report). Spatial data collected
through field surveying or Global Position Systems (GPS) can be downloaded directly to the GIS
database to update the data in almost real-time. Using low cost aerial photography and remote
sensing techniques to collect, verify, and update the data is also becoming more affordable and
accessible to municipalities. Also, the use of 3D laser scanning to collect accurate as-built data of
existing facilities and infrastructure assets is expected to become more affordable and widely
used in the near future.
7.2. Condition Data Collection
Assessing and evaluating the current asset condition is a major activity that consumes
considerable time and effort, and largely determines the nature and extent of the maintenance
operations to be performed on the asset. The quality of the maintenance decisions largely
depends on the accuracy, reliability, and timeliness of the asset condition data. Condition data
are typically collected using various methods of non-destructive testing (NDT) and/or visual
assessment. Traditionally, condition data are collected through periodic inspection, condition
assessment surveys, or when problems arise. Clearly, this approach is inefficient not only
because of the time and resources needed to perform manual inspection, but also because in
many cases it may be too late when problems are discovered. Developing cost-effective methods
for condition data collection is an active research area and many new techniques have already
been introduced. Examples include the use of sensors, SCADA (supervisory control and data
acquisition), NDT techniques, and real time wireless data collection. These new data collection
techniques have the potential to reduce the cost and improve the quality, accuracy, and timeliness
of the condition data.
Several efforts are ongoing to enable the economic and efficient use of wireless sensors and fibre
optic sensing technology to collect condition data in order to remotely monitor the condition of
infrastructure assets in real time. The Intelligent Sensing for Innovative Structures (ISIS)
research network (www.isiscanada.com) is leading such research in Canada.
The use of automated methods to collect “visual” condition data in the form of static images or
video streams is also becoming widely available. For example, collecting pavement condition
data using vehicles that are equipped with cameras, video, or radar is becoming common in
many parts of the world. Moreover, image and video processing software tools that can apply
algorithms to analyze and infer condition from images and video streams can be used to
automatically identify and detect any malfunctioning, deficiencies, or distresses in the assets.
This technique has been applied mainly in pavement management, however, not on a large scale.
Using modern data collection technologies can significantly improve the data collection process
and make it feasible to collect accurate data in a more efficient and cost-effective manner.
Collected physical and condition data can also be directly linked and integrated to the GIS
database, and thus, keeping the database up-to-date to reflect the actual condition of the assets.
Asset management systems supporting these methods of data collection can provide
municipalities with a significant advantage to reduce cost and save resources.
7.3. Data Access
The collection, recording, and access of asset data in the field have also become easier and more
efficient. Portable data entry/access devices, such as Personal Digital Assistant (PDAs) and bar
code readers, help to reduce the time needed to collect and access data while reducing data
redundancies and entry errors. Field technicians using PDAs can access a central server to
download or upload data through wireless Internet connections.
The advent of web-enabled enterprise-wide GIS software has allowed wired and wireless access
of GIS database over the Internet. Users within or across the organizational boundaries of
municipalities can share and query the same GIS database. Field personnel, users in remote
offices or other organization, as well as the general public, can access and query the same GIS
database. Using PDAs, field personnel can easily access work orders and any associated data and
maps, filling out inspection reports, and updating the GIS database right from the field.
7.4. Data Modeling
Traditionally, GIS data models represented spatial features using geometric primitives and
defined attribute data associated with these features. The limited semantics embedded in these
models have limited the sophistication of data analysis that can be performed. Dependencies
between different features were not explicitly modeled, and therefore, the model could easily
become inconsistent if the user incorrectly modified the data. For example, if the user incorrectly
edits the invert elevation of a sewer line at one node or moves that node, the model has no means
to inform the user of this violation. To avoid this or similar scenarios, some GIS data models
(e.g. Halfawy et al. 2000) have embedded more semantics to ensure data correctness and
consistency by explicitly representing the inter-relationships between different features and any
constraints that should be satisfied by the model.
Recently, due to the need to better support the management and sharing of spatial data and their
integration with non-spatial data, there is an increasing trend toward storing both the spatial and
non-spatial (i.e. attribute) data in the database. Database vendors implement and support some
form of a spatial data schema. For example, Oracle Spatial software have adopted and
implemented a spatial schema, defined by the Open GIS Consortium (OGC), to store and
manage spatial data. Storing spatial data in the DBMS also enables the integration of the spatial
data with other non-spatial data stored in the DBMS.
8. Data Requirements of Municipal Asset Management Systems
Implementing successful municipal asset management systems requires addressing a wide range
of challenges: (1) modeling and managing the physical, functional, and performance data of the
facility or infrastructure, as well as operational, maintenance, and cost data; (2) integration of
different function-specific and enterprise-wide software tools and the possibility these tools
interoperating and exchanging data in an efficient manner; (3) linking the data with deterioration
models, simulation models, cost models, optimization models, and maintenance operations; (4)
supporting functionality such as performance analysis, maintenance planning, and operations
management; and (5) modeling, management, and coordination of workflow processes to enable
effective communication of accurate and timely information. Municipal asset data are typically
characterized by their sheer size, complexity, inter-dependencies, and dynamic nature, which
present several challenges that asset management systems need to address.
Asset data are typically distributed in many documents and formats which include maps,
drawings, maintenance records, and design documents. In many cases, some data may be
outdated, inaccurate, or unavailable. This poses a serious problem especially when dealing with
buried utilities or infrastructure assets. Structural changes during construction or as a result of
maintenance operations are rarely incorporated back into the maps or drawings. Outdated maps
and drawings that do not reflect the current as-built status are very common in many
municipalities, especially for paper maps and drawings. Moreover, different documents may
contain inconsistent or conflicting data.
The first step in developing an asset management system is to collect complete, accurate,
consistent, and up-to-date as-built information of the municipal assets. This may require
conducting field surveying to collect the missing data and to verify the data to resolve
inconsistencies, and to reconcile data from the different sources. Afterwards, the data should be
continually updated and maintained to reflect the actual condition of municipal assets. In
addition, systems should support efficient and timely gathering of condition data.
A primary objective of a successful municipal asset management system is to maintain the
accuracy, consistency, and integrity of the data. Efficient data management is the most crucial
factor in almost all the issues mentioned above. An asset management system should support
efficient access and sharing of data. Also, asset management systems should support different
modes of data access and exchange such as centralized database, application-to-application file
exchange, and Intranet/Extranet access.
Different stakeholders in municipalities typically have different data requirements such as
different feature details or map scale and accuracy. In many cases, non-technical high-end users
(e.g. in administration) may find accessing or analyzing the data unintuitive or difficult. An asset
management system should provide users with a number of options to represent and access the
stored data. Different methods to access, analyze, and report the data are needed to satisfy the
varying requirements of different stakeholders.
Municipal assets are typically identified and referenced by their geographic location and spatial
relationships. This kind of data lends itself naturally to the domain of GIS technology. A GIS-
based asset management system would build a spatially-based asset inventory which will
facilitate the storage, query, analysis, and management of asset data. Therefore, spatial asset data
is regarded as a key component in the development of an accurate asset inventory, and hence, an
asset management system. The GIS spatial analysis and visualization capabilities will provide
better support to asset managers in the planning and decision-making process.
Municipal asset management systems should support the integration and interoperability of
legacy software tools. Given the large number of standalone function-specific software tools
currently in use to support various aspects of asset management, an asset management system
should support interfacing with and easily exchanging and sharing data between these tools. For
example, a sewer management system may need to link with analysis applications to perform
rainfall runoff or sewers flow calculations. To achieve this goal, standard data models and
neutral data exchange formats are required. This issue will be discussed in detail in later sections.
Managing municipal assets is becoming increasingly knowledge-intensive and requires accessing
and managing a multitude of knowledge sources. Given the fact that it will be extremely difficult
and expensive for municipalities to achieve expertise in all knowledge areas, the need for
municipalities to access “knowledge repositories” is becoming crucial. Currently, there is an
ongoing effort in Canada to “capture” the knowledge of infrastructure management in the form
of “best practices.” To maximize its use, this knowledge needs to be formalized and structured in
a format that would enable its efficient access, sharing, and reuse by various stakeholders. A
municipal asset management system will be more useful if it could support efficient
representation, management, sharing, and reuse of knowledge through implementing repositories
that incorporate various forms of applicable knowledge. Techniques for data mining and
knowledge discovery could be implemented in the system to enable extracting useful knowledge
from the stored infrastructure data.
Municipal asset management is a multi-disciplinary process that typically involves a large
number of inter-dependent operations that need to be managed in a coordinated manner. A
municipal asset management system should support the efficient flow of information among
various activities, integration of data with the workflow processes, and the coordination of
workflow processes. Enabling easy and efficient access to the distributed data sources is a
primary requirement through which efficient communication and collaboration among
stakeholders can be supported.
Satisfying the above mentioned requirements present several challenges that the developers of
municipal asset management systems need to address. An underlying theme of these challenges
is the ability to represent data in a consistent, integrated, and standardized form. The next two
sections highlight the need for data standards and discuss various efforts to develop these
standards, with primary focus on the domain of municipal asset management.
9. Need for Interoperability and Spatial Data Standards
Implementing municipal asset management systems involves defining a data model that
represents the asset spatial and non-spatial data. The data model defines the objects, their
attributes, and inter-relationships. The scope of the data model may vary depending on the
sophistication and required functionality of the software. Developing data models is by large the
most critical and time consuming step in developing asset management software.
Many asset management software solutions have been developed during the last decade in an
attempt to improve the asset management processes. However, the majority of these tools were
developed to operate as stand-alone systems to support a particular task with limited or no
capability to share and exchange information with other systems. Also, each tool typically used a
proprietary data model and stores data in a proprietary file format which, in most cases, cannot
be accessed by or shared with other software tools. Standardizing the representation of municipal
data would enable these different systems to interoperate (i.e. to share and exchange data), and
thus provide users of these systems the capability to reuse existing data, coordinate work
processes, and share vital information in an efficient and effective manner.
Without data standards, integrating different software tools into an integrated system requires
implementing adapters to translate the ad-hoc data models, that different tools use, to and from
proprietary data models. A standard data model would facilitate efficient data sharing and
exchange, and improve the consistency and quality of asset information.
Spatial data constitute the core of most municipal information systems and are central to many
activities and decision-making processes in municipalities. The majority of municipal data can
be related to some form of spatial data. It can be argued that the development of standard data
models for municipal asset management applications will have to be founded upon
comprehensive and standardized spatial data models. A spatial data standard would serve as the
foundation for the specification of comprehensive data models for municipal asset management
systems. Therefore, developing or adopting a vendor-neutral standard for municipal spatial data
is the first step towards developing standard data models for municipal asset management
systems. Section 10 reviews some of the predominant spatial data standards that are related to
developing standard spatial data models for municipal assets. After adopting or developing such
standards, future work may focus on augmenting the spatial data models with specific non-
spatial data elements of particular assets classes, for example linking condition data to spatial
To any municipality, spatial data needs to be accessed, not only by its own employees, but also
by several other organizations and agencies such as utility companies. Similarly, a municipal
engineer might need to access spatial data stored and managed by other organizations. Adopting
a standardized data model and format will enable various organizations to access, use, and
disseminate spatial data in a common and consistent manner. A standard data model provides the
means of data “reusability” between different software tools and organizations. It also helps to
eliminate the duplication of efforts in collecting and developing spatial data. Consultants and
contractors can make their data compatible with the standards, and hence, make these data
readily available and reusable for maintenance and operations management.
By standardizing the data semantics and format, a standard data model ensures the integrity and
consistency of the data across various disciplines involved throughout the life cycle of municipal
assets. The standard data model will provide common definition of the asset data (e.g. entities,
attributes, units of measurements, data quality metrics). The standard will also promote data
consistency and enable the effective creation, use, management, and automation of municipal
data. Sharing spatial data between municipal asset management systems and other enterprise-
wide systems (e.g. ERP) or other maintenance management systems can also be aided by using
standard spatial data models. When a data model is standardized and endorsed by major bodies
in the industry, software vendors try to make their products and solutions compatible with these
standards, which further ensures data consistency and the interoperability between different
In their effort to integrate and streamline their work processes, many municipalities want to
ensure that their existing computer applications can interoperate and exchange information in an
efficient manner. Standardized data can be easily accessed and integrated with various function-
specific applications such as cost estimating, operation management, and maintenance planning
applications. Adopting a standard data model and format enables municipalities to better
streamline and coordinate their work processes by integrating data across different function-
specific software tools.
A major advantage of using GIS in municipal applications is its capability to integrate spatial
data from different sources that could exist across several organizations. For example, creating a
complete map of a specific municipality would require overlaying different layers to show
infrastructure, utilities, and land information for that municipality. Again, integrating and linking
spatial data from different sources requires the use of a consistent and standardized spatial data
model that could enable linking and exchanging spatial data regardless of the software or
hardware used, or the method by which the data was created. This will be particularly useful
when spatial data is shared and exchanged over the Internet.
The life cycle of municipal data is extremely long compared to the life span of any data model,
format, or software technology. Software systems and proprietary data formats typically go
through extensive changes and upgrades, which may render their use after few years a
cumbersome task that requires the use of special vendor-specific software to translate from older
data models/formats to newer ones. The use of an open, standard, and neutral data format is one
way to ensure that the data created will likely survive the changes in software or data
Finally, a spatial data standard is a key to the development of a spatial data infrastructure at the
local, national, or international levels. In Canada, the Canadian Geospatial Data Infrastructure
(CGDI) is an ongoing effort to harmonize Canada’s spatial data and make them accessible on the
internet (www.geoconnections.org/CGDI.cfm). The CGDI effort focuses primarily on the
development and adoption of spatial data standards to enable the access, reuse, and maintenance
of spatial data on a national level. In the U.S., there is a similar ongoing effort, known as the
National Spatial Data Infrastructure (NSDI). Spatial data standards are considered to be the
cornerstone for developing spatial data repositories.
10. Spatial Data Standards for GIS Interoperability
Data modeling standards, in general, define structures and semantics as well as mechanisms for
modeling and exchanging information. In the context of GIS, a data model would specify the
structure and organization of spatially related information, including the representation of both
graphic and non-graphic data. Spatial data address primarily the content and accuracy of the
positional and attribute data of spatial features. In general, exchanging data between different
GIS systems is currently being achieved in two main ways:
1. Through the use of special-purpose tools to translate the data between different formats
and data models. Many GIS systems use de-facto file format standards developed by
vendors. Examples include ESRI Shape File format and Autodesk DWG/DXF format.
Data translators map the data between different systems and typically involve a
tremendous amount of redundant data extraction, interpretation, and re-entry by the end
users. This process is known to be inefficient, time-consuming, and prone to
inconsistencies due to mapping and/or interpretation errors. Also, the tool-specific nature
of the translators imposes unnecessary constraints on end users by requiring the use of
proprietary vendor-specific data models and software systems. Lack of industry wide
standard data models has been viewed as the main cause of these deficiencies.
2. Through accessing a set of APIs (application programming interfaces) or software
components (e.g. COM components). Many commercial applications offer API interfaces
to allow other applications to access the internal data model and to input or extract data
directly to and from the application. Although some of these APIs may be an industry de-
facto standard (such as COM or the Open Database Connectivity, ODBC), the majority
of the APIs in GIS software are proprietary and tool-specific. As a result, this form of
vendor-specific API-based interoperability is also limited.
Due to the inherent limitations of the two aforementioned methods of data exchange, there has
been a wide consensus in the industry and research community that the use and adoption of
vendor-neutral industry-wide standard data models and formats constitute the most viable option
for software interoperability. The standard data models define common semantics of the data as
well as a vendor-neutral file format to exchange these data. The standard data models would also
need to meet the diverse requirements of the various disciplines of all potential users, and should
be based on industry wide consensus.
During the past decade, there have been several efforts to standardize GIS spatial data models
and format. Parallel to these efforts, several software vendors and researchers have been
developing data models for municipal asset management applications in various domains. Many
of these data models have now reached a level of maturity that could enable their wide use and
adoption as standard data models in the industry. For example, ESRI has provided users with
application-specific data models in about 24 different application domains such as water utilities,
energy utilities, land parcels, and transportation networks (support.esri.com/datamodels).
However, vendor-specific data models are not generally intended to enable interoperability with
other software systems. Rather, the main purpose of these vendor-specific data models is to
simplify and expedite the process of implementing GIS systems using the vendor’s toolset by
providing ready and tested data models. Another reason is to promote the use of a consistent data
model within a particular application domain based on the vendor’s software suite.
The following three sections provide an overview of some of the efforts most relevant to the
issue of standardizing spatial data in GIS systems, with a primary focus on municipal GIS
systems. Data standards that deal with the standardizations of digital or paper maps, such as the
Map Standards developed by the U.S. Geologic Survey (mapping.usgs.gov/standards/), are out of
the scope of this report. Four major spatial data standards will be presented: the Federal
Geographic Data Committee (FGDC) standards, the CADD/GIS Center standards, the Open GIS
Consortium (OGC) standards, and the International Organization for Standardization (ISO)
standards. No attempt is made to cover the specifications involved with these spatial data
standards, but rather to discuss the main parts of these standards that are most relevant to the
development of GIS-based municipal asset management systems.
11. Federal Geographic Data Committee (FGDC) Standards
Established in 1990, the Federal Geographic Data Committee (FGDC) (www.fgdc.gov) is a U.S.
federal government organization, made up of 16 federal agencies, that oversees the development
and use of spatial data within different federal government agencies and organizations. The
FGDC has been developing spatial data standards with the main objective to enable the
interoperability between GIS systems implemented in different agencies and organizations of the
federal government. This work has been recently merged into a larger governmental effort to
develop a National Spatial Data Infrastructure (NSDI).
To facilitate the development and use of spatial data standards, the FGDC has been involved in
the work of the InterNational Committee for Information Technology Standards for Geographic
Information (INCITS Technical Committee L1) (www.incits-l1.org). INCITS serves as the U.S.
Technical Advisory Group (TAG) to the ISO International Committee for Geographic Standards
(ISO/TC 211 - Geographic Information/Geomatics) (see Section 14). INCITS evaluates the
FGDC spatial data standards for possible recommendation and adoption at a national or
Over the past decade, the FGDC has developed several spatial data standards (about 19 standards
to date) that have been adopted by almost all organizations within the U.S. Department of
Defence (DoD). Many other federal and local government organizations, municipalities and
utility companies have adopted the FGDC data standards. Several software vendors have also
implemented support for the standard in their software products.
The FGDC defines three main sets of data standards: data content standards, data transfer
standards, and geospatial metadata standards. The following three subsections will summarize
each of these standards. More details can be obtained from the FGDC web site.
11.1. Data Content Standards
FGDC standard data models were defined with a primary focus to support areas of facilities and
infrastructure management. Within the FGDC, the Facilities Working Group (FWG) coordinates
and oversees the development of data models to support municipal and civil works applications.
The data content standards (www.fgdc.gov/standards/standards.html) describe the “semantics” of
a set of domain objects along with their attributes and inter-relationships. A data content standard
in a specific domain describes the structure and content of what is known as the “Feature
Attribute Tables” (FAT). A typical FAT includes a comprehensive listing of domain features and
their associated spatial and attributes data that are within the domain of interest. The content
standards also define a number of constraints to ensure data integrity and consistency. For
municipal types of applications, the most important data content standards are the Cadastral
Content Standard (FGDC-STD-003), and the Utilities Content Standard (FGDC-STD-010).
The Cadastral Data Content standard (www.fgdc.gov/standards/documents/ standards/cadastral)
aims to support the representation, integration, and sharing of land ownership records
information. The main objective of this standard is to provide common and consistent definitions
for cadastral information and to standardize attribute values which facilitate the effective use and
sharing of land records data. The standard outlines the information that needs to be defined for
cadastral features in GIS such as surveying data, property limits and property description. This
standard is currently supported by a number of software vendors. For example, ESRI has already
implemented data models for land parcels based on this standard in their ArcGIS suite of
The Utilities Content Standard (www.fgdc.gov/standards/status/sub3_1.html) aims at
standardizing the spatial information of utilities systems. This standard is intended for use in
engineering and maintenance management applications of utility systems. The standard defines
utility system components by specifying the names and description of feature types and their
spatial and non-spatial attributes, and specifying the domain (i.e. values range or list) of various
attributes. The standard describes eleven (11) feature classes which include: water distribution,
wastewater collection, storm drainage collection, saltwater, natural gas distribution, compressed
air, electrical distribution, electrical monitoring/control, fuel distribution, heating/cooling
systems, and industrial waste. The standard also incorporates several modeling concepts such as
the concept of grouping utility system components into feature classes.
It should be noted that the FGDC data models are intended to be implementation-neutral and not
to reflect any implementation specifics. Software implementers can customize or modify the
FGDC data models to suit their particular requirements and purposes. Implementers can also use
any software technology or platform to develop their systems.
The FGDC data models are mainly described using the Unified Modeling Language (UML).
Although UML serves as a robust and rich data modeling method, UML models cannot be used
for encoding and exchanging data. Therefore, another encoding and exchange data format is
needed to encode and exchange the data. One commonly used encoding scheme is the XML-
based Geography Markup Language (GML) (see Section 13 below). GML was defined by the
Open GIS Consortium and is adopted by ISO/TC 211 as a Draft International Standard. Many
GIS software programs currently support GML.
11.2. Spatial Data Transfer Standards (SDTS)
The Spatial Data Transfer Standards (SDTS) (mcmcweb.er.usgs.gov/sdts) define methods and a
data format to represent and exchange the spatial data in a technology-neutral and vendor-neutral
manner. SDTS has also been endorsed as a Federal Information Processing Standard (FIPS 173).
The SDTS specification is organized into two sets of specifications: the base specification
(SDTS Parts 1, 2 and 3) and the profile specifications (SDTS Parts 4, 5, 6, 7, and potentially
others). The base specifications describe the spatial data model structure and content, and a
format for exchanging spatial data. The remaining parts define what are known as profiles. A
profile specification includes the definition of some rules and formats for applying SDTS (i.e. the
base specification) for the exchange of particular types of spatial data. Figure 2 shows the
relationship between SDTS base standard, profiles, and software products.
Figure 2: Relationship between SDTS base standard, profiles, and software products
11.3. SDTS Base Specification
The base SDTS specifications provide standards for representing and exchanging spatial data.
SDTS Part 1 of the standard defines a conceptual model for spatial data. The conceptual data
model represents real-world entities, their geometric and topological attributes and inter-
relationships, using a set of spatial objects (or features). Spatial objects are represented by vector,
raster, topological, planar, or other models of geospatial data. The SDTS data model defines 32
spatial object types that include point features (0-D), line features (1-D), and area features (2-D)
as vector and raster objects. It also defines components of a data quality report, and the layout of
SDTS modules that contain all needed information for a spatial data transfer. The specification
organizes the spatial data into a set of modules, and each module has a number of fields that
define a specific type of spatial information. Overall, the standard defines the structure and
content of 34 modules grouped into five areas: Global, Spatial Objects, Attribute, Data Quality,
and Graphic Representation. A number of options related to projections, coordinate systems, and
data quality are also defined.
SDTS Part 2 defines spatial features which could be a physical object or an occurrence that can
be located geographically. It describes a standardized list of spatial features (entity types) and
their associated attributes. The features are defined using the 32 spatial object types described in
SDTS Part 3 defines rules and formats for encoding the data model using ISO 8211 into a
neutral and structured file format that can be exchanged in text or binary format. SDTS Part 3
also specifies the method to map SDTS logical constructs (e.g. spatial features, modules, fields)
into ISO 8211 Data Descriptive File (DDF).
11.4. SDTS Profiles
Subsequent parts of the SDTS standard (i.e. Part 4 and above) address the implementation of the
SDTS standard for exchanging different types of spatial data. SDTS is intended to be a generic
spatial data standard that supports all or most of the different types of spatial data. From an
implementation point of view, this generality seems to be almost prohibitive. The need to
develop software systems that can virtually map any spatial data (i.e. vector and raster-based) to
any other type seems to be impractical and virtually impractical to implement, especially given
the vast number of options that can be used during the mapping (e.g. projections, coordinate
systems). Therefore, to support more feasible implementations of SDTS, the standard has
defined a set of “profiles”. A profile defines the rules to “restrict” the mapping of SDTS spatial
data described in the base specification (SDTS Part 1, 2, and 3) to a specific type of spatial data.
Specifically, a profile specifies rules for: (1) restrictions for the use of the specific SDTS spatial
object types; (2) conventions for naming modules and files; (3) specifying of mapping options
such as projections and coordinate systems; and (4) the use of the ISO 8211 textual or binary
encoding. Software implementing support for the SDTS standard typically support one or more
of the defined profiles.
SDTS profiles are differentiated primarily by their spatial data models (i.e. the type of spatial
data that can be mapped). Examples of existing or proposed SDTS profiles include: topological
vector, raster, computer-aided drafting and design (CADD), non-topological vector, object-
based, cadastral, graphic, geodetic point, and Digital Geographic Exchange Standard/Vector
Product Format (DIGEST/VPF).
The most mature profiles are the Topological Vector Profile, TVP (defined in SDTS Part 4) and
the Raster Profile, RP (defined in SDTS Part 5). TVP describes the implementation of the SDTS
standard for exchanging vector-based spatial data that can be described as a planar graph. On the
other hand, the RP describes the implementation of the standard for exchanging raster-based
data. The RP supports the use of the ISO Basic Image Interchange Format (BIIF) or
Georeferenced Tagged Information File Format (GeoTIFF).
It is worth noting that an SDTS profile could be used to support several user data formats and
data products. For example, the Topological Vector Profile (TVP) can handle many data formats
including the USGS Digital Line Graph (DLG), the U.S. Bureau of Census’ “Topologically
Integrated Geographic Encoding and Referencing” (TIGER) format, ESRI Coverage format, and
many other topologically structured vector data set.
SDTS Part 6 covers Point Profile (PP) and describes the specification of high precision point
data. SDTS Part 7, the Computer-Aided Design and Drafting (CADD) profile, defines an SDTS
profile for use with vector-based geographic data as represented in CADD software. The profile
supports the representation of two- and three-dimensional geographic vector data from CADD
systems to be transferred via the SDTS standard. The CADD profile also enables the exchange of
spatial data between CADD and GIS software.
11.5. SDTS Implementation
Many GIS software vendors, such as ESRI, MapInfo, Intergraph, ERDAS, among others, have
already implemented support for SDTS in their software products. In general, a software
supports the standard by providing users with two commands to import and export data in SDTS
format. Other commands may also be provided to obtain general information about the data
transfer, and list the individual modules within a transfer. A comprehensive list of SDTS
implementers can be found at (mcmcweb.er.usgs.gov/sdts/implement-priv.html).
There are also several software development kits that can be used to develop SDTS-compliant
systems. One such tool is the SDTS++ (mcmcweb.er.usgs.gov/sdts/sdtsxx/index.html), which
provides a set of C++ classes for reading and writing SDTS datasets.
11.6. FGDC Spatial Metadata Standards
The proliferation of spatial data sources, combined with the need to organize, maintain, share,
reuse, and disseminate spatial data, have created a strong demand to assist spatial data users and
GIS implementers in searching, reusing, and assessing the usefulness and suitability of a
particular data set. The FGDC has developed a new standard for Digital Geospatial Metadata
(www.fgdc.gov/metadata/constan.html) to respond to these requirements and to reduce the
possibility of duplicating surveying and mapping work.
The metadata standard allows spatial data creators and producers to use a standard schema to
describe the content and format of their geographic data. GIS user organizations, such as
municipalities and utility companies, can search and retrieve spatial information by searching
meta-databases that contain information about the content, quality, and characteristics of spatial
data sets. Also, municipalities can use the standard to provide municipal spatial information to
other interested parties (e.g. utility companies) and make this information available for online
search, query, and retrieval. Interested parties can then use metadata about the municipal spatial
information to easily search, query, and access the data.
The FGDC’s Metadata Content Standard defines a standard schema to document the content and
structure of the metadata of spatial data. The metadata typically include information such as the
data source, identification, quality, positional accuracy, extent, and other quantitative and
qualitative characteristics that describe the data sets. Metadata could also provide information
about the precision, consistency, and integrity of the data. The metadata schema defines a large
list of metadata elements. However, only a subset of these elements, known as the core elements,
must be defined by data creators. Other elements are left optional.
In 1994, a presidential executive order required U.S. Federal agencies to use the FGDC Metadata
Standard. As part of the National Spatial Data Infrastructure (NSDI) initiative, a national
repository for spatial data, called National Geospatial Data Clearinghouse, was established. The
Clearinghouse was intended to enable users to search for spatial data across the Internet, and to
be able to evaluate the quality and suitability of the data, and to access or order data sets online.
The metadata standard was the main enabling factor behind this effort. In Canada, a similar
effort, called the Canadian Geospatial Data Infrastructure (CGDI),
(www.geoconnections.org/CGDI.cfm), also requires the adoption of the same or a similar
ISO/TC 211 has also defined a similar metadata schema (ISO 19115) that is based on the Open
GIS Consortium metadata standard. This standard was recently published in July 2003. The
INCITS L1 committee (www.ncits.org) is currently undertaking an effort to harmonize and
integrate both the ISO and FGDC metadata standards in order to develop a unified and more
robust standard. More information about this work can be found at
Spatial metadata can generally be generated using a variety of software tools. An example of a
metadata authoring tool that supports the FGDC metadata schema is the U.S. Army Corps of
Engineers’ software called “corpsmet” (corpsgeo1.usace.army.mil). An example of commercial
software that supports the ISO 19115 metadata standard is ESRI’s ArcCatalog
12. Spatial Data Standard for Facilities, Infrastructure, and Environmental
One of the first spatial data standards that have been developed with a primary focus on facility
management and civil infrastructure systems was the "Tri-Service Spatial Data Standards"
(TSSDS) and "Tri-Service Facility Management Standards" (TSFMS) which were first published
in 1992. The CADD/GIS Technology Center (tsc.wes.army.mil) developed these standards to be
used by various organizations within the U.S. Department of Defence (DoD). These standards
primarily address the issues involved in engineering and management of facilities and
infrastructure systems throughout their life cycle.
In July 1999, these standards were renamed to "Spatial Data Standards" (SDS) and "Facility
Management Standards" (FMS), and renamed again in January 2001 to become "Spatial Data
Standard for Facilities, Infrastructure, and Environment" (SDSFIE), and "Facility Management
Standard for Facilities, Infrastructure, and Environment (FMSFIE), respectively. Also, in 1999,
the Facilities Working Group (FWG) within FGDC was merged into the CADD/GIS Technology
Center, and many of the FGDC data content models standards have been integrated into the
SDSFIE standards. This move has helped to harmonize and integrate the two standards, which
will eventually promote and advance the implementation and adoption of the standards across
the industry. Recently, the SDSFIE standard was approved as a national standard by the U.S.
National Committee for Information Technology Standards (NCITS) and became known as
The SDSFIE standards provide a consistent data model to define spatial features, such as
buildings and utilities, in GIS or CAD systems, and to describe a relational database schema to
store the attribute data associated with these features. Attribute data associated with a particular
feature are defined in an “attribute table” attached to that feature. The most recent version of the
standards (Release 2.300) was made available on September 2003. The complete SDSFIE/
FMSFIE standards can be downloaded from (tsc.wes.army.mil/products/tssds-
tsfms/tssds/idef1x/tssds.asp). Figure 3 shows a screenshot of the model schema browser
software, also downloadable from the same site.
Figure 3: SDSFIE/FMFIS Data Model Schema Browser
The SDSFIE/FMFIS standards have already been widely adopted as GIS standard throughout the
U.S. DoD as well as in several other Federal, State, and local government organizations,
municipalities, universities, and utilities companies in the U.S. Many software vendors have
already implemented support for the SDSFIE standard in their products. Examples include
ESRI’s ArcGIS, Intergraph’s MGE and GeoMedia, Autodesk’s AutoCAD Map, and Bentley’s
The FMSFIE standard defines a spatial data model for FM applications. A major goal of this
standard was to enable the integration and data sharing between several existing and commercial
FM software systems. The standard also models data related to construction, operation,
inspection, maintenance, and repair of facilities. The FMSFIE standard references the data
models defined in the SDSFIE as well as the architectural, engineering, and construction (A/E/C)
CADD Standards, also developed by the CADD/GIS Technology Center. In 2000, development
started on a new version of FMSFIE, called Transactional FMSFIE. This new version provides
closer integration with the SDSFIE schemas and focuses on the “areas of asset management,
work management, environmental management, public safety management, organization
management, information security management, and financial management”
It is worth noting that the SDSFIE/FMFIS standards do not provide implementation level
specifications or define a neutral data format for exchanging data and enabling interoperability.
The standards are mainly concerned with defining attribute tables and a database schema that can
be implemented by different software systems to support infrastructure and facilities
management applications. In other words, the SDSFIE/FMFIS standards are mainly intended to
define consistent and comprehensive content data models, in the form of a relational data model,
to satisfy the needs of various parties involved in the infrastructure and facilities management
work. However, the standards provided some guidelines for implementing the schemas in some
software that is commonly used within the U.S. DoD organizations, primarily ArcView and
Autodesk Map. Implementation Guides for ESRI ArcView and AutoCAD Map can be accessed
13. The Open GIS Consortium (OGC) Standards
The OGC was founded in 1994 with the goal “to provide a single 'universal' spatio-temporal data
and process model that will cover all existing and potential spatio-temporal applications; to
provide a specification for each of the major database languages to implement the OGIS data
model; and to provide a specification for each of the major distributed computing environments
to implement the OGIS process model” (www.opengis.org). The OGC standards aim to support
the development of a single integrated infrastructure that is vendor-, technology, and platform-
independent. Heterogeneous and distributed GIS systems complying with the standards can
transparently communicate and exchange spatial data. OGC standards are widely accepted in the
industry and are already supported by many software vendors.
The OGC develops two main sets of standards: abstract-level and implementation-level
specifications. The specifications define a set of interfaces that can support standardized access
to distributed spatial data and processes, and thus enables the interoperability and integration of
spatial data resources across the Internet.
Abstract Specifications define conceptual data models and interfaces that can be used to develop
more detailed implementation specifications. Implementation Specifications, on the other hand,
are a detailed and unambiguous description of software system Application Programming
Interfaces (API) that are based on the abstract standards. The OGC standards also define a set of
services and metadata standards to enable the discovery and access of spatial data and services
across the Internet. The OGC reference architecture has adopted an XML-based web services
model for integration and interoperability of different spatial data resources and data processing
systems. According to OGC’s definition, web services are “… self-contained, self-describing,
modular applications that can be published, located, and invoked across the Web. Web services
perform functions that can be anything from simple requests to complicated business processes.
Once a Web service is deployed, other applications (and other Web services) can discover and
invoke the deployed service."
13.1. The OGC Abstract Specifications
The OGC abstract specifications are organized into topics and each topic is developed and
overseen by a separate Working Group. The abstract specifications can generally be categorized
into two main groups. The first group focuses on standardizing the data models to enable sharing
spatial data and interoperability between different systems, while the second group focuses on
specifying interfaces (or APIs) of web services to enable interoperability of distributed spatial
systems in a plug-and-play fashion. This second group includes topics 12, 13, 15, and 16, while
the first group includes all other topics. Figure 4 shows the relationship between the different
topics in each group.
(Adapted from opengis.org)
Figure 4: Relationship of OGC Abstract Specification Topics
Topic 1 provides the geometry data structures for spatial features. This topic standard was
adopted by ISO/TC 12 and became known as ISO 19107. Topics 2 and 3 deal with the area of
geographic or spatial location in two different domains: Topic 2, Spatial Referencing by
Coordinates, provides the Spatial Reference Systems by which features are related to positions
on earth by coordinates, while Topic 3, Locational Geometry Structures, defined methods for
providing spatial referencing models for GIS systems. Topic 8 deals with defining and modeling
of spatial relationships between features. Topic 11 provides for the modeling and query of
metadata. Topics 1, 2, 3, 8, and 11 directly support Topics 5, 6, and 7.
Topics 5, 8, and 10 discuss the modeling of spatial information using features. Topic 5 discusses
the use of features to model real-world spatial objects. A feature object corresponds to a real
world or abstract entity. Topic 8, Relationships between Features, describes an abstraction for the
relationships between features. Topic 10, Feature Collections, describes an abstract object
consisting of a collection of Feature Instances and their Feature Schema.
Topics 6 and 7 are focused on modeling spatial information using Coverages and Earth Imagery.
The Coverage is a special case of an OGC Feature, while an Earth Image, in turn, is a special
case of a Coverage.
Topics 12, 13, 15, and 16 of the Abstract Specifications are primarily concerned with
standardizing geospatial services, while other topics address the standardization of the spatial
data models. Topic 12, the OGC Service Architecture, specifies a comprehensive set of
geospatial services such as the Web Map Server, the Web Feature Server, the Web Coverage
Server, the Web Annotation Server, and the Web Catalogue Server. Topic 12 has become ISO
19119. Topics 13, 15 and 16 describe specific categories of spatial services. Topic 13 describes
catalogue and information access services; Topic 15 describes the categories of image
exploitation services needed to support the use of images and certain related coverage types; and
Topic 16 describes the image coordinate transformation services.
13.2. The OGC Implementation Specifications
The OGC implementation specifications provide detailed description of software systems API
which enable software developers to build software that can plug-and-play with other standard-
compliant systems. To promote and support the implementation effort of the standards, OGC has
adopted a program, called the Interoperability Program, to implement, test, and refine these
interfaces through the development of a series of pilot projects and test-beds.
The implementation specifications describe the schemas for web services interfaces, outlined in
Topic 12 in the abstract specifications, and the Geographic Markup Language (GML). GML
provides a set of XML-based schemas to the modeling and encoding of spatial and non-spatial
attributes of objects in GISs. Example schemas include geometry for spatial features (0, 1, or 2
dimensional), coordinate reference systems, topology, and coverages, among others. Schemas for
GML and various services can be accessed at (schemas.opengis.net).
Being an XML-based technology, GML is particularly useful to enable the integration, sharing,
and interoperability of GIS data and software across the Internet. GML is intended to be generic
enough to enable the definition of domain-specific schemas. The current GML version is 3.0.1,
was released in June 2003. Most major GIS vendors have already started supporting GML in
their products. Also, several software products are implementing support for OGC Web Service
interfaces and can serve spatial information as GML documents. Many GML-based schemas can
be accessed at (schemas.opengis.net/gml).
14. ISO/TC 211 Geographic information/Geomatics Standards
ISO/TC 211 develops and oversees the development of international standards for representing
and exchanging digital geographic information used in GIS. These standards provide
specifications for the methods, tools and services for managing, acquiring, processing, analyzing,
accessing, and presenting spatial data (www.isotc211.org). The standards also provide a
framework for developing interoperable spatial applications where spatial data can be shared and
exchanged between different systems. The committee includes representatives from 33
participating countries and 17 observing countries, and many external observer organizations
such as OGC. A complete list of approved and under development standards can be accessed at
the committee’s web site. Currently, work is ongoing to develop about 32 spatial data standards.
ISO/TC 211 has about 40 different standards in active development. Up until now, the
committee has defined about 9 international standards (IS) and 10 draft international standards
(DIS), while the remaining is still under preparation. A complete list of these standards with brief
description can be accessed at (www.isotc211.org/scope.htm). The first ISO/TC 211 standard in
the series was published in 2000. The existing IS and DIS are listed below.
14.1. International Standards
International Standards (IS) include the following
• ISO 19101: Reference model (published in 2002)
• ISO 19105: Conformance and testing (published in 2000)
• ISO 19107: Spatial schema (published in 2003)
• ISO 19108: Temporal schema (published in 2003)
• ISO 19111: Spatial referencing by coordinates (published in 2003)
• ISO 19112: Spatial referencing by geographic identifiers (published in 2003)
• ISO 19113: Quality principles (published in 2002)
• ISO 19114: Quality evaluation procedures (published in 2003)
• ISO 19115: Metadata (published in 2003)
14.2. Draft International Standards
The Draft International Standards (DIS) include the following:
• ISO 19104 - Terminology
• ISO 19106 - Profiles
• ISO 19109 - Rules for application schema
• ISO 19110 - Feature cataloguing methodology
• ISO 19112 - Spatial referencing by geographic identifiers
• ISO 19116 - Positioning services
• ISO 19117 - Portrayal
• ISO 19118 - Encoding
• ISO 19119 - Services
• ISO 19125 - Simple feature access – Part 1-2
The most important ISO/TC 211 standards for municipal GIS applications are: ISO 19107
Spatial schema, ISO 19115 Metadata, ISO 19111 Spatial referencing by coordinates, and ISO
19112 Spatial referencing by geographic identifiers. These standards can be directly
implemented in municipal GIS systems.
ISO/TC 211 standard data models are defined using the Unified Modeling Language (UML).
The UML models of the defined standards can also be accessed on the committee’s web site at
In 1999, the ISO/TC 211 and OGC signed a cooperative agreement in order to harmonize and
integrate the standardization efforts of the two organizations. As a result, ISO has adopted a
number of OGC specifications. Most notable is the OGC’s Spatial Schema (ISO 19107) that was
defined as abstract specification Topic 1 on Feature Geometry. The spatial schema describes the
spatial attributes of geographic features as well as a set of operations for data access, query,
exchange, and management. Also, the OGC Service Architecture (Topic 12) was adopted as ISO
19119. On the other hand, OGC adopted ISO 19115 on Metadata where it replaced the Abstract
Specification Topics 9 and 11.
GML development is currently undertaken by a joint team of OGC and ISO/TC 211, and is
planned to be released as ISO 19136. A number of OGC specifications, such as the Web
Mapping Services and the Web Feature Services, have also been submitted to ISO/TC 211for
In this section, we present some proposed strategies that provide a road map for Canadian
municipalities to further exploit GIS data standards and technologies to promote systems
integration and interoperability, as well as to enhance the capabilities to share and exchange
spatial data of municipal assets.
15.1. Harmonization of AEC/FM Standard Data Models with Spatial Data Standards
One of the most important ISO data modeling standards that has been in active use within the
AEC/FM domain throughout the last decade is the ISO 10303 Standard for the Exchange of
Product Model Data (STEP). STEP consists of a series of parts that cover areas of modeling
methods, integrated resources, application protocols, implementation and data exchange
methods, and conformance testing. A STEP-based standard data model provides a standard
schema for representing the data and a neutral file format that enables different applications to
efficiently interoperate by sharing and exchanging information. The AEC/FM industry has
recognized the importance of such standards and there has been an increasing interest in
standardizing and using STEP-based standard information models in various domains. Many of
the AEC/FM modeling effort in STEP is performed under ISO/TC184/SC4 (www.tc184-
sc4.org). A number of Application Protocols (APs) (i.e. domain-specific data models) have
already been developed such as AP 225 for elements of buildings using explicit shape
representation, AP 228 for services (HVAC), and AP 230 for structural steel frameworks.
Another significant STEP-based data modeling effort was undertaken by the Industry Alliance
for Interoperability (IAI) (www.iai-na.org) to define a standard data model for buildings projects.
The data model, called the Industry Foundation Classes (IFC), is the culmination of over a
decade of research and development. The IFC model defines an integrated schema that
represents the structure and organization of project data in the form of a class hierarchy of
objects. The IFC class hierarchy covers the core project information such as elements of
buildings, the geometry and material properties of products, project costs, schedules, and
Although STEP provided robust and powerful methods for information modeling and exchange,
it is the author’s opinion that new data modeling efforts, especially in the municipal asset
management domain, can achieve better and more cost-effective results by adopting UML and
XML instead. The data modeling capabilities of UML and XML schema have proved to be
equivalent to those found in STEP. The new 1.1 XML schema standards
(www.w3.org/XML/Schema) can support the development of semantic rich object-oriented data
models. Also, compared to the graphical notation of STEP data models (EXPRESS-G), UML
offers far more capabilities. Many commercial software tools can easily translate between UML
models and XML schemas. Besides, the availability of many affordable (or free) software tools
that aid in the modeling and implementation of UML and XML-based data models can
potentially make these models widely accessible and usable throughout the industry. In contrast,
STEP modeling and implementation software tend to be far more expensive, more difficult to
develop and maintain, and generally in limited use in the industry. Unlike STEP-based data,
XML data can be easily integrated with other forms of data, which can potentially facilitate
integrating data from various sources. Moreover, accessing and exchanging XML data over the
Internet can be facilitated by the existing web tools and infrastructure already in place in most
There are several efforts in the industry to develop XML-based data models, or schemas. Most
prominent of these efforts are the development of aecXML for buildings projects and facilities
management applications (www.iai-na.org/aecxml), and the development of LandXML
(www.landxml.org) for land, roads, sewers, bridges, utilities, and cadastral data management
applications. More recently, a research project was launched to develop a set of XML schemas,
called TransXML, for transportation applications. TransXML will address areas such as
surveying/roadway design, highway bridge structures, and transportation construction/materials.
There are also several efforts to re-use existing STEP-based data models and re-define these
models using XML. For example, IAI has developed an IFC XML schema (ifcXML) to be used
as an equivalent data model to the IFC STEP schema. Similar efforts need to be undertaken to re-
define STEP-based and other content data models that have been developed over the years, such
as FGDC models, SDSFIE, or vendor-specific data models, using XML schemas. These schemas
can then be harmonized and integrated with other ISO or OGC’s spatial data schemas such as
GML. In fact, many of the elements and data types defined in GML can readily be used to
represent many features and objects defined in aecXML and LandXML schemas. This topic will
be the subject of a future study.
15.2. Adopting and Adapting Spatial Data Standards for Municipal Applications
A number of spatial data standards have been presented. Although some of these standards can
be adopted “as-is” to support municipal asset management applications, more work is still
needed to adapt most of these standards to the municipal asset management domain. Some of the
aforementioned standards have already been supported by commercial off-the-shelf software; for
example, the latest releases of ESRI’s ArcGIS (vers. 9.0) support GML and ISO Metadata
As it is apparent from the discussion above, many standardization efforts have reached a high
level of maturity, and many efforts are underway to merge and harmonize these different sets of
standards into fewer and more comprehensive standards. For example, SDSFIE is adopting
FGDC content data models, and ISO and OGC are cooperating to integrate their standards and to
develop new ones. FGDC is also studying the harmonization of its metadata standard with ISO
Municipalities will need to participate in the ongoing standardization efforts. The main sources
of spatial data standards related to municipal asset management applications primarily include
the SDSFIE and FGDC for data content standards, and ISO TC-211 and OGC standards, for
modeling, encoding, and interoperability. The OGC has adopted an approach for developing and
defining domain-specific spatial data models, called Topic Domains, based on the more generic
existing standards. OGC has already defined a Topic Domain for telecommunications Networks.
Developing similar data standards for municipal applications seems to be a viable and feasible
undertaking. In so doing, the FGDC and SDSFIE data content standards can be used to define
OGC Topic Domains for various municipal assets such as roads, water mains and sewers.
Municipalities will also need to adopt and encourage the use of software systems that comply
with known data standards.
15.3. A Model-Based Approach for Developing Municipal Asset Management Applications
Generally, the workflow processes in most municipalities are not engineered to support
integration. Traditional workflow processes, supported by legacy software systems, have created
the “islands of information” phenomenon in most municipalities. As a result, inefficiencies in
data flow and coordination of work processes have been commonplace. Enabling the
interoperability and efficient data exchange between different software systems, though can
potentially improve the efficiency of data flow, will not offer a complete solution to this
problem. The data standards discussed in the previous sections are intended to address systems’
interoperability and data exchange issues. However, to maximize the benefit from
interoperability and data standards, the integration of the data and the associated workflow
processes needs to be achieved in order to improve the efficiency of data flow and coordination
of work process. In other words, workflow processes will need to be re-engineered and linked
with the data in a manner that would bind together various perspectives of asset information and
integrate the workflow processes.
Adopting a model-based approach for municipal asset management systems can potentially
enable this integration between workflow processes and their associated data. The approach
involves the use of a central data model to represent the asset inventory data and link (or cross-
reference) various aspects of asset information, such as inspection data, performance data,
maintenance data, and cost data. The resulting “integrated model” would represent a
comprehensive view of the multi-disciplinary aspects of municipal assets. It would also support
efficient gathering, organizing, management, and distribution of information. Accessing the
integrated data model through a GIS interface will further enhance the ability of the management
teams to explore, navigate, access, and query infrastructure data. The role of the integrated asset
data model to support information sharing and exchange is illustrated in Figure 5. The model-
based approach can also facilitate integrating different inventory systems, such as sewers, roads,
bridges, and facilities into a single database that integrates information across various municipal
The model-based approach provides many benefits to the various municipal asset management
activities. First, in addition to the inventory and condition data, the model enables the
representation of a wide range of information about other aspects of assets (e.g. performance
characteristics, maintenance, operations, cost) as well as other forms of information such as
inspection reports, maintenance records, and drawing files. The integrated data model provides a
single point of access to all relevant information about the asset. Second, the model can
significantly improve communication among stakeholders by making various aspects of asset
information accessible from one centralized data repository. Stakeholders from different
disciplines can use the integrated data model to access information in their respective domains.
Third, the model will enable tools interoperability and efficient sharing and exchange of
information. By sharing the data model, the need to map and translate the data from one tool’s
format to another will be minimized or eliminated altogether.
Infrastructure System Stakeholders
Maintenance Data M odel Sim ulation m odels/results
Deterioration/life cycle cost
References to knowledge sources References to other databases
(Halfawy et al. 2002)
Figure 5: Applying a Model-Based Approach to Municipal Asset Management
15.4. Developing and Deploying Sharable Municipal Spatial Data Repositories
Municipalities, utility companies, and many other organizations typically use and share common
spatial datasets. As previously shown, inconsistent and different representation of spatial data has
always been an impediment towards efficient use and sharing of spatial data. Spatial data
standards can play a critical role to enable the sharing and efficient access of the data.
One possible approach is to build and maintain a centralized repository of common municipal
spatial data that need to be shared and accessed. The spatial data repository can enable the
publishing and distributing of municipal spatial data over the Internet in a format that can be
directly used by different and heterogeneous software tools. The repository can also enable easy
integration of spatial data within the same organization or across the organizational boundaries.
Sharing a municipal spatial data repository can also help coordinate the development, access, and
maintenance of the municipal spatial data in various federal, local, and private parties and
organizations. The repository can be later linked with the Canadian Geospatial Data
Infrastructure (CGDI) (www.geoconnections.org) to make the data easily accessible over the
15.5. Framework for Developing Integrated GIS-Based Municipal Asset Management Systems
Integrated GIS-based municipal asset management systems are complex software solutions that
require special design to satisfy their wide range of requirements and objectives. Defining a
robust and flexible architecture for these systems can facilitate the development of successful
and practical municipal asset management software solutions. Also, a common architecture can
potentially improve the consistency of applications in the domain, and enable better
interoperability and reusability of different components and applications. This section briefly
presents a framework for developing integrated GIS-based municipal asset management systems.
The framework aims to integrate different tools and technologies into one coherent environment
to enable municipalities to address various aspects of asset management from an integrative
perspective. Details about the framework and its implementation can be found in work by
Halfawy et al. (2002).
The framework serves as a reference architecture to support the implementation of integrated and
distributed GIS-based municipal asset management systems. The framework supports the
integration of asset management processes by adopting a model-based approach (Section 15.3)
whereby a central data model is used to integrate different aspects and views of the assets.
A primary goal of the framework is to facilitate the development of “integrated” software
systems that can support managing multiple municipal assets (e.g. roads, watermains, and
sewers). In a typical municipality, different systems are used to manage different assets, and each
system typically manages a separate database about that asset information. An integrated system
tries to store asset data in a centralized repository that can maintain cross references and
relationships between different assets in a consistent manner. Such integrated systems can
support studying and investigating the interactions and dependencies between different assets
that may be co-located, overlapping, or in close proximity. The systems can also eliminate
duplication and inconsistencies in the collecting, processing, and storing of asset data. Assisted
by the GIS functionality, this integration can improve the efficiencies, cost-effectiveness, and
coordination of maintenance plans and work processes, and lead to minimizing rework and waste
The proposed framework has a modular component-based architecture to accommodate future
modification, extension, and relevant technology improvements. Access to asset data
repositories, knowledge sources, and software applications is enabled through a unified and
integrated GIS interface. The framework separates the responsibilities between the set of
function-specific software applications and the generic services components. Function-specific
software applications provide users with the functionality to perform specific tasks, while the
components provide the functionality to integrate and manage different processes.
Given the complexity and scalability requirements of asset management systems, a multi-tier
component-based architecture seems particularly suitable. The framework architecture has been
created by breaking down the required functionality into a set of specific services and then
mapping these services into a set of components. Components are coarse-grained, reusable,
software subsystems that perform a specific set of functions. The component-based architecture
helps to maintain the framework flexibility and extensibility since upgrading or extending any of
the components will have little impact on the rest of the framework. An important feature of the
component-based architecture is that different parts of the framework can be running in a
distributed environment over the Internet. The tiered architecture, on the other hand, helps to
separate the responsibility and functionality between the GIS interface, the function-specific
applications, and the common domain-specific components.
GIS Interface Tier
Mapping Condition Modeling/ Real-time Report Cost
Tools Assessment Simulation Data Acquisition Generation Estimating
MR&R Risk EMS Work Order Optimization Performance
Planning Analysis Tool Management Tool Prediction
Assets Management Services Tier
Common Asset Management Services Tier
Data Knowledge Modeling Maintenance Operations
Management Management Components Planning Management
Components Components Components Components
Assets Management Assets Data
Knowledge Repository Repository
Halfawy et al 2002
Figure 6: The Component-Based Framework for Integrated Asset Management Systems
The proposed framework four-tier architecture includes: the GIS interface tier, applications tier,
common asset management services tier, and data/knowledge repository tier. The GIS tier
implements the spatial interface to the integrated spatial data model and allows users to access
the framework functionality through a unified intuitive graphical interface. The tools at the
applications tier support specific tasks, while the common services components at the middle tier
implement a set of generic domain-specific services (e.g. information management, operations
management). Figure 6 illustrates a conceptual view of the framework and its main components.
The following sub-sections briefly describe each tier.
15.6. GIS Interface Tier
This tier implements the GIS interface in the integrated spatial data model that is constructed and
maintained by the framework tools and components. The interface serves as a graphical front-
end to the asset data repository that provides the ability to access data locally, or over an Intranet
or Internet. Other function-specific applications would employ the GIS functionality to enable
users to efficiently explore and analyze spatial aspects of the infrastructure systems. Users could
query the system for its current performance and physical parameters, and to visualize the spatial
characteristics of the assets. Assets are represented as map features with certain spatial
relationships, and attributes are assigned to these map features and used to associate the features
with their corresponding data tables and records in the data repository. The GIS interface
provides means to navigate through and query the database and to carry out “what if” analysis.
Users can simply point to map features, representing assets physical components, and query the
system or retrieve data related to the selected feature(s).
15.7. Applications Tier
This tier integrates a set of function-specific applications so that they can effectively and
seamlessly interoperate and share data through the use of the integrated asset data model.
Mapping the integrated model to and from the ad-hoc information models supported by
individual tools is achieved by using adapters. However, the need to implement adapters will
continue until industry-wide standard data models are developed and supported by different
applications. Applications access the framework through the components’ interfaces, and thus
the framework makes no assumptions about the implementation details of individual tools.
Making the framework tool-independent provides the flexibility to upgrade or replace any tool
without impacting other components.
15.8. Common Asset Management Services Tier
This tier includes the following five main components.
Data Management Component: This component offers data management services such as
maintaining the data repository, mapping between the integrated data model and application-
specific data models, concurrency control, and transaction management. This component is
developed on top of the database management system used to implement the data repository. The
component is used by different applications to interface with the asset data repository. An
important functionality of this component is to support data collection and organization. The
component may provide functionality to support different data collection technologies (e.g.
visual inspection, NDE, remote monitoring using networks of sensors), and to automatically
update the data repository to reflect the up-to-date status of the assets.
Knowledge Management Component: Besides reliable and updated physical, condition, and
performance data, asset management decisions also employ a vast amount of domain knowledge
from a multitude of sources. Capturing, organizing, and representing this knowledge is of
paramount importance in the successful implementation of asset management systems. The
knowledge management component can enable agencies to develop and maintain a knowledge
repository about best practices and experience, and provide tools to enable effective search and
retrieval, sharing, and reuse of this knowledge. The component may also maintain a web portal
to post information such as inspection manuals, maintenance operation guides, new and
emerging technologies of interest, and design manuals.
Modeling Component: This component provides functionality to support life cycle modeling of
the assets. Infrastructure management systems typically use several models to accurately predict
future performance, plan maintenance operations, evaluate performance and costs of alternative
strategies, and to optimize the allocation of maintenance funds. Modeling various aspects of an
infrastructure asset is generally a costly and time-consuming task. However, techniques could be
developed to generalize a class of models for particular types of infrastructure systems in the
form of “a library of model templates” that can be later customized for specific infrastructure
systems. This functionality can significantly facilitate the process of model construction and
linking the models’ data with the data repository.
Four main classes of models can be identified: simulation models, deterioration models,
optimization models, and cost models. Simulation models are used to simulate the system
performance. Deterioration models are used to estimate future infrastructure condition.
Optimization models are used to determine the optimum allocation of resources in order to
sustain acceptable levels of performance. Cost models are used to estimate items such as user
cost, organization cost, and maintenance cost. The modeling component will facilitate
constructing these models and their integration with the data repository.
Maintenance Planning Component: This component implements methods to enable planning,
scheduling, and tracking of maintenance operations and estimating maintenance costs. The
component will use different modeling approaches to determine the best maintenance options
based on life cycle cost analysis, to prioritize maintenance operations, estimate budget
requirements for future maintenance, and to determine cost-effective maintenance plans.
Operations Management Component: This component provides services to coordinate,
monitor, and manage maintenance operations. The component implements a workflow model,
supplied by end-users, to represent and guide the maintenance operations and to define the inter-
dependencies of various operations. The workflow model shows the data flow across various
maintenance operations. The workflow model can also assist in automating routine tasks such as
scheduling periodic inspections and preventative maintenance activities. Another function of this
component is to manage work orders and service requests. Work orders can be automatically
generated based on service requests, complaints, and condition data. The component can also
log, assign, track, and control work orders.
15.9. Data/Knowledge Repository Tier
The data repository provides a single point of access to all asset data, which can significantly
improve the collection, organization, consistency, and availability of information. The repository
is typically implemented using a Database Management System (DBMS) that can potentially
support distributed data sources and implement concurrency control mechanisms. The repository
schema is defined based on the integrated spatial data model of the assets. The repository
contains data about the inventory, condition, and performance characteristics. Inventory data
includes physical attributes that define design parameters of the assets’ structural components.
Condition attributes define the current status of these components and may include data from
visual inspection, NDE tests, or from remote sensors. Performance attributes define the current
performance metrics (or indices) of the structural components.
The knowledge repository supports the representation, organization, sharing, and reuse of asset
management knowledge. This knowledge may include structure-specific knowledge related to
the infrastructure physical characteristics, inspection requirements and procedures, life cycle
management, assessment, maintenance and rehabilitation knowledge, operations management
knowledge, and procedures or best practices for field tests, performance evaluation, and
maintenance tasks. Developing the knowledge repository requires systematizing asset
16. Summary and Conclusion
Municipalities are facing unprecedented challenges due to the increasing number of aging
infrastructure assets combined with declining maintenance budgets. Leveraging the use of
information technology, in general, and of GIS and asset management systems, in particular, to
improve the efficiency and effectiveness of asset management work processes is considered a
crucial strategy to address these challenges. Lack of interoperability and inefficient data
exchange between municipal asset management software has been a major impediment to the
efficient access and communication of asset information. Efficient data sharing and software
interoperability play a crucial role in supporting efficient data access and retrieval, which in turn
is important to support efficient operations and cost-effective decision-making processes at all
levels of municipal asset management. Much inefficiency has been attributed to the use of
inconsistent data models and formats across different software applications.
Developing and adopting standardized data models can aid in solving this problem. As
previously shown, the use of standard data models can significantly improve the availability and
consistency of asset data across different software systems and platforms, can serve to integrate
data across various disciplines, and can facilitate the flow and exchange of information between
the various parties involved, resulting in removing or eliminating deficiencies of information
access and exchange.
This report discussed several issues related to leveraging the role and functionality of GIS
technology in enhancing the municipal asset management processes by increasing the efficiency
of managing the asset information. The report discussed the data requirements of GIS-based
asset management systems and presented some of the challenges of collecting and maintaining
municipal asset data in a form that enables efficient access, query, analysis, and retrieval.
The importance of interoperability and spatial data standards from an asset management
perspective was highlighted. The report has also surveyed a number of existing spatial data
standards that can be adopted or adapted for municipal asset management applications. It also
presented a methodology and a framework for developing integrated GIS-based municipal asset
management systems. The framework employed a model-based approach that exploits the notion
of standard data models and the use of a centralized data repository. The proposed framework
can potentially provide municipalities with a tool to leverage the use of their existing systems
and technologies, and thus helps municipalities to protect their existing investment in
information technology tools, while re-casting these tools into integrated and more efficient
systems. Also, the framework can help municipalities focus on the big “integrated” picture,
instead of the details of individual software tools and technologies.
Implementing and deploying integrated GIS-based asset management systems in municipalities
can be regarded as a long term goal that can be realized through a number of incremental steps.
However, in order to realize this long-term goal, municipalities must take some short-term
actions towards this objective. First, municipalities need to be actively participating in the
development and adoption efforts of standard data models. Future studies are needed to study
existing standard data models in detail and to harmonize different models, before new
development is carried out. Second, municipalities need to ensure the reusability of design and
construction information delivered at the end of construction or maintenance projects, and to
integrate this information back into the asset database to reflect the updated status of assets.
Information loss during hand-over processes is very common in the industry, and many problems
may arise as a result. Addressing this issue would require devising a new information hand-over
strategy that specifies the kind and format of the as-built information to be handed over. Again,
the use of standard data models can be particularly useful in this regard. Third, municipalities
should start adopting software solutions that support existing data standards and encourage
software vendors to implement these standards. And finally, municipalities should invest in
training their technical staff to keep pace with the innovations in geospatial technologies, and
learn how to maximize the benefit of these technologies to aid various asset management
Specifically regarding the MIIP project and its deliverables, the author thinks it is best to look at
GML in more detail and to re-encode some of the FGDC or SDSFIE data models based on GML.
GML can be used to complement and replace parts of the LandXML and aecXML schemas.
Since the project is focusing on the data model of sewer systems, the author also recommends
studying ESRI’s water utility’s data model in more detail. The data model (ESRI 2004) is fairly
detailed and provides a large set of objects and feature classes typically required to model sewer
systems. Re-encoding this data model using XML should be a straightforward task.
This report was prepared under a Collaborative Research Agreement between UBC and NRC.
Financial support for this work was provided by the NRC as part of the MIIP project. The author
is deeply indebted to the input and guidance of Dr. Dana Vanier of NRC and Dr. Thomas Froese
of UBC. Their insightful vision helped shape and direct this work.
Danylo, N. and Lemer, A. (1998) Asset Management for the Public Works Manager: Challenges
and Strategies, Findings of the APWA Task Force on Asset Management,
<www.apwa.net/documents/resourcecenter/ampaper.rtf > (April 2004).
ESRI (2004) Water Utilities ArcGIS Data Models, <downloads.esri.com/support/datamodels/
index.cfm?fa=downloads.dataModels.filteredGateway&dmid=16> (April 2004).
Frangopol, D. M., Lin, K. Y. and Estes, A. C. (1997) Life-cycle cost design of deteriorating
structures, J. Struct. Engrg., ASCE, 123(10), pp. 1390–1401.
Grigg, N.S. (1999) Infrastructure: Integrated Issue or Tower of Babel?, Journal of Infrastructure
Systems, 5( 4), December.
Halfawy, M.R., Pyzoha D., Young R., Abdel-Latif M., Miller R., Windham L. and Wiegand R.
(2000) GIS-based sanitary sewer evaluation survey, 20th Annual ESRI International User
Conference, Jun., San Diego, CA. <gis.esri.com/library/userconf/proc00/professional/
papers/PAP158/p158.htm> (April 2004).
Halfawy, M.R., Pyzoha D., and El-Hosseiny T. (2002) An Integrated Framework for GIS-Based
Civil Infrastructure Management Systems, Proceedings of the CSCE 2002 Conference of the
Canadian Society for Civil Engineers, Montreal, Canada, June 5-8.
Kyle, B.R.; Vanier, D.J. and Lounis, Z. (2002) The BELCAM Project: a summary of three years
of research in service life prediction and information technology, 9th International
Conference on the Durability of Building Materials and Components, Brisbane, Australia,
pp. Paper 138, pp. 1-10, April 01, <irc.nrc-cnrc.gc.ca/fulltext/nrcc45189> (April 2004).
Lemer, A.C. (1998) Progress Toward Integrated Infrastructure-Assets-Management Systems:
GIS and Beyond, APWA International Public Works Congress, NRCC/CPWA Seminar
Series: Innovations in Urban Infrastructure, <irc.nrc-cnrc.gc.ca/fulltext/apwa/
apwaintegrated.pdf> (April 2004).
Lounis, Z. and Vanier, D.J. (1998) Optimization of bridge maintenance management using
Markovian models, Proceedings of Developments in Short and Medium Span Bridge
Engineering Conference, Calgary, Alberta, pp. 1045-1053, <irc.nrc-cnrc.gc.ca/fulltext/
nrcc42829.pdf> (April 2004).
Lounis, Z., Vanier, D. J., Lacasse, M. A., and Kyle, B. R. (1998) Effective decision-making tools
for roofing maintenance management, Proceedings of 1st International Conference on New
Information Technology in Civil Engineering, 425–436, <irc.nrc-
cnrc.gc.ca/fulltext/nrcc43721.pdf> (April 2004).
Shahin M.Y. (1992) 20 Years Experience in the PAVER Pavement Management System:
Development and Implementation, Pavement Management Implementation, F.B. Holt and
W.L. Gramling, editors, ASTM, West Conshohocken, PA.
Vanier, D. J. 2001) Why industry needs asset management tools, Journal of Computing in Civil
Engineering, 15(1), pp. 35–43, <irc.nrc-cnrc.gc.ca/fulltext/nrcc44702> (April 2004).
Vanier, D. J. (2000) Municipal Infrastructure Investment Planning (MIIP) Project: Statement of
Work, Inst. for Research in Construction, National Research Council Canada, Ottawa,
Vanier, D.J. (2004) Towards geographic information systems (GIS) as an integrated decision
support tool for municipal infrastructure asset management, CIB 2004 Triennial Congress,
Toronto, pp. 1-11, May, <irc.nrc-cnrc.gc.ca/fulltext/nrcc46754> (April 2004).
Vanier, D.J. and Rahman S. (2004) MIIP Report: A Primer on Municipal Infrastructure Asset
Management, Internal Report B-5123.3, National Research Council Canada, Ottawa, 68p.,
<irc.nrc-cnrc.gc.ca/uir/miip/docs/primer.pdf> (July 2004).