On the Lifecycle of DOT Class and HCA Structures Modeling

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On the Lifecycle of DOT Class and HCA Structures Modeling
Jared Tooley, NiSource Gas Transmission and Storage
On the Lifecycle of DOT Class and
HCA Structures
Modeling, Collecting, and Managing Data to
Support 49 CFR 192 Regulations
Michael White, Global Information Systems, LLC
Brett A. Rape, Global Information Systems, LLC
Background
• NGT&S maintains data for:
– Over 460,000 structures
– Roughly 15,000 miles of pipeline
– Over 9,000 miles of ROW
• Past:
– Created “DOT Sheets” which were sent to Operations for field review
• Large and time consuming effort
– Operations tasked with red-lining DOT Sheets and returning for update
• Varying levels of response
– GIS update frequency inconsistent on returned DOT Sheets
• Present:
– Staff reductions underscored the need for a more efficient approach
– Now leveraging advanced technologies to meet new corporate goals of
continuous improvement
– Utilizing electronic field data collection system to link data updates directly to
GIS via automated completion workflow
NGT&S Structure Game Plan – TRUST
• Large scale structure data collection from publicly available ortho-imagery
to capture new, and update existing, structures in a highly scalable and cost
effective manner.
– 92% confidence level that
existing structures were
accounted for
– 4.5% of newly captured
structures’ type could not be determined from imagery.
– Remaining 3.5% lack in structure
data capture confidence is due
to source imagery characteristics
(varying dates, times of year,
image quality)
NGT&S Structure Game Plan – VERIFY
• Field verification of structure data where structure type could not be
determined from Imagery
– Result is a DOT structure type for all known structure locations
• Data is now at a confidence level that it can be utilized for Class Location
and HCA calculations (proposed)
• Field Verification of all structures
in areas of proposed Class or HCA
change
• Ongoing program for continuous
maintenance and update that
utilizes NGT&S Field personnel
within their daily activities
Lifecycle of Structures for DOT Class
and HCA Analysis
Modeling: How is the data stored in the database?
Retire
Maintain
Capture
Structure Lifecycle
Verify
Classify
Modeling: How will the data be stored?
Predominant Data GIS Data Storage Methodologies
– GIS Feature Based
• Each feature object contains all of its data, both attributes and geometry.
– Examples include implementations of APDM
– Arc feature classes implement point or polygon structures, each with
associated attributes.
– Tabular Geometry Based
• Geometry and attributes are in one or more than
one tables
– Examples include implementations in ISAT
or PODS
– Coordinates are queried from one or more
tables and then rendered as geometric
figures (points or polygons).
Modelling: What do we need to know?
– Base Structure Attributes (Per CFR192)
• Is it a building or is it an outside area?
(Structure Type)
• Is it occupied?
(Number of Occupants)
• Is there more than one dwelling unit in the building?
(Number of Units)
• Are there 20 or more persons five days per week?
(Days per Week)
• Are there 20 or more persons ten weeks per year?
(Weeks per Year)
• Are there 20 or more persons, fifty days per year
(Days per Year)
• Does it have four or more stories?
(Number of Stories)
• Does it contain occupants with impaired mobility?
(Impaired Mobility)
• When was it discovered?
(Capture Date)
• Does it still exist?
(Status)
Modeling: What would we like to know?
– Extended Attributes (Support Additional Business Initiatives)
• Type of building or area
(Structure Subtype)
• Name of Building or Facility
(Structure Name)
• Facility Description
(Description)
• Does it have a Day Care?
(Day Care Present)
• Physical Address
(Street, City, Sate, ZIP)
• Contact Info
(Name, Phone, E-Mail)
• Discovery Method
(Discovery Method)
• Verification Information
(Verification Type)
• Spatial Accuracy Information
(Accuracy)
Modeling: Avoid Remodeling
Don’t allow modeling to become a part of the structure lifecycle!
• Recognize that there are separate costs associated with collecting and
maintaining data.
– The cost for up-front collection of additional data may seem like a good value,
but the cost to maintain some data elements can be prohibitive.
– If collected data cannot be effectively maintained, it will fall out of date and
become unreliable.
– Once data is perceived as unreliable users will ignore it, or worse, question the
reliability of data as a whole.
• Keep the Model Simple!
– Consider the method of data collection for each modeled attribute
– Consider the maintenance requirements for each modeled attribute
Structure Data Capture and Classification
• Routine system-wide structure updates
– Free, public imagery (Google, Bing Imagery, etc.)
– ESRI digital imagery
– GeoEye satellite imagery
• Existing structure points and footprints are overlaid
on the imagery
• New structures are captured and classified if
possible (DOT, Non-DOT, Unknown)
• Attributes regarding the structures characteristics
are collected
– Photo Interpretation Techniques
– “Street View” function in Google
– Undeterminable characteristics are classified as
“Unknown”
Verification: Pre Class and HCA Calculation
• Targeted system-wide verification of “Unknowns”
– Generate electronic forms for each
• Contains all known attribution –possibly only X,Y
• Contains ortho image of the immediate area
• Allows systematic and comprehensive update process
– Electronic Forms are loaded to field data collection unit
• Alternately Electronic Form IDs and coordinates Loaded to GPS and field computer
loaded with electronic forms
– Field personnel verify structures and update attribution
Verification: Post Class and HCA Calculation
• Preliminary Class & HCA Runs Identify Areas of Potential Change
– Identify controlling structures & proximity areas that require higher accuracy
location information
• Preparation for Field Verification
– Create electronic forms for every existing structure, unknown or otherwise
• Create digital field maps with structures and forms IDs
• Identify where sub-meter positional accuracy is required for controlling
structures and proximity areas
• Load forms to field data collection unit with sub-meter GPS and offset capability
Verification: Post Class and HCA Calculation
• Field Activities
– Utilize electronic forms verify or edit attribution (including location) as required
• Data driven approach promotes efficiency, consistency and accuracy
– Capture any new structures not on the imagery using the electronic forms
• Capture location using GPS offset tools if possible
• Completed Electronic Forms are Reviewed and Loaded to the GIS for
Re-run of Class and HCAs
• Verified Class and HCA Changes are
Loaded to the NGT&S Class and HCA
Change Workflow
Maintenance
• Maintain history of structure location and attributes
– Attributes change over time and continue to affect Class, HCAs, and their limits
– Field data can be used as document trail for audits, research
• Location accuracy
• Occupancy changes
• GIS tools for maintaining structure data
– Single, targeted purpose ensures data consistency and reliability
– Both Class / HCA specific “Other” regular field activities are connected via
automated workflow utilizing electronic data collection system
• Foot patrols for leaks, exposures, line marker installations, etc.
– Collect new structures as they are identified
– Modify attributes for existing structures as use or occupancy changes are identified
Retirement
• Structures are “retired”, not deleted
– Historical view of system
– Permits reconstruction of Class and HCA changes
• Structures can be “un-retired”, but are more likely to be replaced
• Can be viewed as an aspect of “maintenance” Conclusion
• Collection and maintenance of structures is a major undertaking
• Careful consideration of how you model, collect, and maintain structures
ensures you get the most from them, not just for Class and HCAs
• Utilize appropriate tools for maximum efficiency and accuracy
• Improved data reliability encourages continued field efforts to maintain
structure data
Thank You!
• For more information:
Jared Tooley
[email protected]
Michael White
[email protected]
Brett A. Rape
[email protected]

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