8 MINUTE READ
The most ambitious public data project attempted in Canada: Acorn's roadmap to success
Building Data-Smart City Solutions
Consultant: STEERING
Copyright: SSMIC
The Sault Ste. Marie city/utility GIS implementation took five years and was one of the most ambitious GIS projects attempted in Canada. The data capture required for the city and PUC was massive and included a complete inventory of all water, wastewater, electric, transportation, telecom, land base and administrative features. “We started with road line features, moved to sewers, manholes, catch basins. Anything that had an address or point in space, we wanted it in there,” says Don Elliott, Director of Engineering for the City of Sault Ste. Marie who oversaw the GIS project from the municipality’s side for three years. “It was down to nitty-gritty details and we went at it week by week.”
One of the early challenges the Acorn team faced was the number of unknowns in early features and attributes to be added to the GIS. “If our physical records didn’t show what’s in the ground we couldn’t determine things like material or pipe size,” said Elliott. This absence of data was a gap that needed to be filled in.
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To solve the problem of missing or incomplete information, action reports were created to initiate further analysis and resolve questions related to the data entered into the GIS. “Every time something was missing an action report was created,” said Elliott. “The city would have to work through thousands of these on top of their regular responsibilities. We emphasized that the data had to be reliable. That’s why the process of updating it while it was entered was and still is critical. If you don’t know, don’t put it in. When the data is ready, put it in and make it accurate.”
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The accuracy of data was and remains paramount to the success of the CIU project. To ensure the data was both exact and consistent, Acorn developed pick-lists as a method to enter data into Esri’s software. The reason pick-lists were essential is twofold - it formed the foundation in which data could be entered without free form typing, thus greatly reducing the margin for human error in the data entry process, and it would permit users to query information based on set parameters in the system allowing for exact results based on keywords for text-based spatial search.
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To prevent editors from typing incorrect values and avoid typos, Paul Beach and his team worked extensively with the city and PUC to create a pick-list inventory that spanned the range of value fields across the data layers. The outcome achieved was data quality at the highest level.
Example of pick-list used in the CIU to identify characteristics of underground water infrastructure
Intelligent data modelling
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It took four years of effort working with the City of Sault Ste. Marie and PUC staff and engineers to design the data models for water, wastewater, electric, transportation and administration. This would form the intelligence of the GIS solution. These data models contain all of the features, attributes, symbology, relationships, domains and connectivity rules to represent complex municipal and utilities systems. Features in the CIU are not just lines and representations of municipal infrastructure, but form object oriented intelligence that communicate how these objects connect, interact and react to one another.
The data capture process to populate these models involved inputting field inventory and original source documents into the GIS. With stockrooms of drawings and plans (some dating back over one hundred years) and in-field verification of infrastructure, this was a labour-intensive process.
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The data capture process to populate these models involved inputting field inventory and original source documents into the GIS. With stockrooms of drawings and plans (some dating back over one hundred years) and in-field verification of infrastructure, this was a labour-intensive process.
“You don’t put garbage into the GIS to begin with. The best information went into the GIS and what we didn’t know went in as ‘unknown’. If we couldn’t tell based on the drawing what it was, material or size, we didn’t put it in.”
– Don Elliott, Director of Engineering, City of Sault Ste. Marie
Initial data deliverables from
The City of Sault Ste. Marie and PUC
Data warehouse development
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To tackle the data capture and data warehouse development, both the city and the utility committed dedicated staff to the project to scrub records, prepare for the conversion of source documents and complete a field inventory of infrastructure.
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Records conversion involved discrepancy resolution; scanning and digitizing of all infrastructure drawings, schematic drawings, plans and profiles; loading digital assets from existing databases including customer databases, electrical infrastructure databases, municipal land databases; and manipulating existing digital data out of their current programs.
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The completion of the initial field inventory included a full inventory of all overhead primary and secondary distribution features of hydro-electric infrastructure and confirmation of electricity meter locations in relation to physical addresses; field verification of the location of hydrants, valves and manhole locations; and a full inventory of all overhead fibre optics network features.
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Once source documents were prepared and entered into the GIS along with images and details captured during field inventory, this information was hyperlinked to geospatial reference points in the GIS solution creating deep, intricate layers of information in one place that moved beyond mere infrastructure layouts. The imagery of assets and features, video of distribution networks, and one-click access to plans, layouts and drawings were to make finding accurate data needed by the city, public utility, and eventually other partners simple and efficient. “This is a picture, a visual representation of what’s out there in our city. The plus/minus accuracy is about a foot, but that’s why things like survey data are hyperlinked,” said Elliott. “If you need a reference document, you don’t have to make information requests from other departments because the data you need is in the GIS. It’s a time and money saver. You can be so much more productive.”
"If this was an internal project it wouldn’t have been successful. SSMIC took the lead as a not for profit and it allowed them to develop their expertise in GIS while city staff focused on their key accountabilities. It paid off for us in the end because it allows us to perform our jobs better and more efficiently.”
– Don Elliott
Director of Engineering, City of Sault Ste. Marie
Data that never goes out of style
The success of Acorn and the implementation of the Community Information Utility hinged greatly on data accuracy, data handling procedures and data succession planning. As data was entered into the CIU, measures were put into place to ensure the data would never go out of date. This process included creating data literacy with community partners and data entry and review processes performed by Acorn.
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Creating a digital twin of the community
Through the development of an intricate, highly detailed data warehouse, standard operating procedures to keep data up to date, and intelligent data modelling Acorn created a ‘digital twin’ or virtual model of the community.
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A GIS digital twin is an information-based spatial representation of a community. This representation of a community is based on data that is:
Accurate: Accurate data is captured through high standard quality control methods, constrained data entry, comprehensive editing rules and accurate data sources. To create a digital twin, all data must be of the same level of accuracy.
Detailed: Defining characteristics of reality must be captured to support a mirror image.
Current: GIS information must match the status of the real item in the field. This is accomplished by:
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Realtime mobile field data capture.
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Work order management systems driving day to day changes. Information is added to the CIU based on work orders and planning documents. This information is updated for accuracy and currency once a project is completed.
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Standard operation procedures that enforce the accuracy of real time data capture.
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Intelligent: All features in Sault Ste. Marie’s data-smart city solution are connected to other features through database relationships, to address as a spatial reference point, and to geometric network connectivity.
A GIS solution that is a digital twin allows a community to:
Plan
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Use historic and current data to model the future.
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device and tracing to see the effect.will happen in reality, such as turning off an electrical What you do to the digital system should predict what.
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Maintain
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Efficiently maintain and manage the municipal / utility assets.
React
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React to changing conditions and emergencies.
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A smart city will utilize a GIS digital twin solution to know the environment, gain up to date insights into the community and allow for collaboration to improve quality of life. Having access to a digital twin of the city allows Acorn to monitor systems on behalf of stakeholders to address problems before they occur, develop new opportunities and design solutions based on real-world simulations.
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