Esriโs Web GIS technology can be used anywhere, anytime, and on any device.
The biggest challenge in the utility industry is not so much the physical trials of an occupation at the mercy of the elements, as it is the sharing of knowledge.
In the operations division of an electric company in New England, an area in the United States which is prone to storms, a man named Stanley worked his way up from line worker to supervisor. Managing all the crews in the region, he developed a manual routine to deal with the severity and unpredictable nature of the regionโs thunderstorms. Once crews headed home after a dayโs work, he checked the local forecast for the location a storm was likely to hit. Then he double-checked where electrical poles were leaning and wires were frayed. He recalled where particularly fussy customers lived and where there might be trees that had not yet been trimmed. Finally, Stanley walked outside, smelled the air, and informed the union steward of exactly how many crews to keep overtime in the field.
If he overshot, Stanley might have cost the company countless hours of unnecessary time in the field. If he were too optimistic and sent no one, travel time would have cost the company as well. But Stanleyโs wisdom enabled him to make precise decisions that saved money and improved public safety. Then, like thousands of other utility workers his age, he retired.
Fortunately, the next generation of utility decision-makers is using Esriโs geographic information system (GIS) technology to collect and share knowledge and gain insight from a new network of data sensors, strengthening their ability to respond and evolve over time. In addition, personal wisdom like Stanleyโs is being incorporated, along with hard data, into Esriโs ArcGIS platform. So, individual experiential information is a boon โ rather than an impediment โ to sharing this knowledge.
From mapping to connecting: GIS evolves
Historically, GIS has been used in the utilities industry to make hard copies of maps illustrating the location of assets. While GIS was developing into a technology that allowed more information to be incorporated into geospatial analysis in different ways, utility companies were placing more sensors into the field. These sensors were practical in nature for utility companies. New electronic meters are sensors themselves. They tell the electric company how much electricity someone uses and when. Sensors, known as fault indicators, let utility companies know when equipment is malfunctioning in real-time. While these sensorsโ initial purpose was to help a utility companyโs office access information in the field, they have evolved into conduits for transmitting parts of a larger geospatial picture.
Utility companies are now combining this collection of sensor data with spatial analytics to gain insight and improve decision-making. Sensor data from the field is not only shared in real-time with the home office but also fed back to the workforce in the field, who can access this shared data via mobile devices like smartphones and tablets. This is made possible with Esriโs Web GIS technology that can be used anywhere, anytime, and on any device.
Stanley invested years of his life accumulating knowledge based on history, observation, and intuition that he used to perform his invaluable duties. However, this also meant that an entire civic infrastructure and millions of dollars often hinged on peopleโs intuition and years of experience. Now that this information is accessible instantly by anyone. Electric utility companies and emergency responders, for example, have a much stronger foundation on which to operate efficiently.
The winning equation of diverse data
There are five types of data taken into account when conducting geospatial analyses that have become crucial to utilities:
- Authoritative data is the most basic and invariable form of information since it is based on a system of record, such as the age of a piece of equipment in the field or the average rainfall in an area over a period of years.
- Predictive data is any type of forecast of a probable incident, such as a flood or storm, based on past trends. For instance, the data on the frequency of tornadoes occurring in the US Great Plains region, known as Tornado Alley, enables experts to make a predictive data analysis on the frequency of tornadoes occurring in the future within a reasonably precise geographic area.
- Measured data is derived from sensors in the field recording quantifiable incidents. Sensors, for example, can record the amount of energy individual homes are using per day or even the frequency of lightning strikes in an area.
- Social data is observational information gathered through a social networking service such as Twitter. And while social media might be a comparatively informal method of data capture, this form of crowdsourcing information forms a valuable supplement to authoritative and measured data in making comprehensive predictive analyses.
- Experiential data, which is inherited wisdom such as the knowledge Stanley gleaned over the years and hopefully passed on to his successor, forms the last piece of the puzzle. With all these different forms of knowledge combined โ some learned and some quantified โ a clearer picture emerges.
Showing the big picture with Big Data
Weighted overlay analysis is one of the most visually accessible tools enabling GIS to use data in decision-making. Layers are placed on top of a basemap representing values, such as data on lightning strikes and other weather patterns recorded by stationary field devices. On top of that, historic weather data is assigned a new layer. Another layer, which corresponds to the frequency of reported fallen power lines and tree trimming history, accounts for the largely intangible social and experiential data, which we can now see all on the same map. There is now a clear picture of where and when the utility company is likely to have a problem with power lines during a storm.
What the ArcGIS platform allows utility companies to do is make the most of the data they have and use it more effectively. In particular, it enables workers anywhere to access GIS data in real-time by making it mobile. By having access to shared information in the field or at the office, workers can rapidly respond and assess issues regardless of location. And now that crowdsourced data has been incorporated into geospatial models, the kind of inherited historic wisdom that men like Stanley possessed is accessible without delay and analyzed in concert with other forms of quantified data. Workers can now have true situational awareness by having real-time knowledge and are, thus, able to stage crews much more effectively.
Disasters tend to cause so much damage because relevant information needed for prevention is often not effectively organized. City departments that rely on vital data often do not have a holistic picture to work with, so most of their effort is spent on response and recovery rather than remediation. If emergency responders have a better idea of the likelihood of flooding in an area prior to a storm (in a region prone to tropical storms), they are more likely to be able to evacuate ahead of time. This is what the ArcGIS platform avails anyone with access to data โ the ability to effectively organize information to make better decisions.
The combination of data from the Internet of Things and spatial analytics from GIS enables taking a prescriptive step toward saving lives and money, rather than simply predicting outcomes
In the future, sensors will also be used to make targeted decisions about power usage at a more precise level, as well as inform consumers. Soon, sensor technology will be available in energy consuming appliances like refrigerators, enabling utility companies to monitor exactly which devices are consuming the most electricity. Rather than announcing a community-wide flex alert, electric companies would now be able to make targeted recommendations to those households that are using more power, instead of also including the ones who are already conserving. Additionally, electric cars, which have a limited range, equipped with sensor technology will alert drivers where the nearest charging station is. Once data from vehicles is collected on a large scale and integrated with weather data, GIS can be used to predict where and when slippery conditions will occur, preventing accidents.
The combination of data from the Internet of Things and spatial analytics from GIS enables taking a prescriptive step toward saving lives and money, rather than simply predicting outcomes. A geospatial ecosystem of shared data makes it easier for utility companies, emergency providers, and anyone else providing community services to understand data, act in real time, and ultimately improve the lives of citizens.
Bill Meehan
Director of Utility Solutions, Esri
