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The Future of GIS – Part 1: In a Relationship

Sept. 28, 2018
We must all stay focused on where GIS is heading in the next year, three years, and even five years

Like any other technology, GIS continues to evolve at a rapid pace, adding new avenues of access, rising to meet new business requirements, and even reinventing itself in some respects to stay current with the tech trends. With that said, we must all stay focused on where GIS is heading in the next year, three years, and even five years.

The Future of GIS: Let’s Go Back to Basics

Every utility employs some form of mapping to support their organization. And just about every utility originated mapping with some form of paper maps – linens, mylar, etc. Somewhere along the way those utilities shifted to a digital platform, and most of our readers have implemented GIS at this point (though they may have utilized a CAD-based product for some period as a stepping stone).

While GIS has now become a household acronym, it is worth taking a step back to look at the definition of GIS to provide a foundation for how we got here and where we are headed.

A geographic information system (GIS) lets us visualize, question, analyze, and interpret data to understand relationships, patterns and trends.

I get excited reading this standard definition because I believe utilities are one of the best case studies for the benefits of GIS. Let’s unpack the definition.

The visualization part is easy for utilities. An electric, gas or water utility adds their facilities in GIS to create digital maps. The shift from paper to digital makes the maps available on many more devices and allows for performing centralized updates, which directly results in much better maps. And if you are still distributing paper maps, your job of mass-producing paper maps via a GIS just got a whole lot easier.

Now let’s turn to the most important word in the definition for utilities and the key component to the Future of GIS. In my opinion, that word is “relationships.” The relationships in GIS drive value at a utility by establishing the patterns and trends for analysis that empower decision making. There are three key types of relationships we should focus on:

Database technology provides classic relationships – I know this seems obvious, but the first and foremost relationship we maintain in a GIS is a true database relationship. The ole primary key / foreign key approach. Generally speaking, we take this for granted in a GIS, but there are many file-based mapping platforms out there that do not support DB relationships, so it’s an absolutely foundational aspect to the value provided by GIS.

For a utility some common examples might be:

  • A location related to asset details. Ex. the transformer bank on the map often shows the location of a transformer whereas the related (non-mapped) transformer units actually represent the transformer asset data.
  • An asset related to inspection history. Ex. When we model pole inspections in the GIS, we often do so as a related table to the pole feature class. This allows us to track multiple inspection events over time for the asset which provides the ability to track the change (deterioration) of the asset over time.
  • Load point to customers. This is a more advanced example that many utilities are implementing. Here we create an explicit relationship between a load point (i.e. a transformer) on the map and all of the service points which represent the customers. This enables a lot of analysis.
  • Customers to usage. Taking the above example one step further, we might relate the customer points in GIS to the corresponding usage data from the Customer Information System (CIS). Once again, this is usually handled via classic DB relationships.

Spatial Awareness Creates New Relationships – once we model our data into the GIS, the simple fact that we have geospatial awareness drives all types of new relationships within our data. Many times, these spatial relationships are not possible or even evident prior to enabling the data on a map.

I would challenge you to think about the data you maintain at your utility and to find any data that cannot be related to a geospatial location. When you really dig into it, this challenge is pretty difficult. The fact is that almost every data point at a utility can be tied to the map via a customer, an asset, a crew, or even the work being performed. I’m often surprised at the new relationships that become apparent when comparing two pieces of disparate data on a map!

Some common examples include:

Proximity to facilities – we can now drive new relationships between any location on the map and its proximity to our utility or telecom facilities at the touch of a button – and this has far reaching implications.In gas we use buffers off of gas mains to determine high-consequence areas and their impact on structures and homes. In telecom we target new sales opportunities based on proximity to our installed fiber. I won’t go on, but you can think of about a hundred other examples.

One Call Boundaries – we all have to call before we dig, right? Our one call boundaries can be directly derived from our underground facilities, and as long as our data is accurate this takes a lot of the guess work out of where we need to perform field locates.Face it, we all do a lot of locates (or pay contractors to do them) and managing the scope of that work can save significant money. We’re now envisioning a future where our GIS data is so accurate and accessible to crews that the field locate needs are heavily reduced or even eliminated.

Nearest Points – when we begin to drive operations off of determining the nearest points on a map, we can streamline work, reduce drive time, and save on operating costs. Think about optimizing your crew assignments in an emergency situation based on which crew is closest, cross-referenced with the equipment and material each crew has on their truck. Less windshield time increases our efficiency while decreasing our costs.

Assets within a district – think of all the districts you manage, tax districts, operating areas, city boundaries. And some of these boundaries can change on a regular basis.By using spatial relationships instead of database relationships we can dynamically manage the relationships between those district polygons and any other entity on the map. This is much more efficient than manually maintaining the relationship on every asset or customer.

Networks drive flow and hierarchy – the final type of GIS relationship goes even beyond the spatial aspect and models true connectivity between our assets. This allows for tracing through our connected network, and when we add the concept of source and consumption, we can establish flow direction and a hierarchy of related assets based on that flow. This new relationship is not to be taken for granted!

Some examples are:

  • Electric, gas, water and telecom connectivity – at the most basic level, we use a network to establish connections between our pipes, conductor and fiber assets. And each connection is really a relationship between those two assets. They now move together and allow for tracing from one asset to the next.When you add in devices along the way that affect the flow of power, gas, water or data you can dynamically manage the flow of those commodities directly in the GIS. Or in other words, dynamically drive relationships.
  • Service Point to Transformer – I mentioned earlier that many utilities have an explicit database relationship between a service point and a transformer, and that is true. But many utilities couldn’t establish or maintain that relationship without using the network to get there.We’ve implemented custom functionality to trace and maintain relationships between a transformer and the service points it feeds at many utilities now and it’s all based on the network connection from the transformer to the secondary/service conductor to the service point.
  • Upstream protective devices – in outage prediction, we can use the network hierarchy to find the next upstream protective device (fuse, switch, recloser, etc.). The entire concept of upstream and downstream is based on the network.
  • Telecom route tracing – telecom tracing gets complex and is based on many different factors, but at the root level, it is enabled once again by network connectivity. In this case, the connectivity joins each fiber to a splice, to a device or even to a microwave path. The network provides all of the value in place of simple points and lines.
  • Gas and water pressure systems, isolation traces, cathodic protection traces – the list goes on and on.

These three relationship types are enabled, embraced, and heavily intertwined in your GIS. GIS is what makes them possible.

The Future of GIS: The Foundation

Tying it back to our definition of GIS: the relationships within the GIS establish the patterns and trends that allow for analysis to occur – and this drives operational decision-making around efficiency, reliability and safety.

The “relationship” is foundational to the future of GIS because we have to set the stage for where we are today to allow us to take full advantage of the path ahead. Utilities often spend their first several years establishing the data quality to support these intrinsic GIS relationships. And the result is that your organization will reap a tremendous return on that investment while also preparing you for the future.

If you’re not all the way there yet, keep at it and get those relationships established! When it comes to GIS you definitely want to be “in a relationship!”

In my next installment, we’ll talk about what happens as a utility matures and begins to relate outward. Until then…

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