Is It Time for “Inside the Box” Thinking?

I’m worried our field has become insular - perpetuating our own language and ideas, while missing the opportunity to influence broader technological trends.

The Geographic Information Systems (GIS) industry has always been an innovative field. For decades, spatial professionals have pioneered the very technologies and concepts that are now being rebranded and claimed by other sectors. Terms like "Digital Twin" and "Artificial Intelligence" (AI) are making waves, but they are hardly foreign to GIS practitioners. In fact, while we cannot claim to have invented the concepts of AI and Digital Twins, GIS has laid some of the groundwork in these areas, practicing elements of spatially aware AI and city-scale simulations long before these terms became buzzwords.

Despite this foundational role, GIS professionals often find themselves on the sidelines, watching as industries adopt these concepts in ways that bypass GIS principles and practitioners. The issue is no longer about “thinking outside the box” but rather inviting other disciplines to think inside ours. The power of spatial thinking can bring transformative benefits to AI, digital twins, and beyond - if GIS can integrate its own unique strengths into these popular frameworks. But to do so, GIS professionals may need to adapt to new terminologies, present spatial insights in ways that resonate with wider industries, and bridge the gap between GIS and these rising trends. The question then becomes: how do we bridge that gap and bring the value of spatial thinking into mainstream practice?

The Echo Chamber of GIS

As GIS practitioners, we have a robust community that understands the intricacies of spatial data, geostatistical modeling, and geospatial technology. This community does an excellent job of sharing insights, technologies, methodologies, and continually refining and advancing the field. But in this close-knit circle, there’s a risk of creating an echo chamber - where GIS is discussed among GIS professionals without an eye to how others might benefit from or interpret the spatial insights we bring to the table. I’m worried our field has become insular - perpetuating our own language and ideas, while missing the opportunity to influence broader technological trends.

Meanwhile, as new concepts and technologies emerge, they are quickly rebranded, reframed, and put to use by other sectors without tapping into the deep well of GIS expertise. For example, digital twins - virtual replicas of physical assets - are suddenly a hot topic. Yet GIS professionals have long been creating sophisticated spatial models that predict changes in urban development, environmental systems, and infrastructure. The same can be said for AI, where GIS practitioners have been employing predictive models to analyze spatial trends and classifying imagery data long before machine learning became a headline-maker.

The problem isn’t that these fields are advancing without us; the issue lies in the lack of cross-discipline dialogue that would ensure GIS principles are integral to these developments.

Digital Twins: A Missed Opportunity for Spatial Professionals?

The recent surge of interest in digital twins is a prime example. The concept of mirroring a real-world entity within a digital model isn’t new to GIS. From urban planning and transportation logistics to environmental monitoring, GIS professionals have long been creating detailed spatial models that go beyond mere 2D representations on a map. A GIS-driven digital twin doesn’t just replicate a structure - it incorporates topographical, environmental, and socio-demographic data that lend depth and insight into how that structure interacts with the surrounding area. These insights are unique to the spatial field and could vastly enrich digital twin technology.

However, many of these advancements in digital twins are happening without GIS specialists at the table. The risk here is twofold: not only is GIS expertise being sidelined, but also the digital twins created without a spatial focus are less effective, missing out on critical contextual information that could make them genuinely transformative.

Artificial Intelligence Needs Spatial Thinking

Similarly, the boom in AI applications could benefit immensely from GIS-driven spatial thinking. In recent years, AI has been employed to predict customer behaviors, streamline logistics, and automate decision-making. But adding spatial dimensions to AI models can provide insights that non-spatial models simply cannot offer. For example, consider predictive policing models: a traditional AI model might identify times or seasons when crime rates are likely to surge, but only a spatial model will pinpoint specific locations where resources should be deployed.

GIS practitioners have a wealth of experience building spatially-driven predictive models. The challenge is not only to showcase the utility of spatial data in AI but also to translate GIS practices into mainstream AI frameworks in ways that are accessible and resonate with AI developers and decision-makers.

The Path Forward: Inviting Others Into the GIS Box

To step out of the GIS echo chamber, it’s essential that we invite other professionals - AI specialists, urban developers, environmental scientists, anyone really - into our spatial “box.” This requires a shift in our approach. Here are some strategies to consider:

1. Adapt to New Terminology: GIS practitioners need to be nimble with language. If the industry calls it “digital twins,” then we must too. But instead of simply adopting the terminology, we should redefine it from a spatial perspective, demonstrating how GIS has been realizing these concepts for years.
2. Build Cross-Disciplinary Alliances: Forge partnerships with professionals in other disciplines who are actively engaged in AI, digital twins, and other emerging technologies. By working alongside them, we can emphasize the indispensable role of spatial thinking in these projects.
3. Publish Spatial Success Stories in Mainstream Forums: It’s not enough to share GIS success stories within GIS circles. Publish them in AI, tech, and urban development journals. Present at conferences that are outside the traditional GIS track. Showcasing spatial thinking in new venues could bring critical recognition to the role of GIS in these advancements.
4. Create Open libraries or Interoperable tools and APIs for Other Fields: By building tools that allow non-GIS professionals to integrate spatial insights into their work, we can expand GIS’s influence. Make it easy for AI enthusiasts to incorporate spatial data, for urban planners to use GIS-built digital twins, and so on.

These are just a few things we can do. I'd love to hear your ideas in the comments below. Are there examples of these already in place?

Bringing the World into GIS

As GIS professionals, we need to shift from only thinking of "outside the box” as a way to creatively solve our own challenges to inviting others to see what is "inside our box". The time is ripe for GIS to assert its place in these rising fields, not merely by adopting their terminology but by demonstrating how spatial thinking can take them to the next level. By fostering cross-disciplinary collaboration and communicating our expertise in accessible ways, GIS can take a central role in shaping the future of digital twins, AI, and beyond.

GIS may have evolved in its own specialized world, but spatial thinking has applications that transcend that world, offering a richer and more comprehensive perspective on data, technology, and innovation. Let’s make sure the rest of the world knows that.