Deconstructing GIS

Unleashing the Power of Geography & Time

Pixabay

Imagine a world where innovation isn’t fueled by profit. What if vendor lock-in and bloated enterprise systems weren’t the ultimate objectives? This is the premise from which I consider the future of GIS. What if technology’s ultimate purpose was to truly serve society, humanity, and the natural world?

The technological landscape was vastly different at the inception of Geographic Information Systems (GIS). Computing hardware and software were prohibitively expensive, and digital geospatial data was practically nonexistent. At the time, relational databases were only beginning to gain traction, and scripting relied on proprietary languages.

Even as the fundamentals of technology have evolved, enterprise software like GIS has often merely layered new capabilities onto outdated frameworks. Bloated, feature-heavy products are then moved wholesale to the cloud. Meanwhile, data remains locked at the center of the enterprise universe, protected by deceptive guards around its moat.

Breaking down GIS

Deconstructing various geospatial tools into their individual components has long been a vision for many in the geospatial industry. Common, simple actions—such as determining the proximity of objects or confirming if assets are connected to a linear feature like a water main, electrical line, or road—become effortless when data is enriched with contextual knowledge. This unbundling of capabilities allows them to be deployed precisely when and where they are needed within a connected system, a necessary step toward creating true digital twins.

Let’s break down the elements of a GIS and look at what the future holds for geospatial.

Hardware

Current View: The physical infrastructure that runs GIS software and processes large volumes of spatial data. This includes everything from mobile devices and GPS units used for field data collection to powerful centralized servers.

Future View: Hardware is rapidly evolving beyond centralized systems. Advances in hardware, communication, and security will allow individual algorithms to be linked together through intelligent agents operating on different devices. For example, edge computing will support an electrical device in a distribution system that can connect to satellite, weather, temperature, and moisture data to run a model in the field. It can then alert headquarters of the risk of a fault or, if the situation is critical, automatically operate the necessary devices to shut down the circuit, or send a drone with retardant-dropping capabilities if sparking has occurred.

Software

Current View: GIS software includes applications that store, analyze, and display geographic data. Platforms like the industry-standard ArcGIS Pro and open-source tools such as QGIS enable users to perform geoprocessing, conduct spatial analysis, and create high-quality maps.

Future View: Geospatial analysis and modeling tools are evolving into standalone functions that can be seamlessly integrated into any operational system or workflow. Consider how easily an app can geolocate you. Imagine if more complex geospatial tools were just as simple to incorporate.

The need for GIS platforms is fading. Instead, spatiotemporal capabilities—the integration of location and time—will be readily available for any operational system, transforming how we solve problems with a geospatial component.

Data

Current View: Data is the most critical component of a GIS, encompassing geographic features (spatial data) and their associated characteristics (attribute data). This information is typically sourced from topographic surveys, satellite imagery, LiDAR, or an organization’s internal databases.

Future View: The distinction between data, software, and hardware will blur as they converge. Digital data will become an active technology, embedded with its own information and algorithms. Instead of being a static file, data will be a dynamic entity that agents can access for analysis. For instance, a digital representation of a land parcel won’t just contain owner and survey details; it will also include embedded code capable of modeling and understanding the parcel’s changes over time. Data will contain instances of itself that can be instantiated depending on what algorithm or tool uses it. At a simple level, think coordinate or projection. Or a valve that is open or closed, installed or removed.

People

Current View: The human element of GIS encompasses everyone from the expert analysts and developers who design and build complex systems to the field surveyors and end-users who rely on GIS data for daily decision-making. These individuals represent the intellectual capital and operational workforce responsible for designing, operating, and interpreting the GIS.

Future View: Knowledge of data, products, and procedures will be embedded directly within the data itself or available as standalone tools. This will empower people to design workflows that focus on processes and outcomes. Data scientists, software designers, user experience experts, hardware developers, citizens, app developers, and more will have geospatial capabilities at their fingertips. GIS Professionals will be integrated into expanding fields of research as geospatial becomes operationalized.

The End of GIS Enables A Dynamic Geospatial Future

In the near future, the role of GIS will become more specialized and focused. While it will remain a useful tool for data automation, cleanup, and producing traditional maps for defined tasks like planning, its dominance will fade. Operational tasks that depend on geospatial data, modeling, and visualization will increasingly be constructed from a combination of distinct geospatial functions (open-sourced) and intelligent data embedded with its customized logic. Agent-based systems will manage the complex handoffs between these functions, while artificial intelligence (AI), integrated with other technological advances, will provide the cohesive logic to bring the entire system to life.

Eventually, true digital twins will emerge, created from both individual parts and the whole. Digital twin AI models will help inform and expand capabilities. Systems that never communicated before will soon engage in constant conversation, replacing old workflows with newer, faster, and more powerful capabilities. Much like computers transformed our work in the last century, digital twins are set to revolutionize our professional lives. The first step is to deconstruct existing systems, rebuild them with modern technology, and share them freely.

Interoperability of systems will be critical, and renewed efforts to bring all players to the table are growing. There are many challenges to this vision, but the building of geospatial tools and data is already happening outside the walls of GIS. Core geospatial innovations at companies like Uber, Apple, and Google are just hints at the future. Many of these efforts are open-sourced. In this new paradigm, the true power lies not in the software itself, but in the processes and data it creates and enables.

The Name Positions the Technology

The term “GIS” is loaded. It often conjures images of complex, niche map-making tools that are both expensive and difficult to use. Does “GIS” represent the future technology that understands and behaves appropriately to address geographic, connectivity, and temporal challenges? Whenever I bring up GIS, I’m often met with indifference. It’s becoming increasingly difficult to defend the traditional GIS model as technology evolves. Geospatial capabilities are a natural part of this evolution, and they’re being developed now outside of GIS.

As the GIS industry grapples with its identity and future direction, other fields are forging ahead. Is GIS an anchor holding back the future of geospatial technology, or merely a waypoint in its historical evolution? Or both?