6 GIS Trends Shaping GovTech In 2026
2025 was an exciting year for the GIS community, with meaningful software releases across both proprietary and open ecosystems, continued experimentation with new data formats, and steady progress toward more scalable and integrated geospatial workflows. Releases such as QGIS 3.44 and ArcGIS Pro 3.6, along with emerging database and storage approaches like DuckLake, reflect a broader shift toward performance, interoperability, and analytics-readiness.
As we head into 2026, GIS is continuing its evolution from a system of record and visualization platform into a foundational component of enterprise data and analytics stacks. The trends below reflect broader direction we are observing across the industry and in our work with clients.
1. Data Engineering as a Core GIS Capability
The volume, velocity, and diversity of spatial data continue to grow, driven by sensors, imagery, models, and increasingly automated workflows. As a result, data engineering is becoming inseparable from GIS, rather than a parallel discipline.
Across GIS platforms, we are seeing stronger alignment with modern data engineering practices such as orchestration, repeatable pipelines, schema management, and integration with analytical ecosystems. Python-based tooling continues to play a central role, with broader GIS support for workflow orchestration frameworks like Prefect and continued growth in Python-first geospatial libraries.
At the same time, GIS vendors are lowering the barrier to entry for data engineering concepts. Visual and managed pipeline tools, such as ArcGIS Data Pipelines, allow analysts to operationalize recurring data preparation and transformation tasks without requiring deep software engineering expertise. These approaches complement, not replace, code-based pipelines and reflect the diverse technical maturity levels across GIS teams.
This trend also encompasses how data is stored and delivered. Columnar formats such as parquet/geoparquet, now supported in ArcGIS Pro illustrate a move toward analytics-optimized geospatial storage. These formats enable more efficient querying and are well suited for temporal and large-scale datasets.
We at NiyamIT have also been shifting towards a combined approach between data engineering and GIS, and more of our projects have been leveraging DuckDB/DuckLake, parquet file storage, and Python-based tooling. These technologies can be seen in some of our projects, such as SPHERE (the graphic below depicts our typical workflow on these projects).
Rather than treating file formats and web services as standalone concerns, these developments signify an integrated data engineering mindset focused on performance, scalability, and interoperability across the GIS lifecycle.
2. Open Ecosystems and Interoperability
While proprietary software continues to play a central role in many GIS programs, open-source initiatives have continued to deliver practical, production-ready solutions. Over the past year, there has been noticeable momentum behind open tools and formats that address real operational needs, particularly as data volumes increase and workflows grow more complex.
Permissively licensed technologies are being used to manage, analyze, and deliver large geospatial datasets more efficiently. These tools are gaining traction because they are well suited to modern data challenges around scale, performance, and integration with broader analytics ecosystems.
Commercial GIS platforms are also increasingly supporting open formats, APIs, and integration patterns, while open-source tools benefit from enterprise use cases and operational rigor. In 2026, we expect this balance to continue, with open and proprietary solutions coexisting and reinforcing one another as part of flexible, interoperable geospatial architectures.
3. Increased Cloud Adoption
Cloud adoption continues to influence how GIS systems are designed, deployed, and operated. This includes GIS-specific platforms such as ArcGIS Online, growing interest in cloud-hosted ArcGIS Enterprise environments, and broader use of general-purpose cloud infrastructure and services provided by platforms like AWS and Azure. Together, these environments are shaping how organizations think about scalability, resilience, and integration with enterprise systems.
As data volumes increase, there is growing interest in storage and table formats designed for analytics at scale. Data lake architectures and technologies such as DuckLake and Apache Iceberg are gaining attention as ways to manage large, evolving datasets while supporting efficient querying and versioning. In parallel, cloud-native delivery mechanisms such as PMTiles illustrate how serverless approaches can support performant web mapping by transferring only the data required at runtime.
Our work at NiyamIT is also evolving to use cloud-based technologies, such as Databricks. We recognize that these developments reflect a broader shift toward flexible, cloud-aligned GIS architectures that can support a mix of operational, analytical, and web-based geospatial workloads.
4. Real-Time, Near Real-Time, and IoT
There is an abundance of real-time data being generated every single day from sensors, connected devices and monitoring systems, serving needs from motor vehicle traffic analysis to natural disaster prevention and mitigation. More and more technologies are incorporating IoT-enabled sensing as well; the sensor market is expected to increase substantially by the end of the decade and according to IoT Analytics, IoT devices have grown in number by 14% since 2020.
When combined with cloud-native and serverless technologies, these data sources are expanding what is feasible for near real-time ingestion, processing, and analysis. In contrast to single-mode operation, real-time GIS increasingly exists along a spectrum, where streaming data, frequent updates, and historical context are used together depending on the application.
We see this trend highlighting a shift away from purely static datasets toward GIS environments that can accommodate both live signals and historical context within the same analytical framework.
5. 3D GIS and Digital Twins
2025 saw continued advancements in the realms of 3D GIS data and the application and evolution of digital twins for cities and metropolitan areas. Digital Twins are “virtual representation[s] of reality” that are “time aware” and “scalable”.
Digital twins provide a way to model the real world that can be thought of as a physical miniature, enhanced with powerful data analytics capabilities. When paired with time-enabled data and, in some cases, real-time inputs, 3D and digital twin models offer new ways to examine change, test assumptions, and communicate complex spatial dynamics. Instead of replacing traditional GIS, these capabilities extend existing spatial analysis into more immersive and temporally aware representations of the built and natural environment, supporting applications such as planning, infrastructure management, and scenario analysis.
6. Ethical Use of AI in GIS
As the use of and investment in AI become more integral to decision-making at various scales, discussions around ethical data and AI use in GIS are becoming more prominent.
GIS increasingly intersects with sensitive data, automated decision support, and real-time analysis, raising important considerations related to privacy, bias, and accountability. There is growing recognition that the responsible use of data and AI is not solely a technical challenge, but also one of governance and communication.
Establishing clear safeguards, documentation, and oversight mechanisms is becoming an important aspect of deploying advanced geospatial capabilities, particularly where public trust and real-world impacts are involved.
Niyam and GIS in 2025
2025 was an exciting and busy year for Niyam's GIS practice.
We turned GIS Day into GIS month during November, making it a fun filled organization wide activity (like building an Oktoberfest breweries map) while highlighting the geospatial work we are doing on different projects.
Our GIS Center of Competency (CoC) was re-charted early in 2025. It has grown to more than 20 members who represent a wide range of backgrounds, including GIS analysts, hazard modelers, project managers, scrum masters, data scientists, and developers.
Niyam also achieved ArcGIS Cloud Services Specialty designation from Esri last year. This designation validates our expertise in deploying, managing, and supporting ArcGIS Enterprise and related technologies in cloud environments such as AWS, Azure, and GCP.
As 2026 rolls out, we are looking to building on our 2025 momentum and hit even more milestones while serving our GIS clients.
This post is written by Daniel Okuniewicz, GIS Developer and GIS CoC Member