More than a decade ago, the Spatial Data Ecology diagram was conceived by Richard Simpson of Meta Moto, with design by Kevin Finn, for the Spatial Industry Business Association (SIBA). Its purpose was ambitious yet clear—to distil the complexity of a rapidly evolving and often fragmented spatial sector into a single, coherent visual framework. At the time, SIBA was Australia’s peak industry body, representing the full breadth of spatial disciplines, from surveying, GIS, and remote sensing to cartography, data services, and emerging geospatial technologies.

Following successive mergers with the Surveying and Spatial Sciences Institute (SSSI) and the Geospatial Information & Technology Association (GITA), SIBA’s legacy now continues through the Geospatial Council of Australia (GCA). Despite these structural changes—and in the face of rapid technological advancement—the Spatial Data Ecology remains one of the most comprehensive, functional, and forward-looking frameworks for representing the capabilities of the spatial sector.

More than a visual reference, the diagram serves as a strategic navigation tool, integrating a conceptual value chain, functional classifications, provisioning layers, and a maturity model into a single, coherent framework. In doing so, it enables businesses, government agencies, and the broader industry to locate their current position, define their desired future state, and plan the partnerships, skills, and technologies needed to achieve it.

A six-stage journey from data to insight

The centre of the diagram shows a simple but powerful idea: data gains value as it moves through a process of transformation. Each layer builds on the one before it:

1. Data – Raw Observations
   Measurements and observations captured through surveying, GNSS, satellite imagery, aerial photography, drones, in-situ sensors, and crowd-sourced feeds.

2. Information – Structured Data
   Organisation of raw inputs according to defined schemas or models, such as Building Information Modelling (BIM) or ISO metadata standards, enabling interoperability and shared meaning.

3. Evidence – Validated and Trusted
   Assessment of geo-veracity, the accuracy, lineage, update frequency, and internal consistency of data, through multi-source verification and quality assurance processes.

4. Knowledge – Connected Meaning
   Linking verified data through ontologies, formal structures defining concepts and relationships, ensuring semantic as well as structural integration across datasets.

5. Patterns – Recognised Relationships
   Discovery of pattern language, recurring spatial configurations and solutions, through advanced analytics and machine learning applied to Big Data.

6. Metaphors – Communicated Understanding
   Translation of insights into compelling and actionable forms, maps, dashboards, XR experiences, and tactile interfaces, designed for human engagement and decision-making.

The Radiating Edge – Industry Activities

The diagram’s outer perimeter lists over 100 specific activity types, grouped into ten thematic clusters that capture the operational diversity of the spatial industry. These are based on the activities that members of SIBA claimed to be undertaking in 2013:

• Platforms and Enabling Infrastructure – GIS platforms, cloud hosting, broadband, big data analytics, visualisation engines.

• Data Capture and Acquisition – Cadastral, hydrographic, engineering, and mining surveying; photogrammetry; aerial and satellite imagery; climatological, geological, and hydrological surveys.

• Data Management and Integration – Data brokering, compilation, integration, fusion, quality control, decision support systems.

• Planning and Modelling – Urban, regional, and environmental planning; transport modelling; policy studies; demand/load forecasting.

• Design and Engineering – Architecture, structural engineering, erosion control, hydraulic modelling, CAD authoring.

• Asset and Project Management – Asset renewal, facility operation, risk management, project financing and reporting.

• Emergency and Risk Management – Disaster planning, preparedness, mitigation, response, and recovery.

• Legal, Governance and Contracting – Titling, cadastral documentation, expert witness services, insurance, contracts.

• Communication, Media and Outreach – Data journalism, public communication, interactive media, animation.

• Specialist and Support Services – Technology transfer, recruitment, procurement, sociological research, technical assistance.

This taxonomy makes clear that the spatial sector is not a set of isolated disciplines, but a networked ecosystem where technical, operational, and advisory functions reinforce one another.

Supporting rings - the enablers

Inner Rings -  Provisioning Activities

Four provisioning layers connect the value chain to delivery:

• Platforms – Foundational systems such as SDIs, cloud architectures, and high-speed networks.

• Tools – GIS/CAD software, drones, LiDAR, and sensor arrays.

• Services – Skilled practitioners who operate and integrate these systems.

• Operations – Deployment in asset management, navigation, environmental monitoring, and emergency response.

Outer Rings & Inner Core  – Transformations

The core value chain of the Spatial Data Ecology can be understood as a series of six progressive stages, each a stepping stone in the journey from raw observation to actionable insight.

Between these stages lie transformational functions, the active processes that add value, refine meaning, and deepen trust in the information.

These same transformations are mirrored in the outer orbiting rings of the diagram, where they are mapped directly to the operational activities of the spatial industry. This dual representation connects conceptual transformation to practical application.

The transformation processes are:

1. Capture – The systematic acquisition of spatial data from diverse sources, including field surveys, GNSS positioning, LiDAR flights, hydrographic mapping, satellite imagery, and IoT sensor networks. Capture is the act of observing and recording reality in measurable form.

2. Design / Author – Converting raw datasets into structured information models through schema definition, metadata creation, and contextual framing. At this stage, unstructured observations are given form, classification, and relational meaning.

3. Representation – Validating and encoding information models so that they function as evidence. This includes quality assurance, datum transformation, georeferencing, and ensuring semantic and topological integrity. The goal is to make the dataset trustworthy, authoritative, and interoperable.

4. Simulation – Using validated evidence to model, test, and predict real-world scenarios. Simulation builds knowledge by revealing the dynamics, sensitivities, and potential futures of systems, from flood inundation modelling to urban traffic simulations.

5. Extrapolation – Analysing knowledge to identify patterns and correlations, often through advanced statistical methods, AI, or machine learning applied to Big Data. This transformation moves beyond what is known into what can be inferred or predicted.

6. Visualisation – Synthesising patterns into compelling metaphors, visual, interactive, or immersive outputs that make complexity comprehensible. This may include dashboards, thematic maps, XR environments, or tactile haptic models.

7. Sharing – Distributing metaphors through appropriate channels, open data portals, SDIs, secure APIs, or industry briefings, to stimulate curiosity, engagement, and further inquiry. This final transformation not only communicates insight but also creates feedback loops, reigniting demand for deeper understanding of people, places, and systems.

Nodal Loops, Use case demand, & Maturity

At the north pole of the Spatial Data Ecology diagram sits “You” - the user, client, or decision-maker with a defined purpose for the data. Every workflow is visualised as a nodal loop, a closed path that begins with raw data capture, moves through a series of transformation stages, and returns to “You” with an output suited to the purpose at hand.

The number of nodes (or stages) visited in each loop is both:

• A practical measure of the use case  - some tasks require only minimal transformation before the data is fit for purpose.

• A proxy for organisational capability and maturity - the more nodes an organisation can competently traverse, the greater its ability to integrate, analyse, and repurpose data across contexts.

In the diagram, dotted green arrows represent the “circuit breaker” for each loop. The arrows mark the point at which the workflow returns to the user and the loop closes. This visual cue highlights where the process stops and where additional transformation stages could be added if required.

Not all loops need to be long. Efficiency matters as a leaner, shorter loop may be entirely appropriate if the project’s needs are met without unnecessary processing. Conversely, more complex problems, larger datasets, or multi-stakeholder contexts often demand longer loops to maximise value extraction.

Use Case Examples for Loops (Circuits)

1. Two-node loop – Capture → Sharing
Simplest participation in the spatial industry.

• Use case: A drone operator captures aerial photos of a rural property and directly provides the imagery to the landowner for visual inspection.

• Characteristics: Minimal processing; rapid turnaround; fit for purpose when context and interpretation are handled by the client.

2. Three-node loop – Capture → Design/Author → Sharing

• Use case: A survey company conducts a topographic survey, processes it into a point cloud or basic 2D CAD drawing, and delivers it to an engineering firm.

• Characteristics: Adds initial structure to raw data, making it easier for downstream users to integrate into their workflows.

3. Four-node loop – Capture → Design/Author → Representation → Sharing

• Use case: A BIM modeller converts as-built survey data into an IFC-compliant 3D model for integration into a federated design environment.

• Characteristics: Enables interoperability and data reuse; ensures the output conforms to industry standards and supports multi-party collaboration.

4. Five-node loop – Capture → Design/Author → Representation → Visualisation → Sharing

• Use case: An urban planning consultancy produces a photorealistic 3D city model, integrating survey data and planning information, then presents it via an interactive dashboard for community engagement.

• Characteristics: Enhances communication; enables stakeholders to explore scenarios visually before making decisions.

5. Six-node loop – Capture → Design/Author → Representation → Simulation → Visualisation → Sharing

• Use case: A transport authority simulates traffic flows for a proposed highway extension, integrating geospatial data, travel demand models, and AI-driven analytics to visualise future conditions.

• Characteristics: Supports optioneering; allows testing of “what-if” scenarios; integrates predictive modelling into decision-making.

6. Beyond six-node loop – Capture through all core stages, plus extended analytical integration

• Use case: A national water agency integrates real-time sensor feeds, climate models, satellite imagery, and AI-driven forecasts into a continuously updating digital twin of the water network.

• Characteristics: High complexity; ongoing data ingestion and analysis; supports continuous operational decision-making and long-term strategic planning.

Why these matter

By mapping your organisation’s current workflows to these loops, you can:

• Assess whether your current capability matches the complexity of the problems you are solving.

• Identify opportunities to extend loops for greater value or shorten them for efficiency.

• Communicate clearly to stakeholders where your strengths lie in the Spatial Data Ecology.

In short, these loops are not just a measure of what you do; they are a lens for deciding what you could do.

Advancing from a shorter to a longer nodal route typically requires strategic investment in:

• Skills and workforce capability

• Governance and quality assurance

• Interoperable data standards

• Technology integration and infrastructure

In practice, nodal routes provide both a diagnostic, revealing the current maturity of an organisation, and a roadmap for targeted capability development.

A Dynamic Dashboard

When the diagram can also serve as an online dashboard and present relationships as a  dynamic heat map:

• Tender Mapping – Plotting tender requirements against the classification rings and activity clusters identifies required competencies and potential delivery partners.

• News and Market Scanning – Tagging announcements and innovations to relevant nodes reveals emerging trends and underdeveloped opportunities.

• Business Self-Assessment – Asking How are we doing? Where are we going? What competencies must we develop next? Turns the diagram into a roadmap for organisational growth.

• Knowledge Organisation – Indexing research, case studies, and technical standards to the diagram’s structure builds a navigable, living library of spatial expertise.

This transforms the Spatial Data Ecology from a static reference into an active operational dashboard, a tool for decision-making, resource alignment, and strategic collaboration.

Final Thoughts

The Spatial Data Ecology was never intended as wall art. It was designed as a functional map for an industry in flux, capturing its moving parts, revealing interdependencies, and providing a structured way to think about capability growth. Its longevity stems from a foundational logic: value is created by transforming data into evidence, evidence into knowledge, and knowledge into action within a connected, collaborative ecosystem.

In today’s geospatial environment, where we speak in terms of concepts such as digital twins, AI is being woven into workflows, and interoperability is both a market expectation and a regulatory necessity. This framework offers a rare combination of stability and adaptability. It anticipated many of today’s defining innovations, from real-time monitoring to AI-enhanced asset management, and it remains an essential lens for navigating the sector’s future.

Its relevance is tangible:

• Disaster response – High-altitude drones (HALE/HPS aircraft) monitoring fires or floods in real time.

• Urban planning – Modelling how zoning changes will impact infrastructure and transport.

• Environmental monitoring – Predicting groundwater contamination using integrated land-use and weather data.

For businesses, the imperative is not simply to acknowledge their relevance, but to use it actively:

• Map current and target capabilities against the value chain.

• Track market shifts and emerging technologies to anticipate demand.

• Plan partnerships that extend reach and expertise.

• Build knowledge systems that align with the industry’s full spectrum of functions.

Whether you are a policymaker, industry leader, researcher, or community advocate, the Spatial Data Ecology offers a strategic compass for showing where you are, illuminating where you could be, and helping you chart the path to get there.

In a world where the pace of change is constant, having a shared, enduring map of how we think spatially may become a tool of value. The question is no longer whether to engage with the spatial industry, but how far you are prepared to let it transform business as usual.

** Download the SIBA Spatial Data Ecology Poster **

About Meta Moto

Meta Moto is a globally recognised consultancy specialising in digital engineering, strategic foresight, and the implementation of next-generation digital twin frameworks. With deep expertise in infrastructure, geospatial intelligence, and data-driven transformation, Meta Moto partners with governments, industry, and academia to deliver measurable outcomes and enduring public value. Our advisory services are independent, evidence-based, and grounded in international best practice.

To discuss how Meta Moto can help your organisation to navigate the future, or to learn more about our work, please visit Meta Moto's website or contact us directly by email