Have you ever used a map to find your way, check real estate boundaries, catch a train, or understand flooding risks? Behind every one of these everyday tools is a powerful and largely invisible system that is a vast and evolving spatial data ecology.

This infographic, created over a decade ago, shows how raw data from the world around us becomes useful knowledge for decision-making. Whether you are a student, a planner, a farmer, a surveyor, or a mayor, understanding this data journey is key to making informed, future-ready choices.

Developed by Meta Moto's Richard Simpson and illustrated by designer Kevin Finn, the Spatial Data Ecology helps us see how different technologies, professions, and tools work together to transform data into understanding.

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 – the raw materials

This is where it all starts. Sensors, satellites, mobile phones, drones, and surveyors collect observations of the world. These are raw facts -  like temperature readings, ground heights, or GPS locations. On their own, these data points are like grains of sand: individually small, but potentially powerful when assembled.

Technical note: “Spatial data” refers to any information that includes a reference to location, usually latitude and longitude. This can be 2D (like maps), 3D (like building models), or even 4D (adding time) … and beyond.

2. Information – organised data

Next, we group and structure the data so it can be understood. For example, a list of coordinates becomes a property boundary, or a 3D scan becomes a building shape.

This step uses predefined templates or schemas that give meaning to the data.

Technical term – BIM (Building Information Modelling): A digital model that combines geometry (shapes) with metadata (e.g. materials, costs, maintenance schedules). BIM is a common schema for organising building-related spatial data.

3. Evidence – trustworthy, validated information

At this stage, we begin to test and verify what we know. We ask whether the information we’ve structured is accurate, reliable, and fit for purpose. This is the point where spatial information starts to earn our trust. We examine large datasets to detect errors, resolve inconsistencies, fill in missing details, and test underlying assumptions. This stage moves us from what we think to what we can prove.

Often, this involves comparing multiple co-located datasets—such as satellite imagery, GPS traces, survey records, and real-time sensor feeds—to check whether they agree. The more independently gathered sources align in time and space, the higher our confidence in the result. This is the essence of building spatial evidence.

As we superimpose data from different systems, we begin to assemble what are known as spatial biographies of place, rich, multi-layered views that capture not just what is there now, but how that place has evolved, who has shaped it, and how it functions. These biographies become foundational to digital twins, asset management, and place-based decision-making.

Technical note: This process is supported by principles of geo-veracity - a measure of how believable and verifiable spatial information is. It considers factors like positional accuracy, data lineage, update frequency, and internal consistency. Higher geo-veracity improves the integrity of spatial analytics and helps mitigate risks in decision-making.

Spatial evidence also draws on fundamental spatial laws, such as Tobler’s First Law of Geography: “Everything is related to everything else, but near things are more related than distant things.” This explains why location matters—and why patterns in space are rarely random.

4. Knowledge – connected meaning

Knowledge emerges when we link evidence together to understand relationships. It’s the step where facts become insight. For example, knowing that certain land types are prone to flooding during specific weather patterns.

Technical term – Ontology: In computer and spatial science, an ontology is a formal structure that defines concepts and relationships within a domain. For example, it helps define how a "river" connects to a "floodplain" and how both relate to land ownership.

5. Patterns – machine-recognised relationships

Some insights are too complex or subtle for humans to spot easily. This is where machine learning and artificial intelligence help identify patterns, such as predicting landslides or traffic congestion.

Technical term – Pattern Language: Originally coined by architect Christopher Alexander, in this context, it refers to recurring solutions or motifs within spatial environments that can be reused and adapted, like the way a city might consistently use green space buffers around schools.

6. Metaphors – telling the story visually

Finally, we transform patterns into visual or interactive forms, such as maps, models, animations, alarms, or dashboards. These are the tools we use to communicate findings, guide action, and engage communities. Metaphors spark our neurons for further action and response.

Technical terms: Extended Reality (XR) encompasses Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). These human interfaces are transforming how we experience spatial information. AR overlays data onto the physical environment, such as utility networks or hazard zones, while VR allows users to explore fully digital representations of terrain, infrastructure, or cityscapes.

Increasingly, tactile interfaces such as StretchSense gloves and haptic feedback systems are enabling more natural interaction with spatial data. These tools register fine motor movements and simulate physical sensations, allowing users to manipulate 3D models through touch and gesture.

Together, XR and embodied interfaces make spatial information experiential, enhancing comprehension, improving collaboration, and expanding access to complex geospatial systems across planning, engineering, and community engagement.

Four supporting rings - the enablers

Surrounding this central value chain are four essential layers that make the system work:

• Platforms – These are the backbones: cloud computing, 5G networks, and spatial data infrastructures (SDIs) that host and deliver spatial information across regions or countries.

• Tools – These include Geographic Information Systems (GIS), CAD software, drones, and sensor arrays – the instruments that capture, analyse and visualise data.

• Services – Skilled professionals such as surveyors, engineers, analysts, and developers who know how to use the tools effectively.

• Operations – Real-world applications such as asset management, disaster response, transport navigation, or even autonomous vehicles.

Growth and maturity – the nodal orbits

The diagram also suggests that organisations grow in their ability to engage with this ecosystem.

• 3-node orbit: Basic data collection and visualisation.

• 4-node orbit: Structured use of information and basic analytics.

• 5-node orbit: Advanced use of AI, predictive modelling, and systemic insight.

Each “orbit” represents a level of digital maturity—how deeply an organisation integrates spatial thinking into its decisions. Moving from orbit to orbit requires investment in people, policies, and technology.

Technical term – Epics and Stories: Borrowed from agile project management, “epics” are large bodies of work (like adopting a digital twin strategy) broken into “stories” (smaller tasks like training staff, upgrading sensors, or integrating data platforms).

Three ways of seeing the system: provisioning, engagement, and insight

This ecosystem can be understood through three overlapping lenses:

• Provisioning – How data is captured, structured, and delivered.

• Engagement – How people and machines use and respond to the information.

• Insight – How patterns are identified and translated into decisions, policies, or designs.

Each lens emphasises a different part of the cycle, but they work best when aligned.

Why it still matters

The Spatial Data Ecology model anticipated many of today’s innovations, like digital twins, real-time monitoring, and AI-enhanced asset management. As we face increasingly complex challenges—from climate change to urban growth—understanding this ecosystem is more important than ever.

Examples of relevance include:

• Disaster response – Using high-altitude drones (HALE aircraft) to monitor fire or flood conditions in real time.

• Urban planning – Visualising how zoning changes impact infrastructure and transport patterns.

• Environmental monitoring – Predicting groundwater contamination from land-use data and weather forecasts.

  
  

Final thoughts

This diagram is more than a static image. It’s a guide to how we observe, understand, and shape the world. Whether you are a student, policymaker, engineer, or community leader, this model helps you see where you fit—and how your choices matter.

Want to take it further?

• Reflect on which “node orbit” your team or organisation currently occupies.

• Explore how better data, smarter tools, and shared language (like ontologies and pattern languages) could help you move forward.

• Use this diagram to guide workforce planning, curriculum design, and strategic investment.

In a world where change is constant, a shared map of how we think spatially might be one of our most powerful tools.

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 www.metamoto.com or contact us directly at info@metamoto.com.