Faster and better spatial choices for liveable and resilient cities
Housing, energy, mobility, climate adaptation, nature, health, nitrogen emissions and economic activities all require the same scarce space. TNO helps public authorities and businesses make better spatial choices, faster. We combine domain expertise with data, spatial models, AI and optimisation to translate policy ambitions into feasible development scenarios and support transparent decisions about the future use of space.
The challenge: turning multiple ambitions into spatial choices
Spatial planning increasingly involves interconnected objectives, competing demands and decisions across different scales. However, policy ambitions are often developed separately and are not immediately suitable for spatial analysis or design. The information required to assess them is distributed across organisations, datasets and models, while assumptions and assessment criteria are not always transparent.
This makes it difficult to establish what is spatially feasible, compare alternative configurations and understand how a decision in one domain affects other functions, areas or population groups. Promising combinations may remain unexplored, while early choices can unintentionally restrict future development options.
Decision-makers therefore need methods that make ambitions, spatial requirements and dependencies explicit. They must be able to explore multiple scenarios, compare their impacts and understand how priorities, constraints and uncertainties influence the available options.
Independent knowledge for integrated spatial decision-making
TNO brings together expertise in mobility, climate, energy, infrastructure, health and human behaviour with spatial impact assessment, advanced modelling, AI and optimisation. This combination allows us to analyse spatial challenges across domains and scales and to develop scenarios that reflect the complexity of real-world area development.
We help translate policy ambitions into measurable objectives, spatial interventions and assessment criteria. Our models reveal where functions can be accommodated, how spatial choices interact and which combinations of interventions are feasible. Where relevant, we also assess what scenarios mean for access to housing, essential services, mobility, green space and healthy living environments in different areas and for different population groups.
Optimisation methods allow a wider range of development scenarios to be explored systematically. These methods do not prescribe a preferred outcome. They expand the solution space and show how different priorities, constraints, phasing decisions and uncertainties affect the available options.
This provides public authorities, developers and technology partners with a transparent and shared basis for decisions concerning policy, investment and spatial design. Together with market partners, we integrate our platform-independent methods and components into existing digital environments and apply them to concrete spatial challenges.
Our solutions
1. Making policy ambitions spatially explicit
Policy ambitions are not always directly suitable for spatial analysis or design. TNO develops integrated methods for translating these ambitions into interventions, indicators and spatial requirements. Depending on the challenge, we use assessment frameworks, AI methods and connected knowledge structures.
The Spatial Impact Assessment Framework, or SIAF, provides a structured approach for translating policy ambitions into interventions and spatial effects. It shows how an intervention affects other policy domains and scales, and helps identify the parties, information and assessment criteria required for integrated spatial decision-making.
TNO develops AI methods that support the translation of policy and vision documents into concrete interventions and measurable indicators. This accelerates the operationalisation of policy ambitions, while keeping assumptions and assessment criteria transparent and verifiable for domain experts and other parties involved.
A knowledge graph connects policy objectives, indicators, areas and spatial scales. It helps identify which ambitions and constraints apply to a location, how they are related and which effects should be included in a spatial analysis.
2. Developing and comparing spatial scenarios
When multiple functions require the same space, individual spatial claims cannot be assessed in isolation. TNO supports the development and comparison of scenarios in which functions, constraints, phasing and spatial scales are considered together. This reveals feasible combinations, synergies and tensions at an early stage.
Suitability models assess which locations are appropriate for a particular function based on explicit criteria and constraints. Allocation models then show where functions could be accommodated, which spatial claims arise and how choices in one domain affect other functions and scales.
Scenario analyses show how alternative spatial configurations contribute to policy objectives. Sensitivity analyses reveal how different priorities, assumptions, constraints and phasing decisions influence the outcomes. This supports transparent decision-making and prevents a single set of assumptions from being treated as fixed.
3. Optimising scenarios and supporting joint decision-making
The challenge is often not only to assess existing plans, but also to identify new development scenarios that combine multiple ambitions. TNO uses AI and optimisation methods to explore the solution space systematically and identify promising alternatives that may remain outside the scope of conventional planning processes.
Optimisation methods help generate and compare development scenarios based on multiple objectives, indicators and constraints. They reveal which combinations contribute most effectively to the stated ambitions and how changing priorities or limitations affects the available options.
TNO combines modelling with participatory methods to make objectives, criteria, spatial claims and differences between stakeholders explicit. Scenarios provide a shared basis for discussing impacts, identifying common interests and arriving at transparent and explainable choices.
Our impact
Smarter and faster construction: TNO helps Eindhoven Metropolitan Region with historic leap of scale
Balanced Spatial Choices
Developing digital solutions for area development together
TNO supports public authorities and area developers in making spatial choices that connect multiple ambitions, functions and scales. Together with providers of data, digital twins, GIS, design software and decision-support solutions, we develop platform-independent methods and components that strengthen existing products and services. These include assessment frameworks, AI modules, spatial models, indicators and access to enriched datasets.
We work with partners to validate, integrate and scale these innovations in concrete area-development projects.
Would you like to accelerate spatial decision-making, explore more development scenarios or integrate knowledge and technology into digital solutions for area development? Contact us to develop and validate a concrete application together.
Contact us
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Peter van Buijtene
Functie:Senior Manager Innovation Partnerships – Mobility & Built EnvironmentMy mission is to achieve demonstrable social impact with scalable digitalisation solutions and technologies in the field of urbanisation, mobility and related energy transition issues. This is never possible alone and that is why I focus on initiating, setting up and developing public/private partnerships to help governments, regions and cities achieve their social goals.
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Standplaats:Den Haag - New Babylon
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Email:Email Peter
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LinkedIn:Peter on LinkedIn
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