Making use of new tools within urban development

The complexity of modern cities demands innovative solutions. From infrastructure planning to public service management, urban decision-makers rely on vast amounts of data, regulations, and community input to shape sustainable and efficient cities. However, much of this information is scattered across reports, policy documents, and technical datasets, making it difficult to extract insights quickly.

Large Language Models (LLMs), a type of AI designed to process and generate human-like text, are emerging as a powerful tool in urban development, governance, and planning. By making information more accessible, searchable, and interactive, language models have the potential to greatly change how cities manage knowledge, make decisions, and engage with communities.

The challenge: Information overload in urban planning

Urban development involves navigating regulatory frameworks, infrastructure data, social policies, and economic trends—a process that requires extensive research and analysis. Some key challenges include:

• Complex and fragmented data – Urban policies, land use regulations, and infrastructure plans exist in dense reports that are difficult to cross-reference.
• Time-consuming research – Planners and policymakers spend hours searching through lengthy documents to find relevant guidelines, precedents, or case studies.
• Limited accessibility – Community stakeholders and smaller organizations often lack the resources or technical expertise to interpret urban data and planning documents.

Large language models offer a new way to organize, retrieve, and analyse urban data, making it easier for stakeholders to make informed decisions faster.

How large language models can support urban decision-making

Language models are trained on vast datasets and can process text-heavy information, extract key insights, and generate responses based on user queries. Their capabilities can be applied to urban development in several ways:

1. Supporting policy and regulatory analysis

Navigating urban policies, zoning laws, and building regulations is often a daunting task. Language models can:

• Quickly summarize relevant policies based on specific project requirements.
• Compare regulations across jurisdictions, helping planners align with best practices.
• Identify inconsistencies or gaps in urban policies to improve governance.

Instead of manually searching through thousands of documents, reports and spreadsheets, planners and policymakers can use tools leveraging language models to access relevant information in seconds.

2. Facilitating public engagement and community involvement

Engaging communities in urban planning often requires breaking down complex information into clear, accessible language. Language models can:

• Translate technical planning documents into easy-to-understand summaries.
• Generate interactive FAQs that address common citizen concerns about urban projects.
• Assist in multilingual communication, making urban planning discussions more inclusive.

By providing on-demand explanations of policies, public consultations can be more transparent and participatory.

3. Improving urban data interpretation and reporting

Urban analytics generates vast amounts of data—ranging from housing statistics to mobility patterns. However, not all stakeholders are skilled in data interpretation. Language models can:

• Summarize trends in urban reports, making insights more accessible.
• Generate automated reports from structured datasets, reducing manual workload.
• Identify patterns in infrastructure performance to help cities optimize services.

This ensures that data-driven decision-making is not limited to technical experts but can be used by policymakers, community leaders, and businesses alike.

4. Supporting urban planners with large language model assistance

Planners often need quick references, best practices, or case studies to support their proposals. Language models can act as a research assistant, providing:

• Summaries of past urban projects with similar challenges.
• Insights from global planning practices.
• Suggested strategies based on urban development principles.

By reducing research time, planners can focus on strategy and implementation rather than information retrieval.

Bridging the gap: Making large language models accessible for urban development

Despite the potential of language models, their impact depends on accessibility. Many AI-driven tools remain limited to researchers and data scientists, leaving urban professionals without user-friendly solutions.

For AI to truly benefit urban planning, it needs to be:

• Integrated into existing workflows, rather than requiring technical expertise.
• Designed for public sector needs, such as policy evaluation, service monitoring, and community engagement.
• Trained on urban-specific datasets, ensuring relevance and accuracy in responses.

Our approach

Language models offer new approaches for how cities manage knowledge, engage with communities, and make decisions. While urban planning will always require human expertise, negotiation, and strategic vision, emerging tools such as language models can support the process by reducing information barriers and improving efficiency.

As language model-powered tools become more accessible, they will play a growing role in shaping more responsive, inclusive, and better-informed urban environments.