In recent years, the concept of a digital twin of a city has become a central topic in urban planning, geospatial analytics, and smart city strategies. The term is widely used by architects, municipal authorities, and technology vendors. However, it often refers to very different solutions, ranging from simple 3D visualizations to complex analytical systems for modeling urban processes.

This article explains what a city digital twin actually is, how it evolved from traditional GIS, what distinguishes it from 3D city models, and why data and analytics matter more than visual detail.

From Maps to Urban Digital Twins

The evolution of geoinformation systems has been gradual and cumulative. First, maps became digital and interactive. Then they moved to web platforms, allowing collaborative access and updates. Over time, GIS projects began to incorporate analytical dashboards, time-based data, and three-dimensional representations.

As a result, maps stopped being static illustrations and became working environments for analysis and decision-making.

In this context, the idea of an urban digital twin appears as a logical continuation of GIS development. It reflects the need to combine city geometry, heterogeneous datasets, and analytical tools in a single system that supports simulation and forecasting.

What Is a Digital Twin of a City?

A digital twin of a city is a geospatial, data-driven system that links spatial models with analytical and forecasting tools to simulate, analyze, and compare urban development scenarios.

Unlike a visual model created for presentation purposes, a digital twin is designed for continuous analysis and decision support.

A typical city digital twin includes:

• Spatial layers representing buildings, transport networks, utilities, land use, and public spaces

• Attribute and statistical data describing social, economic, and environmental parameters

• A temporal dimension that allows users to track changes over time

• Scenario modeling and comparison tools for evaluating planning and policy decisions

If these elements exist independently, the system remains a map or a 3D model. A digital twin emerges only when geometry, data, and analytics are tightly connected.

Digital Twin vs GIS vs 3D City Model

Understanding the differences between these concepts is essential for realistic expectations.

A digital twin does not replace GIS. Instead, it builds on GIS capabilities and extends them with temporal analysis, simulation, and forecasting.

Data as the Foundation of a City Digital Twin

Any digital twin is only as useful as the data behind it. Core data sources typically include cadastral and topographic datasets, municipal registers, remote sensing imagery, and open data portals. In some cases, sensor data related to transport, utilities, or environmental monitoring is added.

However, real-time data is not mandatory. Many urban planning tasks can be effectively solved using periodically updated datasets combined with historical analysis and scenario modeling.

The critical factor is not data volume, but data relevance, consistency, and update logic.

Real-World Examples of City Digital Twins

Virtual Singapore

Virtual Singapore is one of the most cited examples of a city digital twin. It combines detailed 3D city geometry with demographic, environmental, and climate data. The system is used to analyze building density, solar exposure, pedestrian flows, and emergency response scenarios before changes are implemented in the real city.

Helsinki 3D+

Helsinki’s digital city model supports urban planning, climate research, and public engagement. Open access to the data allows residents, researchers, and planners to explore development scenarios together, making the digital twin a tool for transparency and participation, not just internal analysis.

Digital Twin of Rotterdam

Rotterdam is developing its digital twin to manage a complex urban and port environment. The system integrates buildings, transport infrastructure, utilities, and water systems. It is used to simulate flood risks, test infrastructure changes, and assess climate adaptation strategies in a city where large areas lie below sea level.

Who Uses Digital Twins of Cities?

City digital twins are primarily used by:

• Urban planners and architects

• Municipal and regional authorities

• Infrastructure and utility operators

• Real estate developers and investors

• Environmental and climate risk analysts

For each group, the value lies in scenario comparison and risk reduction, not in visual realism.

Limitations and a Critical Perspective

Despite their popularity, many digital twin initiatives fail to deliver long-term value. Common problems include outdated data, unclear use cases, and an overemphasis on visual presentation.

Key limitations include high implementation costs, complex data integration, and a lack of analytical expertise within organizations. Without a culture of working with geospatial data and models, digital twins quickly become static showcases.

In many cases, investing in data quality, analytical workflows, and user training is more important than building a detailed 3D model.

Why Digital Twins Matter in Urban Planning

Beyond marketing terminology, digital twins represent an attempt to integrate geoinformation technologies and urban planning into a single analytical framework. Their main purpose is to help specialists understand urban dynamics, evaluate alternative development paths, and make decisions based on spatial evidence rather than isolated assumptions.

A digital twin is first and foremost a forecasting and analytical tool. Its value is determined not by visual complexity, but by the quality of questions it helps answer.

FAQ

Does a universal digital twin of a city exist?
No. The structure and level of detail depend on specific objectives, available data, and institutional capacity.

Is 3D visualization required for a digital twin?
No. Many planning and analytical tasks can be solved using 2D maps combined with time-based data and analytics.

Does a digital twin require real-time data?
Not necessarily. Regularly updated datasets are sufficient for most urban planning scenarios.

What problems does a city digital twin solve best?
Scenario comparison, infrastructure planning, climate risk assessment, and long-term development analysis.

Key Takeaways

A digital twin of a city is an analytical and predictive system built on geospatial data.
Its effectiveness depends on clearly defined tasks and reliable data.
Starting with use cases and scenarios is more important than starting with a model.