What is a digital twin?
A digital twin is a virtual representation of reality, including physical objects, processes, and relationships. When built on a foundation of geography, it becomes a geospatial digital twin.
Geospatial
Accurate representations of complex systems require robust maps and spatial analytics from geographic information system (GIS) technology.
Time aware
Digital twins go beyond 3D models as they reflect change over time—showing historic, current, and future states.
Scalable
From single facilities to large built and natural systems, geospatial digital twins scale to meet changing needs.
Real Time
Digital twins continuously collect data from physical assets, keeping the virtual model updated in real-time. This is achieved through sensors and other data collection tools. Real-time data is essential for monitoring processes and making quick interventions.
Explore an unmatched holistic view of assets, operations, and possibilities with a real-time integrated digital twin.
Visualize comprehensive data
Dynamic, interactive visualizations unite vast amounts of data from sources including business systems, sensors, and information models.
Simulate and predict outcomes
Comprehensive models show how plans and ideas would impact places, people, and the bottom line in a risk-free, virtual environment.
Make complex systems easier to understand
One view shows infrastructure, assets, and movement to streamline workflows and decision-making.
Unite diverse datasets, gain spatial context, and build a mature digital twin with the robust capabilities in GIS technology.
Advanced data integration
Connect proprietary data and add layers such as terrain, demographics, and climate risk to deeply understand relationships and outcomes.
Dynamic modeling
Use interactive models built with precise location data and other essential inputs such as building information to answer critical questions.
Immersive environment
Bring buildings and cities alive. Transport stakeholders to a site with game-like 3D models that transform collaboration and problem-solving.
Digital twins continuously collect data from physical assets, keeping the virtual model updated in real-time. This is achieved through sensors and other data collection tools. Real-time data is essential for monitoring processes and making quick interventions.
A digital twin must accurately represent the physical asset. This requires precise modeling of the system’s mathematical, physical, and behavioral characteristics. Inaccurate or incomplete data can compromise the validity of simulations and analyses.
Digital twins should be able to simulate and predict future events and scenarios. By testing different scenarios, the system’s performance can be optimized, and potential issues can be identified in advance.
Digital twins must have the ability to adapt to continuously changing conditions. Supported by machine learning and artificial intelligence algorithms, a digital twin can learn from past data and make more accurate predictions in the future.
Digital twins enable continuous monitoring of physical assets, allowing for early detection of malfunctions or performance drops. This helps reduce maintenance costs and improve operational efficiency.
A digital twin should have an interface that allows users to easily interact with the data. Visualization, data analytics tools, and user-friendly control mechanisms make decision-making easier and make the digital twin more accessible.
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