Digital Twin
Digital Twins are used increasingly to support urban planning processes – by visualizing urban data, show-casing future scenarios and many other use cases. In general Digital twins are virtual representations of an object, process or system that can be used to run simulations to optimise efficiency and examine what-if scenarios. The technology has been primarily used for manufacturing to test products (e.g. as of 2018, GE had 1.2 million digital twins for 300,000 types of assets) but is quickly expanding to buildings, supply chains and entire cities as digital planning technology advances ( Castro, 2019 ). Integrating data from the Internet of Things (IoT) with the advanced modelling capabilities of technologies such as geospatial information systems (GIS), virtual and augmented reality (VR/AR) and building information modelling (BIM) allows governments and industry to create predictions of how systems will react and respond to real-world data. Creating a feedback loop between the virtual and real worlds results in substantial improvements of processes and impacts, with time-saving and financial benefits. The concept of digital twins is not new; for example, NASA has been running simulations of spacecraft for decades, but the rapid growth of connected sensors and endpoints with the rise of the IoT and advancements in artificial intelligence has opened up a myriad of possibilities for the planning and analysis tool. Potential uses for digital twins are still being imagined. Uses for cities currently include using digital twins to plan transportation systems, prepare for natural disasters and identify optimal locations to install solar panels. Future uses could include predicting how a disease will spread and informing optimal lockdowns and hospital reservations or using the tool to facilitate collaborations with other cities that have shared problems and mutual goals. Problems to be solved: Ease complex decision-making Enable more efficient urban planning Enable more efficient project implementation and monitoring Create better preparedness for emergency situations Make urban data easier to understand