Jascha Grübel, ETH Zürich

Improving the quality of life for people in the 21st century is intrinsically tied to the development of sustainable environments. According to UN estimates, the number of city dwellers will rise from 4.2 billion to 6.7 billion in 2050 Cities will grow denser, and these changes will be accompanied by environmental pressures resulting from climate change and the need for humans to reduce their carbon footprint. The concept of smart cities aims at adopting novel technologies in order to make cities more efficient, habitable and ecologically sustainable. Recently, several improvements have been made in terms of large scale efficiency (e.g., optimising traffic flows and public transport), habitability (e.g., eco-cities and resilient cities) and ecological sustainability (e.g., reducing energy consumption and recyclable construction materials). However, translating these successes to the smaller scale (e.g., buildings) remains a challenge, given that many of the metrics used to measure pedestrian spatial behaviour require more nuanced and fine grained methodologies. Research in spatial cognition can provide human-centred metrics, that focus on the perceptual and cognitive processes, that take place, while humans acquire and use spatial information to navigate.

Unfortunately, spatial cognition research is often constrained to artificial lab environments, that sometimes fail to capture the complexity of real world challenges. Fast paced developments of the Internet of Things (IoT) are at the heart of smart city developments. IoT is an umbrella term for a network of devices including home appliances (e.g., computers, heaters), vehicles and sensors, that are capable of collecting and exchanging information about the real-world. This technology offers new opportunities, for measuring the intricacies of human behaviour and can close the gap between laboratory and real-world experiments. Here, data on real-time behaviour, measured in situ, can be used, to make predictions about behaviour and design interventions, focused on creating more liveable and efficient spaces.

At the same time, the circumstances in the lab can mirror the real world ever closer. Developments in computer graphics and hardware have propelled Virtual Reality (VR) and Augmented Reality (AR) into everyday use. Using VR has already changed, how behaviour research is conducted in labs but also in industry. VR allows architects and urban planers, to visit new buildings and city quarters, before they are built. This offers an opportunity, to develop liveable and efficient spaces from the beginning. However, the core question of external validity remains disputed: Can human navigation behaviour be studied without full embodiment in the environment?

This project will leverage an IoT setup in order to capture, visualise and predict human spatial behaviour inside public spaces as a basis for comparision to human spatial behaviour in a virtual correspondence of the space.

The proposed IoT setup will passively measure spatial behaviour, using a network of high density multimodal environmental sensors (e.g., noise, brightness, temperature, CO 2 , motion) inside two museums in Switzerland. Data collected from these sensors will be used, to create a “live” digital twin of the surveyed environment, in which the collected sensor data will be visualised and analysed. The digital twin offers novel opportunities for using empirical data, to validate models of spatial behaviour that can be used by designers and building managers for soft (e.g., closing doors, closing rooms) and hard (e.g., rearrange exhibits, change the layout of space) interventions. I will first prototype the digital twin concept, using a network of approximately 400 sensors inside a single gallery of the Rietberg Museum Zürich. In a second stage, I will extend this network of sensors, to cover a large section of the Historical Museum of Basel. These sensor networks will provide millions of data points on different aspects of visitor behaviour (e.g., paths taken, time spend at exhibits, interactions within a group) inside museums. This data will be the foundation for a comparative study of human spatial behaviour in a virtual copy of the museum environments.

In summary, this project will (1) introduce new methods for using IoT sensor data in order to capture human spatial behaviour, (2) develop novel techniques for visualising and analysing IoT data through a digital twin , (3) validate the capacity of passive sensor networks for generating accurate insights on human spatial behaviour, and (4) compare human spatial behaviour in real and virtual spaces contributing to the discussion on the external validity of experiments in VR.