Research projects

The project will use a Digital Twin model named GEMINI to support policy decisions for emission monitoring and prediction in cities. The GEMINI combines multisource data into a single platform and holistically uses data/AI analytics and simulation techniques to create system understanding and knowledge.

It also connects IoT sensor data as the basis for knowledge-based decision-making targeting different aspects of sustainability and policy interventions in cities. The project will prototype and demonstrate the GEMINI platform in Kista and scale up to the City of Stockholm. The GEMINI platform is designed in a general and modular way that could be extended for applications beyond the project, such as energy consumption and noise emissions.

Project period: 2023-2024

The project intends to investigate a broad research question: 

How could carbon budgeting, embracing embodied emissions of buildings and consumption behaviour, be done for a city district like Kista? 

Investigating the built environment allows us to quantify the carbon footprint of static urban assets: buildings. However, it would be highly misguided to perform an analysis that thinks of the city as purely static: buildings are used by companies, shops, and people who constantly move to occupy these buildings—therefore, the assessment of any urban feature must deal with multiple temporal and spatial scales. Thus, we intend to answer the research question by adding Stockholm's temporal and spatial consumption variation, using Kista as a starting point. 

Project period: 2023-2024

Safety can be experienced differently depending on whether you live in an area, work in an area or visit an area. In this research project, the researchers will take a closer look at perceived security in Kista, a knowledge that is important when planning and building housing.

Project period: 2023-2024

We combine the deployment of low-cost sensors on vehicles – the City Scanner – with existing data on forest and other tree-cover habitats to measure ambient and ground temperature and computer vision models to quantify greenery at the street level to assess the hyperlocal benefits of urban greenery at a fine temporal and spatial granularity. This is compared to the influence of the urban canyon and natural topography on microclimate and to the result of tree-cover habitat patterns at the neighbourhood and landscape scales.

This knowledge will be essential to counter the health impacts of heat events on several spatial scales relevant to urban planning.

Project period: 2021-2022

In this study, we explore a combination of data sources to produce an AI safety perception map of the city of Stockholm, Sweden. The ultimate goal of this research is to develop a prototype of a long-term monitoring platform using AI that is reproducible.

What does make a city safe, and which are the most common urban features that make people declare feeling safe? How do safety patterns relate to the physical and social landscape of urban environments according to different data sources? Do they vary by groups of the population, and how?

Project period: 2021-2022

New construction may have important intended and unintended implications for its immediate vicinity and larger urban context. This project intends to investigate and evaluate the impacts of specific development projects to create a deeper, more refined, and more thorough understanding of what kinds of contextual effects projects have, and pilot a monitoring system to detect these changes.

Project period: 2021-2022

Urban segregation and unequal living conditions are urgent contemporary challenges. The ongoing expansion of Stockholm is an opportunity to develop a more socially sustainable city, characterized by reduced segregation and more equal living conditions. This requires an explicit design for diversity and a design allowing for differences in public space.

The phenomenon of urban segregation will be studied from many perspectives also beyond residential segregation by using new techniques and develop creative mapping in order to increase the understanding of how people use the city.

Air quality, heat, noise. Those are the three environmental variables that affect the well-being of people living in cities the most. Yet, those are also hyperlocal, fast-paced changing phenomena hard to snapshot.

The Sensing Stockholm project aims at bringing environmental data to the doorstep of citizens: By collecting, analyzing and visualizing hyperlocal environmental data to study a number of urban phenomena (e.g. air quality, road quality, and thermal leaks from buildings) we aim at delivering actionable insights for the public good.

The development and implementation of AI applications for Stockholms Stad will actualise various ethical issues and value conflicts. Identifying and addressing them responsibly and sustainably (socially as well as environmentally) is necessary for the successful development of the smart city.

In addition to a commitment to academic excellence the project will be stakeholder informed and have a focus on practical relevance. The two parts complement each other and give the project a good balance between theoretical and empirical a combination which, plausibly, generates applicable and useful results.

This project aims to strengthen Stockholm’s transport system by analysing “flow imbalance”. Flow imbalance refers to a wide disparity in the travel times between two locations in a city for different travel modes.

This project will identify relevant flow imbalances in Stockholm. It will also identify solutions, both in a short and long term capacity, and within the toolbox of the stakeholders of KTH Royal Institute of Technology and MIT Senseable City Lab. The overall aim of the project is to find the right locations of the new transport modes and a better mix between housing and office/service etc. such that people use more efficient, less costly, and more environmentally friendly transport modes.