Research themes

Our research focuses on analysing spatial and temporal interactions between people, and between people and their environment. Much of our work examines human mobility/accessibility from different perspectives as a proxy for interactions from local to global scales. ​To do so, we apply new big data sources and spatio-temporal big data analytics while paying attention to critical thinking and ethical issues.

Our research is divided into three closely interrelated themes: 1) socio-spatial interactions between people, 2) human–nature interactions, and 3) development of methods and tools for utilising big and open data sources to study these interactions.

Our dynamic society is shaped by interactions between people, and between people and the broader society. To understand how societies function and change in time and space, we need to know more about these interactions. The mobility of people is a key proxy for indicating socio-spatial interactions. It is linked with the underlying spatial patterns of accessibility, a prerequisite for potential encounters. Our group uses a range of novel data sources complemented with traditional data to analyse population flows and dynamics, the individual mobility of people, spatial accessibility patterns, and how these change in time. We are particularly interested in user-generated data, such as mobile phone, GPS tracking and social media data and how they allow us to examine individuals’ activity spaces and travel behaviour, and how these link to equity, inclusion, sustainability and health. Our work contributes to the fields of urban geography and regional studies, transport and urban planning, tourism and health research.

Related projects:

Sustaining good environmental conditions, including high levels of biodiversity and ecosystem services, is crucial for the health and wellbeing of humankind. The possibility to access, use and enjoy green areas has been considered an issue of social justice. People’s valuing and use of green areas are important for maintaining many green areas of high nature value, including urban parks and many protected areas. Naturally, intensive use may also be a threat, or exposure may sometimes be negative. We study human–nature interactions from various perspectives: How do people use green areas, how equal are their opportunities for being exposed to nature and how can we ensure the co-existence of biodiversity and people through conservation planning? Our group uses various novel data sources to study human–nature interactions, combining data from social media with expert mappings of biodiversity, and GPS tracking with air quality models and street view greenery indices. We apply a wide range of methods developed for data science, conservation planning and prioritisation, and environmental exposure analysis. Through our analyses, we address key issues in sustainable land use planning, biodiversity conservation, and environmental health research.

Related projects:

Related tools:

The amount of data being collected about individuals and the environment, either actively or passively, is growing exponentially. Open data movement has made many publicly produced datasets easily available for research. Private companies are collecting unprecedented amounts of data on individuals through apps and mobile devices, which we seek to use for the common good. Taking full advantage of the growing body of data resources for increased understanding of spatial phenomena requires novel data mining, analysis and visualisation methods. In our work, we take advantage of the recent advancements in the fields of (geo) data mining, geoinformatics, Artificial Intelligence and visualisation techniques to develop tools and data products that can help to study socio-spatial interactions and human–nature interactions. Whenever possible, we share our tools and data openly. We have extensive global data resources collected from social media platforms (Twitter, Instagram and Flickr) and have published a widely used Travel Time Matrix for the Helsinki region in 2013, 2015 and 2018.  

Data and tools:


Related projects: