Spatio-temporal data Analysis and Reasoning (STAR)


Our research group is part of the Automated Design of Algorithms (ADA) embedded in the Leiden Institute of Advanced Computer Sciences (LIACS) at Leiden University. We aim to design algorithms for effectively processing spatial, time-series and spatio-temporal data. In principle, every process around us is spatio-temporal, as we can attach time and space to it. Spatio-temporal datasets represent the development of these processes by sampling them in time and space. Modern sensing technologies such as Earth Observation satellites, GPS, or wearables sensors have allowed the collecting such datasets at large scales. We explore the design of algorithms that can automatically handle all necessary data processing tasks from the point of data collection to high-level modelling, extraction of information, and effective decision-making from such data. Our research targets complex applications in a broad range of urban, environmental, and industrial domains. We strongly focus on developing algorithms for wearables data, Earth observations and open spatio-temporal data sources. Find more about our new initiative AutoAI4EO, where we push for advancing machine learning algorithms for Earth observations.


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Since 2011

Trajectory data
Spatio-temporal trajectories show the movement of moving objects (e.g., humans, cars, animals) over space. We design algorithms that can extract patterns from such data. We have previously designed algorithms to analyse the movement of crowds and animals using location-sensing technologies. Within one of our projects funded by the LDE Center for Big, Open, Linked Data (BOLD) cities, we are now looking at trajecories of pupils while playing at schoolyards. Through designing trajectory pattern mining algorithms, we aim to understand the underlying social dynamics.
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Since 2018

Time series
Time series data are observations of a single process of regular time intervals. We have previously designed different algorithms for time series data for different applications (e.g., energy load forecasting, remaining useful life estimation, and COVID-19 forecasting).
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Since 2022

Astronomical observations
 We study how machine learning algorithms developed for data acquired from telescopes can help understand the world better. For this goal, we strongly collaborate with astronomers in the Leiden Observatory.

Since 2023

Wearables data
Within the LABDA (Learning Network for Advanced Behavioural Data Analysis) EU project, we would like to understand how 24/7 observational activity data collected by wearable sensors (e.g., smart watches) can be used to identify and recommend effective changes in daily activities (i.e., possible behavioural interventions) that are expected to result in concrete health improvements.
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Since 2017

Urban Computing
We would like to know how open and ubiquitous data sources can help find computational solutions for urban challenges. Notably, in 2019, three of our projects made it up to the top 10 selected contributions in the Future Cities Challenge.


[All Publications]
  1. Unsupervised discretization by two-dimensional mdl-based histogram
    Yang, Lincen, Baratchi, Mitra, and Leeuwen, Matthijs
    Machine Learning, 2023
  2. A GNN-based Architecture for Group Detection from spatio-temporal Trajectory Data
    Nasri, Maedeh, Fang, Zhizhou, Baratchi, Mitra, Englebienne, Gwenn, Wang, Shenghui, Koutamanis, Alexander, and Rieffe, Carolien
    In Proceedings of the 21th International Symposium on Intelligent Data Analysis (IDA 2023), 2023
  3. A Novel Data-driven Approach to Examine Children’s Movements and Social Behaviour in Schoolyard Environments
    Nasri, Maedeh, Tsou, Yung-Ting, Koutamanis, Alexander, Baratchi, Mitra, Giest, Sarah, Reidsma, Dennis, and Rieffe, Carolien
    Children, vol. 9, 2022


Address: Niels Bohrweg 1, 2333 CA Leiden
Email: m.baratchi[at]