MoST – Modeling, querying and interactive visualization of spatiotemporal data


José Manuel Matos Moreira (IEETA - Institute of Electronics and Informatics of Aveiro)

CESAM Responsible researcher

Ana Isabel Miranda


Programa Operacional Regional do Centro (02/SAICT/2017)


01/06/2018 - 30/09/2021

Funding for CESAM

40041 €

Total Funding

232645 €

Participating Institutions

  • INESC-TEC Porto


Recent technological advances made it possible to collect data on the evolution of spatiotemporal phenomena far superior to the existing capacity to analyze and to extract relevant information from them in various scientific areas. Thus, there is a growing need of tools to automate processes of quantitative analysis of spatiotemporal data, guaranteeing levels of objectivity, precision and reproducibility compatible with the execution of scientific work.
Nowadays, there are already well-known tools for the processing of static spatial data (e.g., Geographic Information Systems), but the support for the modelling of dynamic phenomena is limited, and it is often necessary to make a great effort in implementing complex algorithms that are specific to solve a single problem.
This project focuses on the development of advanced tools for modeling and analyzing spatiotemporal data using continuous models of space and time. The key element is a data management system designed to model generic spatial transformations (e.g., shape’s transformation, movement, rotation, aggregation or fragmentation of entities or objects) representing the evolution of the phenomena across time. The system will be accessible through a query language offering high-level functions for the management, retrieving and processing of large volumes of data.
We will also develop methods to create spatiotemporal representations from sequences of images or videos, and spatiotemporal data visualization tools. The result will be an integrated system designed to perform studies about spatiotemporal phenomena, lessening the effort and the time required to implement complex data management and processing procedures, and thus releasing resources to accomplish the studies themselves.
The proof of concept is based on two case studies involving the modeling of spatiotemporal phenomena with highly distinct features. The first consists of the modeling forest fires propagation from aerial images, in order to carry out studies on carbon emissions to the atmosphere. The second consists of the creation of a database characterizing the morphological changes of cells as they move, using data extracted from microscopic videos. The quantification of these characteristics is important in biological processes such as embryonic development or tumorigenesis. It is also expected that the results of this project will be applied in other fields in the future, such as, studies on coastal erosion, silting of rivers or others.