See Research Themes

Spatio-temporal analytics, maps and processing

Top 10 Shanghai Ranking

Organizations, as well as, the general public alike are in constant need of information about spatial phenomena and about processes driving change in their environment. In other words, they all demand up‐to‐date, or (near) real-time information, about virtually anything, anywhere, and anytime. The digital, mobile and sensor revolution has made such requirements practically feasible. Thus, nowadays, some of the data is collected on an ad hoc basis, or even 'by accident', at such a high frequency and volume, by both physical and human sensors, that data availability becomes less of an issue. What remains an issue is how to optimally convert the data into useful geoinformation and how to further process this information to generate knowledge.

Our mission is to provide optimal support for the provision of modern and fit-for-purpose (geo)information products and services that are useful in many different user contexts. The objective of the programme is to develop methods and techniques that process (organize, model, analyze, and visualize) spatiotemporal data into valuable and accessible geo-information, and tools that improve our understanding of complex and dynamic systems and helps in decision-making at a variety of spatial and temporal scales.

RESEARCH THEME :

Spatio-Temporal Analysis, Maps and Processing (STAMP)

The objectives outlined above often require tightly connected (Geo)IT-based information processing and human interaction through visualization and systems modeling. To realize this, the STAMP team has organized itself around five interconnected subdomains shown in the scheme below, each with a typical research objective:


keywords

Agent-Based Modelling, Eye Tracking, 3D (geo) visualizations, Big geo-data, Reproducible Workflows, Cloud & distributed Computing, System Engineering, Data Models, Crowdsourcing & Citizen Science

Research group leaders

Spatio-temporal Analytics
Chair: Prof.dr.  R. Zurita Milla

Geovisual Analytics and Cartography 
Chair: Prof.dr. M.J. Kraak

  • More detailed info

    RESEARCH QUESTIONS DRIVING EACH OF THE FIVE SUBDOMAINS ARE INTRODUCED BELOW:

    MODELLING

    How to make sense of geo-information amongst the deluge of data by building new and using existing simplified representations of reality, models that help us to analyze systems in time and space, guide our efforts in collecting data, and communicate knowledge with stakeholders?
    How to integrate models and model components, especially when they are based on different modeling approaches?
    How to integrate human mental models with computer simulations?

    CLOUD & CROWD

    How to technically and socially devise geoinformation sharing systems for diverse user groups (professionals, citizens), aiming at better informed near real-time spatial decision making, by utilizing developments in crowdsourcing, mobile sensors networks and cloud computing.

    DATA ORGANISATION

    How to represent our spatial environment and processes by spatiotemporal data in information systems?
    How to design systems for the processing and management of spatiotemporal information and for services to access and use this information in spatial data infrastructure environments?

    ANALYTICS

    How to extract and filter usable geoinformation out of massive amounts of heterogeneous spatiotemporal data in a reproducible and robust manner?
    How to realize data-driven explanatory and predictive models that guide decision making at appropriate spatiotemporal scales?
    How to integrate novel data sources and analytical methods into traditional geospatial workflows to extract valuable insights from complex systems?

    GEOVISUALISATION

    How to offer a diversity of visual representations that support the user during any phase of the spatiotemporal data handling process?
    How can these visualizations help to understand the information displayed, improve insight, and support reasoning and decision making?
    How to design and create map-based representations that summarize temporal and spatial trends in large heterogeneous data-sets?

Group memberships