Modeling population dynamics with AI: A hands-on workshop with the Population Dynamics Foundation Model

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  • Date
    18 February 2025
    Timeframe
    15:00 - 16:30 CET Geneva
    Duration
    90 minutes
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    Explore the transformative potential of the Population Dynamics Foundation Model (PDFM), a cutting-edge AI model designed to capture complex, multidimensional interactions among human behaviors, environmental factors, and local contexts. This workshop provides an in-depth introduction to PDFM Embeddings and their applications in geospatial analysis, public health, and socioeconomic modeling. 

    Participants will gain hands-on experience with PDFM Embeddings to perform advanced geospatial predictions and analyses while ensuring privacy through the use of aggregated data. Key components of the workshop include: 

    • Introduction to PDFM Embeddings: Delve into the model architecture of PDFM and discover how aggregated data (such as search trends, busyness levels, and weather conditions) generates location-specific embeddings.
    • Data Preparation: Learn to integrate ground truth data, including health statistics and socioeconomic indicators, with PDFM Embeddings at the postal code or county level.
    • Hands-On Exercises: Engage with interactive Colab notebooks to explore real-world applications, such as predicting housing prices using Zillow data and nighttime light predictions with Google Earth Engine data.
    • Visualization and Interpretation: Analyze and visualize geospatial predictions and PDFM features in 3D, enhancing your ability to interpret complex datasets. 

    By the end of this workshop, participants will have a strong foundation in utilizing PDFM Embeddings to address real-world geospatial challenges. 

    Target Audience: 
    This workshop is designed for data scientists, geospatial analysts, researchers, urban planners, and professionals in public health, economics, or environmental science who want to integrate AI into their workflows. 

     Prerequisites: 

    • Basic understanding of Python programming and geospatial data concepts is recommended