2025-06-01 — Paper Published in Scientific Data : "Cancer Incidence Data at the ZCTA Level in the U.S. Interpolated by Multi-Constraint Monte Carlo Simulation. Learn more →
2025-05-10 — Dataset on ZCTA-level cancer incidence interpolation released on Harvard Dataverse. Learn more →
2025-07-21 — GeoAI & GenAI Workshop, Harvard CGA, Cambridge MA. Learn more →
2025-03-15 — Presentation at AAG 2025: “GNN for Health Service Area Detection”. Learn more →
Geospatial Analytics Extension for KNIME, v 2.0 — An open-source visual programming platform for geospatial analysis, supporting both local and cloud environments, and used by over 200,000 users (by 2025-4-12). Recognized as the top community extension for KNIME, it led to Lingbo's nomination as KNIME's Contributor of the Month for March 2025. Textbook | GitHub | KNIME Hub workflows | Datasets on Harvard Dataverse
XGeoML, v0.6 — The first Python package for ensemble explainable geospatial machine learning, integrating over 30 machine learning models with local geospatial modelling; downloaded over 15,000 times on PYPI (by 2025-8-4). Download statistics →
MIGIS is an interdisciplinary research initiative that integrates spatial thinking, geospatial modeling, and explainable artificial intelligence to address critical challenges in urban health, environmental justice, and data-driven decision-making. Led by Dr. Lingbo Liu at Harvard University’s Center for Geographic Analysis, MIGIS emphasizes not only the technical rigor of geospatial intelligence but also the ethical, human-centered, and policy-relevant dimensions of spatial data science. MIGIS stands at the intersection of spatial modeling, digital equity, and open science. Its core mission is to develop mindful and responsible spatial analytics—tools and methods that are not only powerful but also interpretable, inclusive, and actionable. The initiative contributes to global research through open-source toolkits, reproducible workflows, and collaborative platforms that empower governments, researchers, and communities to build healthier and more equitable cities.
Explainable geospatial machine learning models for public health and climate resilience.
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Accessibility modeling and urban health analysis based on human mobility and spatial equity.
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KNIME-based visual tools for geospatial workflows, teaching, and reproducible research.
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