Workflow-based GeoAI Analysis with No/Low-Code Visual Programming


In this training session, we will explore the utilization of low-code/no-code visual programming platforms to effectively integrate geospatial analysis with a variety of AI algorithms, including machine learning, deep learning, and Explainable AI. Designed primarily for data science novices, this training enables participants to easily embark on their journey without needing extensive programming expertise. They will learn to harness the platform for advanced spatial analysis and the development of sophisticated AI models.

The training is structured into four comprehensive sections:

Introduction to the Visual Programming Platform: We will begin by introducing the open-source KNIME Analytics Platform (AP), detailing its basic features and user interface. Participants will become familiar with its intuitive visual programming environment.

AI Functions in KNIME AP: This segment will cover the platform's advanced AI functionalities, providing insights into the range and capabilities of its AI tools.

Extension on Geospatial Analysis for KNIME AP: Participants will delve into specific geospatial analysis applications, learning how to manage spatial data and execute spatial analyses within KNIME.

Case Demonstration: The final part will focus on constructing AI models using the KNIME platform, with a special emphasis on deep learning and explainable AI models. A practical case study will be presented to demonstrate these models' application in geospatial analysis.

Through this training, participants, irrespective of their data science background, will gain essential skills to employ the KNIME platform for both geospatial analysis and AI model applications. This will lay a solid foundation for their continued learning and practice in this evolving field.

Time Schedule:

Hands-off Training (1 hour):

10 minutes: Introduction to the KNIME platform
10 minutes: AI functionalities in KNIME
10 minutes: Introduction to the Geospatial Analysis Module
30 minutes: Introduction to AI Models and Case Demonstration


Lingbo Liu, a postdoctoral fellow at Harvard's Center for Geographic Analysis, specializes in merging Open Geospatial Artificial Intelligence (GeoAI) with visual programming to revolutionize urban health initiatives. With a profound background in health-focused city planning and over 730 citations for his scholarly work, Lingbo significantly influences urban public health policy and sustainable design. His pioneering use of open-source AI for urban analytics, coupled with his commitment to education through visual programming, embodies his dedication to making GeoAI accessible to all. This innovative approach to harnessing spatiotemporal big data is pivotal in fostering sustainable, health-oriented urban development and democratizing GeoAI knowledge.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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