#  Workflow-based Case Studies 

 



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 **Open KNIME Workflows on KNIME Hub**

 KNIME Community Hub: [Center for Geographic Analysis at Harvard University](https://hub.knime.com/center%20for%20geographic%20analysis%20at%20harvard%20university)

 This Hub contains workflows for Geospatial Analytics Extension for KNIME. Especially ,this space contains workflows that accompany the book: [Computational Methods and GIS Applications in Social Science (3 Edition) and its KNIME Lab Manual](https://www.routledge.com/Computational-Methods-and-GIS-Applications-in-Social-Science---Textbook/Wang-Liu/p/book/9781032285184). The book details applications of quantitative methods in social science, planning, and public policy with a focus on spatial perspectives. The book integrates GIS and quantitative (computational) methods and demonstrates them in various policy-relevant socio-economic applications with step-by-step instructions and datasets. The book demonstrates the diversity of issues where GIS can be used to enhance studies related to socio-economic issues and public policy.

   ![Computational Methods and GIS Applications in Social Science-Lab Manua](/sites/g/files/omnuum8906/files/styles/hwp_1_1__960x960_scale/public/chinadatalab/files/screenshot_20231121143418.png?itok=pQoJjxyG) 

 

 KNIME AG and Harvard’s Center for Geographic Analysis have launched a joint project to advance spatial data science. The project is part of the Spatiotemporal Thinking, Computing, and Applications (STC) Center which is funded by the NSF to enable pre-competitive research in partnerships among industry, academia, and government. The two-year project “Developing Workbenches for Spatial Data Science (HVD-21-07)” will explore methodologies to advance spatial data science research and teaching. To [open up spatial analytics to a more diverse group of users](https://www.knime.com/blog/harvard-geospatial-analytics-for-all), the partners will develop freely accessible KNIME Analytics Platform extensions for spatial statistics, modeling, and visualization. KNIME’s visual, no-code/low-code environment will enable multi-discipline users to more easily and efficiently collaborate, explore data, and surface insights. Further resources in the form of workbooks, workflow-based case studies, and best-practice workflows will be developed to help users get started with replicable and reproducible spatial data science across scientific disciplines.