24025 KNIME Data App for Computational Methods and GIS Applications in Social Science

Research Topics: Computational Social Science, GIS, Geospatial Analytics

Mentors: Lingbo Liu and Tian Tian, Harvard University

Project Description:

This project focuses on developing a KNIME Data App to enhance the accessibility and utility of computational methods and GIS applications in social science research. Building on workflows from the book Computational Methods and GIS Applications in Social Science and using the Geospatial Analytics extension for KNIME, the project aims to standardize workflows and provide an engaging learning platform for GIS students and researchers.

The project involves two primary objectives:

  1. Standardization of Book Workflows into Webportals
    Interns will review and standardize workflows from Computational Methods and GIS Applications in Social Science and it KNIME Lab Manual, developing webportals for each workflow shared in KNIME Community Hub. They will study examples from the KNIME Hub and learn how to create and deploy KNIME Data Apps to meet standardization guidelines. This process will familiarize interns with using KNIME to automate workflows and effectively design standardized KNIME Data Apps.

  2. Introduction to KNIME’s Geospatial Analytics Extension
    Interns will create KNIME Data Apps for each node in the Geospatial Analytics extension. These apps will provide intuitive, interactive explanations of each node, designed to make learning GIS methods both straightforward and enjoyable for users.

Through these tasks, interns will develop a strong foundation in geospatial analytics in KNIME, enhancing their understanding of GIS techniques and their applications in social science research.

Intern Responsibilities:

  • Workflow Standardization: Develop and deploy standardized KNIME Data Apps for workflows covered in the book.
  • Geospatial Node Documentation: Create interactive KNIME Data Apps for each node in the Geospatial Analytics extension, making GIS learning accessible and engaging.
  • Skills Development: Interns will acquire a comprehensive understanding of geospatial workflows in KNIME and the development of KNIME Data Apps.

Learning Outcomes:
Interns will gain hands-on experience with KNIME Hub deployment, geospatial analytics, and the process of standardizing and documenting workflows. Additionally, they will learn to use KNIME’s Data Apps to create effective educational tools.

Prerequisites:
Completion of the prerequisites for the SDL Project Internship/Fellowship Program is required.

Term of Project: 1 Year