Democratizing reproducible GIS in digital humanities: a workflow-based approach to historical spatial analysis using KNIME

Geographic Information Systems (GIS) offer powerful analytical capabilities for digital humanities research, yet technical barriers and reproducibility challenges limit their adoption by humanities scholars. This paper presents a workflow-based approach that democratizes reproducible spatial analysis by transforming complex GIS operations into accessible, executable workflows using the open-source KNIME platform. Our methodological framework addresses three critical challenges: technical complexity through intuitive workflow design, reproducibility through complete process documentation, and accessibility through browser-based deployment. The approach integrates specialized components for historical data processing – including temporal uncertainty handling and spatial disambiguation – within standardized, shareable workflows that preserve analytical transparency while requiring no GIS expertise. We demonstrate the framework’s effectiveness through a comprehensive case study analysing spatial mobility patterns of pre-modern Chinese literati, where researchers successfully performed complex network analysis, trajectory visualization, and spatiotemporal pattern detection using our workflow-based tools. Results confirm that the approach enables exact analytical replication while significantly lowering technical barriers for humanities researchers. The framework’s modular design supports adaptation to diverse historical spatial research contexts, establishing a replicable methodology for democratizing GIS in digital humanities. This workflow-based approach contributes to more inclusive and reproducible spatial humanities scholarship by making advanced geospatial analysis accessible to researchers regardless of technical background.