Integrating machine learning into public participation GIS: trends, challenges, and future directions for spatial information science

Identificador:  imarina:9599191

Handle: https://hdl.handle.net/20.500.11797/imarina9599191

Autores:  Gheitasi, Mahdi; Albert, Yolanda Perez; Schnabel, Marc Aurel; Gine, David Serrano

Resumen:
This paper presents an exploratory narrative review of machine learning (ML) applications in Public Participation Geographic Information Systems (PPGIS)-related studies. Based on a structured Scopus search and screening process, the review examines how ML techniques are used to process and interpret collaborative spatial data and identifies associated challenges. The findings show that supervised learning methods, including random forest, boosted regression trees, artificial neural networks, and deep learning, are most commonly applied, while unsupervised methods are used to detect spatial and perceptual patterns. These approaches can strengthen PPGIS by supporting the analysis of complex citizen-generated datasets and linking participatory inputs to spatial decision-support processes. However, concerns remain regarding data representativeness, participant bias, spatial uncertainty, privacy, and model interpretation. The paper highlights the need for more transparent, evidence-based, and explicable PPGIS frameworks, which would enable the use of ML to support participatory spatial decision-making.