Fusing Machine Learning with Place-Based Survey Methods: Revisiting Questions Surrounding Perceptual Regions
History & Political Science
This article explores questions on perceptions of the location of the ‘Midwest’, a contested vernacular region of the United States. We created a custom online survey with R’s web framework Shiny, in which participants were presented with a blank web map and asked to ‘draw’ their definition of the Midwest. Instead of simply describing the aggregated results, we employ machine learning algorithms – Naive Bayes, Random Forest and Categorical Boosting – in an attempt to classify users into groups, with a focus on the features that most effectively separate responses. We also demonstrate a way to engineer features from a single spatial response question and provide an implementation through a small R package. Furthermore, we discuss misclassified observations and suggest some driving factors in the construction of regional perception. This research is important not only for its contribution to perceptual regions but also for the approach, which could be applied to place-based survey analysis more broadly.
International Journal of Geographical Information Science
Haffner, Matthew, Patrick D. Hagge, C. Brown, R. Heyrman, and C. Perkins. 2022. “Fusing machine learning with place-based survey methods: Revisiting questions surrounding perceptual regions”, International Journal of Geographic Information Science. (published online July 2022) https://doi.org/10.1080/13658816.2022.2097683