A data-synthesis-driven method for detecting and extracting vague cognitive regions

Publication Type:

Journal Article


International Journal of Geographical Information Science, Volume 31, Issue 6, p.1245–1271 (2017)




cognitive regions, data synthesis, latent dirichlet allocation, place, vagueness


Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a recent study, Montello et al. (2014) devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundarydrawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from Montello et al. (2014), identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience