New Paper Published on IJGIS

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Happy New Year! Our team published a new research article on the International Journal of Geographical Information Science:

Song Gao, Krzysztof Janowicz, Daniel R. Montello, Yingjie Hu, Jiue-an Yang, Grant McKenzie, Yiting Ju, Li Gong, Benjamin Adams, Bo Yan. (2017) A Data-Synthesis-Driven Method for Detecting and Extracting Vague Cognitive Regions. International Journal of Geographical Information Science, DOI:10.1080/13658816.2016.1273357.

Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. They arise from the complex interactions of individuals, society, and the environment. 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. Consequently, cognitive regions and places are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general.

In an earlier study published in the journal IJGIS, 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 boundary-drawing 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 the human-participants study of 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.