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The resulting multidimensional raster predicts the prevalence of emotions across the landscape, with each dimension representing a different emotion. The results show the potential for both DL and GPT methods in spatial emotion detection using contextual windows, achieving high accuracy rates in most measures, though indicating the need for improvement in precision (DL) and recall (GPT). The methodology provides further direction forward in developing a geographic information system (GIS) of place that is spatially accurate and sensitive to human experience. 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