Relationships between cityscape patterns and culture groups revealed by a global genealogy

Abstract The absence of large-scale cityscape measurements hampers further exploration of the relationship between cityscapes and cultures, a key focus in cultural geography. As a response, a data-informed analytical method has been developed to enable the measurement of cityscapes at a global scale. Specifically, six key cityscape features were identified and measured using a Large Language Model and deep learning algorithms. Computed cityscape patterns via neural embeddings and UMAP across ten cultural groups were systematically identified for the first time. A significant correlation was found between cityscapes and cultural group classifications, stronger than natural environmental factors. The First Law of Geography holds within a 5,000 km range, beyond which cityscape similarity and geographical distance become largely independent. Long-distance similarities arise from shared configurations shaped by colonial legacies and modernist planning, and are better explained by the Third Law. By integrating intelligent algorithms and big data, this study offers a big picture of global cityscapes and provides new insights into core debates in cultural geography. It aims to inspire a paradigm shift and establish a new frontier at the intersection of cultural geography and computational urban science.