2022
DOI: 10.1111/tgis.12957
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Inter‐city association pattern recognition by constructing cultural semantic similarity network

Abstract: Inter‐city association patterns can be embodied in many aspects, such as transportation, immigration, and the spread of diseases. Among these aspects, culture, as an important content of human society, is also a manifestation of inter‐city association. The recognition of inter‐city cultural association patterns plays an important role in understanding the spatial distribution pattern of culture. This article defines cultural eigenvectors to represent city cultural characteristics by mining the semantics of pla… Show more

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Cited by 7 publications
(1 citation statement)
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References 57 publications
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“…Thanks to the emergence of geospatial big data collected from location-based services (LBSs) and the Internet of Things (IoT), an increasing number of researchers have taken advantage of GCNNs to investigate urban issues, e.g., traffic prediction [43,44], urban land-use recognition [24,45], urban scene classification [25], urban security perception [46], public health evaluation [47], weather forecasting [48], and cultural association mining [49]. Nevertheless, the implicit semantics and contextual information of geospatial big data are underexploited in the identification of urban functional features.…”
Section: Place Embedding With Graph Convolutional Neural Networkmentioning
confidence: 99%
“…Thanks to the emergence of geospatial big data collected from location-based services (LBSs) and the Internet of Things (IoT), an increasing number of researchers have taken advantage of GCNNs to investigate urban issues, e.g., traffic prediction [43,44], urban land-use recognition [24,45], urban scene classification [25], urban security perception [46], public health evaluation [47], weather forecasting [48], and cultural association mining [49]. Nevertheless, the implicit semantics and contextual information of geospatial big data are underexploited in the identification of urban functional features.…”
Section: Place Embedding With Graph Convolutional Neural Networkmentioning
confidence: 99%