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 place names. On this basis, a cultural semantic similarity network (CSSN) is constructed to recognize inter‐city cultural association patterns. Meanwhile, the related algorithm is designed to discover the cultural spatial structures using China as a case study. Finally, four types of cultural hubs, four typical cultural belts, and 13 cultural circles are identified. This article not only recognizes the cultural importance and associations of Chinese cities, but also provides a reference for other city association studies.

Semantic-Enhanced Graph Convolutional Neural Networks for Multi-Scale Urban Functional-Feature Identification Based on Human Mobility

“…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.…”