City association pattern discovery: A flow perspective by using cultural semantic similarity of place name

Wang, Haoran; Zhang, Haiping; Jiang, Shangjing; Tang, Guoan; Zhang, Xueying; Zhou, Lei

doi:10.1016/j.apgeog.2021.102629

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Thus, the cultural characteristics of a region can be expressed by analyzing the distribution and semantics of common names. This article selects common names with a frequency larger than 10,000 from Chinese village names due to the above characteristics of common names, and these names are referred to as high-frequency common names (Wang et al, 2022). In addition, they can be divided it into nine cultural semantics: Broken, Completed, Water, Religion, Military, Han, Building, Agriculture, Relative, and Absolute.…”

Section: Cultural Semantic and Eigenvectorsmentioning

confidence: 99%

“…The similarity between cultural eigenvectors, which is defined as cultural similarity in this article, can then be obtained in accordance with Spearman's rank correlation coefficient. The form of origin–destination flow is used to express the cultural similarity relationship, that is, cultural semantic similarity flow (CSSF; Wang et al, 2022). Simultaneously, a cultural semantic similarity network (CSSN) is constructed using Chinese cities as nodes and CSSF as links and pattern mining of place name culture from the perspective of a complex network.…”

Section: Introductionmentioning

confidence: 99%

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Inter‐city association pattern recognition by constructing cultural semantic similarity network

Wang

Zhang

Tang

et al. 2022

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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.

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Section: Cultural Semantic and Eigenvectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inter‐city association pattern recognition by constructing cultural semantic similarity network

Wang

Zhang

Tang

et al. 2022

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Self Cite

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“…Wang, H. et al study aimed to apply place names as a cultural symbol in measuring the cultural similarity study of cities in order to reveal their cultural characteristics. A cultural feature vector based on the semantics of place names was designed to identify and explore the cultural similarities between different cities [8]. Membrado-Tena, J. C. targeted to analyze historical place names, taking the example of the names of towns and cities in the region of West Asia, Spain, and pointed out that it is necessary to incorporate linguistic as well as landscape features in analyzing the original place names.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Map Study on the Rules of Place Names Setting in Ethnic Minority Areas in China

Yang,

Duan

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, for the information categorization of toponyms in ethnic minority agglomerations, different cultural partitioning results are derived by combining the region of interest model, which constitutes a specific semantic map of the region of interest model, i.e., a semantic map of the toponymic structure. The preference index of toponymic culture is taken as a feature variable, and the calculation of toponymic similarity in ethnic minority agglomerations is realized by adopting the method based on the difference of feature vectors. Dynamically determining the weights is used to evaluate the semantic similarity of spatial data for place names. To examine the distribution characteristics and distribution patterns of place names in ethnic minority agglomerations, kernel density and logistic regression models are utilized. The ratio of the number of place names of each ethnic group to the total number of county units is analyzed by Moran’s I index to obtain the spatial autocorrelation distribution direction of the distribution of place names in Yunnan Hani Autonomous County. The analysis shows that the spatial autocorrelation index of the Hani ethnic group is 0.3524, which mainly falls in the first, second and third quadrants, where the first quadrant indicates that the area around the region with a higher proportion of Hani ethnic group place names is likewise a positively correlated region with a higher proportion of Hani ethnic group place names. As per the semantic map analysis results, the spatial distribution of the population closely corresponds with the distribution of toponyms in ethnic minority areas.

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Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism

Jiang,

Yin

2023

Environ Sci Pollut Res

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City association pattern discovery: A flow perspective by using cultural semantic similarity of place name

Cited by 8 publications

References 40 publications

Inter‐city association pattern recognition by constructing cultural semantic similarity network

Inter‐city association pattern recognition by constructing cultural semantic similarity network

A Semantic Map Study on the Rules of Place Names Setting in Ethnic Minority Areas in China

Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism

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