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Article summary:
1. This article explores the cultural similarity between cities and proposes a method to measure it using place name data.
2. The K-S index and Mahalanobis distance are used to quantify cultural similarity, and the Euclidean distance is used to calculate the cultural eigenvectors of Chinese cities.
3. Three patterns of cultural semantic similarity flow are discovered, including one-to-one, one-to-many, and one-to-all patterns.
Article analysis:
The article “City Association Pattern Discovery: A Flow Perspective by Using Cultural Semantic Similarity of Place Name” is an interesting exploration into the concept of cultural similarity between cities. The authors provide a comprehensive overview of existing research on place names and their use in GIS cultural geography and tourism, as well as international business research related to investment policies and cross-regional acquisitions. They also discuss various definitions of cultural similarity from different fields such as economics, sociology, international relations studies, etc., which provides a good foundation for understanding the concept before delving into their proposed method for measuring it.
The authors then propose a new method for quantitatively measuring the cultural similarity among cities based on the semantic analysis of place names. This method involves constructing common name semantic trees, selecting high frequency common names as data sources, calculating weights for them, constructing initial cultural eigenvectors which are then weighted according to distribution area to obtain final eigenvectors. Finally they use Spearman's rank correlation coefficient to calculate cultural similarity between cities and construct a cultural semantic similarity flow (CSSF).
The article is generally well written with clear explanations throughout; however there are some potential issues that should be noted when considering its trustworthiness and reliability. Firstly, while the authors provide an extensive overview of existing research on place names and their use in various fields such as GIS culture geography and tourism, they do not provide any evidence or examples from these fields that support their proposed method or demonstrate its effectiveness in practice. Secondly, while they discuss various definitions of cultural similarity from different fields such as economics, sociology etc., they do not explore any counterarguments or alternative perspectives on these definitions which could have provided more insight into how this concept can be interpreted differently depending on context or field of study. Thirdly, while they present three patterns of CSSF that were discovered through their case study with Chinese city data (one-to-one patterns; one-to-many patterns; one-to-all patterns), they do not provide any evidence or examples that demonstrate how these patterns can be applied in practice or what implications they may have for urban planning or other related areas.
In conclusion, while this article provides an interesting exploration into the concept of cultural similarity between cities using place name data as well as proposing a new method for measuring it based on semantic analysis; there are some potential issues with its trustworthiness and reliability due to lack of evidence supporting its claims or demonstrating its effectiveness in practice as well as lack of exploration into counterarguments or alternative perspectives on definitions discussed throughout the article.