Transitional streets serve as intermediary spaces between the Central Business Districts (CBDs) and surrounding residential areas, offering diverse functions and activities within urban interiors. However, a practical methodology for accurately classifying these streets has been lacking, due primarily to transitional areas’ spatial constraints and functional complexities. This study leverages Point of Interest (PoI) data from 2023 to develop an innovative methodological framework that addresses these challenges. This framework analyses transitional streets’ functional distribution and typology, employing PoI frequency density and functional type ratios to identify and classify functional zones. It generally delineates eight main types of transitional streets in the CBD of Chongqing, a prototypical hillside city. Utilising advanced data technology from internet maps, this research pioneers new approaches for identifying and analysing the functionality of transitional streets. The findings underscore the effectiveness of PoI data in precisely recognising the functional types of transitional streets, thereby providing a robust theoretical and practical foundation for the in-depth study of transitional streets. Moreover, the results enhance urban spatial planning in hillside cities of China, effectively demonstrating the advantages of PoI data in defining street typology compared to traditional methods. This approach provides a more detailed understanding of urban functional dynamics by allowing for a more nuanced data analysis of street functions.

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Based on the IPA analysis method, this paper explores tourists' satisfaction with the image of Lijiang Old Town. It provides a reference for the green development of Lijiang Old Town's image. Methods This study used the questionnaire survey method to investigate tourists from different regions and ages. According to the five dimensions of MI, BI, BI, AI, and EI of the image of the Old Town of Lijiang, tourists have analyzed and compared the satisfaction and importance of 19 demand indicators. The volume and dignity of the index points have presented through the four quadrants of the IPA analysis method. Results: The analysis of 19 needs hands concluded that the disadvantaged areas include A1 ancient town positioning, B3 public services, C2 characteristic elements, D2 regional sound, and F2 architectural style, and summarizes the corresponding strategies to adapt to the image of the ancient city of Lijiang. Conclusion Tourists' satisfaction with the notion of Lijiang Old Town in Lijiang is essential to the green development of Lijiang Old Town. Only by continuously updating the image of Lijiang Old Town according to the needs of tourists and its characteristics can Lijiang Old Town develop sustainably and healthily.

This study aims to improve the loyalty of digital natives in the digitalization of the brand by constructing an innovation model for a national pharmaceutical brand image. This study uses Literature review to understand the current demand for identify the digitalization of the brand and the national pharmaceutical brand image. Later it uses Case Study to analyze the factors of how national pharmaceutical brand images could construct an innovation model in the digitalization of the brand. Results of the study would include a background understanding of the current demand for national pharmaceutical brand images. Categorize the national pharmaceutical brand image and find the best way to digitize the brand. Results of the study would include a background understanding of the current demand for national pharmaceutical brand images. Categorize the national pharmaceutical brand image and find the best way to digitize the brand. In addition, the expected outcome will document the background description of the national pharmaceutical brand image. Through the analysis of latent feature variables and latent categorical variables, it recommends finding the best method for brand digitization. Identifying the potential for digitalizing the brand culture variables required construction(variable) for the construct construction model. The study contributes to the model for increasing the loyalty of digital natives by building a strategic development of the national pharmaceutical brand image.