Prediction of network public opinion features in urban planning based on geographical case-based reasoning

As a significant part of sustainable urban development proposed by the United Nations, urban planning is related to the ecological environment and transportation, especially affecting quality of life and social well-being. In the process of urban planning, the public express their opinions on open network platforms, resulting in large quantities of network public opinion data, which has important implications for evaluating urban planning. Based on the idea of geographical case-based reasoning (CBR), this paper constructs an expression framework for urban planning cases in the form of a 'case problem-case attribute-case result' triad. On this basis, this paper proposes a similarity calculation method of urban planning cases that integrates case attribute. Finally, based on an improvement to the traditional k-nearest neighbors method, the proposed public opinion feature calculation model considers similarity weights, which allow us to predict network public opinion features, including viewpoint-level emotional tendency and concerned groups of urban planning cases. The experimental result shows similarity weights (SWs) model could effectively improve the prediction accuracy, where the average MIC-F1 score reached more than 74%. Based on CBR, the proposed method can predict the development trends of public opinion in future planning cases, and provide scientific and reasonable decision support for urban planning.

Automated summary

This paper proposes a similarity calculation method of urban planning cases based on the geographical case-based reasoning (CBR) framework. By integrating case attributes and considering similarity weights, the proposed method can predict network public opinion features and provide decision support for urban planning. The experimental results demonstrate the effectiveness of the method with an average MIC-F1 score of over 74%.