Progress and prospects of future urban health status prediction

Predicting future urban health status is significant in terms of identifying urban diseases and urban planning. Current studies have focused on using machine learning and deep learning models to predict future urban populations, urban safety, and traffic conditions. The city is a complex system, and perceiving the future urban health status requires accurate predictions of the multifarious components that make up the urban system. In this work, firstly, the improved meta-analysis approach is applied to the smart city domain. Secondly, considering the city as a complex system, the concept of urban factors is proposed. Eight urban factors are selected and they are graded into three tiers. Finally, methods suitable for predicting urban factors reflecting the health of a city are discussed in depth, and which prediction method is most suitable for a particular urban factor is summarized. Among the final 40 studies included in the meta-analysis, the maximum values for Prediction Improvement Rate (PIR) and Weighted PIR (WPIR) reach 95.98% and 22.31%, respectively.

Automated summary

Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.