Cloud model-based public spaces safety risk assessment in Xi’an old residential areas

To precisely and efficiently improve the living environment quality in urban old residential areas, and to enhance their disaster prevention and mitigation capabilities, this study proposes a cloud model-based method for the assessment and identification of safety risks in public spaces. Specifically, 13 typical residential areas in Xi’an were selected as research samples, and an evaluation indicator system comprising four primary indicators, nine secondary indicators, and 30 tertiary indicators was established. Subsequently, the analytic hierarchy process was employed to determine the subjective weights, while the entropy weight method, combined with the criteria removal effect method, was used to determine the objective weights. Finally, the multiplicative ensemble method was applied to derive the combination weight for each indicator. Building on this, cloud computing theory was introduced, and a cloud model-based safety risk assessment system for public spaces in old residential areas was constructed using MATLAB. Subsequently, the Residential Area of Xi’an Fourth People’s Hospital was used as a case study to apply the aforementioned model. The results demonstrated that the model can assign a safety risk rating to public spaces within the residential compound while accurately identifying potential safety risks requiring urgent attention.