Big data analysis and evaluation for vitality factors of public space of regenerated industrial heritage in Luoyang

Abstract

Abstract The regeneration of idle industrial heritage buildings needs to take into account many factors such as history, science, art, and social aspects. Current research on industrial heritage regeneration has not developed quantitative research in each factor of assessment, and cannot provide clear guidance and program reference for industrial building regeneration weighting ratios. In this study, we put forward a spatial vitality factor range prediction method based on big data analysis for the regeneration of an industrial heritage public space in Luoyang, aiming to establish a model for the regeneration of industrial heritage abandoned industrial buildings and to provide implementation and regeneration planning. The specific evaluation process and regeneration evaluation design scheme based on Luoyang’s industrial heritage were restored. The weights and percentages of the key domain indicators and branch indicators of the idle industrial building regeneration model were predicted, which provided clear guidance and reference for the planning of regeneration of public spaces of industrial heritage. The prediction accuracy of each key assessment factor was maintained above 90%, and the sequential ranking result of the assessment factors was given. The spatial regeneration prediction scheme provided an effective guide and indicator reference for the case design, highlighting the effectiveness of our approach.