High-quality public spaces in industrial parks play an important role in enhancing the environmental image of the parks, employee well-being, and talent attraction. However, research on the factors influencing the attractiveness of public spaces in industrial parks remains relatively limited. Taking three typical industrial parks in Suzhou Industrial Park as the study objects, this study employs big data and GIS analyses, questionnaire surveys, and other methods to construct a public space attractiveness evaluation system comprising three dimensions: physical characteristics, environmental perception, and sense of place, thereby revealing generalizable characteristics and patterns across the parks. The results show that the physical characteristics dimension makes a relatively substantial contribution to attractiveness values, whereas sense of place and environmental perception play reinforcing and supplementary roles, respectively. The physical characteristics dimension carries a relatively high weight, but functional diversity, landmark level, and vitality perform poorly and are key factors requiring improvement. The place meaning indicator makes a relatively high contribution and constitutes an advantageous factor of public spaces. Comfort, aesthetic quality, and place identity make moderate contributions and are potential optimization factors. Activity support and interest also show moderate performance and represent important directions for improvement, among which the provision of functional facilities and the creation of a cultural atmosphere are priority needs for enhancing public space attractiveness. This study can provide a reference for the optimization and high-quality development of public spaces in industrial parks.

Xuewei Wu. Evaluation of Public Spaces Attractiveness and Its Influencing Factors in Urban Industrial Parks: A Case Study of Suzhou Industrial Park. Academic Journal of Architecture and Geotechnical Engineering (2026), Vol. 8, Issue 1: 134-144. https://doi.org/10.25236/AJAGE.2026.080117.

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