Research on the Landscape Renewal Path of Public Spaces in Old Residential Areas Based on Eye-Tracking Technology

With the focus of urban construction shifting to inventory space renewal, the landscape transformation of public spaces in old residential areas has become an urgent priority. Taking the old residential neighborhoods of Fengcheng Sub-district in Jinan City as the empirical research objects, this study innovatively integrates eye-tracking physiological technology with the Kano model and Importance–Performance Analysis (IPA) to construct an objective–subjective integrated landscape evaluation framework. Subjective survey data (n = 122, determined via Yamane’s finite population formula) and objective eye-tracking cognitive data (N = 30 valid participants, screened with a <80% data capture rate exclusion threshold) were collected across representative community spaces. Utilizing heatmap visual analysis, one-way ANOVA, and Gestalt/color psychology theories, the research reveals residents’ explicit needs and implicit visual preferences for plant colors, water body geometries, and activity venue types. A stepwise multiple linear regression model establishes that Average Pupil Diameter (X1, positive predictor, β = 0.542, p < 0.001) and Fixation Count (X2, negative predictor, β = −0.381, p = 0.006) reliably predict Subjective Satisfaction (S) with a strong model fit (R2 = 0.618, F = 21.84, p < 0.001). A multi-method convergence matrix resolves apparent metric divergences by distinguishing visual search friction from functional demand. Based on these findings, an actionable renewal framework structured as ‘demand priority–space type–design method’ is operationalized across three dimensions: functional repair, spatial reconstruction, and sensory optimization.