Urban greenery is increasingly recognised for its potential to address urban challenges, necessitating effective methods for its measurement to inform research and guide practical interventions. Conventional greenery measurements primarily rely on indicators derived from remote sensing and street view imagery, whereas emerging social media image data offers novel opportunities to capture greenery from a subjective, user-oriented perspective. However, the characteristics and comparative performance of these greenery measurements remain unclear, and limited attention has been paid to their appropriate application scenarios. Employing Rotterdam as a case study, this research compares three urban greenery measurements: greenery derived from social media, the Green View Index (GVI), and the Normalised Difference Vegetation Index (NDVI), and examines their interrelationships at street and neighbourhood scales. The results indicate that, although these measurements are interrelated and broadly reflect consistent patterns, notable differences exist. Social media greenery aligns closely with NDVI and captures overall greenery conditions, but exhibits greater variability and subjectivity. GVI, while moderately correlated with NDVI, shows higher sensitivity but may underrepresent densely vegetated areas. Spatial analysis reveals coherent structures across measurements, with low clusters in the city centre and high clusters toward the periphery, but further examination shows varying local heterogeneity in measurement relations, with strong coefficients concentrated in selected central areas and fluctuating or negative associations in peripheral and residential or commercial districts. These findings highlight the influence of measurement characteristics, biases, and analytical scale selections in urban greenery measurements, and clarify the strengths and limitations of each measurement to guide context-appropriate method selection. ... Read more

Understanding users’ experiences and evaluations in urban public spaces is essential for human-centric design and management. While traditional survey methods are costly, large-scale user-generated content (UGC) offers new possibilities for capturing public insights. However, existing approaches mainly assess evaluations and experiences through oversimplified, single-dimensional assessments, while deriving users’ multidimensional experiences beyond basic sentiments and capturing their underlying mechanisms remain critical research gaps. This study proposes a novel integrative analytical model for assessing urban public space experiences and evaluations. Drawing on large-scale online review data, it employs a Low-Rank Adaptation (LoRA)-fine-tuned RoBERTa language model for multidimensional user evaluation identification, combines a lexicon-based method with an Aspect-based Sentiment Analysis (ABSA) pipeline to examine specific experiential qualities and satisfaction, and integrates textual and visual modalities through Bootstrapping Language-Image Pre-training (BLIP) vision-language model. Focusing on Amsterdam as a case study, validation demonstrates that the proposed model outperforms baselines, achieving a mean accuracy of 85% and exceeding widely used XGBoost and LSTM methods by 12% and 23%, respectively. The application effectively quantifies nuanced and detailed user experience and evaluation patterns, revealing differentiated associations between experiential qualities and overall evaluation positivity, thereby providing a deeper understanding of human-environment interactions in urban public spaces. The proposed approach offers an integrated and scalable method for investigating user insights and can inform future responsive, human-centric public space design and management. ... Read more