Dynamic Analysis of Social Capital in Commercial Areas Using Connection Networks

Yuya Ieiri, Ryo Okutani, Hiroshige Dan, Osamu Yoshie

Abstract: Social capital (SC), defined as the network of relationships, trust, norms, and mutual support that facilitate cooperation within a community, plays a vital role in sustaining local economies, fostering community resilience, and preserving urban social cohesion. Despite its recognized importance, particularly within traditional commercial districts, quantitatively assessing SC remains a major challenge. This study introduces a dynamic analytical framework for evaluating SC in such districts by constructing interaction networks that capture relationships between individuals and retail establishments. A field experiment was conducted in a Tokyo shopping district by incorporating two interaction modalities: visitor-driven word-of-mouth (WOM) and store-driven communication. SC was measured using five questionnaire-based indicators encompassing necessity, attachment, trust, and social norms. Connection networks were constructed as weighted graphs based on interaction intensity. The results revealed that both WOM and store-driven communication significantly enhanced SC values, as confirmed through pre- and post-intervention statistical tests. A predictive model was developed to explore the SC and network structure relationships using eight network indicators. The model achieved reasonable accuracy and interpretability, with comparisons between complete and reduced feature sets providing insights into the trade-offs between generalization performance and explanatory power. This integrated approach of network analysis and machine learning offers a robust framework for understanding and quantifying SC dynamics. The proposed framework contributes methodologically and practically to the study of urban revitalization by capturing how specific interactions reshape network structures and community cohesion.

External IDs:doi:10.1007/978-3-032-10156-3_13