No abstract

Recent studies demonstrated associations between physical environment (especially greenery) and people's health, well-being, and crime rate by using street-level imagery as 'big data' and automated image recognition methods. However, few prior studies focused on interrelations between physical environment and residents' social relationships. This study investigated associations between physical environments and psychological tendencies of neighboring communities in Japan by using a mail survey and Google Street View images. The mail survey was collected from 156 regions across eight prefectures in western Japan. Google Street View images of these regions were collected and classified by machine learning models and human observers. The results indicated mainly negative correlations between the survey items related to feelings towards participants' neighbors such as social capital and the rate of outdoor gardening by region. Additionally, these correlation patterns differed by type of community, namely, fishing, farming, and other types of communities.

As individuals are susceptible to social influences from those to whom they are connected, structures of social networks have been an important research subject in social sciences. However, quantifying these structures in real life has been comparatively more difficult. One reason is data collection methods—how to assess elusive social contacts (e.g., unintended brief contacts in a coffee room); however, recent studies have overcome this difficulty using wearable devices. Another reason relates to the multi-layered nature of social relations—individuals are often embedded in multiple networks that are overlapping and complicatedly interwoven. A novel method to disentangle such complexity is needed. Here, we propose a new method to detect multiple latent subnetworks behind interpersonal contacts. We collected data of proximities among residents in a Japanese farming community for 7 months using wearable devices which detect other devices nearby via Bluetooth communication. We performed non-negative matrix factorization (NMF) on the proximity log sequences and extracted five latent subnetworks. One of the subnetworks represented social relations regarding farming activities, and another subnetwork captured the patterns of social contacts taking place in a community hall, which played the role of a “hub” of diverse residents within the community. We also found that the eigenvector centrality score in the farming-related network was positively associated with self-reported pro-community attitude, while the centrality score regarding the community hall was associated with increased self-reported health.