A wide range of research across academic disciplines including brain sciences, psychology and social sciences are paying attention to the cooperative or altruistic aspects of human behavior or human beings. This can be regarded as an essential departure from the basic paradigm of modern science or the modern model about human beings, which characterizes them as independent or atomistic individuals maximizing self-interests. While there may be a myriad of factors behind such a shift, attention can be focused on one socio-economic factor: the state of a society as determined by the “finite” versus “infinite” character of the total volume of resources and environment. This perspective is explored in this article through theoretical and historical viewpoints and future directions for the “reintegration of economy and ethics” in the post-growth society are presented.

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.

This paper discusses the concepts of aesthetic and nonaesthetic in streetscapes. The nonaesthetic refers to physical elements and spatial compositions that make the aesthetic emerge differently depending on the observer who sees them. Using Ashihara’s (1979) methodology for evaluating aesthetic townscapes and Sibley’s (1959) concept of aesthetic properties, this paper proposes a quantification of the nonaesthetic of streetscapes, focusing on streets in Ginza, Tokyo, and examining the spatial clustering of their nonaesthetic. For this, we used deep learning to quantify the distribution of physical elements and their spatial composition and a spatial clustering analysis to uncover hidden patterns in the streetscapes. These methodologies enabled us to assess streets across neighborhoods and districts and produce a comparative microscale analysis that covers a wider area. Thus, our study combines the fields of aesthetics, architecture, urban planning, and community design.