  Fashion styles adopted every day are an important aspect of culture, and
style trend analysis helps provide a deeper understanding of our societies and
cultures. To analyze everyday fashion trends from the humanities perspective,
we need a digital archive that includes images of what people wore in their
daily lives over an extended period. In fashion research, building digital
fashion image archives has attracted significant attention. However, the
existing archives are not suitable for retrieving everyday fashion trends. In
addition, to interpret how the trends emerge, we need non-fashion data sources
relevant to why and how people choose fashion. In this study, we created a new
fashion image archive called Chronicle Archive of Tokyo Street Fashion (CAT
STREET) based on a review of the limitations in the existing digital fashion
archives. CAT STREET includes images showing the clothing people wore in their
daily lives during the period 1970--2017, which contain timestamps and street
location annotations. We applied machine learning to CAT STREET and found two
types of fashion trend patterns. Then, we demonstrated how magazine archives
help us interpret how trend patterns emerge. These empirical analyses show our
approach's potential to discover new perspectives to promote an understanding
of our societies and cultures through fashion embedded in consumers' daily
lives.
