The arrival of a super-aging society due to demographic changes is a growing concern in many parts of Japan. Ohnishi et al. (2015), through their approach to the problem of shopping refugees using large-scale telephone directory data, selected Maebashi City in Gunma Prefecture (2017-) as the subject of this study and actually conducted a field survey. The city has been selected from the perspective of conducting an actual field survey and conducting an analysis linked to movement trends. In this analysis, an overview of the movement patterns of this data both domestically and internationally was conducted in consideration of anonymity, as well as trends around stations, universities, and other institutions, and a discussion of movement patterns in relation to the effects of weather which is expected to show the trend of human flow in normal period because it is the data before the COVID-19 disaster after 2020.

In this paper, we will use the results of fitting with the sociophysical method regarding online media interaction in sports competitio etc (mainly this paper topics of rugby in 2016 to 2018 on broadcast) that has begun as a pioneer of large-scale broadcast events since 2000 in Japan.

Using seasonal topics as the study subject, in this study, we focus on the timing gap between social media writing and online search behavior. To conduct our analysis, we used the mathematical model of search behavior, comprising the sociophysics approach. The seasonal topics selected were cherry-blossom viewing for spring, bikinis for summer, autumn leaves for fall, and skiing for winter. We also picked up the event like Christmas and Halloween. We analyzed the influence of blogs and Twitter on search behavior and found a deviation of interest in terms of timing. We also analyzed seasonal topics in newspapers after 2010 and observed deviations in the number of searches and spikes in the number of references in newspaper magazines.

We study national elections using a mathematical model of hit phenomena. Additionally, we adapt the theory of mathematical model of hit phenomenon to national elections. Then, using the data written in the SNS, predict the ranking of the number of seats acquired by proportional representatives of each political party.

The excitement and convergence of tweets on specific topics are well studied. We can also investigate the position-sensitive subjects by utilizing the position information of tweets. In this research, we focus on bomb terrorist attacks and propose a method for separately analyzing the number of tweets at the place where the incident occurred, nearby, and far. We made measurements of position-sensitive tweets and suggested a theory to explain it. This theory is the theory of social physics that can analyze epidemic phenomena such as movies and hit products from the time change of the number of social media writing. This paper expanded this theory to take into account the position where Tweet was transmitted. With this theory we found that it is possible to explain the spread of information by the tweets with location information on the bomb case.

In previous research that has been done, there were issues like the precision of GPS data for an analysis of tourist behavior patterns, difficulties with long-term studies, and it was difficult to conduct an analysis based on detailed data like what language people were speaking, what nationality they were, what they bought and when and where they bought it. However, this study reports that the efforts and enthusiasm in the creation of a system that works in close cooperation with local guides are making it possible to perform an analysis that overcomes the limitations of the research surveys that have been done until now. Additionally, the data collection handled in this text that uses a system that works closely with local guides is not only in place for the Tokyo Metropolitan Area. It is also expanding to areas like the Kansai, Tohoku, and Kyushu regions, and more new items are being added. This makes it possible to follow the elaborate and detailed trends of tourists to Japan.

We performed an analysis of live EXILE TRIBE performances using a mathematical model. EXILE TRIBE is phenomenon represented by several diverse group of artists representing Japan. Therefore, it is an interesting object for analysis. Analysis was carried out using a mathematical model of the hit phenomenon by considering each parameter obtained from Twitter and blog posts concerning EXILE TRIBE live as input. There were some differences in parameters depending on the size of the tour. This paper quantitatively explains changes in the number of people involved in one concert event and the propagation tendency of rumors as a result.

We apply sociophysics approach using the mathematical model for hit phenomena to analyze Pokemon GO influence in society and the theoretical calculation describes the observed social media response well.

We propose a new model for hit phenomena. Our model is based on the Integer-Valued autoregressive model in form of a stochastic difference equation, and it describes behaviors of count data sequences. Utilizing our model, we give a theoretical formulation of the concept “hit”, and a systematic method deciding whether given time series count data contains “hit”.