Many studies have indicated that most crimes occur close to an offender's home. However, recent studies demonstrated that substantial Journey-to-crime Distance (JtcD) variation was observed at the inter-offender level. This study aimed to examine whether the relationship between a central point calculated from a distribution of crime sites (Center of Minimum Distance: CMD) and each crime site and the crime scene characteristics (students vs. other victims and indoor vs. outdoor settings) could explain the variation in JtcD at the inter- and intra-offender level. The data comprised 222 serial sexual offenders who committed seven or more offenses from 2003 to 2015 in Japan. The analyses of generalized linear mixed modeling showed that the CMD model constructed from the relationship between CMD and each crime site accounted for 47.9% of intra-offender variance in JtcD compared to the random intercept model. Additionally, when crime scene characteristics are included in the CMD model, the model accounted for 35.6% inter-offender variance in JtcD compared to the random intercept model. These findings indicated that the substantial variation in JtcD was explained by the relationship between CMD and each crime site and the crime scene characteristics, but the extent of this influence varied between offenders.

It is not easy for companies to know how consumers react to advertisements. In recent years, the idea of occasion recognition has been proposed as a method for knowing the advertising effect. This is a state in which consumers remember the situation when they see an advertisement, and it is considered that the advertisement effect is higher in this state. However, a questionnaire survey is required to observe the occasion cognition. In the present research, we estimate the consumers who are in the occasion cognitive state by measuring the operation status of mobile phones. It is possible to identify the layer (digital occasion clusters) that are presumed to be occasion cognition without conducting a survey.

US President Donald Trump is known for his unique character. However, does the notion imply that his policy is unique? Existing studies on presidents have assumed that Trump's policy significantly differs from that of former President Barack Obama due to extremely biased policies, especially foreign policy. To test the validity of such a common view, the study conducted a quantitative text analysis of executive orders issued by both presidents. Results show that (1) there are systematic differences between Obama's and Trump's policy; (2) tracklessness, rather than deflectiveness highlights the uniqueness of Trump's policy; and (3) the uniqueness of Trump's policy is pronounced in domestic rather than foreign policy.

In February 2020, Japan began experiencing outbreaks of COVID-19 infection and deaths due to the disease. Open data on the number of positive PCR test results are available from the Ministry of Health, Labour and Welfare (MHLW). We applied the SARIMA models to these time series data and selected an ARIMA(1,1,2) model as the best model among this class of models for the number of infection deaths during the main period, which includes the period covered by the declaration of emergency. We applied bivariate VAR models to the data of the number of PCR-positive persons and the number of infection deaths and selected a VAR(15) model as the best model for the main period. With regards to Granger's causality of the number of PCR-positive persons relative to the number of infected persons and deaths, we obtain clear causality in the data only up until June 17, with causality not so clear after June 18, when MHLW changed the definition of fatalities.