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Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

The study approaches the social problem of overconsumption by examining how dispositional characteristics (Trigunas) affect self-control capacity and buying impulsiveness.

A survey of 181 Indian students was conducted to collect data on Trigunas, self-control and impulse buying tendency (IBT). Partial least squares-based structure equation modeling package ADANCO was used for data analysis.

The results indicate that two out of the three gunas were related to impulsive buying tendency. As hypothesized, self-control mediated these relationships. The findings prove that Trigunas carry differential influence on self-control capacity and impulsive buying behavior of individuals.

The results of this study offer new insights and ideas to practitioners and researchers pursuing the problem of overconsumption. This study delves into ancient Hindu knowledge of mindfulness and offers fresh psychological constructs that broaden scholarly understanding on personality-related drivers of overconsumption.

Most research on overconsumption and related issues has been conducted using western personality models. Additionally, many of these findings are inconsistent. This article broadens this discussion by applying indigenous Indian psychology constructs to the study of consumer behavior and provides empirical support for the same.