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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.

Toyota's internationally coordinated production system in Asia and its selection of supply bases in South America and South Africa highlights the significance of recognizing global network firms and the global hub-and-spoke logistics system that has been developed to meet their needs. This system underpins the expansion of container shipping, air freight and telecommunications. Recognition of Main Street, linking Europe, Asia and North America with cui-desacs in Africa, Australasia and Central and South America, provides a framework for examining the relative importance of the system's hubs and terminals across different modes and regions. This analysis provides the basis for identifying and ranking key regional logistics platforms in Northeast Asia and their attraction as headquarter sites for global network firms. Examining the logistical situation pertaining after the end of the Cold War in the early 1990s and a decade later is used to gauge progress towards regional economic integration in Northeast Asia.