서울시 범죄 토픽의 시공간 변화 분석 : 빅카인즈 뉴스 데이터와 KoBERTopic 모형을 활용하여

초록

Crime negatively impacts daily activities and diminishes the quality of life for urban residents. Analyzing the spatial distribution and types of crime, along with formulating strategies to reduce crime, is essential for enhancing citizens' well-being. In Korea, crime location data is private, while crime rate data, categorized by type, is available at the city, county, and district levels. Recently accumulated digital news data provides details on crime locations, types, and targets. However, extracting relevant crime-related information from the extensive collection of news articles amassed over the past decades has posed challenges. This study examined the spatial distribution and changes in major crimes by analyzing news article data from 2000 to 2023, sourced from Big Kinds, utilizing the KoBERTopic text mining methodology and focusing on Seoul. Crime-related news was filtered using text mining technology, and location information tied to administrative districts was extracted from the articles to analyze crime concentration over time. Moreover, shifts in major crime topics were identified using the KoBERTopic methodology. The analysis revealed that the types of crimes and crime-concentrated areas in Seoul changed annually, influenced by significant factors. Furthermore, changes in crime topics over time were visually represented spatially. This study is notable for leveraging large-scale news data to analyze shifts in crime locations and concentrations, leading to policy recommendations for crime prevention.

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