The goal of the question-answering system is to respond to user queries expressed in natural language. Unlike search engines, the closed domain question answering systems are specialized to specific domains, providing concise and precise answers often derived from structured data. This paper focuses on a question-answering system tailored for crime events, capable of addressing both statistical and contextual inquiries. In terms of crime statistics, the fine-tuned GPT-3 model outperforms the USE, TAPAS, TAPEX, and GPT-3 models, while for context-based crime-related queries, the fine-tuned RoBERTa model surpasses the BERT and RoBERTa models. This system is capable of providing the responses in natural language format, supplemented with relevant data visualizations. The models are train on Q2A and NewsQA datasets while it is tested on NCRB and NewsTimes datasets. The Q2A and NCRB datasets are used for statistical queries while NewsQA and NewsTimes datasets are used for contextual inquiries. The paper presents an analysis of various models and showcases results for sample case studies. Such a system can prove valuable in applications where users seek to study criminal cases or gather pertinent insights for specific cases. Furthermore, it can assist in understanding patterns and trends in criminal events, particularly concerning geospatial information. Linking crime event-based question-answering systems to geospatial information facilitates exploration of niche areas and furnishes precise details about local crime with minimal hype and hence worth exploring.