Spatio-Temporal Cybercrime Prediction in Mumbai Using CNN-LSTM and GIS-Based Hotspot Analysis

Sandeep Kamble and Ankit Temurnikar

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 2, 827-840

Abstract: The increasing use of digital technologies has led to a rapid rise in cybercrime, especially in metropolitan cities like Mumbai. Traditional statistical methods are often insufficient to capture complex spatio-temporal crime patterns. This paper proposes a hybrid deep learning approach for spatio-temporal cybercrime prediction in Mumbai using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks integrated with GIS-based hotspot analysis. The CNN model extracts spatial features from location-based crime data, while LSTM captures temporal dependencies in crime occurrences. The integration of GIS enables visualization of high-risk areas and emerging crime hotspots. The proposed model is evaluated on real-world cybercrime data and demonstrates improved accuracy, precision, and recall compared to conventional methods.The results highlight the effectiveness of combining deep learning and geospatial analysis for proactive cybercrime prevention and intelligent policing.

Keywords: Cybercrime Prediction; CNN-LSTM; Spatio-Temporal Analysis; GIS; Hotspot Detection; Mumbai (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612331
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i2:id:2006

DOI: 10.32628/CSEIT2612331

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