Open Access
Research Paper
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Paper Title

Spatio-Temporal Forecasting of Urban Cybercrime

Article Identifiers

Registration ID: IJNRD_323048

Published ID: IJNRD2604260

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Keywords

Spatio-temporal forecasting, urban cybercrime, hotspot detection, machine learning, Random Forest, LSTM, geospatial analytics, predictive policing.

Abstract

Urban cybercrime has emerged as a critical threat to digital infrastructure and public safety in modern cities due to rapid digitalization and increasing online connectivity. This study introduces an intelligent web-based spatio-temporal forecasting system for predicting urban cybercrime patterns using machine learning and geospatial analytics. The proposed system analyzes historical cybercrime data enriched with spatial attributes such as geographic location and hotspot density, along with temporal features including time intervals, seasonal trends, and frequency patterns. The collected data is preprocessed and integrated into a centralized analytical framework for real-time prediction and visualization. Machine learning algorithms such as Random Forest and Long Short-Term Memory (LSTM) networks are employed to model non-linear spatial relationships and temporal dependencies in cybercrime incidents. Additionally, time-series forecasting techniques are used to identify trends and predict future crime patterns across urban regions. The system enables early identification of high-risk zones by detecting abnormal spatial and temporal variations associated with increased cybercrime activity. The integration of predictive modeling, geospatial heatmaps, and interactive dashboards provides an efficient, scalable, and data-driven solution for proactive cybercrime prevention. The proposed framework supports law enforcement agencies and policymakers in optimizing resource allocation and strengthening urban cybersecurity management.

How To Cite (APA)

Hariharen V, Aravind Karthik S V, & Gokul S (April-2026). Spatio-Temporal Forecasting of Urban Cybercrime . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), c499-c504. https://ijnrd.org/papers/IJNRD2604260.pdf

Issue

Other Publication Details

Paper Reg. ID: IJNRD_323048

Published Paper Id: IJNRD2604260

Research Area: Other area not in list

Author Type: Indian Author

Country: Coimbatore, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2604260.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604260

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Publication Timeline

Paper Submission
04-04-2026
Peer Review
Through Scholar9.com Platform
Paper Acceptance
09-04-2026
Paper Publication
14-04-2026

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