CityGuard++: Crime Detection System and Hotspot Analysis

Abstract

Crime reporting and analysis remain essential components of modern public safety. CityGuard++ is a hybrid, web-centric system that enables citizens to report incidents online, supports administrator verification, and offers ML-driven analysis and geospatial visualizations for hotspot detection and trend forecasting. The platform combines deterministic preprocessing, lightweight NLP classification, clustering for hotspot discovery, and a bounded forecasting pipeline to preserve compute predictability. Data sources include citizen reports, historical crime datasets, and optional public records. CityGuard++ outputs verified incident records, interactive dashboards, and calibrated confidence scores for predicted hotspots. We evaluate the system on a regional crime dataset and simulated citizen reports; results show robust classification performance (accuracy ~89% on balanced test splits), meaningful hotspot detection (Silhouette score ~0.7), and actionable visualizations. The system is designed for auditability, privacy-preserving storage, and incremental deployment across municipalities. Key contributions include (i) an end-to-end verified reporting ? analytics pipeline, (ii) a deterministic ML orchestration pattern for predictable cost at inference time, and (iii) an evidence-provenance mechanism for flags and hotspot suggestions.

Introduction

Rapid urbanization has made crime prevention and public safety increasingly complex. Traditional reporting methods—such as in-person complaints, phone calls, and paper documentation—are slow, prone to incomplete information, and limit citizen participation. These challenges hinder law enforcement agencies from maintaining accurate records and developing proactive strategies. The rise of digital technologies calls for smarter, data-driven platforms that enable real-time reporting, automated analysis, and predictive insights.

CityGuard++ is a digital ecosystem designed to modernize crime management by allowing citizens to submit reports through a web interface, including descriptions, categories, and optional multimedia evidence. Submitted reports undergo verification by authorized administrators or police officers, ensuring data reliability. Verified reports are processed using machine learning (ML) and geospatial analysis:

• Natural Language Processing (NLP) extracts patterns from text and classifies crime types.

• Geospatial clustering identifies crime hotspots.

• Time-series forecasting predicts short-term crime trends.

The platform’s dashboard provides interactive visualizations, including heatmaps, trend graphs, and cluster summaries, facilitating intuitive exploration of crime patterns. Data privacy, role-based access, and explainable ML outputs enhance transparency and trustworthiness.

System Architecture and Pipeline:

1. Report Ingestion: Citizens submit reports with metadata, descriptions, coordinates, and evidence.

2. Validation & Normalization: Ensures completeness, cleans text, validates locations, and checks evidence formats.

3. Verification Queue: Admins or police verify reports; rejected reports are stored for audit.

4. Feature Engineering: Extracts TF-IDF text vectors, temporal features, and geospatial features.

5. Crime Classification: ML models (Random Forest/SVM) predict crime category, severity, and confidence scores.

6. Hotspot Detection: Clustering algorithms (DBSCAN/K-Means) generate spatial crime heatmaps.

7. Trend Forecasting: Historical data is used to predict short-term crime patterns.

8. Dashboard Visualization: All results are displayed interactively for decision-making.

The system emphasizes determinism, reproducibility, and computational efficiency, making it suitable for real-time or batch municipal deployment. CityGuard++ bridges the gap between citizens, administrators, and analytical tools, providing a scalable, AI-enabled framework for modern digital policing and public safety management.

Conclusion

The experimental evaluation of CityGuard++ demonstrates that an integrated approach combining structured crime reporting, machine learning analysis, and geospatial intelligence can significantly improve the accuracy and reliability of crime monitoring. The system offers a transparent and interpretable workflow in which each predicted classification, cluster, or hotspot can be traced back to the underlying crime report and its features. This provenance-based design increases trustworthiness and enables authorities to review and validate decisions before taking real-world action. In practice, the clustering results showed stable hotspot formation across multiple testing cycles, indicating that the system can reliably identify regions experiencing recurring criminal activity. Similarly, the classification model exhibited strong performance on both common and moderately imbalanced categories, suggesting that lightweight ML models coupled with well-engineered features can outperform heavier architectures in resource-constrained public-sector settings. Although CityGuard++ performs effectively as a reporting and analysis platform, several advanced capabilities can further extend its impact. A major direction for future work is the integration of deep learning models, such as transformer-based text encoders and sequence prediction architectures, to enhance classification accuracy and long-range trend forecasting. Another promising area involves the incorporation of CCTV or drone-based visual analytics to automatically detect suspicious behavior, allowing the system to correlate on-ground camera feeds with reported incidents. Extending the platform into a multilingual and voice-enabled application would make it more accessible to communities with diverse linguistic backgrounds, while an anonymous reporting feature could help increase the detection of sensitive crimes that often go unreported. CityGuard++ successfully demonstrates how a unified digital framework can transform traditional crime reporting and analysis processes into an intelligent, scalable, and data-driven solution. By integrating citizen-driven reporting, admin verification, machine learning-based classification, and geospatial hotspot detection, the system provides actionable insights that strengthen public-safety decision-making. The experimental evaluation confirms that the architecture is robust, computationally efficient, and capable of producing accurate crime predictions and meaningful spatial patterns. Through visual dashboards and transparent provenance tracking, CityGuard++ ensures interpretability and builds trust among both users and law-enforcement personnel. The findings indicate that the system can serve as a foundational tool for municipalities seeking to modernize crime-monitoring operations and adopt evidence-based policing practices. While current results are promising, the platform offers significant opportunities for further enhancement, including real-time surveillance integration, advanced forecasting, and large-scale deployments across multiple districts. Overall, CityGuard++ contributes a practical and future-ready approach to digital crime management and sets the groundwork for intelligent public-safety ecosystems.

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Copyright

Copyright © 2025 Dr. Vidyarani H J, Dr. KavithaDevi C S, Prakash R Annigeri, Nehaal R, Kishan AS. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.