Authors : I. Tavya Sri; G. Vaishnavi; V. Lohitha; S. Rishika; T. Swathisri

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3u7u2rjd

Scribd : https://tinyurl.com/2zyrscfp

DOI : https://doi.org/10.38124/ijisrt/26apr1363

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Rapid urbanization and population growth in Indian smart cities have increased the complexity of crime prevention and public safety management. Traditional crime analysis methods often on previous records and manual interpretation, which are limited in handling dynamic spatial and temporal crime patterns. This research paper proposes an Enhanced AI-Spatio Crime Prediction and Hotspot Visualization System for Indian Smart Cities that integrates artificial intelligence, geospatial analytics, and data visualization to improve crime forecasting and decision-making. The proposed system uses machine learning algorithms to analyze historical crime data, location-based factors, demographic patterns, and time-series trends to predict potential crime occurrences. Advanced clustering techniques are applied to identify crime hotspots, while interactive visualization dashboards provide real-time maps, heatmaps, and analytical insights for law enforcement agencies and city administrators. The model is designed specifically for the Indian urban environment by considering city-specific challenges such as population density, traffic flow, socio-economic diversity, and rapidly changing infrastructure. Experimental results indicate that the proposed approach enhances prediction accuracy, enables proactive policing, optimizes resource allocation, and supports safer urban planning. The study demonstrates how AI-driven crime intelligence systems can contribute significantly to the development of secure, efficient, and sustainable smart cities in India.

Keywords : Crime Prediction, HDBSCAN, SARIMA, Machine Learning, Smart Cities.

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