AI-Driven Pattern DNA & GEO-Spatial DRIFT Analysis for Missing Person Cases in India
Dr. Satyawati S. Magar 1, Dr. Sandip R. Udawant 2
1 Associate Professor, E & TC, DVVP COE, Ahilyanagar
2 Associate Professor, E & TC, DVVP COE, Ahilyanagar
Abstract
This study introduces an artificial intelligence- based analytical model created to identify patterns, anomalies, and leads in long-standing open missing persons cases in India. By utilizing publicly available demographic, geographic, and temporal data from the Missing People Dataset (Kaggle), NCRB data, and district reports, the system employs several analytical layers: Pattern DNA clustering to reveal hidden demographic trends, Geo-Spatial Drift Analysis to predict potential movement areas using spatial attributes and logistic regression, and a Semantic Case Matching module that uses BERT embedding to connect cases with similar narratives for cold case support. The pipeline also features a risk prediction module based on time-series models like Prophet to forecast upcoming peaks in missing persons cases by state and time intervals.
Extensive data preprocessing, exploratory data analysis, feature creation, and clustering were performed using Python. The resulting geo-risk heatmaps, similarity scores, and trend forecasts offer actionable insights to aid investigators in resource allocation, hotspot identification, and case prioritization. This framework demonstrates how AI can improve investigative efforts and highlights the potential for future integration with law enforcement databases and humanitarian programs, further emphasizing the value of technology-driven solutions for addressing key social issues.
Keywords: Missing Person Analysis, Artificial Intelligence, Pattern Clustering, Geo-Spatial Analysis, Semantic Case Matching, Natural Language Processing, BERT embedding, Time Series Forecasting.
