Spatiotemporal Analysis and Prediction of Crime Hotspots Using Artificial Intelligence and Machine Learning
Dr. Dudiki Narahari,
Thokala Jyothika,
Yarru Sirisha,
Yarraguntla Manasa,
Raavi Naga Mallika,
Poojitha Gurrala,
G. Ananth Rao
Crime hotspot prediction is a critical task for proac¬tive policing, urban safety management, and
efficient resource allocation. Traditional crime analysis methods rely heavily on retrospective reports,
manual inspection, and static statistical summaries, which limits their ability to anticipate emerging
high-risk areas. This paper presents a spatiotemporal crime hotspot analysis and prediction framework
that combines data preprocessing, spatial-temporal tensor construction, a Convolu-tional Long Short-
Term Memory (ConvLSTM) network, and an explainable artificial intelligence (XAI) layer. Historical
crime records are aggregated at the police-station and grid-cell lev¬els to model temporal trends and
spatial dependencies. The ConvLSTM-based architecture forecasts future crime intensity and classifies
regions as potential hotspots using threshold-based risk scoring. To improve interpretability, the system
generates natural-language explanations based on recent crime trends, crime-type distributions, and
seasonal variations. The frame¬work is deployed through an application interface that allows authorized
agencies to visualize hotspot predictions and associ¬ated explanations. The proposed approach provides
a scalable, transparent, and analytically robust decision-support system for proactive crime prevention
and monitoring.
