Advanced Integration of Geoinformatics and AI with Cyber Security for Real-Time Data

Tirumala Bala Koteswara Rao Kamisetty — Thu, 02 Jul 2026 00:00:00 +0000

Modern digital ecosystems generate massive volumes of heterogeneous, high-velocity data, making it increasingly difficult for conventional security systems to detect, interpret, and respond to threats in real-time. This paper presents a unified framework that integrates Geographic Information Systems (GIS), artificial intelligence (AI), and cybersecurity principles to enhance situational awareness and automated threat response across complex environments. The proposed approach leverages spatial analytics, machine learning, computer vision, and distributed sensing to identify anomalies, predict risk patterns, and support rapid, data-driven decision-making. By combining geospatial intelligence with AI-driven inference, the system enables continuous monitoring of dynamic urban and infrastructural settings, providing a richer understanding of threat evolution. To improve scalability and resilience, the framework incorporates edge computing for processing, cloud analytics for large-scale computation, and secure data exchange mechanisms to protect sensitive information. Federated architecture further supports privacy-preserving model training across multiple agencies. Key challenges related to interoperability, governance, and ethical data handling are examined to ensure responsible deployment. Experimental evaluation demonstrates that the fusion of GIS and AI significantly strengthens cybersecurity operations, enhances predictive capabilities, and supports the development of robust smart city security infrastructures. The framework offers a scalable foundation for next-generation real-time monitoring systems.

Spatio-Temporal Analysis of Crime, Rainfall and Temperature in Abraka and Its Environs, Delta State, Nigeria

Mon, 20 Jul 2026 00:00:00 +0000

This study examined the spatial–temporal distribution of crime in Abraka, Delta State, Nigeria, with particular emphasis on the influence of weather variables, namely rainfall and temperature, on crime occurrence. The study employed Geographic Information Systems (GIS) and statistical techniques to analyze crime records obtained from the Abraka Divisional Police Station and meteorological data collected between 1992 and 2014. Crime types examined included assault, theft, fraud, rape, burglary, murder, and character deformation. Pearson product-moment correlation, Analysis of Variance (ANOVA), nearest-neighbor analysis, hotspot analysis, kernel density estimation, and directional distribution techniques were used. The study found that there exists a strong relationship between rainfall and temperature on crime occurrences.

Furthermore, crime propensity tends to be high during the dry season and low during the wet season. In the spatial analysis of crime, the study revealed that crime propensity is high in developed areas, places of human habitation, and areas with a high presence of social and economic activities. It is therefore hoped that, when planning crime-control operations in the study area, the aforementioned factors will be considered to ensure success.

International Journal of Geoinformatics Science and Technology

Advanced Integration of Geoinformatics and AI with Cyber Security for Real-Time Data

Spatio-Temporal Analysis of Crime, Rainfall and Temperature in Abraka and Its Environs, Delta State, Nigeria