International Journal of Geoinformatics Science and Technology

A Socio-Technical Analysis of Deep-Sea Mining Risks in the Bight of Benin: Revisiting Norway and the Cook Islands Protocols

Jimmy U. J — Thu, 20 Aug 2026 00:00:00 +0000

This research presents a socio-technical analysis of the risks associated with Deep-Sea Mining (DSM) in the Bight of Benin, a critical maritime corridor characterized by complex bathymetry and high ecological sensitivity. Utilizing a Policy-Oriented Multi-Level Perspective (MLP), the study evaluates the "knowledge-governance gap" between Nigeria’s strategic "Blue Economy" ambitions and the technical realities of subsea extraction. The methodology integrates data from high-level official documents, including the Nigerian Blue Economy Strategy (2024) and the International Seabed Authority (ISA) Draft Regulations, alongside comparative analyses of the Norway and Cook Islands protocols. The findings indicate that the Bight of Benin faces systemic vulnerabilities across the Political, Economic, Social, Technical (PEST) spectrum. Technically, the rugged "mile-deep" environment of the Benin Abyssal Fan, coupled with thick terrigenous sediment, poses significant risks for hydraulic extraction and "Normal Accidents." Socially, the study identifies a "technology of dispossession," where automated industrial mining threatens the artisanal fishing livelihoods that form the bedrock of regional food security. Politically, the research highlights a "governance void," as Nigeria’s terrestrial-focused Minerals and Mining Act (2007) lacks the "institutional elasticity" required for transboundary marine management. The study concludes that Nigeria is currently at risk of "Carbon Lock-in" and stranded subsea assets. It recommends a strategic precautionary moratorium, the establishment of a specialized Seabed Minerals Authority, and the adoption of "Digital Twin" monitoring technologies. By aligning with the restorative justice principles and anticipatory governance models seen in Norway, Nigeria can ensure that its maritime frontier contributes to "ocean thriving" rather than irreversible ecological and socio-economic decay.

GIS-Based Landslide Susceptibility Assessment and National Highway Vulnerability Analysis Using the Frequency Ratio Model: A Case Study of Lamjung District, Nepal Himalaya

Fri, 21 Aug 2026 00:00:00 +0000

National highways in Lamjung district, Nepal, are frequently disrupted by landslides during the monsoon season, causing significant human casualties, infrastructure damage, and economic losses. This study developed a landslide susceptibility map for Lamjung district using the bivariate statistical Frequency Ratio (FR) method integrated with GIS and assessed the exposure of National Highways NH-03 and NH-25 to landslide hazards. A total of 264 landslides were identified through visual interpretation of high-resolution Google Earth Pro imagery (2015–2025), supported by field verification and Rapid Visual Assessment (RVA). Eleven geo-environmental factors were analyzed: slope, aspect, elevation, geology, LULC, NDVI, TWI, curvature, distance to road, distance to drainage, and rainfall. Factor importance was evaluated using the Prediction Rate (PR) index, with NDVI (PR = 14.03), geology (PR = 12.91), and rainfall (PR = 10.42) identified as the most influential factors. The model achieved strong predictive performance, with AUC values of 81.03% (training) and 80.76% (validation). Approximately 46% of the district lies in high to very high susceptibility zones, which account for 79.2% of the mapped landslides. Highway corridor analysis revealed that NH-25 is substantially more exposed than NH-03, with 84.7% of high and very high susceptibility points along NH-25. The Ghermu–Sattale section and bridges at Syange Khola and Sirung Khola were identified as critical hotspots. The resulting susceptibility map serves as a robust decision-support tool for landslide risk mitigation, infrastructure resilience planning, and sustainable land-use management in the Nepal Himalaya.

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.

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.