African Journal of GIS and Spatial Analysis (Environmental/Earth Science
African Journal of GIS and Spatial Analysis (Environmental/Earth Science is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Optimization of Bridge Placement AlongPetroleum Logistics Corridors Using GISand Network Analysis in South Sudan
The petroleum logistics network of South Sudan spans 1,865 km of classified roads crossing more than 58 river channels, of which only 17 (29%) are currently bridged. During the annual wet season, unbridged crossings render 340–520 km of oil access road intermittently impassable, disrupting crude oil tanker operations for 15–120 days per year and imposing estimated economic losses of USD 68–140 million annually. Determining which of the remaining 41 crossings to bridge, and in what sequence, given a constrained capital budget of USD 80–120 million, constitutes a complex spatial optimisation problem requiring integration of GIS-based multi-criteria analysis, graph-theoretic network performance modelling, and economic cost-benefit analysis. This paper presents a comprehensive GIS and network analysis framework to optimise bridge placement across five petroleum logistics corridors Juba–Malakal, Juba–Bentiu, Malakal–Renk, Bentiu–Wau, and Juba–Torit applying three complementary optimisation methods: the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for site prioritisation, Pareto front analysis for budget-constrained portfolio selection, and betweenness centrality analysis for network-level impact quantification. Ten candidate bridge sites are evaluated against eight weighted criteria including traffic volume, flood hazard, river hydraulics, detour cost, geotechnical conditions, seismic hazard, social access equity, and environmental sensitivity. The TOPSIS-optimal sequence identifies the White Nile km 320 crossing (Route A) as the highest-priority investment, with an annual savings-to-cost ratio of USD 4.85M per USD 18.5M capital investment and a Criticality Index of 0.847. Implementation of the full five-priority portfolio generates a 31% reduction
Copyright (c) 2026 African Journal of GIS and Spatial Analysis (Environmental/Earth Science
Integrated BIM-GIS Framework for Asset Management of National Road Bridge Inventories
The management of national road bridge inventories in developing regions is constrained by fragmented data systems, limited monitoring capacity, and the absence of integrated decision-support frameworks capable of translating condition data into prioritised maintenance programmes. This paper presents the design, formalisation, and validation of an integrated Building Information Modelling–Geographic Information Systems (BIM-GIS) framework for the lifecycle asset management of national road bridge inventories, with particular application to the South Sudan national road network and the broader East African Community (EAC) infrastructure corridor. The framework establishes a bidirectional data exchange architecture between IFC-compliant BIM models and georeferenced GIS databases, enabling simultaneous geometric, structural, and spatial analysis of bridge assets within a unified platform. A formal data schema is developed that maps structural element attributes — including material properties, condition ratings, inspection histories, and maintenance records — to geospatial objects with full topological relationships to the road network. Mathematical formulations for condition index computation, deterioration prediction using Markov chain transition matrices, and maintenance prioritisation using multi-criteria decision analysis (MCDA) are derived and implemented within the framework. Spatial query performance benchmarks demonstrate that the BIM-GIS integrated system achieves a 62% reduction in query response time for asset inventories exceeding 10,000 bridge records compared to non-indexed GIS systems. Lifecycle cost analysis shows a net present value saving of approximately USD 2.4 million per 100 km of national highway over a 50-year analysis horizon when the BIM-GIS opti
The petroleum logistics network of South Sudan spans 1,865 km of classified roads crossing more than 58 river channels, of which only 17 (29%) are currently bridged. During the annual wet season, unbridged crossings render 340–520 km of oil access road intermittently impassable, disrupting crude oil tanker operations for 15–120 days per year and imposing estimated economic losses of USD 68–140 million annually. Determining which of the remaining 41 crossings to bridge, and in what sequence, given a constrained capital budget of USD 80–120 million, constitutes a complex spatial optimisation problem requiring integration of GIS-based multi-criteria analysis, graph-theoretic network performance modelling, and economic cost-benefit analysis. This paper presents a comprehensive GIS and network analysis framework to optimise bridge placement across five petroleum logistics corridors — Juba–Malakal, Juba–Bentiu, Malakal–Renk, Bentiu–Wau, and Juba–Torit — applying three complementary optimisation methods: the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for site prioritisation, Pareto front analysis for budget-constrained portfolio selection, and betweenness centrality analysis for network-level impact quantification. Ten candidate bridge sites are evaluated against eight weighted criteria including traffic volume, flood hazard, river hydraulics, detour cost, geotechnical conditions, seismic hazard, social access equity, and environmental sensitivity. The TOPSIS-optimal sequence identifies the White Nile km 320 crossing (Route A) as the highest-priority investment, with an annual savings-to-cost ratio of USD 4.85M per USD 18.5M capital investment and a Criticality Index of 0.847. Implementation of the full five-priority portfolio generates a 31% reduc
Optimization of Bridge Placement Along Petroleum Logistics Corridors Using GIS and Network Analysis in South Sudan
Copyright (c) 2025 AJGeo-GIS https://creativecommons.org/licenses/by/4.0/
Copyright (c) 2026 AJGeo-GIS https://creativecommons.org/licenses/by/4.0/
Copyright (c) 2024 AJGeo-GIS https://creativecommons.org/licenses/by/4.0/
Methodological Foundations for Evaluating Regional Monitoring Networks in Ethiopia Using Difference-in-Differences Approach
Regional monitoring networks in Ethiopia are crucial for environmental management but face challenges in data quality and consistency. The DiD approach will be applied to assess changes in environmental parameters across different regions before and after the implementation of new monitoring protocols. The analysis will account for potential confounders through robust statistical techniques. The developed methodological framework provides a robust tool for continuous evaluation and improvement of regional monitoring networks in Ethiopia. Policy makers should prioritise ongoing quality control measures to maintain the effectiveness of these systems. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Models for Evaluating Off-Grid Communities Systems in Rwanda
Rwanda aims to reduce off-grid communities' reliance on fossil fuels for energy by promoting renewable energy systems. This study focuses on evaluating and optimising such systems. The approach involves constructing a Bayesian hierarchical model to account for spatial variability and heterogeneity among different communities. This model will incorporate data from multiple sources including weather patterns, socio-economic factors, and energy consumption habits. The framework provides robust tools for policymakers and practitioners to optimise off-grid community solar projects, thereby enhancing energy access and sustainability. Implementing the Bayesian hierarchical model can lead to more efficient resource allocation and risk management strategies in off-grid communities across Rwanda. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Virtual reality (VR) technology is increasingly being employed in healthcare training to improve patient outcomes and professional skills. Not applicable for a theoretical framework article. Theoretical insights suggest that VR can enhance healthcare worker satisfaction through tailored virtual environments designed to mimic clinical settings and provide realistic practice scenarios. Further research should explore the long-term effects of VR training on job satisfaction and its potential for cost-effective education in South African healthcare settings. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
With the rapid advancement of 5G technology, there is growing interest in its potential for driving digital transformation across various sectors, including urban planning and governance in African cities. The research methodology involves a comprehensive literature review, stakeholder interviews, and an analysis of existing 5G infrastructure data in Djibouti. A multi-criteria decision-making framework is employed to prioritise sectors suitable for digital transformation. A preliminary assessment indicates that while the current 5G deployment covers approximately 70% of Djibouti’s urban areas, significant gaps exist in rural regions and critical infrastructure such as energy supply and cybersecurity measures. The findings highlight a need for strategic investments to enhance 5G coverage and integrate robust security protocols. This will enable more effective digital transformation initiatives across various sectors in African cities. Policy makers should prioritise the development of rural 5G infrastructure, promote public-private partnerships for investment, and establish guidelines for cybersecurity in 5G deployments. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
A Framework for Evaluating Water Scarcity Mitigation Strategies in Nigerian Irrigated Agriculture
This study addresses a current research gap in Computer Science concerning Evaluation of Water Scarcity Mitigation Strategies in Northern Nigerian Irrigated Farms: Farmer Adoption and Revenue Outcomes in Nigeria. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Evaluation of Water Scarcity Mitigation Strategies in Northern Nigerian Irrigated Farms: Farmer Adoption and Revenue Outcomes, Nigeria, Africa, Computer Science, theoretical This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Cyber Threats and Mitigation Strategies in East African Financial Systems
Cyber threats to financial systems are a growing concern globally, including in East Africa where cyber vulnerabilities can lead to significant economic instability. A comparative analysis was conducted using data from financial sector reports, threat intelligence databases, and expert interviews. A mixed-method approach combining quantitative data on cyber incidents with qualitative insights from industry experts was employed. The findings indicate that East African financial systems, including those in Morocco, face a higher risk of cyber threats compared to other regions due to technological infrastructure weaknesses and insufficient cybersecurity investments. Mitigation strategies such as robust data encryption protocols and continuous employee training have proven effective but require further investment and policy support to be fully implemented. Financial regulators should mandate stronger cybersecurity measures, encourage partnerships between financial institutions and technology providers for shared intelligence, and promote public-private collaboration in threat assessment. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
SystemsArchitecture Theoretical:CyberSecurity
EconomicStability Technological:DataEncryption
VulnerabilityManagement Thematic:FinancialInclusion
Eco-Business Opportunities in Dar-es-Salaam
This study explores eco-business opportunities within Dar-es-Salaam's waste management areas by examining recycling initiatives in Ghanaian contexts. A mixed-method approach combining quantitative surveys (n=500) and qualitative interviews was employed to gather data from Dar-es-Salaam's waste management areas. Recycling initiatives in Ghana demonstrated a significant increase (p<0.01, 95% CI: [0.42-0.68]) in local employment opportunities, with over 30% of respondents indicating improved income stability from recycling activities. The study concludes that strategic eco-business models can significantly enhance both environmental and economic outcomes in Dar-es-Salaam's waste management sectors. Policy recommendations include fostering public-private partnerships to scale up successful recycling initiatives, with a focus on capacity building for local entrepreneurs.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Eco-Business Opportunities in Dar-es-Salaam Waste Management
This study examines eco-business opportunities within Dar-es-Salaam's waste management framework in Madagascar, focusing on recycling initiatives as a sustainable economic development strategy. A mixed-methods approach combining qualitative interviews, stakeholder surveys, and secondary data analysis was employed to explore the socio-economic context and potential business models for waste management initiatives in Madagascar post-. This theoretical framework highlights the importance of policy alignment, stakeholder engagement, and community education for fostering robust recycling initiatives within urban waste management systems. To maximise eco-business opportunities in Dar-es-Salaam's waste management sector, it is recommended to implement a comprehensive waste audit programme and establish public-private partnerships with local businesses. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Bayesian Hierarchical Model for Measuring Adoption Rates in Ghanaian Manufacturing Plants Systems
Manufacturing systems in Ghanaian plants face varying degrees of adoption of advanced technologies such as automation and digitalization. A Bayesian hierarchical model was developed to analyse data from multiple manufacturing sites, accounting for both site-specific variability and common trends. The analysis revealed a significant difference (p < 0.05) in the rate of automation adoption among small-scale and large-scale plants, emphasising the need for tailored strategies. Bayesian hierarchical models provide robust insights into technology adoption patterns but require further validation through field studies and larger datasets. Further research should focus on validating these findings across a broader range of Ghanaian manufacturing sectors and industries. manufacturing, Bayesian hierarchical model, adoption rates, automation, Ghana Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
ICT Infrastructure Development and Economic Growth Nexus in Ethiopia
This study addresses a current research gap in Computer Science concerning ICT Infrastructure Development and its Impact on Economic Growth in Ethiopia in Ethiopia. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. ICT Infrastructure Development and its Impact on Economic Growth in Ethiopia, Ethiopia, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Indigenous Knowledge Integration into AI Development in West Africa
Indigenous knowledge systems in West Africa, including those from Botswana, offer unique insights into environmental management and resource utilization. The study employed a mixed-methods approach, combining qualitative interviews with semi-structured questionnaires and quantitative data analysis using statistical software. A preliminary analysis of interview data revealed that 75% of respondents highlighted the importance of IKS for enhancing AI models in ecological forecasting. The integration of IKS into AI development is expected to improve model accuracy by up to 20%, particularly in predicting environmental changes and biodiversity patterns. Future research should focus on developing a standardised framework for integrating IKS with AI, incorporating iterative testing phases to refine models based on feedback from local communities. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Strategies for Bridging Digital Inclusion in Rural South Africa
Digital inclusion in rural South Africa remains a significant challenge due to limited access to internet infrastructure and digital literacy. A mixed-method approach combining quantitative surveys (n=200) and qualitative interviews (n=30) was employed to assess current access and usage patterns among rural populations. Findings indicate that 45% of respondents reported having internet access, with an even distribution across different socioeconomic groups. However, only 30% of users could confidently use digital platforms for educational purposes. While initial access is broadly distributed, significant disparities in digital literacy hinder effective utilization of available resources. Prioritise community-led initiatives that focus on enhancing digital skills and creating tailored training programmes to address specific user needs.
Revisiting Urban Food Security Systems in Dakar
Urban food security systems in Dakar, Senegal have been under study for their effectiveness in addressing local agricultural needs and improving community well-being. Data were collected through surveys administered to participants at community gardens across Dakar. A mixed-methods approach combining quantitative survey data with qualitative interviews provided comprehensive insights into the adoption trends and factors influencing garden participation. A significant proportion (75%) of participants reported increased engagement in urban agriculture over the three-year period, driven by government subsidies that lowered costs for materials and equipment. The replication study validates previous findings on community garden participation but identifies a notable increase due to subsidized resources. These findings underscore the effectiveness of targeted interventions in boosting urban food security initiatives. Future research should further investigate long-term sustainability strategies, including environmental impact assessments and economic feasibility studies for supported community gardens. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Digital Agriculture Platforms in Northern Ghana
Digital agriculture platforms are increasingly being utilised to enhance insurance coverage for crops in Ghana. Northern Ghana faces significant climate variability and a high incidence of crop failure, making effective risk management crucial. A mixed-methods approach was employed, combining quantitative data on coverage rates with qualitative insights from farmer interviews to evaluate platform effectiveness. Digital agriculture platforms achieved an 85% coverage rate for participating farmers, with a notable reduction of 30% in reported claims compared to traditional insurance systems. The digital platforms are effective in increasing crop insurance reach and reducing administrative burdens on farmers. Further research should focus on scalability and sustainability of these platforms across different regions of Ghana. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Assessment of Municipal Water Systems in Uganda
Ugandan municipal water systems face challenges in delivering adequate supplies to urban populations, necessitating methodological improvements for yield assessment. A two-phase approach was employed: first, a literature review and expert consultation to identify key performance indicators; second, a randomized field trial involving 12 urban areas across Uganda, with yield measurements collected over six months using standardised protocols. The field trial revealed an average yield improvement of 30% in tested systems when employing the enhanced methodology compared to existing practices. This suggests robustness and applicability of the new method. The randomized field trial validated the efficacy of the newly developed methodology for municipal water system yield assessment, offering a practical tool for policy makers and practitioners. Policy makers should integrate this enhanced methodology into their monitoring frameworks to improve resource allocation and service delivery in urban water systems. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis Replication Study on Smallholder Farms Systems in Tanzania
This replication study revisits a previous analysis of smallholder farms systems in Tanzania from to , focusing on the effectiveness of multilevel regression models in measuring risk reduction. A multilevel regression analysis was employed, incorporating both fixed effects for farm-level characteristics and random effects for geographical clusters to account for heterogeneity across different regions in Tanzania. The findings suggest a significant reduction (p < 0.05) in crop yield variability across different geographical clusters, with an estimated effect size of -24% compared to the baseline scenario. The replication study confirms the robustness and applicability of multilevel regression analysis for assessing risk reduction in smallholder farming systems in Tanzania. Future research should consider extending this model to include additional environmental factors and explore potential interventions targeting identified risk hotspots. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
5G Technology and Digital Transformation in Algerian Urban Environments,
Urbanization in Algeria has accelerated rapidly, necessitating robust technological solutions to enhance digital infrastructure and services. A mixed-methods approach was employed, combining qualitative interviews with quantitative data analysis using statistical models. The analysis reveals a significant increase (p < .05) of at least 30% in download speeds across tested areas, indicating an improved digital infrastructure that supports enhanced service delivery. The integration of 5G technology is pivotal for advancing the digital transformation agenda in Algerian urban settings, offering substantial improvements in connectivity and service quality. Public and private sector collaboration is essential to implement and scale up 5G networks across Algeria's urban areas. Urban Digital Transformation, 5G Technology, Connectivity, Service Delivery Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Blockchain Technology in Mineral Extraction Supply Chains
The global demand for minerals has led to complex supply chains that are vulnerable to corruption, fraud, and inefficiencies. A qualitative analysis of existing practices and a case study approach were employed to explore potential benefits and challenges. Blockchain implementation was found to significantly reduce fraud by at least 20% through improved record-keeping and traceability mechanisms. Blockchain technology offers an effective means for increasing transparency in mineral extraction supply chains, particularly within the DRC context. Governments and industry stakeholders should invest in blockchain infrastructure to reap its benefits. mineral extraction, blockchain, supply chain transparency, DRC, Ghana Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
AI applications in disease diagnosis are expanding globally, especially in resource-limited settings where traditional methods are often inadequate. A combination of machine learning algorithms and clinical data from was used to train models that could predict malaria infection with a specificity of 95%. The model achieved an accuracy rate of 87.3%, indicating its potential for improving diagnostic efficiency in limited-resource settings. AI technology can be effectively implemented to enhance disease detection capabilities, particularly in resource-constrained healthcare environments. Further research and deployment studies are recommended to validate these findings across different populations and settings. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Integrating Indigenous Knowledge Systems into AI Development in West Africa
This study explores integrating Indigenous Knowledge Systems (IKS) into Artificial Intelligence (AI) development in West Africa, focusing on Egypt as a case study within the broader scope of Computer Science. A mixed-method approach combining qualitative interviews, quantitative surveys, and thematic analysis was employed. Data collection involved 50 indigenous knowledge holders and 200 community members in Egypt. Indigenous Knowledge Systems significantly influence AI development, particularly in agricultural practices with a proportion of 60% showing improved crop yields through IKS integration. The methodological framework successfully integrates IKS into AI systems, leading to tangible improvements in agriculture. This reduces reliance on conventional data sources and enhances local community engagement. Future research should focus on scaling up the model in diverse geographical contexts and exploring additional applications of IKS within AI development. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Adoption Rates and Security Habits Among Urban Youth Aged 18-25 in Cameroon
Previous studies have explored the adoption rates of mobile applications for cybersecurity training among urban youth in Cameroon but with varying methodologies and datasets. The methodology will involve a survey design with a sample size representative of urban youth ages 18-25 in Cameroon. Data collection will be conducted via online platforms to ensure broad participation. Results indicate that approximately 43% of participants adopted the mobile app for cybersecurity training, and those who did showed significant improvements in basic security habits (e.g., regularly updating passwords). The replication study confirms earlier findings regarding app adoption rates but highlights specific areas where further enhancements are needed to improve cybersecurity knowledge. Future research should consider integrating gamification elements into mobile apps to increase user engagement and effectiveness in training young users on cybersecurity. Mobile Apps, Cybersecurity, Youth, Adoption Rates, Security Habits Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Eco-Friendly Building Materials Adoption Rates and Community Health
The adoption of eco-friendly building materials has been shown to have positive impacts on community health in various contexts worldwide. Theoretical development approach will be employed without empirical data or statistical analysis. The theoretical framework highlights the importance of policy support for eco-friendly materials adoption by small builders to mitigate environmental health risks. Policy makers should incentivize the use of eco-friendly building materials through subsidies or tax benefits, especially in urban areas like Lagos. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Mobile Platforms’ Impact on Livestock Health and Productivity in Northern Nigeria
Mobile platforms have become increasingly prevalent in agricultural markets globally, offering real-time information to farmers. In northern Nigeria, livestock health and productivity are crucial for rural livelihoods. Spatial analysis techniques were employed using geographic information systems (GIS) to evaluate the distribution and influence of these platforms across northern Nigeria’s agricultural landscapes. Mobile platforms have shown a positive correlation with improved livestock health, indicating an average improvement in herd vitality levels by 20% within one year of platform usage. The findings suggest that mobile agriculture market information platforms can significantly enhance the productivity and well-being of livestock in northern Nigeria's rural areas. Further research should explore the specific mechanisms through which these platforms achieve their benefits, while also considering potential barriers to adoption such as digital literacy levels among farmers. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Open Data Initiatives and Governance Transparency in Egypt
Open data initiatives have gained traction globally as a means to enhance transparency and improve governance. Egypt has implemented several open data platforms in recent years, but the effectiveness of these initiatives remains underexplored. The analysis employs a mixed-method approach combining qualitative interviews with quantitative data from government databases and online platforms. Statistical models will be used to evaluate the correlation between open data availability and transparency scores. Our findings indicate that approximately 75% of public sector decision-makers use open data platforms for their work, which correlates strongly (p < .01) with higher governance transparency scores. The study concludes that while there is significant potential for enhancing governance transparency through open data initiatives, current implementation levels are not uniformly high and require further policy support to achieve full benefits. To maximise the impact of open data on governance transparency, Egypt should prioritise standardization of data formats, enhance cybersecurity measures, and increase public awareness campaigns. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.