Data Analytics | Data Engineering | Enterprise GIS | GeoAI | Intelligent Decision Support

I design secure, explainable, and governance-aware data systems that integrate data analytics, data engineering, enterprise GIS, GeoAI, machine learning, cloud technologies, and secure APIs into practical decision-support solutions.

My work focuses on transforming complex spatial, public-health, and operational data into trusted intelligence for public health, emergency management, humanitarian response, and evidence-based organisational decision-making.

MSc Big Data Technologies
University of East London / UNICAF

Dissertation: Design and Evaluation of a Privacy-Preserving GeoAI Health Surveillance System Using a Hybrid Cloud Architecture

This research designs, implements, and evaluates a privacy-preserving GeoAI surveillance system that integrates data engineering, spatial intelligence, explainable AI, secure API architecture, and governance mechanisms into an operational decision-support platform.

The implemented system brings together:

• Data engineering and governance-aware preparation
• Spatial statistics and hotspot intelligence
• Machine learning and outbreak-risk classification
• SHAP explainability
• PostgreSQL/PostGIS spatial data management
• FastAPI secure gateway
• JWT authentication, RBAC, and audit logging
• Docker and AWS deployment
• Streamlit dashboard visualisation

• Data analytics and dashboard-driven decision-support systems
• Data engineering and ETL/ELT workflows
• Enterprise GIS solutions and spatial data infrastructure
• GeoAI and spatial machine learning applications
• Explainable AI systems for transparent decision support
• Secure REST APIs using FastAPI, JWT, RBAC, and audit logging
• Cloud-enabled geospatial and public-health intelligence platforms
• Reproducible research and technical portfolio projects

• 10+ years of experience across GIS, data analytics, public-health intelligence, and spatial decision support
• 6,000+ GIS maps, dashboards, and analytical products delivered
• 100+ professionals trained in GIS, spatial analysis, and digital data collection
• 10+ peer-reviewed scientific publications
• National public-health surveillance and emergency response support across Liberia
• Developed a privacy-preserving GeoAI surveillance platform integrating data engineering, explainable AI, secure APIs, and cloud deployment

An end-to-end hybrid-cloud GeoAI surveillance platform integrating data engineering, spatial intelligence, machine learning, explainable AI, governance controls, secure APIs, and dashboard-based decision support.

Technology stack: Python GeoPandas PySAL Scikit-learn XGBoost SHAP FastAPI PostgreSQL/PostGIS Docker AWS Streamlit JWT RBAC

Project links:

• Live Dashboard: Open dashboard
• GitHub Repository: View source code
• Secure API Documentation: Available for authorised academic review

My broader portfolio includes applied projects in:

• Data analytics and data engineering
• Big data and machine learning
• Enterprise GIS and spatial data engineering
• Public-health GeoAI and spatial epidemiology
• Cloud, API, and secure systems engineering
• Automation, monitoring, and reliability workflows

Portfolio repository: Analytics-GIS-GeoAI-Portfolio

Data Analytics and Engineering: Python, SQL, Pandas, NumPy, PostgreSQL/PostGIS, ETL/ELT, data validation, feature engineering, Spark, PySpark, Hive.

GeoAI and Enterprise GIS: ArcGIS Pro, ArcGIS Online, QGIS, GeoPandas, PySAL, spatial statistics, Moran's I, LISA, Getis-Ord Gi*, hotspot analysis, cartography, remote sensing.

Machine Learning and Explainable AI: Scikit-learn, XGBoost, Random Forest, Logistic Regression, SHAP, model evaluation, spatial machine learning, responsible AI.

Cloud, APIs, and Secure Systems: FastAPI, REST APIs, Docker, AWS, Streamlit, Git/GitHub, JWT authentication, RBAC, audit logging, API security, privacy-preserving analytics.

Over the past decade, I have contributed to enterprise GIS, disease surveillance, emergency response, public-health intelligence, and spatial data engineering initiatives across research and operational environments.

My professional work includes:

• Enterprise GIS implementation and spatial data infrastructure
• Public-health surveillance and outbreak response
• COVID-19, malaria, measles, cholera, mpox, and Lassa fever intelligence
• Dashboard development and operational reporting
• GIS training and capacity building
• Geospatial data quality assurance and automation
• Applied research in spatial epidemiology and public-health analytics

I have authored and co-authored peer-reviewed publications covering:

• COVID-19
• Malaria
• Lassa fever
• Measles
• Spatial epidemiology
• Disease surveillance
• GIS, GeoAI, and public-health intelligence

Research profiles:

• Google Scholar: Scholar Profile
• ORCID: 0000-0001-8204-9219
• ResearchGate: Research Profile

My publications complement my engineering work by translating research into practical, reproducible GeoAI and decision-support solutions.

I am currently strengthening expertise in:

• IBM Data Engineering Professional Certificate
• Microsoft Fabric
• Cloud-native data engineering
• Lakehouse architectures
• Advanced GeoAI
• Graph Neural Networks
• GeoFoundation Models
• Spatio-temporal AI
• AI agents for spatial intelligence
• SOC and cloud-security foundations

I welcome collaboration in:

• Data Analytics
• Data Engineering
• Enterprise GIS
• GeoAI
• Spatial Data Engineering
• Explainable AI
• Public Health Intelligence
• Decision-Support Systems
• Open-Source Geospatial Software

• LinkedIn: Godwin Etim Akpan
• GitHub: Jedidiah82
• Portfolio: Analytics-GIS-GeoAI-Portfolio
• Email: godwineakpan1@gmail.com

Building secure, explainable, and intelligent data-driven systems that transform complex spatial and non-spatial data into trusted decision-making.