This tool is designed to visualize and analyze forest data in Nigeria over time using remote sensing datasets. It allows users to explore various forest-related indices, providing insights into the health, extent, and dynamics of forests across the country. Currently under development, the tool enables users to visualize forest data and download it for further analysis.
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FarmWatch is a geospatial pipeline that monitors agricultural lands using satellite imagery and OSM data. It computes vegetation indices like NDVI and NDMI, storing and analyzing data in Google Cloud for interactive visualization in Carto Dashboard.
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This study investigates the spatial patterns of HIV prevalence across the United States, utilizing spatial statistical techniques to uncover geographic disparities and clusters. By integrating socio-demographic variables such as income, education, race, and access to healthcare, the analysis aims to understand how these factors influence HIV distribution.
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This app leverages Google Earth Engine (GEE) to visualize and analyze land use changes in the Amazon. Users can explore deforestation trends and download satellite imagery for further analysis.
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I used Google Earth Engine (GEE) with a Random Forest classifier to map mangroves and other land use classes in Benin Republic for 1986, 2010, and 2011, achieving over 90% accuracy. The classification provides insights into land cover changes for environmental monitoring..
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I developed an advanced geospatial pipeline using ArcGIS Notebooks and Google Earth Engine Data Cloud to automate data collection, processing, and visualization. This streamlined system seamlessly integrates real-time environmental data into an ArcGIS Dashboard, enabling efficient monitoring of tree health, growth patterns, and environmental impacts..
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Using GIS to analyzed education levels and healthcare accessibility in Zamfara State, Nigeria, integrating diverse datasets to drive data-informed decision-making.
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Analysis of EPA monitoring station data to model and predict distribution of Ozone, Nitrogen, and PM2.5 conditions.
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ML model predicting flood likelihood based on topographical and environmental factors using various data APIs.
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