Augustino-Abraham/Geospatial-Analysis-Portfolio-Augustino-Abraham

This portfolio does a good job showing Augustino’s range across practical GIS and remote-sensing problems rather than just listing tools. The strongest part is the way the projects are anchored in recognizable decision-support use cases: TTCL network fault prediction in Dodoma, burn severity mapping, public-facilities hotspot analysis, and climate/flood vulnerability in Dar es Salaam. The TTCL project especially feels substantial because it includes source datasets, map outputs, a dissertation PDF, and R scripts for correlation analysis, terrain analysis, data cleaning/geocoding, and GLM-based model validation. The reported AUC of 0.979, plus the ROC and predicted-vs-actual map outputs, gives readers a concrete sense of model performance instead of leaving the project at the “made a map” level.

The Dar es Salaam climate vulnerability project is also a strong addition. The README explains the workflow clearly: DEM-derived slope, rainfall, temperature, soil moisture, population exposure, flood risk, heat risk, and final multi-hazard vulnerability. The included urban_climate_vulnerability_analysis.R script makes the methodology inspectable, and the generated flood, heat, and vulnerability maps help nontechnical viewers understand the outputs quickly. The MIT license is a good signal, and the repository was recently updated, which helps it feel maintained even though it is still early and has no stars, forks, or open issues yet.

The biggest improvement would be reproducibility. Several scripts use absolute local Windows paths like D:/DATA & OUTPUTS/..., even when similar data is committed under the project folders. Moving to relative paths, adding a small renv.lock or package-install section, and documenting exact run order would make the work much easier for another analyst to rerun. Some folders also need cleanup: the TTCL repo has a nested duplicate folder name, some scripts are stored as .txt instead of .R, and a few README references still contain placeholder citation artifacts such as :contentReference[...]. The burn severity and public facilities projects are visually useful, but they would be more convincing with the source workflow, data provenance, parameters used, and interpretation of what the resulting classes or hotspots mean.

Overall, this is a promising geospatial portfolio with real applied context and good map-based communication. With more consistent project structure, reproducible scripts, data-source notes, and a short “how to run” section per project, it would become much easier for employers, collaborators, or reviewers to evaluate the technical quality behind the outputs.