GEOPROCESSING & SPATIAL ANALYSIS

Spatial Statistics

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Borders Violence Regression

Quantifying the increase of violent conflict proximate to borders (national, colonial, religious, ethnic) with spatial regression.

Objective: determine the most significant proximity to which border type yields higher reported violent conflict events.

Method: perform multivariate regression analysis on the increase of violence with distance to borders controlling for confounding variables.

Result: statistically significant result showing proximity to religious borders, rivers, and coasts as having higher violence and fatalities.

Role: School Project

Skills: Google Earth Engine, Python, QGIS

Raster Data

Antivenom Accessibility Risk Index

Airborne Laser Scanning and tree identification methods with Canopy Height Models to quantify tree loss in avalanche paths.

Objective: test the possibility of using LiDAR data to measure post-avalanche tree loss.

Method: utilize tree detection algorithms from Canopy Height Models to quantify tree loss for plots in an avalanche path.

Result: measured average tree loss of 5% resulting from 2020 Utah South Monitor Bowl Avalanche, and identified locations of highest tree loss in the avalanche path.

Role: School Project

Skills: ArcGIS, Raster Calculation, Python

Vector Data

Regional Inventory Data Reporting

First local-level visibility into inventory data for HIV products.

Objective: to display latest inventory levels at all 3k+ pharmacies and last-mile distribution warehouses across Zambia.

Method: ETL Pipeline with reported inventory data (excel) to arcGIS.

Result: new access into local inventory levels for supply chain teams.

Role: Project Lead, USAID Global Health Supply Chain

Skills: Python, Esri Arcade

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