2. Mapping Clandestine Drug Laboratories in the US
Summary
This project analyzed DEA records of clandestine drug laboratories across the United States. The goal was to clean and georeference the data, apply spatial econometric analysis, and create a visual narrative that could explain patterns of distribution in connection with organized crime and public security concerns.
Methodology
1. Data acquisition
- Downloaded and reviewed DEA datasets on clandestine drug labs.
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2. Data cleaning and geocoding in Python
- Standardized addresses and corrected inconsistencies.
- Automated geocoding of locations to produce GIS-ready layers.
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3. Spatial analysis in QGIS and Python
- Imported processed data into QGIS.
- Conducted spatial econometric analysis using Exains to identify clusters and distribution patterns.
- Mapped regions with high density of clandestine labs.
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4. Narrative visualization
- Exported spatial outputs into After Effects with GeoLayers.
- Animated maps to highlight concentration zones and their evolution.
- Produced a television-ready segment that communicated technical results in an accessible format.