Contract-program task / Geospatial benchmark development
Geospatial Benchmark:
Residential-Location Inference
Inferring residential location from imperfect geographic evidence.
I developed and evaluated a synthetic geospatial benchmark grounded in surveillance field experience. It examined how analytical systems infer residential location and express uncertainty when place names are ambiguous, geographic evidence conflicts, and landmark reports are imperfect.
My roleGeospatial Benchmark Developer · Contract-program task. Separate from my ongoing geospatial AI review and quality-assurance engagement.
A location reference is not always a residence.
A surveillance record may refer to a community, facility, or landmark without establishing a household’s location. A precise coordinate can still represent the wrong place for the question being asked.
The prototype modelled this distinction with synthetic data. The benchmark concerns residential-location inference and geographic uncertainty, rather than assuming that every coordinate or place label is definitive.
Geospatial engineering and reproducible evaluation.
I built Python geospatial data-generation and validation components and reproducible evaluation workflows.
Automated quality-assurance checks supported benchmark development and experimental validation. Containerized workflows supported reproducible execution.
This overview summarizes professional competencies; protected data and confidential program materials are excluded.
Testing inference, uncertainty, and reliability.
I designed and executed controlled experiments assessing inference methods, uncertainty, methodological robustness, and reproducibility.
A registered 30-repetition pilot was completed and documented against predefined criteria. The pilot did not meet all predefined advancement criteria; completing the experiment did not establish scientific acceptance or production readiness.
Development concluded.
Development concluded after the registered 30-repetition pilot. Implementation and experimental evidence were preserved; no further development is currently underway.
Synthetic reporting processes are modelling assumptions, not measurements of real surveillance reliability. The work does not establish operational accuracy on real households or transfer to new regions.
Public presentation is limited to a general description of the work. Protected data, internal evaluation rules, and confidential program materials are excluded.