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Geospatial Data EngineeringFlood RiskHazusResilience PlanningBenefit-Cost Analysis

Statewide Flood Loss Avoidance Study

A statewide geospatial data integration workflow that connected flood hazard datasets with building, parcel, census, and structure inventory data for Hazus flood model benefit-cost analysis.

Client
Commonwealth of Massachusetts Executive Office of Energy and Environmental Affairs
Location
Boston, MA
Year
2026
Role
GeoAI Engineer

Context

Massachusetts needed technical support for evaluating flood-resilient building code strategies through a statewide flood loss avoidance study. The analysis required a reliable geospatial foundation that could connect flood hazard data with building exposure information across the state.

Challenge

The source data came from multiple systems with different formats, assumptions, spatial resolutions, and coverage areas. Effective and preliminary NFHL data, Q3 flood data, CoreLogic flood data, census information, parcel data, building footprints, and National Structure Inventory records all had to be reconciled before they could support statewide loss analysis.

My Role

I led geospatial data preparation and integration support. My role focused on combining flood hazard and building inventory datasets, associating structures with flood risk information, and preparing the final analytical dataset for Hazus-based benefit-cost analysis.

Approach

I processed and merged statewide flood hazard layers with parcel, building, census, and structure inventory datasets. The workflow emphasized spatial consistency, attribute alignment, and building-level association of risk information so that the final dataset could support defensible flood loss analysis.

Output

The project produced a statewide geospatial dataset prepared for Hazus flood model benefit-cost analysis. The dataset connected buildings and related exposure information with relevant flood hazard attributes.

Impact

The work supported statewide flood resilience planning and building code evaluation. It created a stronger analytical basis for comparing potential loss avoidance benefits across communities, building types, and flood hazard conditions.

Tech & Techniques

ArcGIS ProPythonHazusNFHLQ3 Flood DataCoreLogicNSIParcel Data