Polygon Overlap

The Polygon Overlap method, which assumes complete overlap between census tracts and flood zones, produced the highest population exposure estimate, with 2,291,761 individuals identified as being exposed. This method, while simple to implement, tends to overestimate exposure by failing to account for the uneven distribution of populations within census tracts. The results demonstrate that this approach might not be suitable for precise hazard assessments, especially in heterogeneous urban areas.

 

Centroid Containment

In contrast, the Centroid Containment method identified 586,462 exposed individuals. By only considering tracts where the centroid falls within the inundation zone, this approach provides a conservative estimate. However, this method risks underestimating exposure in cases where large tracts with significant populations are partially affected by flooding but the centroid remains outside the hazard zone.

 

Areal Weighting

Areal Weighting refined the exposure estimate to 517,217 individuals by proportionally allocating populations based on the area of census tracts intersecting flood zones. This method accounts for spatial heterogeneity but assumes an even population distribution within tracts, which may not always hold true in densely populated or mixed-use urban regions.

 

Dasymetric Mapping

Dasymetric Mapping yielded a mid-range exposure estimate of 628,692 individuals by incorporating ancillary data to redistribute populations more accurately within tracts. This approach is particularly effective in urban settings, as it aligns population distribution with land use and other relevant factors. The results illustrate the potential of dasymetric mapping for enhancing the spatial precision of hazard exposure assessments.

 

Comparison and Implications

The wide range of population exposure estimates across methods underscores the critical influence of technique selection on hazard mapping outcomes. While Polygon Overlap provides a broad overview, it may inflate exposure estimates, making it less reliable for policy-making. Conversely, Centroid Containment may overlook significant at-risk populations, especially in large and diverse census tracts. Areal Weighting and Dasymetric Mapping strike a balance, with the latter offering superior accuracy for urban applications by leveraging detailed spatial data.

 

These findings highlight the need for context-sensitive approaches in GIS-based hazard assessments. For policymakers and planners, integrating dasymetric mapping or similar advanced techniques into risk analysis workflows could provide more accurate exposure estimates, aiding in targeted disaster response and mitigation efforts.