![]() ![]() ![]() The second hypothesis is that this dasymetric technique can be used to aid in the detection of the presence or absence of environmental injustice by disaggregating the racial and ethnic sub-populations and also compare the degree of over- or under-estimation for the various sub-populations. The first is that when the CEDS method is used to disaggregate population data, the estimation of the number of persons potentially affected by flooding in New York City (NYC) may be more “realistic” than the more traditional centroid containment and areal weighting (AW) methods (see section below for explanation of CEDS, AW, and centroid containment methods). Because of these multiple goals, there are two distinct, yet related, hypotheses. This study has several goals: applying a newly-developed dasymetric population mapping method estimating population potentially impacted by flood hazard and conducting an environmental justice assessment of flood risk. Underestimating more vulnerable sub-populations impairs preparedness and relief efforts. Minorities are disproportionately undercounted using traditional methods. Ethnic/racial populations are also spatially disaggregated to determine any environmental justice impacts with flood risk. Undercounting of impacted population could have serious implications for emergency management and disaster planning. Compared to CEDS, 37 percent and 72 percent fewer people are estimated to be at risk from floods city-wide, using conventional areal weighting of census data, and centroid-containment selection, respectively. A case study estimating population potentially impacted by flood hazard in New York City compares the impacted population determined by CEDS with that derived by centroid-containment method and filtered areal weighting interpolation. A newly-developed mapping method, the Cadastral-based Expert Dasymetric System (CEDS), calculates population in hyper-heterogeneous urban areas better than traditional mapping techniques. This paper demonstrates the importance of disaggregating population data aggregated by census tracts or other units, for more realistic population distribution/location. ![]()
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