vices might be needed most urgently. Specific data variables considered in this stage include population density, age breakdown (with particular emphasis on the elderly, who are more likely to need—and use—emergency shelter service), language barriers, and combinations of age and ethnicity that might correlate with dietary and nutritional requirements. A variety of economic variables—percentage living below the poverty level and housing tenure (renter/owner)—are also important to assessing outcome—so, too, is the variable on the extent of housing vacancies (including seasonal homes) in the area. Mapping these data is often the most effective way to focus services and resources to the subareas of most acute need.

In disaster response, Paulsen said, the “name of the game is getting ahead of the curve as fast as you can.” In its planning, the Red Cross works to have people on hand with the specialized skills needed to address particular problems—but it still takes time to get them into position where they can do the most good. A community like Pascagoula, Mississippi, might ordinarily have a local Red Cross staff of two people; during the response to Katrina, the Red Cross needed to deploy on the order of 25,000 people to Pascagoula, many (if not most) from outside the local area. So, he stressed, the data-driven assessments of impact and need in the wake of a disaster must be completed quickly, and the value of data in making effective resource allocations decreases sharply with the oldness of the data. Put bluntly, he said, “we are going to make bad decisions” if all that is available are 10-year-old data. Paulsen diverged from his presentation to comment on the challenge raised by Census Bureau staff in the earlier discussion session (Section 2–F) on whether a shift of a few weeks or months in data release time would really affect results. As an end user and a manager doing disaster response, he said, “I am really hungry for speed because I feel like I am going to make better decisions the more current the data is”; the actual effective difference in response of a few weeks or months of increased data recency would depend on the magnitude of the change caused by the disaster. For longer-term planning and for disaster preparedness—as he would discuss next—change is slower and so lags might not matter. But fresh, recent data are invaluable in the immediate response and recovery phases of the disaster cycle.

Paulsen echoed Plyer’s comment about the usefulness of timely, accurate data in the next step in the cycle—recovery from the disaster. However, given that Plyer had discussed recovery in great depth, Paulsen moved on in the cycle to discuss the role of data in disaster preparedness. He said that preparedness is his new major focus in work at the Red Cross; having led recovery from Katrina and having worked in disaster response for a long time, he said that he wants to really get ahead of the curve and try to reach a point where people do not have to suffer so much when disasters occur. As he put it, Red Cross services cannot be instantaneous at every disaster everywhere, no matter how big the Red Cross is; “people have to do some stuff on their own, and they can reduce their own risk.”



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement