36 ISPRS: Climate Variability and Health

Tuesday, 27 August 2013
3:30 PM - 4:30 PM
Monroe

SHORT-TERM VULNERABILITY OR LONG-TERM ADAPTATION: QUANTIFYING ADVERSE HEALTH CONSEQUENCES OF EXTREME WEATHER EVENTS WITH SPATIAL REGIONALIZATION

Paper number: 36-3
Program: Sessions
Time: 4:10 PM – 4:30 PM

Authors

Abstract

Identification of regions with comparable climate patterns has the potential to substantially improve forecasting of adverse health outcomes caused by extreme meteorological events. We aim to design an objective spatial regionalization for assessing vulnerability and health risks. We propose to define specific climate regions for the United States using remote sensing and to compare the classifications with the Köppen-Geiger divisions. Using a nationwide database of hospitalizations we examined the utility of a Remote Sensing-based classification to assess vulnerability to extreme weather, defined by the number of residents prone to adverse effects, the rate of severe health outcomes, and the likelihood of extreme events.

Satellite image composites were aggregated, masked, and compiled into a 229-dimensional dataset. Longitudinal and temporal data redundancies were reduced by applying principal components transformation. The study area was classified into 8 distinct regions determined by the Calinski-Harabasz cluster validity index. Proposed regions exhibited a high degree of consistency with Köppen-Geiger regions and a well-defined regional delineation by annual and seasonal temperature and precipitation values.

Regional climate classifications were used to model the incidence of hospitalization due to exposure to extreme weather among elderly Americans. We determined that the relatively warm and humid Southeastern region of the United States had the highest rate of hospitalizations due to exposure to cold (28.3 cases per 10,000 person-at-risk) and the largest population at risk (10.9 million). The dry Southwestern region had the lowest rate of hypothermia hospitalizations (14.6 cases per 10,000 persons-at-risk). Based on these results we can conclude that climate classification based on remotely sensed data demonstrates strong potential for predicting and mitigating the adverse effects of severe weather on human health and its utility needs to be further explored.