1. 1.Introduction Heat waves are common occurrences around the world and have been projected to increase in frequency, intensity, and longevity in the future (Luber and McGeehin 2008), and therefore a better understanding of these events and their impacts on human health is needed. Heat Waves are especially dangerous within urban environments due to the phenomenon known as the Urban Heat Island (Bornstein, 1968). The Urban Heat Island in Oklahoma City has been well documented (Basara et al. 2008), and has shown that urban areas can often be several degrees warmer than in rural areas outside the city. Coupled with the increased temperatures in urban environments, several demographic attributes can also play a role in an individual’s vulnerability to extreme heat events, such as income and age (Reid et al., 2009). In the summer of 2008 from July 31 st to August 7 th the Oklahoma Mesonet (McPherson et al., 2007) and the Oklahoma City Micronet (Basara et al. 2011) documented a heat wave that impacted central Oklahoma (Basara et al., 2010). 2. Methodology Using the spatial resolution provided by the Oklahoma City Micronet (Figure 1), U.S. Census information, and ArcGIS software this project aims at determining heat vulnerability at a neighborhood level by examining four different attributes (cluster data, population density, maximum temperature, and minimum temperature), thus providing the means for better mitigating against the risk of extreme heat waves. Census Tracts throughout Oklahoma City (defined by the U.S. Census Bureau) are used for risk determination. The minimum and maximum temperature plots were created by calculating the maximum and minimum temperature of each Mesonet & Micronet station for each day, interpolating using the Kriging method, and then calculating the statistical mean temperature for each census tract (Figure 2). Figure 1. Locations of the Oklahoma Mesonet and Oklahoma City Micronet stations that were used. Figure 2. Model created for data processing in ArcMap10 Heat Waves and Their Impacts on Human Health in Urban Areas of Central Oklahoma Emma Fagan 1, Kyle Thiem 1, Jessica Voveris 1, Jeffrey Basara 1, and Heather Basara 2 1 School of Meteorology, University of Oklahoma, 2 Department of Geography, University of Oklahoma 4. References Bornstein, R. D., 1968: Observations of the urban heat island effect in New York City. Jour. of Appl. Metr, 7, 575-582. Reid, C. E., M. S. O’Neill, C. Gronlund, S. J. Brines, D. G. Brown, A. V. Diez-Roux, J. Schwartz, 2009: Mapping Community Determinants of Heat Vulnerability. Environ. Health Pers., doi: 10.1289/ehp.0900683 [Available online at http://dx.doi/.org] Meehl, G. A. and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat waves in the 21 st Century. Science, 305, 994-997. McPherson, R. A., C. A. Fiebrich, K. C. Crawford, R. L. Elliot, J. R. Kilby, D. L. Grimsley, J. E. Martinez, J. B. Basara, B. G. Illston, D. A. Morris, K. A. Kloesel, S. J. Stadler, A. D. Melvin, A. J. Sutherland, J. Shrivastava, J. D. Carlson, J. M. Wolfinbarger, J. P. Bostic, and D. B. Demko, 2007: Statewide monitoring of the mesoscale environment: a technical update on the Oklahoma Mesonet. Jour. of Atmos. and Oceanic Tech., 24, 301-321. Hall, J. and H. Basara, 2010: Mapping Vulnerability in Oklahoma City: An Examination of Connections between Demography and Location in an Urban Context. Population Association of America: 2010 Annual Meeting, 15-17 April 2010, Dallas, Tx. [Abstract available at http://paa2010.princeton.edu/abstracts/101434] Census tracts were organized into “Clusters” based off of multiple demographic characteristics using a Self-Organizing Map algorithm by Hall & Basara (2010) (Figure 3). The area of each census tract was determined by using The ‘Calculate Geometry’ tool in ArcMap. Then population density was calculated using the population of each census tract as determined by the 2000 U.S. Census Bureau (Figure 4). The maximum & minimum temperatures from each day of each census tract were then averaged throughout the time of the study period to determine the average maximum & minimum temperature of each census tract (Figure 5). Figure 3. Characteristics and locations of Clusters defined by the Self-Organizing Map used by Hall & Basara 2010 Figure 4. Population density of census tracts in Oklahoma City Figure 5. Average maximum & minimum temperatures of each census tract during the study period. 3. Results & Conclusions The vulnerability of maximum & minimum temperature was given a value corresponding to each degree Celsius the census tracts were over the climatological mean (32.76ºC & 20.56ºC, respectively), then the scale was normalized to a 0-1 scale, where 1 represents a value of 10ºC above the climatological mean (Figure 6). The population density vulnerability was given a value of 1 for each 500 persons/km 2, and then normalized to a 0-1 scale (Figure 6). Age, education, and income in each cluster were given a value of 1-5 based off of the conclusions of Hall and Basara, 2010. The 3 values were then averaged, and normalized to a 0-1 scale (Table 1 & Figure 6). The results were then averaged to find the average vulnerability for the 2008 central Oklahoma heat wave (Figure 7) Our results show the most at risk areas during this particular heat wave were south central and central Oklahoma City, eastern Woodland Park, and The Village. Table 1. Table of vulnerability levels of each cluster. 5. Acknowledgements We would like to thank the Oklahoma Climatological Survey for the allowing us the use of both Micronet and Mesonet data for this project. Figure 6. Vulnerability plots for each for (in reading order) Maximum Temperature, Minimum Temperature, Population Density, & Cluster Demographics This study has shown risk assessment is possible on the neighborhood scale and may one day be used for heat risk mitigation. Figure 7. Overall Vulnerability from 0-1 during the 2008 central Oklahoma heat wave Cluster #Vulnerability - Age Vulnerability - Education Vulnerability - Income Average Vulnerability Level Normalized Vulnerability Level 12242.660.415 23222.330.333 33554.330.833 43454.000.750 55112.330.333