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Geocoding African Disasters Jesse Brinkman Mentor: Pam Cowher.

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Presentation on theme: "Geocoding African Disasters Jesse Brinkman Mentor: Pam Cowher."— Presentation transcript:

1 Geocoding African Disasters Jesse Brinkman Mentor: Pam Cowher

2 Project Overview Data Prep Gather Data Organize Data Geocode the Data Analysis What effect do El Nino/La Nina years have on droughts in Africa? What role do development indicators play in the severity of disasters?

3 What is Geocoding? Geocoding is a way to give something a reference point on a map. Address Latitude/Longitude Other grid systems

4 Pacific Disaster Center 1305 N Holopono St # 2 Kihei, HI 96753

5 Gather the Data Obtain the data from the Centre for Research on the Epidemiology of Disasters’ (CRED) Emergency Events Database (EMDAT)

6 Organize the Data Using the locations provided by EMDAT assign latitude and longitude information to each disaster. Re-format the data to be easily inserted into the GIS software.

7 Geocode the Data

8 Observations/Trends 1,580 Disasters recorded from Epidemics and Floods account for 71% 585 and 532 respectively

9 Observations/Trends cont. Increase in disasters in recent years

10 Floods and Epidemics by Year

11 Disaster Density Disaster Density

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13 Analysis What effects do El Nino/La Nina years have on droughts in Africa? What role do development indicators play in the loss of life associated with disasters?

14 El Nino/La Nina Hypothesis: There are more droughts during El Nino years, and less during La Nina years. Methods: Gather the list of years that El Nino/La Nina occurred. Look for an increase in drought occurrence.

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16 Conclusions No apparent correlation between # of droughts and El Nino/La Nina years

17 Sensitivity to Disasters Hypothesis: Indicators such as infant mortality, literacy, and government corruption contribute to the amount of deaths per disaster. Methods: Take the average infant mortality, literacy, corruption perception, water stress, digital access, and human development rates. Compare with average death/disaster

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19 Conclusions There is no strong correlation between the two.

20 Future Work Possibilities Do specific regional analysis on the El Nino/La Nina years, as opposed to Africa as a whole. Improve the sensitivity analysis/map Better Scale Disaster by type Individual factors which may affect particular disaster types. Explain the increase in disasters.

21 Acknowledgements Pam Cowher Colin Lindeman Rich Nezelek Centre for Research on the Epidemiology of Disasters (CRED) Emergency Events Database (EMDAT) The Akamai Internship Program is funded by the Center for Adaptive optics through it’s National Science Foundation Science and Technology Center grant (#AST ) and by grants to the Akamai Workforce Initiative from the National Science Foundation and Air Force Office of Scientific Research (both administered by NSF, #AST ) and from the University of Hawaii Lani LeBron Lisa Hunter Lynne Raschke Scott Seagroves


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