1. ASSESSING THE LOCAL EARTHQUAKE RISK Justin Czarka, Lehman College, CUNY – May 2013 Agung Swastika/AFP/Getty Images

2. Purpose of Presentation  To compare datasets for assessing earthquake risk using ArcMap  Various factors must be considered when assessing local areas at risk from earthquakes.  Accurate use of earthquake data could determine future use of risk mitigation funding allocations

3. Earthquake Effects  Ground rupture  Shaking  Liquefaction  Tsunamis  Landslides  Fires  Disease Fire as aftermath from 1906 San Francisco Earthquake

4. Obtaining Essential Data  Required Data: Earthquake data: magnitude, epicenters, and locations Population data: who lives where? Geographic data: administrative boundaries

5. United States Geological Survey (USGS): Locating Earthquake Data

6.  Earthquake datasets from United States Geological Survey (USGS) Utilized archived collection of earthquake data from the “Global Earthquake Search” based on a rectangular area search Parameters: Complete Catalog from 1/1/1973 through 4/25/13 Magnitudes 2-10 Depth 1-100 km  Data Website: http://earthquake.usgs.gov/earthquakes/eqarchives/e pic/ http://earthquake.usgs.gov/earthquakes/eqarchives/e pic/

7. United States Census Data: Locating Geographic and Population Data

8.  United States Census Bureau Tiger Products 2010 Census Demographic Profile 1- Shapefile Format Provides Population Distribution at the individual county level  Data Website: http://www.census.gov/geo/maps- data/data/tiger.htmlhttp://www.census.gov/geo/maps- data/data/tiger.html

9. GRUMP Data: Locating Population Data

10. Global Rural-Urban Mapping Project (GRUMP)  Global Rural-Urban Mapping Project Located at NASA’s Socioeconomic Data and Application Center (SEDAC) GRUMP allows one to more accurately represent population distribution over a particular administrative unit. Utilizes nighttime lights as indicator of population density and population distribution. Access at: http://sedac.ciesin.columbia.edu/data/collection/grump-v1 http://sedac.ciesin.columbia.edu/data/collection/grump-v1

11. Spatial Join: USGS and Census Data  Allows individual earthquakes to be quantified within each county  Overlays earthquake locations within each county administrative unit  Creates a simplistic earthquake risk map

13. Problem with General U.S. Census Data  U.S. Census Data assumes a theoretical uniform distribution of population across the geographic extent of the county.  Actual county population tends to be concentrated- in urban areas, suburbs, towns, and villages. Based on geographic (mountains, streams, etc.) and economic reasons (location of jobs, housing, etc.)

14. Problem with General U.S. Census Data Visualizing U.S. Census data fails to effectively reflect population distribution with a county. The only data available: 1) total population in the county, and 2) the county size/area. Thus one can only determine a uniform population distribution. Population By U.S. County “DP0010001" is the short name for the data table containing total population.

15. Case Study: Albany County, New York Albany County Population: 304,204 20 >M2 earthquakes since 1973 Albany County, New York

16. U.S. Census vs GRUMP Population Case Study: Albany County, New York  Census Population Map Describes: Number of Earthquakes Magnitude of Earthquakes Uniform Population Distribution within the county (administrative unit) Mapping of Probable Fault Line  Census Population Map Fails to Describe: Accurate distribution of population within the county Depict earthquake risk for population centers

17. Case Study: Albany County, New York  GRUMP Population Density Grid, Vol. 1 Allows: Distribution of county population based on economic activity A likely indicator where the population lives Detailed view of population most at risk for earthquake Based on proximity to fault and historical local earthquakes  In ArcGIS, Use GRUMP Population Data to: Form a zone of proximity surrounding the fault/epicenter I.e. a buffer zone Develop population statistics within the buffer zone I.e. population density

18. ArcGIS: Create Shapefile to Create Buffer Zone Albany County, New York Created a shapefile to represent “epicenter” of earthquakes.

19. ArcGIS: Develop Buffer Zones  5 km and 10 km buffer zones surrounding the epicenter More damage closer to the epicenter

20. ArcGIS: Zonal Statistics as Table Able to determine the population density within a given area (buffer).

21. Attempting a Buffer Zone Using Census Data U.S. Census data depicting earthquake risk using population density. Data only allows for county population density, shown here in shapes of green (lighter green represents higher density).

22. Using GRUMP Data With Buffer Zone GRUMP data depicting earthquake risk using population density within buffer zones. Data allows for population density devised through zonal statistics, shown here as brown (5km) and tan (10km) buffer zones.

23. Limitations  Depth of earthquakes not considered  Magnitude of earthquakes not considered  US Census (vector) data not analyzed below county level

24. Conclusions  GRUMP data can be very effective at determining the population at risk from earthquakes.  GRUMP data can be flexible enough to analyze population density risk within various formal and informal administrative units GRUMP Population Density 2000 Map Layer (http://sedac.ciesin.columbia.edu/data/se t/grump-v1-population-density)

25. Citations  United States Census Bureau http://www.census.gov/geo/maps-data/data/tiger.html  United States Geological Survey http://earthquake.usgs.gov/earthquakes/eqarchives/epic/  Global Rural-Urban Mapping Project (GRUMP) http://sedac.ciesin.columbia.edu/data/collection/gpw-v3  “Potential for Global Mapping of Development Via A Nightsat Mission.” Global http://ngdc.noaa.gov/eog/pubs/Nightsat_GeoJ_2007.pdf  “Earthquakes May Endanger New York More Than Thought, Says Study.” The Earth Institute at Columbia University. August 21, 2008. http://www.earth.columbia.edu/articles/view/2235