The aim of GED4GEM is to build a comprehensive, multi-scale and statistically accurate database of population and buildings, to asses the physical and economic exposure of a given area to earthquakes.

1. Development of the Global
Exposure Database (GED)
Kishor Jaiswal, Synergetics Inc./USGS Golden CO
with contributions from:
P. Gamba, University of Pavia, Italy,
C. Huyck and Z. Hu, ImageCat Inc.
S. Vinay, R. Chen, and M. Becker, CIESIN
O. Odhiambo, G. Mboup UN-Habitat, Nairobi Kenya
S. Ferri, E. Goldoni, D. Ehrlich JRC Italy
P. Henshaw, GEM Foundation
D. Wald, USGS Golden CO
10NCEE, Anchorage Alaska
July 23rd, 2014
@GEMwrld #10NCEE

2. Objectives
• The aim of GED4GEM is to build a comprehensive, multi-scale and
statistically accurate database of population and buildings, to asses the
physical and economic exposure of a given area to earthquakes.
• The database had therefore to be:
– state-of-the-art, i.e. including all existing (and freely available) data sets;
– global, i.e. valid all for each country;
– consistent, i.e. capable in providing statistical and spatial consistency (in
a region or a country);
– easily upgradable through ad-hoc scripts.
• Including more information than just the building structural data, the Global
Exposure Database (or GED for short) could eventually be useful in
multi-hazard applications, e.g., earthquakes, floods, landslides,
hurricanes and other disasters.

3. Consortium
Partners
• University of Pavia (UNIPV)
• The Center for International Earth Science Information Network (CIESIN)
• Global Urban Observatory (GUO) of UN-HABITAT
• ImageCat Inc.
• The Joint Research Centre of the European Union (JRC)
Advisory Partners
• US Geological Survey (USGS)
• EUCENTRE
• Geoscience Australia (GA)

4. Source Taxonomy Grid/Ve
ctor
Statistics Validation
Level 0 GPW, PAGER,
GRUMP, UN-
HABITAT, NERA
PAGER
GEM
30” Country Internal:
consistency with
PAGER
Level 1 Sub-country db
(Census, DHS, MICS,
HAZUS, regional
programmes)
GEM
HAZUS
30” Region
(Admin 1
& Admin
2)
Internal: test
site information
at aggregated
levels
Internal: quality
of input data
Level 2 National/regional/lo
cal database(s)
GEM 30” Ad hoc
Level 3 Ground survey
Building database(s)
GEM vector Single
building
External:
regional and
selected users
Global Exposure Database: levels

5. GED Level 0: data from PAGER

6. IMPROVED Level 0
• Additional information available from UN-Habitat to improve GED has been
processed and sample results checked before ingestion into GED.

7. Census records
• Sample design
– Systematic sample of every twentieth household.
• Sampling unit: Households
• Sample fraction: 5%
• Sample size (person records): 1,407,547
• Sample weights: Self-weighting.
Expansion factor = 20.

8. Demographics and Health Survey records
• The Demographics and Health Survey (DHS) sample is designed to
represent each of the country’s administrative regions. In each region, a
stratified sample design was employed. Primary sampling units (PSUs) are
selected with probability proportional to the estimated number of households
from the Census.

9. Level 1: for the first time sub-national information
struct_code struct_ratio
W+WLI//R99 0.026
CR+CT99//RC+RC99 0.001
MUR+STRUB+MOM//R99 0.370
MUR+CL99//R99 0.043
MUR+CL99//RO 0.558
MUR+ADO//R99 0.002
struct_code struct_ratio
W+WLI//R99 0.020
CR+CT99//RC+RC99 0.00
MUR+STRUB+MOM//R99 0.375
MUR+CL99//R99 0.040
MUR+CL99//RO 0.558

10. Level 1: less coverage
• Check for region matching included
(issues with GADM versus the population model versus national databases)
• GADM v.2 compliant

11. Level 2 data: room for detailed databases
struct_id struct_code struct_ratio
1 W+WLI//R99 0.026
9
CR+CT99//RC+RC
99 0.001
12
MUR+STRUB+MO
M//R99 0.370
19 MUR+CL99//R99 0.043
31 MUR+CL99//RO 0.558
102 MUR+ADO//R99 0.002
103
MATO//RME+RM
E99 0.000
Stone 4.3%
Cane/palm 0.7%
Adobe / "tapial" 37.0%
"Tabique, quinche" 0.1%
Wood 1.9%
Bricks 55.8%
OTHER 0.2%

12. Level 2: aggregated data from existing GIS files
Guadeloupe: density of buildings + dwelling fractions from Level 0 (JRC +
UNIPV)

13. Replacement cost data sources
• Published construction cost
guides
– Common in countries like
Europe, North America,
Australia, et
– Available for a selection of
countries in Africa, South
America, Asia
• Purpose commissioned
reports from local quantity
surveyors

14. Example: Malaysia

15. Procedure
• It is proposed that the global range of GDPpc is subdivided into five bins and
an index country (together with a full range of factors) is provided for each
bin.
• Then, a factor for replacement rate is computed.

16. Current Replacement Cost Coverage
• A few countries with detailed information by expert opinion
• Rest of the world (almost) with “default” values

17. A few examples of data in GED

18. A few question we can answer with GED 1.0
Level 0 (national) questions
• Estimate of the total residential exposure of Russia
– X billions USD (computed using level 0 dwelling fractions from PAGER,
average floor per capita, default replacement cost)
Level 1 (subnational) questions
• Estimate of total wooden buildings that are present in the Saravan region of
Laos
– Y (computed using level 1 dwelling fractions from 2006 MICS survey, the
average number of people per dwelling, default numbers for the numbers of
dwelling per building)
Level 2 (local) questions
• Estimate of total masonry buildings in a radius of 3 km around the center of
Brisbane – Lat. 153.03, Lon. -27.44, – or how much would cost to rebuild 60%
of them?
– Z and XX billion AUS (computed using level 2 data from NEXIS)

19. GEM Data and Models on the Platform

20. Thank you !

21. Except where otherwise noted, this work is licensed under:
creativecommons.org/licenses/by-nc-nd/4.0/
Please attribute to the GEM Foundation with a link to -
www.globalearthquakemodel.org

22. Questions?
• Just to help understanding the answers …