Predicting Extreme Events: The Role of Big Data
in Quantifying Risk in Structural Development
by
Oliver Edward Newth
Submitted to the Department of Civil and Environmental Engineering
in partial fulfillment of the requirements for the degree of
Master of Engineering in Civil and Environmental Engineering
j
MASSACHUSETTS IST
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at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUN 1 3 2014
June 2014
J RARIES
@ Massachusetts Institute of Technology 2014. All rights reserved.
Signature redacted
A uthor ............
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Department of Civil and Environmental Engineering
May 9, 2014
Certified by.....
Signature redacted
Pierre Ghisbain
Lecturer of Civil and Environmental Engineering
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Certified by......
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Thesis Supervisor
........... Signature redacted...........
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Jerome J. Connor
Professor of Civil and Environmental Engineering
Thes(1upervis r
Signature redacted
Accepted by . ....................................................... .
Heidi M. Nepf
Chair, Departmental Committee for Graduate Students
E
Predicting Extreme Events: The Role of Big Data in
Quantifying Risk in Structural Development
by
Oliver Edward Newth
Submitted to the Department of Civil and Environmental Engineering
on May 9, 2014, in partial fulfillment of the
requirements for the degree of
Master of Engineering in Civil and Environmental Engineering
Abstract
Engineers are well-placed when calculating the required resistance for natural and
non-natural hazards. However, there are two main problems with the current
approach. First, while hazards are one of the primary causes of catastrophic damage
and the design against risk contributes vastly to the cost in design and construction, it
is only considered late in the development process. Second, current design approaches
tend to provide guidelines that do not explain the rationale behind the presented
values, leaving the engineer without any true understanding of the actual risk of a
hazard occurring. Data is a key aspect in accurate prediction, though its sources are
often sparsely distributed and engineers rarely have the background in statistics to
process this into meaningful and useful results.
This thesis explores the existing approaches to designing against hazards, focussing
on natural hazards such as earthquakes, and the type of existing geographic
information systems (GIS) that exist to assist in this process. A conceptual design for
a hazard-related GIS is then proposed, looking at the key requirements for a system
that could communicate key hazard-related data and how it could be designed and
implemented. Sources for hazard-related data are then discussed. Finally, models and
methodologies for interpreting hazard-related data are examined, with a schematic
for how a hazard focussed system could be structured. These look at how risk can be
predicted in a transparent way which ensures that the user of such a system is able
to understand the hazard-related risks for a given location.
Thesis Supervisor: Pierre Ghisbain
Title: Lecturer of Civil and Environmental Engineering
Thesis Supervisor: Jerome J. Connor
Title: Professor of Civil and Environmental Engineering
3
4
Acknowledgments
I would like to express my sincere gratitude to my supervisor, Pierre Ghisbain, for his
support and assistance throughout my research and study. His knowledge and advice
has been invaluable during the writing of my thesis. I also wish to thank Professor
Jerome Connor, my advisor, for his advice throughout the academic program, and
my parents who have advised me on many points of detail and made many valuable
suggestions.
Funding support was provided through a Kennedy scholarship granted by the
Kennedy Memorial Trust - the British memorial to President Kennedy. I would like
to thank the trustees who have provided me with much advice and assistance over
the past year.
5
6
Contents
1
Introduction
15
1.1
O verview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
1.2
Types of Extreme Events . . . . . . . . . . . . . . . . . . . . . . . . .
16
1.2.1
Naturally Occurring Events . . . . . . . . . . . . . . . . . . .
16
1.2.2
Non-natural Events . . . . . . . . . . . . . . . . . . . . . . . .
19
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
1.3.1
Designing for Risk
. . . . . . . . . . . . . . . . . . . . . . . .
20
1.3.2
Approaches to Geographic Information System Design
. . . .
22
1.3.3
Existing Hazard Geographic Information Systems . . . . . . .
22
Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
1.3
1.4
2
Software Design
31
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
2.2
Functionality
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
2.2.1
Required Functionality . . . . . . . . . . . . . . . . . . . . . .
33
2.2.2
Optional Functionality . . . . . . . . . . . . . . . . . . . . . .
35
2.3
Information Architecture . . . . . . . . . . . . . . . . . . . . . . . . .
36
2.4
User Interface Design . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
2.4.1
User Experience Requirements . . . . . . . . . . . . . . . . . .
37
2.4.2
Overall Interface Design
. . . . . . . . . . . . . . . . . . . . .
38
2.4.3
Risk Views
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
2.5
Implementation of the Proposed System
. . . . . . . . . . . . . . . .
41
2.6
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
7
3
49
Data
3.1
Introduction and Overview . . . . . . . . . . .
49
3.2
Data Sources and Display of Data . . . . . . .
49
3.2.1
International Organizations
. . . . . .
50
3.2.2
Governmental Data . . . . . . . . . . .
51
3.2.3
Other Sources . . . . . . . . . . . . . .
54
3.3
Variation in Data Sets . . . . . . . . . . . . .
55
3.4
Copyright Issues
. . . . . . . . . . . . . . . .
56
3.5
Conclusions . . . . . . . . . . . . . . . . . . .
56
4 Interpretation
5
57
4.1
Introduction . . . . . . . . . . . . . . . . . . .
57
4.2
Types of Risk . . . . . . . . . . . . . . . . . .
57
4.3
Hazard Analysis . . . . . . . . . . . . . . . . .
59
4.4
Systems Design . . . . . . . . . . . . . . . . .
62
4.5
Resolution in Risk Calculations . . . . . . . .
64
4.6
Conclusions . . . . . . . . . . . . . . . . . . .
65
Conclusions and Recommendations
67
5.1
Conclusions . . . . . . . . . . . . . . . . . . .
. . . . . .
67
5.2
Recommendations for Future Development . .
. . . . . .
68
5.2.1
Collaboration . . . . . . . . . .. . . . .
. . . . . .
68
5.2.2
Funding Opportunities . . . . . . . . .
. . . . . .
69
8
List of Figures
1-1
Total damages (USD billion) caused by reported natural disasters
between 1990 and 2012. [11] . . . . . . . . . . . . . . . . . . . . . . .
1-2
Estimated damage (USD billion) caused by reported natural disasters
between 1975 and 2012. [11] . . . . . . . . . . . . . . . . . . . . . . .
1-3
24
A map displaying the likelihood of seismic activity occurring around
the world, where the most likely areas are shown in dark red. [14]
1-6
23
Screenshot of a map output from the HAZUS software package showing
associated structural economic loss due to an earthquake. [1] . . . . .
1-5
18
A choropleth map (top-left), an isoline map (top-right), and a network
m ap (bottom ). [32] . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1-4
18
. .
26
The NATHAN World Map of Natural Hazards. Earthquakes are shown
in orange with varying intensities and tropical cyclones are in green. [26] 26
1-7
Screenshots of the beta version of the Global Earthquake Model. . . .
28
1-8
Screenshot of Raven with live data from Tacloban, Philippines. [13]
.
29
2-1
Proposed design for the interactive risk-focussed GIS. Key navigation
available in the top left corner of the main area. . . . . . . . . . . . .
39
is kept to the left of the screen, with primary search and information
2-2
USGS map of United States with spectral accelerations for a return
period of 50 years. [30] . . . . . . . . . . . . . . . . . . . . . . . . . .
9
40
2-3
The user interface with a view of earthquake risk for a location in
Washington DC. A scale with varying brightness is used to distinguish
risk levels, in this case the predicted peak ground acceleration for a
return period of 100 years. Risk levels are for illustration purposes
only. Mapbox's Tilemill (https://github.com/mapbox/tilemill) is used
to generate the base map. . . . . . . . . . . . . . . . . . . . . . . . .
2-4
41
A comparative view showing the difference in risk levels between two
proposed locations in Washington DC. Note that risk levels are for
illustration purposes only.
2-5
42
A display on data breakdown that has contributed to predictions for
the comparative view.
2-6
. . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . .
43
A potential structure for a hazard GIS. Components run by the system
owner have a red outline, and external components are outlined in
green. Dotted connecting lines signify read-write access, and solid lines
represent read-only access. . . . . . . . . . . . . . . . . . . . . . . . .
3-1
44
Average coordinates of countries from the ISO database have been
plotted using matplotlib's Basemap toolkit. Size of circle indicates
2012 population which was taken from the World Bank database and
matched using the country's Alpha-3 code. . . . . . . . . . . . . . . .
51
3-2
Location of earthquakes between 2004 and 2014. . . . . . . . . . . . .
52
3-3
Location of earthquakes with deaths due to earthquakes in each
country overlaid in red. Size of red circles represents number of deaths.
3-4
53
Location of earthquakes with deaths due to earthquakes in each
country overlaid in red. Size of red circles represents number of deaths
divided by population number . . . . . . . . . . . . . . . . . . . . . .
53
3-5
A comparative map showing deaths due to flooding. . . . . . . . . . .
54
4-1
Variations in quantity of earthquakes over a long time period in
Northern China. [37] . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
59
4-2
Recorded spectral acceleration values correlated with the distance from
epicenter for the 1999 Chi-Chi, Taiwan earthquake. It should be noted
that there is large variability in ground motion. Predicted distribution
is taken from the model by Campbell and Bozorgnia (2008). [2
. . .
60
4-3
OpenQuake workflow for classical PSHA. [8] . . . . . . . . . . . . . .
61
4-4
A suggested approach for structuring an earthquake risk model. [4]
62
4-5
The framework for GEM, identifying the three primary modules. [25]
63
4-6
A possible systems design for n hazards.
Modules are outlined in
red, data storage systems are outlined in blue, and the flow of data
is indicated using arrows. Input and output for calculations completed
in real-time are outlined in green. Annotations at the base indicate
the type of data transferred. . . . . . . . . . . . . . . . . . . . . . . .
11
64
12
List of Tables
1.1
Major Categories of Environmental Hazard [27]
13
. . . . . . . . . . . .
17
14
Chapter 1
Introduction
1.1
Overview
When designing a structure, the fundamental role of the engineer is to ensure that
it will remain safe to use for the entirety of its design life. There is a large range
of extreme events that could lead to structural failure, such as earthquakes, tropical
cyclones and flooding. While tools and guidance exist to aid the engineer in designing
to mitigate these risks, damage still occurs. Between 1989 and 2009, there were at
least 1,029 failure events in the United States due to snow load alone. [21] Risk is
defined as hazard multiplied by vulnerability, where a hazard is defined as a natural or
man-made event that could cause harm to society, and vulnerability is how susceptible
something is when exposed to a hazard. [23] The main way that risk can be better
mitigated is through ensuring the engineer and other parties are fully informed about
the associated risks in a given location.
Designing against hazards is a primary driver in the cost of construction, as a
structure must be designed to be strong enough to withstand any event it is likely to
be exposed to in its design life. As design against extreme events play in important
part of the overall cost, logically the likelihood of such events occurring at a given
location should be considered from the very beginning of the structural development
process.
Currently, extreme events are generally considered when an engineer is
completing the detailed design of a structure, or by an insurance company when
15
calculating premiums. If risks were considered before any design had begun, even
as early as when a developer was deciding where to build a structure, costs could
be dramatically reduced by choosing a location where risks were lower. Through
improved access to information about the likelihood of extreme events taking place,
engineers, real estate developers and other parties will be able to make more informed
decisions to reduce the likelihood of catastrophic damage from occurring, reducing the
cost and associated risks at an earlier stage in the decision-making process. Other
benefits could also result from a better-designed tool, such as a reduction in time
spent quantifying hazard-related risks.
There are a number of geographic information systems (GIS) that aim to visually
provide this type of data, where data is typically superimposed upon a digital map.
This will consider what systems currently exist for calculating and displaying the
probability of extreme events and associated risks, and how these could be improved
upon. The focus will be on what an ideal system would be, what data could be used
by the system, and how this data could be used to predict the risk from hazards.
1.2
Types of Extreme Events
Hazards can be categorized into two groups: naturally occurring and technological
hazards.
When designing a structure, both of these groups must be considered
and designed for to ensure risk is adequately mitigated. Smith (2013) defined the
categories of risk as shown in Table 1.1. This will primarily focus on natural hazards,
namely earthquakes in examples.
Natural hazards are typically geographically
influenced, and therefore a GIS is most useful for understanding this type of hazard.
1.2.1
Naturally Occurring Events
Naturally occurring events can lead to extreme loads applied to a structure.
Depending upon the location, these may include earthquakes, storms and volcanic
eruptions. Such events may cause lasting damage to a structure, or lead to issues such
as flooding which changes the surrounding environment. Figure 1-1 shows how storms
16
Table 1.1: Major Categories of Environmental Hazard [27]
Natural Hazards
Geologic
Atmospheric
Hydrologic
Biologic
Technological Hazards
Transport accidents
Industrial failures
Unsafe public buildings and
facilities
Hazardous materials
Examples
Earthquakes;
volcanic eruptions;
landslides;
avalanches
Tropical cyclones; tornadoes; hail; ice; snow
River floods; coastal floods; drought
Epidemic diseases; wildfires
Examples
Air accidents; train crashes; ship wrecks
Explosions and fires;
releases of toxic or
radioactive materials
Structural collapse; fire
Storage; transport, misuse of materials
have led to the greatest financial damages from natural disasters in the Americas,
though earthquakes are the largest cause in Asia. There is some overlap between
these categories, such as where storms may have led to flooding.
This paper will
focus on earthquakes, which is one of the primary causes of economic disturbance
and leads to a great number of fatalities around the world.
Data collected by the Centre for Research on the Epidemiology of Disasters has
reported that the average danage caused by natural disasters has grown significantly
over the past 20 years well above inflation, as shown in Figure 1-2. The data in their
database was compiled from various sources, including data from international notfor-profit agencies and organizations, insurance firms, and research institutes.
[10]
While the figure suggests a significant increase in the amount of damage in recent
years, data has become more readily available since the program began in 1988 and
it is possible that more data on disasters has become available in recent years. The
view that more damage has occurred in recent years is therefore questionable.
Earthquakes
Earthquakes are one of the main sources of structural damage in the world,
particularly in Asia where earthquakes were associated with over 500 million US
dollars of damage between 1990 and 2012. [11] Risk from earthquakes is currently
17
Total damages ($US billion) caused by reported natural disasters 1990 -2012
I
MAMca
Asia
Amaiercas
Erape
Mca
Oceania
Amercas
Asia
Eu rape
Oceania
Figure 1-1: Total damages (USD billion) caused by reported natural disasters
between 1990 and 2012. [11]
Estimated darnage (US$ bikon) caused by reported natural disasters 1975 - 2012
KOM twrVncK*W
9-
1975
1980
1985
1990
1995
2000
2005
2010
Year
-o
"r.aO-Vca
a
-a
Figure 1-2: Estimated damage (USD billion) caused by reported natural disasters
between 1975 and 2012. [11]
18
primarily mitigated through the use of design codes and guidelines, which provide
instructions on how to design a structure that will withstand extreme load scenarios
(see Section 1.3.1).
Modeling software such as CSI's SAP, Autodesk's Revit and
Oasys Software's GSA Suite are used to programmatically check a structure. Where
static analysis is used to consider how a structure will respond to non-changing load
scenarios, dynamic analysis is generally used to consider how a structure will respond
to changing load cases such as earthquakes. Structural elements and the overall design
of a structure can then be checked for suitability.
The type of subsoil that the structure is built upon is one of the significant
influencers in the risk of an earthquake damaging a structure.
For example, hard
rock presents different load scenarios to a structure than soft clay soil. This is one
of the factors that needs to be taken into account when considering the exposure of
a structure to an earthquake. The Unified Soil Classification System (USCS) is the
standard for classifying soils in North America.
Other natural events
Tropical cyclones are a major cause of damage across the Americas. These include
hurricanes, tropical storms and cyclonic storms, and typically form over the bodies of
relatively warm water. A hurricane is the same as a typhoon, where hurricane is the
regional term used in the northern Atlantic and northeast Pacific. Hurricanes pose
the greatest threat to life and property. The US coastline is typically struck by three
hurricanes every two years, one of which is classified as a major hurricane. Floods
from heavy rain can also cause excessive damage and deaths. In 2001, Tropical Storm
Allison killed 41 people and caused around 5 billion US dollars of structural damage.
[20] It should be noted that this was just a tropical storm and not even a hurricane.
1.2.2
Non-natural Events
The predominant source of non-natural extreme events is fire. Other events such as
those caused by terrorism may also be considered in the design of a structure, though
19
terrorism causes significantly less catastrophic damage than other types of events.
While it is important to consider how a structure could be damaged due to collisions
from vehicles, this tends to be a localized issue, rather than one that is geographically
influenced. A GIS is therefore likely to be of less benefit when designing against this
kind of risk.
1.3
Literature Review
Various resources play an important role in the risk assessment process. Hazardrelated data may be collected from global and national databases to gain an
understanding of the history of events at a given site. Socio-economic data may
also be used to gain a meaningful understanding of the probability of loss of life
and damage occurring. Physical data may be used to understand vulnerability for
a specific structure. Industry standard documents are often used by engineers to
simplify a problem in the design process, giving typical safe values that can be used
in calculations. There are also a number of geographic information systems that aim
to combine hazard-related data with methodologies to provide an overall view of the
associated risks. This section will go through each of these, identifying what currently
exists and any associated issues.
1.3.1
Designing for Risk
Designing for risk in North America
The Association of Structural and Civil Engineers (ASCE) has been the longstanding provider of standards and guidance for the American structural engineering
community. ASCE 7 is a standard that defines the minimum design loads that should
be applied to structures and specifies the dead, live and extreme load cases that need
to be considered.
Section 1.5 in ASCE 7-10 defines different risk classifications and how important
the different load cases are depending upon the structure's intended usage.
20
For
structures that represent a low risk to human life in the event of a failure, the lowest
risk category is used. The other extreme is where the structure is designated as an
essential facility (for example, a facility that processes hazardous waste). [21]
For calculating design parameters for earthquakes, the US Geological Survey
(USGS) provides tools that allow engineers to create values for design based upon the
location of a site and the ASCE (and other) guidelines. All tools allow latitudinal and
longitudinal coordinates to be used, with some tools also permitting postal addresses
to be used. However, these tools provide values for a single site or a selection of
locations (through batch requests), rather than a tool that allows for comparison
between different locations.
Designing for risk in other countries
The Eurocodes are technical rules produced by the European Committee for
Standardisation and were made mandatory for public structures in 2010, replacing
the British Standards that were previously used in many countries. The International
Organization for Standardization (ISO) provide a number of guidelines that aid in
the design and analysis of structures. These have been adopted by countries such as
Ethiopia as the national standards [22], though it is unclear how widely the standards
are used globally.
Some of the other major standards used worldwide are those written by AS/NZS
2002 (Australia and New Zealand), NBCC 2005 (Canada) and AIJ (Japan). A study
conducted at the University of Notre Dame in 2009 aimed to find the differences
and similarities between the different standards with respect to their approaches to
calculating wind actions on structures. In this study, they concluded that while the
general approaches are moderately similar, there is significant variability between the
values that are found from the different equations. [17]
21
1.3.2
Approaches to Geographic Information System Design
The functionality provided by a GIS should contain at a minimum the following
elements:
data input, preprocessing, data management, basic spatial queries,
geographic analysis, and output generation. [5] In the design of such a system, all of
these areas must be considered to produce an adequate solution.
Three approaches to showing geographic data visually are through a choropleth
map, an isoline map and a network map (see Figure 1-3).
A choropleth map is a
thematic map that is designed to show statistical data related to a geographical area.
The area is shaded to represent the variable that it represents, where a darker shade
represents a greater value of the variable. An isoline map is one that uses continuous
lines to join points that represent a variable with the same values. A typical example
of such maps are ones where the lines represent altitude in the form of contour lines.
Network maps show the connectivity in networks, where points are connected to show
the sharing of a specific variable.
1.3.3
Existing Hazard Geographic Information Systems
A number of proprietary and open source systems have been developed for analyzing
risk due to hazards. A subset of these considers catastrophe modeling, also known
as cat modeling, which looks to estimate losses that would occur due to an extreme
event. This is particularly relevant for the insurance industry, where they typically
use such software to calculate the risk of properties in their portfolio, and therefore the
associated premiums. This section considers some of the primary GIS that currently
exist, and their benefits and drawbacks.
HAZUS - Federal Emergency Management Agency
HAZUS is a national tool developed by the Federal Emergency Management Agency
(FEMA) that allows individuals to calculate the losses that would occur due to natural
disasters (see Figure 1-4). It is useful for local and state governments for calculate
risk for their areas. At the time of writing the software is freely available for the
22
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assourp~s
16-:*eIW'huega
:ncrmvlm
Us Paz
MYL
Kok
Tn
9-,,knT
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Ade
~
~
~
orei-j=rcy
Figure 1-3: A choropleth map (top-left), an isoline map (top-right), and a network
map (bottom). [32]
23
Pan
I
Zoom
Point
info
Building Losses
ED $1 - $200,000
EM $200,000 - $500,000
"NIII$500,000 - $1 million
n$1
mllion - $10 million
$10 million - $50 million
More than $50 million
Co
Cu-
nAl*gLoes
~
Trans:
Figure 1-4: Screenshot of a map output from the HAZUS software package showing
associated structural economic loss due to an earthquake. [1]
public to download and use within the United States and available to order in other
countries.
HAZUS combines hazard models with geographic information systems
(GIS) to provide estimates of damage that would occur due to different extreme load
scenarios.
While the software package provides valuable information for engineers and other
parties about the associated risk and potential economic loss and social impact, it is
highly complex and only provides information for the United States. For example,
the guidance on how to use the flood model alone is 499 pages long. [1] The software
is therefore useful in providing detailed technical calculations for risk profiles at
a late stage in the design process for a structure within the United States, but
provides limited assistance in the decision-making process for choosing a location
for a structure.
FEMA has also sponsored the production of a software package named ROVER
(Rapid Observation of Vulnerability and Estimation of Risk) that allows individuals to
conduct safety assessments of structures before and after an earthquake via a mobile
24
device. The tool aims to provide a solution for creating a database of structures, where
users are able to efficiently analyze a structure and its exposure to earthquakes.
Global Seismic Hazard Assessment Program
The Global Seismic Hazard Assessment Program (GSHAP) aimed to produce a
worldwide view of the risk from earthquakes. A map displaying the findings is shown
in Figure 1-5. This proved to be a valuable exercise and the findings helped influence
foreign policy, particularly in Africa, where it was used as a primary source of data
for risk mitigation strategies. [3] The project was terminated in 1999, though the
data continues to be used by organizations globally and Google have produced an
interactive version using Google Maps Engine, where they have additionally produced
interactive versions of earthquake fault lines and real-time earthquake data. [15]
Organizations such as the US Geological Survey (USGS) provide information
specifically looking at earthquakes, where they have a database of historical earthquakes that includes information such as the latitude, longitude, magnitude, date and
time of the events and other more information. Data is also available in the form of
a near real-time feed (every few minutes for California and within 30 minutes for the
rest of the world), and have data and models available for particularly active zones
such as around San Francisco.
NATHAN Risk Suite - Munich Re
NATHAN is a commercial product produced by Munich Re that aims to provide
analysis for risk, primarily targeting insurance companies. They produced their own
models for modeling catastrophe risk and provide online risk analysis tools to their
customers through a digital portal. [26] One of the products from this suite is a world
map of natural hazards, shown in Figure 1-6. While the research was undertaken
separately to the GSHAP, their findings for earthquake risk were similar.
Predictions from these programs should be treated with caution. In one study,
low correlation was found between the earthquakes with the greatest body count and
the predicted high risk areas from models such as that produced by the GSHAP. [16]
25
GLOBAL SEISMIC HAZARD MAP
Figure 1-5: A map displaying the likelihood of seismic activity occurring around
the world, where the most likely areas are shown in dark red. [14]
Figure 1-6: The NATHAN World Map of Natural Hazards. Earthquakes are shown
in orange with varying intensities and tropical cyclones are in green. [26]
26
The Global Earthquake Model (GEM)
GEM is a public-private partnership that began in 2006, created by the Global
Science Forum that is part of the Organisation for Economic Co-operation and
Development (OECD). Its purpose is to create an open-source risk assessment solution
specifically for earthquakes.
They are developing a web-based platform that will
enable users to calculate, visualize and investigate earthquake risk anywhere in the
world.
They released the first version of the OpenQuake engine in 2014, which
is part of a wider suite of tools, including a platform and tools for modeling.
Currently the engine does not have a graphical user interface, so calculations must
be completed using the command line. The engine is largely based upon OpenSHA
(http://www.opensha.org), an open source, Java Based platform which was designed
to process seismic hazard analysis (SHA). [8] Figure 1-7 shows two screenshots of the
beta software, which aims to make the large data-sets easier to access. They have also
created a cloud-based solution called the OpenQuake Alpha Testing Service (OATS),
which a user can request access to through their website. The project is open-source,
and one of the particularly interesting elements is around how they work with local
and regional experts to not only feed in local data, but also to create models specific
for localities. For example, China-specific earthquake models can be built into the
software. One of the main issues, however, is that the views are potentially still hard
to interpret, as there is often a large amount of information shown at any given time.
It is also only for earthquake risk, so does not provide any comparison between the
level that earthquakes should be considered compared to other types of risk.
While GEM does require a technical background to setup and use correctly, it
has many advantages over existing systems. As this project is open-source, data and
source could be potentially integrated into a wider solution. The scientific framework
behind this system is discussed in Section 4.4.
27
OPENOUAKE
)PENOUAKE
(b) Hybrid view of hazard data.
(a) Granular population counts in China.
Figure 1-7: Screenshots of the beta version of the Global Earthquake Model.
Other hazard-related geographic information systems
There are a number of companies in addition to Munich-Re that provide catastrophe
models to industry. One of the notable companies is Risk Management Solutions
(RMS), who provide catastrophe modeling for 9 major types of event, including
natural disasters such as earthquakes, tropical cyclones and windstorms, as well
as man-made disasters including likelihood of terrorism activities and infectious
pandemics.
Other non-hazard geographic information systems
There are a number of commercial platforms that have been created to allow
individuals to explore and interpret data. One example of this is Raven, a software
package created by Palantir described as a "high-performance, web-based geospatial
analysis application." The platform focusses on integrating real-time data and has
primarily been used for natural disaster recovery operations, where it was recently
used in efforts to assist in the response efforts to Typhoon Haiyan, a catastrophic
tropical cyclone that occurred in 2013. [13] A software engineer at Palantir said that
when creating the tool, the focus was to make the data easily accessible, leaving any
interpretation of this data to the end user. (W. Macrae, personal communication,
February 3, 2014) The system primarily uses open source data sets, namely changes
28
-
-
=
I
Figure 1-8: Screenshot of Raven with live data from Tacloban, Philippines. [13]
in OpenStreetMaps, to produce the filterable output. A screenshot of the Haven
system is shown in Figure 1-8.
Dangers related to an easy to use hazard-based geographic information
system
While informing a larger body of users about the risks that various hazards present
at a given geographical location, there is a danger that that the results could be
misunderstood, misinterpreted or incorrect if the system is not properly configured
for their specific use case. It is therefore important that users of such a system are
made aware of the dangers of using this information incorrectly.
There have been a number of programs undertaken to train users on how to use
GIS correctly, with professional organizations pushing to train their workforce on
utilizing these systems. Resources and tutorials on how to use and understand the
system should be provided to end users, though these should be easy to understand
without oversimplifying the contents.
29
1.4
Thesis Outline
The following chapters will focus on three main areas: the design of a tool that
could make hazard-related information easily accessible to a global audience; a study
considering which data sources could be used to provide appropriate data and how
this could be visualized; and an examination of existing risk models with a proposed
systems design for a multi-hazard GIS.
Chapter 2 considers the primary factors behind the design of a hazard-related GIS.
This chapter concentrates on the requirements for the system, what factors should
be considered to ensure a user-centered design process is followed, and which issues a
developer may come across when seeking to implement this solution. Proposed user
interface designs are presented, and an overall structure for the system is proposed.
Chapter 3 focusses on what national and international data sets could be used to
quantify the probability of different hazards occurring. This chapter predominantly
considers data directly related to earthquakes and how this data can be visualized,
in addition to considering how variation between data sets can be accounted for.
Chapter 4 looks at models and methodologies that exist for interpreting riskfocussed data, and how these could be used with the aforementioned data sets to
create an integrated, accurate and transparent solution. The chapter concludes with
a proposed systems design for quantifying risk.
The final chapter reflects upon the key conclusions from this research, and includes
recommendations for future study and development.
30
Chapter 2
Software Design
2.1
Introduction
While there has been a large amount of research into predicting different natural
disasters and the associated risks, the tools available to make use of this data are
complex and generally require expertise and a large amount of time to utilize. The
aim of this is to explore what type of tool would be useful to the engineering and
planning community at the early stages of development that would aid in the decision
making process.
Transparency will be key to such a tool's success. Current solutions usually take a
'black-box' approach, where output is presented without any explanation about how
they were derived. This system should be designed so that it is clear how any values
are calculated. The overall user interface should also be easy to use and learn how to
use. Core functionality should be easy to find and use. The ability of the developer
to fulfill these requirements is likely to have a significant impact over whether such a
tool is adopted.
One of the movements in the software development industry has been to develop
"cloud-based" solutions, where digital resources are stored on a network of servers.
The software application of this is referred to as software as a service (SaaS), where the
software and associated data are stored in the cloud. As this tool will require a large
amount of computational power, large data sets and location-specific information,
31
a SaaS solution is likely to be more relevant than a desktop package and we will
focus on this throughout the chapter. This is because SaaS solutions are able to use
distributed computing networks to perform calculations, large amounts of data can be
stored affordably, and results can be cached so less computing resources are required.
User Groups
There are a number of parties who would benefit from such a system. The system
is likely to be based upon macro-scale data which cover large geographic areas. Site
specific data such as data from geotechnical investigations would still be collected
later in the design process. The system is therefore expected to be primarily of benefit
early in the design and decision-making stages, such as when deciding upon where
a structure should be located. External parties such as governmental organizations
may also use such a system when assessing how to mitigate large-scale risk.
Engineers
This is likely to be the primary user group for the platform as engineers are
usually expected to analyze the risk at the various stages of design in a structural
project. This group typically uses a wide range of complex software and would
be expected to primarily use a hazard GIS to assist in predicting the levels of
risk expected in a structural project.
Real estate developers
Developers generally take on the greatest risk in the creation of real estate. As
such minimizing the associated risk and costs in a construction project would
be of great benefit to them. This system would allow developers to help decide
upon a location for a structure, ensuring the risk of extreme events is considered
at an early stage. Choosing a lower-risk site could therefore benefit developers
by reducing the cost of construction and associated risk.
Governmental organizations
This software could be used to help inform local and national governments about
the likely risks in their areas and could help to design against such hazards.
32
These individuals will be able to approach the data from a social vulnerability
viewpoint, and consider how government funds can be utilized best in their area
of interest.
Not for profit organizations
National and international organizations may be interested in this software as
it may provide information to allow them to focus aid and support in particular
areas. For example, international aid organizations may wish to use such data
to focus financial support for locations that are likely to experience a large
natural disaster in the coming years.
Insurance industry
They could use the system to help evaluate likelihood of loss for their portfolio
of properties under given scenarios, and will be able to evaluate the sensitivity
of their locations of interest.
2.2
Functionality
2.2.1
Required Functionality
The design requirements have been broken down into the main components as defined
by Withall (2007) in the book 'Software Requirement Patterns'. [36] Each of these
required areas is discussed below.
Data entity
This refers to the individual elements of data that will be stored. One of the
main data entities will be location information, most likely accessed through a
mapping application programming interface (API) such as Google Maps API.
Data on different levels of risk (previous earthquakes,
information, etc.)
will also be required.
historical weather
The way that this data is stored
and processed will be important so that the breakdown of any final figures
of risk can be clearly shown. For example, rather than simply showing that
33
the likelihood of a severe earthquake is high at a particular location, historical
data on earthquakes and other factors that affect the prediction should also be
accessible.
Information
The data entities need to be efficiently stored and processed to calculate the
risk levels. Data should also be stored in a way that allows data to be efficiently
compared, allowing the user to query, filter and order this data (for example,
the user may wish to order a set of locations by the level of risk).
User function
This primarily refers to how the data will be accessed by the user. To ensure
the system can be extended for access via a range of devices (such as mobiles
or tablets), the core functionality should be separated from the user interface
by designing it such that data is accessed through an API. This will ensure
that other versions can be produced later relatively easily, which may include
mobile or tablet versions of the application. It also will help to ensure that the
device is designed to follow a core programming principle called 'DRY' (don't
repeat yourself) - a concept that states code should not be repeated if possible.
This aims to improves the serviceability and reliability of software, as changes
to code only need to be made once, compared to multiple times if code did not
follow the 'DRY' principle.
There are a few core user functions that need to be built into the system. These
include views of different types of hazard, location querying (where a user can
search for a specific place by street address, post-code, etc.), location comparison
and risk breakdown (showing a breakdown of the contributing factors for each
type of hazard).
Performance
The system should be designed so that it is capable of supporting the required
peak capacity. Designing the system on a cloud computing platform is likely
to ensure this requirement is satisfied. The system should also be designed to
34
ensure that there is high availability and low response time and tools such as
New Relic (http://newrelic.com) could help to design for this requirement.
Flexibility
The system should be designed modularly to ensure that it can be extended at
a later stage. This will make it easier to add additional features later or allow
the system to be customized for different types of users. Designing the software
using object-oriented principles could help to fulfill this requirement, as this will
increase the modularity of the code and improve readability.
Access control
User accounts will be required primarily to store user data such as locations
of project sites and other user-specific data such as preferences. Access control
would also be necessary for a private management portal, where only authorized
users should have access to monitor and maintain the system.
Commercial
If premium features are added at a later stage, such as data and guidance
from design standards, it may be necessary to add commercial features such
as payment options and corporate/multi-user accounts that allow enterprises
to purchase a license for multiple users. Access control would be required for
commercial features to be implemented.
Optional Functionality
2.2.2
A few proposed optional features have been listed below. It is likely that these will
increase over time as users request additional features.
Real-time view of data
The system will primarily focus on predicting long-term risk, rather than looking
at immediate risk. However, the system could be scaled to provide a view of
current issues affecting people around the world if real-time access to hazard
data is available.
Early warning systems such as ShakeAlert for earthquake
35
warnings have a highly practical application in terms of saving lives, though
their applicability to the long-term planning and construction is limited. It has
also been proposed that individuals are able to contribute to these data sets by
hosting seismic stations and this can provide detailed realtime data regarding
ongoing natural disasters.
Customization for different user groups
As different users of the system will most likely want different types of output
from the system, the system could be customized for their varying requirements.
For an engineer, two examples of parameters they may wish to control are the
type of structure they are considering, and the risk category for this structure.
An example of how the type of structure is important is with regards to the
structure's height.
For high-rise structures, wind is a major consideration,
whereas earthquakes are more important when designing low-rise buildings.
The most relevant information could then be presented to the end user. The
importance of different risks also varies depending upon the use case for a
structure. This could be taken into account if a user selects its intended use.
For example, requirements are considerably different for a non-critical structure
such as a house, compared to a critical structure like a hospital.
2.3
Information Architecture
It is important to consider how the information will be collected, processed and stored
within the system. This will also ensure that the system is able to operate efficiently.
Not all data will need to be stored in the system's own database. Instead, public APIs
can provide this data on-demand. An example is the 'Yahoo BOSS PlaceFinder',
which converts street addresses into geographic coordinates. The basic types of data
that a hazard GIS would require outside of data available on-demand through public
APIs is as follows:
36
Hazard types
An overall collection of the types of hazards stored in the system. This will
include the properties and descriptions about each one. For an earthquake, the
properties would include the location of the event, magnitude and magnitude
scale type.
Hazard data
This will contain the data relating to specific events. This data should correlate
with a particular hazard type, and should include the properties this hazard
type defines as required. As data from different sources is likely to be collected
and matched, the source of hazard data should also be included.
Model data
This would include the data output from programmatic models, which would
be cached to reduce the load on servers and time required to load pages.
Geographic data
Data on countries, continents and other geographic areas would be stored in
the system. This would comprise of information such as border co-ordinates,
the name of the geographic area, and other area-specific information.
User data
Information such as personal preferences and sites of interest may be stored in
the database to allow users to return without repeating previously completed
personalization.
2.4
2.4.1
User Interface Design
User Experience Requirements
One model that outlines how a product should be designed is the Technology
Acceptance Model (TAM) that was developed by Davis in 1989. [9] According to the
model, software will be adopted if it fulfills two basic criteria: it must be perceived
37
to be easy to use, and perceived to be useful. The model has been modified more
recently to include intrinsic motivations for using technology. Wang (2012) concluded
that primary intrinsic contributors to acceptance include emotional response, imaginal
response and flow experience, where each of these significantly influences how likely
a user is to adopt using a technology.
2.4.2
[34]
Overall Interface Design
A proposed user-interface is shown in Figure 2-1. The design features an interactive
map with layers of risk overlaid. Users are able to search for a location and modify
the selected view to show different types of risk. When a location is selected, a pin
shows the point visually on the map and the hazards for this location are shown in
the top left of the screen. Here, the relative probability of severe events occurring for
the given site are shown in a bar chart format. These aim to give the end user a quick
understanding of what the primary risks are that they need to consider for the given
site. In the case of the site in Figure 2-1, the charts indicate that typhoons are the
most severe risk for that specific location. As the user changes the location, they will
be able to see the risk levels adjust accordingly. The interface design aims to achieve
the targets specified in the TAM. The view is purposefully simplistic, with only the
most important information displayed. In order to show the usefulness of data, hazard
information is shown prominently on the screen at all times once a location has been
specified.
2.4.3
Risk Views
The different types of risk are overlaid on the map. This proposal shows three risk
views: a view for earthquakes; typhoons; and flooding. These could be expanded as
other types of risk are considered. Further searching and filtering options could be
added in later versions of the application. It is important that the system is designed
so that it is easy to use.
38
Colmbta. SC 29203
HumdaV
Other-
Figure 2-1: Proposed design for the interactive risk-focussed GIS. Key navigation
is kept to the left of the screen, with primary search and information available in the
top left corner of the main area.
It is important that views of risk are interpreted correctly. Previous views of
earthquake likelihood typically are shown using variation in hue which can be hard
to interpret correctly. Figure 2-2a shows a map produced by USGS where a color
gradient is used. It may be misleading to some that the green areas signify a mediumrisk area and the dark brown signifies higher risk areas than red. Saturation is easier
to correctly interpret, with an equivalent figure shown in Figure 2-2b. It should also be
noted that a non-uniform scale is used, which could lead to further misinterpretation
regarding the meaning of the displayed values.
The type of risk shown on the map can be selected using the navigation bar on the
left of the screen. A proposed design showing the associated risk from earthquakes is
presented in Figure 2-3. The risk levels for earthquakes is indicated through varying
brightnesses of red, with the scale displayed at the bottom of the screen. This allows
the user to understand how risk varies around a given site. Views of other risk types
can then be selected, with icons for typhoons and flooding shown on the navigation
bar. While typhoons may lead to flooding, there can be alternative causes of flooding.
Both are shown so the user can consider whichever is of greatest importance for their
project.
39
4So
4#5,o
-40
-90
10
10A,
(a) Original map.
&00$5
95
(b) Map where colors have been replaced
with a greyscale
gradient
with
varying
brightness.
Figure 2-2: USGS map of United States with spectral accelerations for a return
period of 50 years. [301
Multiple sites can be directly compared, as shown in Figure 2-4. Here, both sites
are indicated using pins on the map with corresponding colors, and the comparative
risk levels are shown in the 'Hazards' box. These two sites are fairly close, so risk
levels do not vary hugely, though by showing risk levels alongside each other, the user
is able to quickly interpret that the risk of earthquakes is higher at the first site. Risk
levels are for illustration purposes only.
A brcakdown of the dlifferenit co)ntrib~utors to a risk calculationi is slhowii wvhen
a type of risk is selected (see Figure 2-5).
This allows the end user to gain an
understanding of the reasoning behind a prediction.
The clarity that this brings
should ensure the end user is fully informed about the associated risk and why it
is the case. An engineer is also able to receive key variables required in technical
calculations.
Default settings are shown below the values, though they can be
manually changed by selecting them and choosing an alternative value using a dropdown menu.
A number of other views could also need to be developed. These would look to
cater for the different user groups and their requirements. For example, if engineers
were to use this system as a replacement for all of the hazard analyzing systems they
previously used, they would require significant control over all variables affecting each
type of risk, and all the relevant output parameters needed to analyze a structure.
40
Figure 2-3: The user interface with a view of earthquake risk for a location
in Washington DC. A scale with varying brightness is used to distinguish risk
levels, in this case the predicted peak ground acceleration for a return period
of 100 years. Risk levels are for illustration purposes only. Mapbox's Tilemill
(https://github.com/mapbox/tilemill) is used to generate the base map.
2.5
Implementation of the Proposed System
Now we have considered what the required features of such a system are and how it
could be designed, we shall consider how it could be implemented. This section will
discuss how the system could be structure, what developer tools exist for creating a
GIS, which technologies may be appropriate for this system, whether native versions
should be created for desktop and mobile, and finally how the public could be made
aware of its existence.
Overall system structure
A proposal for an implementation for such a system is shown in Figure 2-6. In this
diagram, the system is split into three main sections: data storage; data processing;
and external access.
Data storage includes the storage of any core data. Static files would most likely
be stored in a low-cost, redundant system such as Amazon Web Services' Simple
Storage Service (AWS S3).
This would serve static public-facing files such as the
41
Figure 2-4: A comparative view showing the difference in risk levels between
two proposed locations in Washington DC. Note that risk levels are for illustration
purposes only.
HTML, JavaScript and image files, which are stored on servers distributed around
the world and served to a client device from the nearest data center. This reduces load
time for pages, and improves the overall visitor experience. The system's database
will hold any changing data, such as hazard and user data, and could either be stored
on a relational (RDBMS) or non-relational (NoSQL) data storage system. There
are different benefits for each of these, though a developer should investigate the
benefits to each to decide upon an appropriate configuration. Data stored externally,
including hazard data sets, map layers, and location data, would be queried through
their corresponding data access systems. Most providers of data have public-facing
application programming interfaces (APIs) available for accessing this data, and may
charge for usage.
Data processing considers file processing.
Real-time calculations would be
performed on a cluster of real-time calculation servers. Services such as AWS Elastic
Cloud Compute (AWS EC2) allow resources to be dynamically scaled, meaning that
at peak times, the number of servers can be increased within minutes by using a
previously created image of the files for the server. Periodic calculations would be
completed on a separate cluster to ensure performance does not degrade when this is
42
Figure 2-5: A display on data breakdown that has contributed to predictions for
the comparative view.
being completed. Some -static' files may need to be served from a server rather than
static storage, so a separate server could be used to serve these. An example of such
a file might be the HTML page for the home screen, which would possibly need to be
served from a server depending upon the configuration of the system.
External access covers how people access the data. The public-facing portal is
where the public see the system, and this would interact with the static files and the
real-time calculation server. This would be similar for the private management portal
(or administration area), where the managers of the system could control the data
and how the system is operating. An external access API may also be available for
developers to create their own solutions using the data from the system, in a similar
manner to how this system interacts with other companies' APIs for data such as
map images.
The proposed structure is simplistic: it is likely that an API would be created to
control the flow of data between any public-facing areas and the server and storage
areas. However, it should provide an idea for how such a system could be created.
43
Data storage
Static file storage
System database
External data sets
Maps API
Location query
API
Data processing
Real-time
calculation server
Periodic
Cloud static server
Private management portal
(developers; other integrations)
calculation server
External access
Public-facing portal
External access API
Figure 2-6: A potential structure for a hazard GIS. Components run by the system
owner have a red outline, and external components are outlined in green. Dotted
connecting lines signify read-write access, and solid lines represent read-only access.
Mapping tools available for developers
An existing mapping provider could be used to provide the underlying base map.
Assuming the system is likely to be a server-based solution (in contrast to desktop or
mobile software which is often more time-consuming and therefore costly to create),
a JavaScript-based package would best suit this project. Google Maps for Business
(http://google.com/enterprise/mapsearth), Mapbox (http://mapbox.com), CartoDB
(http://cartodb.com) and ArcGIS (http://esri.com/software/arcgis) are four of the
leading providers of server-based mapping developer tools, and generally charge based
upon number of requests sent to their servers. Some of these providers also provide
analytical tools that can provide interpretation and visualization to a developer.
Technologies for back-end system
The two languages most popular in the statistical community are R and Python,
where Python is becoming increasingly widely used. Python is a high-level language
that is built on-top of another language.
The most commonly used version is
CPython which is based upon C. Python has a number of benefits over R and
other languages.
First, it is easy to learn.
The language syntax is purposefully
readable and the language popular in the scientific community, meaning there is
44
a lot of support and documentation available.
Second, Python has an extensive
number of scientific packages available that extend Python's core offering to include
sophisticated functions and other capabilities.
Third, Python is efficient.
Built-in
generators allow tasks to be completed using less memory, and there have also been a
number of projects that have aim to improve the performance of Python such as PyPy
(http://pypy.org), which further reduces Python's speed and memory footprint.
Technologies for front-end interface
The language selected for programming the user-facing interface should be carefully
considered.
Ruby on Rails (http://rubyonrails.org), also known simply as Rails,
is currently a popular language for developing front-end systems as it removes
repetitiveness from the development process. Web applications can therefore often
be built in significantly less time in comparison to other web-based programming
languages such as PHP (though frameworks have been developed for other languages
which aim to reduce time taken for development).
Programming the system in a
language such as Rails may therefore reduce development time and this can therefore
help to reduce the cost of creating the system.
While traditional websites dynamically generate user-facing content on the server,
developers are starting to use JavaScript front-end model-view-controller (MVC)
frameworks that allow pages to be dynamically generated by the end-users' browser.
This can help to reduce the load on servers (as the server is generating less
content), and improve load time for visitors.
Some examples of such frameworks
include Backbone.js (http://backbonejs.org), AngularJS (https://angularjs.org) and
Ember.js (http://emberjs.com).
Open source GIS frameworks
A number of GIS frameworks have been created to make the process of producing a
GIS easier, removing repetitiveness from the development process. One example is
GeoNode (http://geonode.org/) which describes itself as an "open source geospatial
content management system".
This system aims to provide an underlying system
45
that makes it easier to share interactive maps and geospatial data. The system
can then be extended by a third party to produce the desired final system. QGIS
(http://www.qgis.org/) is another system that is free and open source, and available
for desktop, server or online applications.
Native desktop and mobile versions
Generally SaaS solutions are able to bring the belefits that coie from scale to the end
user. A standalone version would lead to issues such as a large amount of disk space
being required on the end users' computer. Two possible solutions to this issue are to
either produce a native application that sends and receives requests from the server
remotely, or to produce an application where an online interface can be integrated
into a native application.
Creating a native application may provide a higher-quality user experience as it
would be written in the operating system's native language, so the application can
utilize native functionality. However, it is typically more time-consuming to create
as the interface effectively has to be recreated. Embedding an online interface in a
native application requires significantly less time and resources to produce, and also
means updates can be rolled out immediately, in the same way as SaaS solutions, as
updates involve updating code stored on a server instead of code stored on an end
user's device. A similar approach can be applied for developing applications for mobile
devices. Two examples of open-source projects that aim to allow developers to embed
online interfaces are PhoneGap (http://phonegap.com), a solution for mobile devices,
and macgap (https://github.com/maccman/macgap), a solution for Macintosh.
Public awareness
If funding is invested into developing such a system, it will be important for the
public to be aware of its existence. One way to bring awareness to the project would
be to encourage organizations that currently publish hazard-related information to
publicize the system through their site. An example would be through USGS, a
primary provider of earthquake-related data to engineers. If organizations such as
46
USGS show support on their websites for the system, this is likely to encourage more
individuals to use it. Introductory seminars could also be given to inform the public
about its existence. These could be tailored to different audiences, where a seminar
for engineers could discuss the more technical aspects of the system.
2.6
Conclusions
For a suitable system to be developed, the user interface, the type of data that will be
displayed and the approach to system development must each be carefully considered
from the outset. The user interface must be designed such that it fulfills the core
requirements, and should aim to fulfill the guidelines specified by the TAM to ensure
users adopt the system. The type of data shown should be sufficient for each of the
core user groups.
The system should be designed so that it is scalable in the future. Approaches
such as object-oriented programming will increase the modularity of the code, making
future developments easier to implement. Open source projects could be utilized to
build the system in less time and with fewer resources.
In Chapter 3, we will consider the type of data that such a system would require,
what sources could be used to provide this data and how this data can be presented.
The chapter will also consider some of the main issues encountered when using
different data sources and what approaches exist for overcoming them.
47
48
Chapter 3
Data
3.1
Introduction and Overview
There are a number of data sets available which document events due to natural
hazards, although they are often hard to find and interpret. This chapter looks into
what types of data sets currently exist, how these data sets can be shown graphically,
and what issues exist when displaying this data. The data discussed in this chapter
could then be made available through a tool such as the one proposed in Chapter 2.
This study was conducted using the Python programming language, and a number
of libraries including Pandas (http://pandas.pydata.org), Matplotlib
(http://matplotlib.org), NumPy (http://numpy.org) and SciPy (http://scipy.org).
The Matplotlib Basemap Toolkit (http://matplotlib.org/basemap/) was also used
for creating the maps.
3.2
Data Sources and Display of Data
Levels of risk vary widely depending upon the geographic location of the site in
question. This study will primarily consider seismic risk as an example, and then
compare the approach to how data could be collected for other types of natural
hazard. For earthquakes, this is mainly dictated by how close a site is to an active
49
fault, which can lead to the strongest shaking and can therefore result substantial
damage occurring to structures in these areas.
In order to compare the levels of risk, data will need to be gathered from a number
of different sources.
International not-for-profit and governmental organizations
provide a large amount of information that will aid in this analysis.
Specialist
organizations (such as the ASCE) and university research also provide valuable data
which can be used in this analysis.
3.2.1
International Organizations
International organizations provide a large amount of freely available data on
countries worldwide.
For this study, geographical data was required for all the
countries around the world. A data set was used that related the Alpha-3 code'
with the mean latitude and longitude values for the majority of countries around the
world. [28]
It is important to go further than basic hazard information, and consider socioeconomic factors to take into account social and economic vulnerability at a location.
Indicators of such factors may include population-related, economic and governmental
factors. To investigate the relationship between natural disasters and socio-economic
factors, population data from the World Bank was used for countries around the
world. [29] This data can be seen visualized in Figure 3-1.
World Health Organization
The World Health Organization's (WHO) Centre for Research on the Epidemiology of
Disasters (CRED) created an international database called EM-DAT. This provides a
tool that allows individuals to create customized datasets filterable by time, location
and type. The output includes number of individuals killed, injured, affected and
made homeless, as well as the total financial damage caused by natural and nonnatural disasters. [12]
1
Alpha-3 codes are three-letter country codes defined within the ISO 3166 standard which provide
a standardized representation for countries, dependent territories or areas of geographic interest.
50
.0-
Figure 3-1: Average coordinates of countries from the ISO database have been
plotted using matplotlib's Basemap toolkit. Size of circle indicates 2012 population
which was taken from the World Bank database and matched using the country's
Alpha-3 code.
3.2.2
Governmental Data
Typically, hazard-related systems will incorporate dozens of different data sets, and
many of these will likely come from governmental organizations.
In this section,
we will focus on data available related to earthquakes, looking at three sources in
particular: the United States Geological Survey; The International Seismological
Center; and the National Climate Change Center.
United States Geological Survey
Event sets from the United States Geological Survey (USGS) Earthquake Archive
Search were used to show the location of earthquakes that occurred around the world
between 2004 and 2013. [31] This data is visualized in Figure 3-2a, where the epicenter
of each earthquake is plotted as an opaque point, and in Figure 3-2b, where the
size of the circle was defined by the magnitude of the earthquake and each point is
translucent.
51
~h4r,
C4h.'
WOW-
Vo
(a) Basic plot of earthquakes
(b) Points are sized by magnitude with an
alpha value of 0.05.
Figure 3-2: Location of earthquakes between 2004 and 2014.
International Seismological Centre
The International Seismological Centre in the UK have produced a catalogue of
earthquakes that covers 110 years of seismic events, including over 20,000 events with
a magnitude > 5.5. They claim it is homogenous to a high degree, and it includes
estimates of uncertainty.
[7]
The relationship between earthquake locations and number of deaths was then
studied.
Figure 3-3 shows the number of deaths from the aforementioned CRED
database superimposed over a map displaying earthquake epicenters.
This showed
a moderate correlation. Figure 3-4 considered deaths as a percentage of a country's
population. This showed a greater correlation, which is likely to be due to the number
of people exposed to an earthquake. As an example, consider how no fatalities will
occur when there is an earthquake in an uninhabited area.
National Climatic Data Center
The National Climatic Data Center (NCDC) is part of the National Oceanic and
Atmospheric Administration (NOAA) and is responsible for collecting and storing
the world's largest live collection of weather data. [19] It provides data on climate
variability, with three key types of data available:
1. Analyses of weather and climate events, which includes monthly reports on
changes in climate and summaries of economically significant events since 1980.
52
A62
r1
Figure 3-3: Location of earthquakes with deaths due to earthquakes in each country
overlaid in red. Size of red circles represents number of deaths.
VJaw
Figure 3-4: Location of earthquakes with deaths due to earthquakes in each country
overlaid in red. Size of red circles represents number of deaths divided by population
number.
53
.
Figure 3-5: A comparative map showing deaths due to flooding.
2. Data on extreme events at a national and state level.
3. Statistical weather and climate information, particularly focussing on changes
in temperature, precipitation and drought levels.
3.2.3
Other Sources
While government departments and international organizations typically provide the
majority of international and national data sets, there are other sources of useful
data, including university research industry associations.
University research has contributed significantly to the data available. A number
of papers have been written on the topic of analyzing associated risk with earthquakes,
and research has often led to the creation of large data sets that have been used in
industry. A number of universities have also created portals for accessing geospatial
data.
One example of such a system is Geoweb, which was created by Massachusetts
Institute of Technology (MIT) and includes over 23,000 geospatial data sets from
54
multiple repositories. The system is based upon OpenGeoPortal, which was developed
by Tufts University Information Technology in partnership with MIT and Harvard,
and the data is collected from contributions made available through The Open
Geoportal Consortium. [18]
Industry associations such as the ASCE tend to focus on creating guidelines and
specifications that can be used to create risk estimates, rather than providing core
data sets.
3.3
Variation in Data Sets
Data in different data sets was found to vary significantly, so data cleansing is likely
to be one of the more time consuming processes. When analyzing the different data
sets, there were a number of issues in the previous data sets used, which were related
to the format of the data stored and missing information in a few of the databases.
One of the issues encountered was around discrepancies in the data stored in
different databases. When collating multiple sources into one large set., generally there
will be issues with differences in titles and formats in the different databases. For
example, one database listed 'Cape Verde', whereas another referred to the country
as 'Cabo Verde', the Portuguese spelling. To avoid such issues, the Alpha-3 code
was used when available, which generally led to a higher percentage of matches. The
usual approach for mitigating this issue is to create an attribute mapping schema
which relates the previous and current keys, and any changes in formats should be
recorded for future reference.
Another difficulty was how some databases did not include information regarding
all of the countries in the world. The World Bank database was lacking information
on 35 countries (14.4% of all countries in the database) which were mostly islands with
small populations, such as the Cook Islands and Niue, an island country in the South
Pacific Ocean. This led to issues when plotting as some datasets were incomplete.
This was solved by removing rows with missing data prior to plotting.
55
Uncertainty should be quantified in order to give a measure of confidence. This
can be achieved by comparing differences from the values measured by independent
sources. Through analyzing how much these values vary by, it is possible to calculate
the accuracy and precision for each reported event.
3.4
Copyright Issues
There may be issues in implementing copyrighted approaches into such a system. For
example, guidance provided by associations such as the ASCE and ICE is generally
chargeable and therefore it may not be possible to integrate information such as risk
categories into software without paying a fee. One option may be to use governmental
funding to pay for these licenses, on the grounds that it will lead to more informed
decision-making and the construction of safer infrastructure and structures in the
long-term.
3.5
Conclusions
There are a number of data sources available from sources such as governmental and
not-for-profit organizations. In order to create a reliable, valid database of multihazard data, research should be undertaken to collate and validate this information.
Attention must be given to ensure variations in the data sets are accounted for, and
any uncertainty between these sets should be quantified to ensure any user of the
data has a measure of confidence in results obtained.
The next chapter will consider how this data can be used to create predictions of
risk, and looks into what models and methodologies currently exist for quantifying
the likelihood of extreme events occurring.
56
Chapter 4
Interpretation
4.1
Introduction
One of the important aspects that should be considered when designing a hazard GIS
is how the risk levels are predicted using existing data sources. A system needs to be
designed that is able to take various data sources for parametric inputs, and output
reliable, easily interpretable predictions for the various types of risk.
This chapter focusses on the types of risk that should be considered in calculations;
general approaches taken to hazard analysis; and how an overall system could be
integrated to produce probability of hazards occurring.
It will also consider the
resolution of the overall system and how this is impacted by the input data and
models used.
Calculations from these models would be computed using a cloud-
based network of servers, with output cached in the system's database in order to
reduce server load (see Section 2.5 for further details on the implementation of the
system).
4.2
Types of Risk
The type of risk calculated is dependent upon the intended purpose for the output.
Insurance companies tend to use catastrophe models to calculate the likelihood of
57
financial loss, whereas a public body is more likely to be concerned about reducing
the likelihood of loss of lives.
There are a number of factors that influence the level of risk. For typical natural
hazards, these include location-oriented, time-oriented and structure-oriented factors.
These all need to be taken into account to produce a valid estimation for how at risk
a particular structure is in a given environment.
Location-oriented
All types of risk vary depending upon the location of a site, and this will be the
governing parameter in any risk calculations. Two such factors include urban
density and soil conditions. Areas with higher urban density (such as cities)
often have higher levels of risk due to changes in soil conditions. For example,
building a number of tall structures in an area will generally lead to compaction
and therefore different soil properties in the surrounding area. These areas can
also have higher numbers of deaths as catastrophic events impact more people.
One way to take this into account is by calculating deaths as a percentage
of population in an area. The type of strata is also likely to influence how
susceptible a structure is to risk.
Time-oriented
Some types of risk are dependent upon time. An example is with earthquakes,
where the probability is largely dependent upon stress release levels. Figure 4-1
shows a theory applied by Zheng and Vere-Jones (1999), where the number of
earthquakes changes over time depending upon stress levels.
[37]
Structure-oriented
Property-specific information will also affect the how exposed a structure
is deemed to be.
This may include data such as structure properties and
characteristics (number of stories, material types, etc.), the use-case, the value
of the property, and insurance and reinsurance information.
58
China El
-- Poisson
Stress Releas
-Year
1500
1600
6
01
1700 Yer
--:r:s
China
E2
1800
s.
1900
2000
1900
2M
.r.. ..
Year
1500
1600
1700
1800
Figure 4-1: Variations in quantity of earthquakes over a long time period in Northern
China. [37]
4.3
Hazard Analysis
Models must be implemented into such a system in order to calculate risk probabilities. These models need to be based upon tested methodologies. As an example,
there are three primary methodologies involved in the process of predicting risk from
earthquakes:
probabilistic seismic hazard analysis; ground motion prediction; and
magnitude scaling. Each of these will be discussed in turn within this section.
Probabilistic Seismic Hazard Analysis (PSHA)
One of the standard methodologies to determine how likely it is that ground motion
will exceed a given value at a location in a specific time period is PSHA. It was
originally based upon research conducted by Cornell and Esteva in 1968 [6], and the
accuracy of the process has been refined in subsequent decades. This correlated the
relationship between spectral acceleration and distance from epicenter.
Figure 4-
2 shows the relationship between spectral acceleration values for the 1999 Taiwan
earthquake.
59
PDF, given
distance= 10 km
I-b
-~0.1~
Recorded ground motions
Mean InSA prediction
- - - Mean InSA prediction +/-one standard deviation
)
0.01
1
10
Distance (km)
100
Figure 4-2: Recorded spectral acceleration values correlated with the distance from
epicenter for the 1999 Chi-Chi, Taiwan earthquake. It should be noted that there is
large variability in ground motion. Predicted distribution is taken from the model by
Campbell and Bozorgnia (2008). [2]
PSHA is one of the primary methods for calculating risk from earthquakes, and
classical PSHA is the primary way to calculate ground motion in the OpenQuake
engine. Figure 4-3 shows how the OpenQuake system is configured to calculate risk
using this methodology. First, the logic tree processor takes the input data and creates
a seismic source model and ground motion model. The seismic sources model is used
to calculate the earthquake rupture forecast, which includes a list of all earthquake
ruptures in the source model with probabilities of occurrence. The earthquake rupture
forecast and ground motion model are then used by the classical PSHA calculator to
create the hazard curves for the desired site. [8]
Ground motion prediction equations
In the calculation of the hazards at a particular location, the ground motion must be
quantified for the design and evaluation of sensitive structures. Several new ground
motion prediction equations (GMPEs) have been developed recently, which have made
use of advancements in empirical ground motion modeling techniques.
60
However, a
PSHA
Input
Model:
- Seismic Sources System
Logic Tree Rocessor
- GMPEs System
Seumc Sources
Forecast Calculator
Earthquake Rupture
bewen
odls eenifth smedaa
Classical Hazard Curves
Mdncalculator
toreaehse
ausde
Thdelsuycocue
Hazad GuvesRisk
that subjectiv
decisions maEanthqe duuesgofmdlsinictyifueedrut;
Figure 4-3: OpenQuake workflow for classical PSHA. [8]
predictions.
[24
study conducted by Peru and Fajfar (2009) found that there is significant differences
between models, even if the same data was used to create these. The study concluded
that subjective decisions made in the design of models significantly influenced results;
including aftershocks in the training generally had a negative effect on median values
and increased scatter; and the adopted functional form had a significant effect on the
predictions. [24]
Magnitude-scaling relationships
There are a number of relationships that need to be calculated in hazard risk modeling.
The magnitude scaling relationship is one of these and relates the magnitude of an
earthquake to the rupture area
(km2)
or length (km). While there have been different
studies into the relationships, the currently implemented relationship in many systems
is based upon research by Wells and Coppersmith (1994). The value for magnitude
for all rupture types is defined as M = 5.08+ 1.16 *log(L), where L represents length.
The variables differ for other rupture types. The associated uncertainty on magnitude
for all rupture types is 0.22. [35]
61
4.4
Systems Design
A number of companies have produced models, though these are generally proprietary
and therefore they do not disclose details behind their models. This section provides
a brief overview of the general architecture behind such models, looking at approaches
taken to create them. This section will primarily consider earthquake risk, though
similar models exist for other types of hazard.
Figure 4-4 shows a typical structure for how an insurance industry-oriented model
may be structured for earthquake risk. This breaks a model into four procedural
modules covering exposure, hazard, vulnerability and financial aspects. Results from
module are passed from one module into the next, eventually leading to a computation
of risk for a location. The system will typically iterate over multiple locations until
risk are completed for the entirety of a company's portfolio.
HAZAMW
Adus DaW
Ground motion
Losses
Figure 4-4: A suggested approach for structuring an earthquake risk model. [4]
The GEM scientific framework (discussed in Section 1.3.3) organizes the risk into
62
Probability
InteSity
Location
Physical
Social
Structures
Population
Cost-benelit
Risk reduction
Figure 4-5: The framework for GEM, identifying the three primary modules. [25]
three modules: seismic hazard; seismic risk; and socio-economic impact. Figure 45 shows a schematic of this framework. The organization have produced an open
source library called OpenQuake Risklib, which includes a number of the modules
that calculate the loss and damage distributions for scenario earthquakes, in addition
to probabilistic risk calculations for events that may occur in a region within a time
period.
The source code (written in Python) for the calculation of risk levels is
publicly available at https://github.com/gem/oq-risklib.
Figure 4-6 shows a proposal for how such a system could be structured. The
first stage is where information about hazard events is passed into the first module.
This module would calculate the likelihood of risk for a given location. The hazard
calculations would include finding the probability of a hazard occurring for a given
site. The results would then be passed into the exposure module, along with
any previous data. The exposure module would assess historical damage that had
occurred at a given location, using this to calculate the vulnerability for different
structure types. Finally, output from these previous stages would be passed into an
economic module, which would take socio-economic factors into account and calculate
probabilities of financial loss.
Calculations would be completed simultaneously for n number of hazards (e.g.,
earthquakes). Calculations would be completed periodically when the output is used
to create hazard maps, to take into account recently input data. This could be as
63
Hazard 1 risk
Hazard DB
Hazard 2 risk
-
Hazard 2 exposure
Hazard n risk
-
Hazard n exposure
Raw hazard data
-+
Previous data +
hazard calculation
output
Figure 4-6: A possible systems design for
data storage systems are outlined in blue,
arrows. Input and output for calculations
green. Annotations at the base indicate the
tImmedte
Hazard 1 economic
Hazard 1 exposure
paramters
Hazard 2 economic
Cached results DB
Hazard n economic]
Previous data +
exposure output
Previous data +
losses output
n hazards. Modules are outlined in red,
and the flow of data is indicated using
completed in real-time are outlined in
type of data transferred.
frequent as once per hour. The system would iterate through a moderately granular
matrix of locations in co-ordinate format which would cover the entire globe. The
output from these computations would then be cached in a results database and used
to produce visual plots on a map and some of the calculated values that would be
displayed in a GIS. Calculations could also be performed on demand for site-specific
results, completing a more comprehensive set of computations where site-specific
parameters are taken into account and output is shown on the user interface.
4.5
Resolution in Risk Calculations
According to Bendimerad (2001), there are two primary dimensions of resolution:
the resolution of exposure data; and the resolution of analysis. [4] When considering
the exposure data, which refers to the granularity and accuracy of the data input
to a model, the validity of any model output is very much dependent upon how
detailed this data is. A highly sophisticated model will take into account data for that
specific location (e.g., at street level). While less relevant to this application, advanced
insurance models may also take into account exposure risks for each property in their
portfolio.
In contrast, the analysis resolution refers to the ability of a model to
statistically model risk levels for a specific location.
64
The resolution of exposure data varies depending upon the type of data being
reported. Many data sets, such as those looking at soil information, tend to be at
much lower resolution, which leads to lower quality in risk calculations. More granular
data tends to be available for specific locations. States may produce more granular
data for their area, though this data is often not available in a standardized format.
If this data were to be integrated into such a platform, standardization of this data
would most likely be necessary.
4.6
Conclusions
For a system to produce valuable results that are representative of the true levels
of risk for a given location, it should provide a view of a risk associated with
a number of different hazards.
The likelihood of these hazards occurring will be
influenced by factors oriented around location, time and structure, and these need
to be accounted for in prediction calculations. Existing methodologies and models
can be integrated into a multi-hazard GIS to provide unified, easily interpretable
predictions that individuals can then use to better understand the associated risks at
a specific location.
Attention must be given to the resolution of the input to ensure that predictions
are representative of the true levels of risk. Both the data used and the assumptions
made by the implemented models and methodologies are important aspects to
ensuring a system produces valid predictions.
There can be significant variability
in the results achieved from similar methodologies, so it will be important to select
ones that are supported by thorough research and are generally accepted by industry.
65
66
Chapter 5
Conclusions and Recommendations
5.1
Conclusions
Digital modeling of risk and associated data and the geographic information systems
have greatly enhanced the construction industry's ability to understand the associated
risks, though many are currently hard to access and interpret. It is evident that the
current tools do not provide a simple way to interpret the likelihood of extreme events
occurring at a given site. Developing a system that makes this process easier would
deliver substantial value to the structural community and surrounding industries,
reducing the time taken to understand hazards and introducing the subject of risk at
an earlier stage in the construction process.
One of the major hurdles in realizing such a tool is regarding the availability of
data. While there are a number of freely available data sources for a number of the
required data sets, many are protected by copyright issues that prevent the integration
in their current form. Partnerships would need to be agreed upon to make parts of
this tool viable, namely with industry associations such as ASCE. Care must also be
taken to ensure that the data will be interpreted and used correctly.
67
5.2
Recommendations for Future Development
A number of complex issues have been discussed that play an important role in the
design of a hazard GIS. Further research needs to be undertaken in the three core
areas discussed: the creation of the system and interface; collating high quality data
sets; and creating models based upon appropriate methodologies.
The external access portals need to be taken from theoretical systems through
to fully developed and tested systems available for public use.
This is likely to
take significant time and resources (potential funding opportunities are discussed in
Section 5.2.2). Collating appropriate data sets for multiple types of hazards will be
time consuming, and it will be important to work with organizations specializing in
each type of hazard to ensure that the aggregated data sets provide a representative
view of the actual situation.
Current multi-hazard prediction models are proprietary. For such a system to
exist, either one of these models must be licensed to be used in the system, or an
open-source model must be developed. The licensing of an existing commercial model
is unlikely as it would remove the necessity for other parties to pay if the data is
freely available online through this system, so developing an open-source model is
likely to be the only available option. This would also bring a significant benefit
that individuals would be able to contribute freely to the system, which could lead
to a more advanced and accurate model being created, though it would be important
to create a process for verification to ensure the quality of user-contributed input
remains high. Previously developed open source code such as that created for the
GEM project (see Section 1.3.3) could be adapted for specific components in the
system, reducing how much original code needs to be written.
5.2.1
Collaboration
The collaboration of different parties would be instrumental to the success of
such a project. These parties would include governmental organizations, research
institutions and companies in related industries. For such a system to work, access to
68
the required data will be a necessity. Some organizations already provide open access
to data and guidelines (see Chapter 3), though a number of data sets are commercial.
These would need to be licensed if they are to be included in the system.
5.2.2
Funding Opportunities
North America
Organizations already exist for funding research into hazards, namely through the
National Science Foundation (NSF). Congress have also formed programs such as the
National Earthquake Hazards Reduction Program (NEHRP) which aim to research
and find opportunities to reduce the damages caused by natural disasters. [7]
Europe
The European Commission funds a number of programs aiming to improve prevention
and mitigation against natural disasters, many of which are under the Civil Protection
unit. Many member states have policies in place such as seismic protection policies
[33], which show commitment to reduce associated risk from natural disasters.
European Union or national governmental funding may therefore be an option to
finance such a project.
The GEM project received funding from a number of European public bodies,
including the Italian Department of Civil Protection and the Swiss Federal Institute
of Technology in Zurich. There were also a number of private founders, such as the
Munich Re Group and Zurich Insurance Group. The initial five year plan included
35 million euros of funding to develop the first version of the system. As much of this
research could be used in the implementation of this system, such as the earthquake
models they created, the resources required for the development of this proposed GIS
could be reduced.
69
Other Countries
There are a number of other country and region-specific programs. However, there
has been a deficiency of research conducted in developing countries, such as across
much of Africa, where there is less funding available than in developed countries. The
high cost of research into flooding and other risks has acted as a barrier preventing
significant amounts of research from occurring in these locations.
70
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