D 4.2

DELIVERABLE

PROJECT INFORMATION

Project Title: Systemic Seismic Vulnerability and Risk Analysis for Buildings, Lifeline Networks and Infrastructures Safety Gain

Acronym: SYNER-G

Project N°: 244061

Call N°: FP7-ENV-2009-1

Project start: 01 November 2009

Duration: 36 months

DELIVERABLE INFORMATION

Deliverable Title: D4.2 - Definition of a group of output indicators representing socio-economic impact for losses from emergency health care facilities

Date of issue: 20 January, 2012

Work Package: WP4- Socio-economic vulnerability and losses

Deliverable/Task Leader: Karlsruhe Institute of Technology (KIT-U)

REVISION: Final

Project Coordinator:Institution:

e-mail:fax:

telephone:

Prof. Kyriazis Pitilakis Aristotle University of Thessaloniki kpitilak@civil.auth.gr + 30 2310 995619 + 30 2310 995693

2

Abstract

In current earthquake casualty estimation models, demand on healthcare systems is

estimated by quantifying the number of fatalities and severity of injuries based on

empirical data correlating building damage with casualties. The expected number of

injured people (sorted by priorities of emergency treatment) is combined together with

post-earthquake reduction of functionality of healthcare facilities such as hospitals to

estimate the impact on healthcare systems. The aim here is to extend these models by

developing a combined engineering and social science approach. Although social

vulnerability is recognized as a key component for disasters and their consequences

social vulnerability as such is seldom linked to common formal and quantitative seismic

loss estimates of injured people which provide direct impact on emergency health care

services. Yet, there is consensus that factors that affect vulnerability and post-

earthquake health of at-risk populations include demographic characteristics such as

age, education, occupation and employment and that these factors can aggravate

health impacts further. Similarly, there are different social influences on the

performance of health care systems after an earthquake both on individual and on

institutional level. We identified social indicators for individual health impacts and for

health care impacts based on literature research, and tested the indicators – were

appropriate - using available European statistical data. The healthcare model simulates

households' decision-making and considers demographic and socio-economic factors

in addition to housing damage and lifeline loss to assess impact on health services

beyond the number of casualties. The results will be used to develop a socio-physical

model of systemic seismic vulnerability that enhances the further understanding of

societal seismic risk by taking into account social vulnerability impacts for health and

health care system, shelter, and transportation.

Keywords: Indicators, Health Impact, Healthcare, Social Vulnerability

Acknowledgments

The research leading to these results has received funding from the European

Community's Seventh Framework Programme [FP7/2007-2013] under grant agreement

n° 244061

4

Deliverable Contributors

KITBijan Khazai Gautam Mondal Tina Kunz-Plapp

Table of Contents

1  Introduction........................................................................................................................................ 9 

2  Health Impact after Disasters ......................................................................................................... 13 

2.1  MORTALITY...............................................................................................................14 

2.2  CHRONIC HEALTH AND DISABILITIES...................................................................15 

2.3  MENTAL HEALTH .....................................................................................................16 

3  Vulnerability Factors Influencing Health-issues in Earthquake Disasters................................. 16 

3.1.1  Integration of earthquakes on health and health care systems .....................17 

3.2  SOCIAL VULNERABILITY FACTORS.......................................................................18 

3.2.1  Age.................................................................................................................18 

3.2.2  Gender ...........................................................................................................19 

3.2.3  Disability.........................................................................................................19 

3.2.4  Social status...................................................................................................20 

3.2.5  Income status/financial resources..................................................................20 

3.2.6  Social Networks .............................................................................................20 

3.3  ENVIRONMENTAL HEALTH FACTORS...................................................................21 

3.3.1  Weather .........................................................................................................21 

3.3.2  Nutrition..........................................................................................................21 

3.3.3  Shelter circumstance .....................................................................................21 

3.3.4  Residential crowding......................................................................................22 

3.3.5  Pre-disaster health characteristics of the affected area.................................22 

3.4  HEALTH CARE ACCESS ..........................................................................................22 

3.4.1  Transport........................................................................................................22 

3.5  INFRASTRUCTURAL FACTORS ..............................................................................22 

3.5.1  Geographical location/Area characteristics....................................................22 

3.5.2  Degree of damages to dwellings....................................................................23 

3.5.3  Building or Dwelling type................................................................................23 

3.5.4  Victims location inside or outside of building during the earthquake .............23 

3.5.5  Height of the building .....................................................................................23 

3.5.6  Victims location within a more high stories building.......................................24 

3.6  SUMMARY.................................................................................................................24 

4  Data Availability .............................................................................................................................. 27 

4.1  EUROSTAT CITY STATISTICS - URBAN AUDIT .....................................................27 

6

4.2  INPUT VARIABLES ...................................................................................................33 

5  Conclusions....................................................................................................................................... 36 

6  Bibliography ..................................................................................................................................... 38 

Appendix A ................................................................................................................................................ 44 

A.1 LIST OF URBAN AUDIT INDICATORS .......................................................................44 

A.2 LIST OF URBAN AUDIT VARIABLES .........................................................................47 

List of Figures

 

Figure 1.1 Emergency shelters in Japan after Tohoku earthquake in March 11, 2011

13

Figure 2.1 Schematic of Impacts on Health and Healthcare Systems after Disasters

15

Figure 2.2 MICRODIS Model for Health Impacts of Disasters 16

Figure 1.2 Social Vulnerability and Health Impacts 19

Figure 1.2 Conceptual Framework showing integration of earthquakes on health and health care systems in a multi-tiered system

19

Figure 1.3 Multi-criteria Framework for Health Impact Analysis 20

Figure 3.4 Number of nominations found for indicators in the 18 studies surveyed.

29

8

List of Tables

Table 3.1 Nominated indicators representing the vulnerability factors affecting health impact after earthquake disaster

27

Table 4.1: Compilation of Urban Audit Variables, available on Sub-City-Districts

33

Table 1: Compilation of Urban Audit Indicators, available on Sub-City-District

34

Table 4.3 Required variables for the Shelter Needs Estimation model and availability on Urban Audit

38

1 Introduction

From the perspective of societal impacts, the interest in measuring impact on health

and healthcare systems is to provide estimates of increased casualty and capture the

performance of healthcare systems in terms of providing post-event health care

services. Casualty estimation methodologies (Coburn and Spence 2002, FEMA 2008)

in ELE software provide estimates of both injuries and fatalities, which is key input to

assist planners in determining the resources required to deal with the increased surge

in the patients. These methodologies generally exclude casualties due to secondary

causes, and do not account for injuries which can digress to fatalities as a result of

systemic failures of healthcare systems and parallel infrastructure (e.g., transport,

power, etc.). Systemic failures in healthcare delivery and lack of access to food and

shelter can also lead to the exaggeration of baseline diseases and increased

transmission of communicable infectious diseases. Several researchers have proposed

methods, whereby interrelated systems - structural, nonstructural, lifelines, and

personnel – are assessed according to performance levels indicating functionality

(Holmes and Burkett, 2006; Chang, 2008, Lupoi et al., 2008).

The destructive earthquake and tsunami of March 11 in Tohoku, Japan is a stark

reminder of how overwhelming the health impacts of such an event can be especially in

terms of mass care of dislocated elderly populations (Figure 1.1). The human losses

incurred by the Tohoku event were extremely severe. As of September 30th, 2011,

15,815 were counted dead and 3,966 missing (19,781 in total). It is unknown how

many victims may have died directly due to the earthquake, not counting tsunami

losses. However, autopsies from the first 13,135 killed indicate that the earthquake

alone did not kill many people. (NPA, 2011). With about 23 percent of Japan's 127

million people older than 65 (Tanaka et al., 2009), Japan has the world’s highest

proportion of elderly in the world. The March-11th disaster highlighted the current and

emerging issues of a “super-aging” society, especially the need for community-based

support systems. Age therefore played a major role in the survival chances of people

escaping the tsunami; as people age, they generally become less mobile. 77 % of all of

the victims counted up to this point were older than 50, and 46% of the victims (nearly

half) were over 70 years of age Figure 1.2.

10

10‐19.; 3 under 10; 3.520‐29; 3.6

30‐39; 5.7

40‐49; 7.1

50‐59; 11.9

60‐69; 19.1

70‐79; 24

80 or older; 22.1

80 or older

70‐79

60‐69

50‐59

40‐49

30‐39

20‐29

10‐19.

under 10

Figure 1.2 Estimated fatalities by age for the dead and missing, given NPA results from April 21st, 2011 as a date of reference.

Several other characteristic geographic, demographic, and cultural factors in Japan

significantly worsened the health consequences from the Tohoku earthquake. The aid

and medical response in the aftermath of the earthquake was complicated by the sheer

scale of devastation, widespread damage to supply routes, loss of power- and

communications networks, and concerns about radiation leaks from the Fukushima I

nuclear power-plants. The existing shortage of healthcare resources in rural areas was

exacerbated by the destruction of hospitals, clinics, and nursing homes, and the loss of

healthcare staff. In-patients in the damaged hospitals had to be transferred to other

hospitals. In some cases, this was extremely difficult, as hospitals and nursing homes

for the elderly were located in the suburbs of the city, or in small towns which were

relatively isolated from public transportation. The isolation of the affected area led to

slower recovery efforts when compared to events such as the Great Hanshin-Awaji

earthquake of 1995, which occurred in one of Japan’s largest cities. Finally, there was

a public health concern about the increased risk of infectious diseases, including acute

respiratory infections, infleuenza, tuberculosis, and measles, under crowded living

conditions about diarrheal diseases and waterborne diseases that are typically sees

after natural disasters. In Japan, an increase in the morbidity rate associated with

pneumonia was reported after the 1995 Hanshing-Awaji earthquake Influenza

wasepidemic from February through March in Japan (Cunha et al.,2011).

The recent great Tohoku earthquake in Japan demonstrates the multi-faceted

implications on individual and public health as a result of major disaster. It also shows

why a dynamic risk-assessment procedure which incorporates dynamic interactions

between natural hazards, socio-economic factors, and technological vulnerabilities is

necessary to investigate the full realm of social losses including health impacts after

disasters. This document sets out to explain the various contributing factors and their

interactions, which is preceded by a definition of terms and explanation of framework

used in the analysis.

12

Source (Top to bottom): Der Spiegel; BBC; LA Times

Figure 3.1 Emergency shelters in Japan after Tohoku earthquake inMarch 11, 2011

2 Health Impact after Disasters

Health impacts after disasters include mortality and morbidity in term of injuries,

disability, psychological effects, inadequate treatment of non-communicable and

chronic diseases (e.g. problem with drug procurement), and increased transmission of

infectious diseases (e.g., caused by parallel systems dysfunction such as water and

sanitation, communication and transport).

Systemic failures of healthcare systems and parallel infrastructure such as the water

and sanitation system or emergency housing system as well as transport and

communication system could lead to increased fatalities. Studies, models, and

guidelines for hospital performance have largely focused on physical damage to

structural systems and in some cases nonstructural systems and equipment (e.g.,

HAZUS model, FEMA 2003). Yet from the perspective of social impacts, the interest is

in measuring hospital performance in terms of capacity (i.e. hospital functionality) to

provide health care services. Several researchers have proposed methods, whereby

interrelated systems - structural, nonstructural, lifelines, and personnel – are assessed

according to performance levels indicating functionality (Holmes and Burkett, 2006;

Chang, 2008, Lupoi et al., 2008). In these studies, impacts on health care systems and

their recovery after disaster in term of service and personnel is less studied. Even

fewer studies consider mid-and long-term consequences of disasters in term of health

impact and health care systems. Figure 2.1 summarizes impacts on health and health

care systems which will be investigated in WP4. The framework shown is adapted and

modified from the EU Project MICRODIS which commenced in January 2011.

Reviewing available literature the present deliverable focuses on linking outputs from

the Earthquake Loss Estimation Models, such as casualty estimates or lifeline and

critical infrastructure losses to a framework representing a broad range of vulnerability

factors related to health impacts following disasters. The following sections will

describe the processes and links between socio-demographic, environmental,

epidemiological and health behavior parameters to increased health short- and mid-

term health impacts. Furthermore, healthcare systems parameters will be integrated in

a healthcare capacity model to assess secondary impacts on the overall health care

delivery to the affected population.

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Figure 2.1 Schematic of Impacts on Health and Healthcare Systems after Disasters

2.1 MORTALITY

Very little information is available about the exact cause of death from earthquakes

primarily due to the difficulty of conducting autopsies on a case to case basis. Injuries

are responsible for a large part of morbidity after earthquakes. The injury epidemiology

data reported in the studies needs to be interpreted with caution as majority of the

studies reviewed single hospital data. Casualty estimation methodologies in ELE

software provide estimates of both injuries and fatalities, which is key input to assist

planners in determining the resources required to deal with the increased surge in the

patients. Casualty estimation methodologies (Coburn and Spence 2002, FEMA 2008)

inherently assume a strong correlation between building damage (both structural and

nonstructural) and the number and severity of casualties. Most take into account the

injuries/fatalities due to indoor structural building, and do not consider outdoor fatalities

from bridge damage and others. They also generally do not include the

injuries/fatalities due to transportation accidents, fire following events, hazmat exposure

and injuries to those assisting in the response effort. In addition, they exclude

casualties caused by heart attacks, car accidents, falls, and power failure which lead to

failures of medical equipment such as respirators, incidents during post-earthquake

search and rescue or post-earthquake clean-up and construction activities,

electrocution, tsunami, landslides, liquefaction, fault rupture, dam failures, fires, and

hazardous materials releases.

Figure 2.2 MICRODIS Model for Health Impacts of Disasters

2.2 CHRONIC HEALTH AND DISABILITIES

Often the exaggeration of baseline diseases and increased transmission of

communicable infectious diseases result from systemic failures. Access to food could

also influence the general condition of the population including malnutrition status and

the lack of adequate immunity thereof. Although there is growing evidence that chronic

disease and disability create a great burden during and in the aftermath of disaster,

these shortcomings are not well documented, and reporting is mostly anecdotal. There

is currently a lack of knowledge and information about the burden of natural disaster on

chronic health problems. Most of the examples come from the experience in Katrina,

with the specific characteristics of Louisiana. In addition, research on the health

prospect of people who have become disabled because of a disaster injury is scarce.

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2.3 MENTAL HEALTH

Although mental health has been recognized as an important issue in the aftermath of

disasters, most studies have taken place in developed countries. Mental health aspects

have been rarely studied in Asian countries and a few publications from Bangladesh,

Sri Lanka, Indonesia, China, Korea, India and the Philippines (e.g. Kar et al. 2007; Kar

and Bastia 2006) appeared only recently. Tools must be adapted to the local cultures

and methods developed in western countries may be inadequate and ineffective in

Asia, as suggested by the experience of the 2004 tsunami.

3 Vulnerability Factors Influencing Health-issues in Earthquake Disasters

Although there is growing evidence that vulnerable population groups (e.g., elderly,

populations with chronic disease and disabilities) create a great burden during and in

the aftermath of disaster, there is currently a lack of knowledge and information about

the burden of natural disaster on chronic health problems. Most of the examples come

from some experiences with recent disasters including 2004 Asian Tsunami, 2005

Katrina Hurricane, and most recently the 2011 Tohoku earthquake and tsunami. In

addition, research on the health prospect of people who have become disabled

because of a disaster injury is scarce. Finally, while mental health has been recognized

as an important issue in the aftermath of disasters, most studies have taken place in

developed countries. Mental health aspects have been rarely studied in Asian countries

and a few publications from Bangladesh, Sri Lanka, Indonesia, China, Korea, India and

the Philippines (e.g. Kar et al. 2007; Kar and Bastia 2006) appeared only recently.

The vulnerability and coping capacities of ‘at risk’ populations affects the complexity of

calculating health impacts of earthquakes. Supporting these findings, the aim is to link

other elements of the health impact model (i.e., casualty estimates, healthcare

functionality, and transportation accessibility) within a multi-tiered health impact model.

The proposed health impact model will describe the processes and links between

socio-demographic, environmental, epidemiological and health behavior parameters to

increased short-term health impacts. Furthermore, healthcare systems parameters will

be integrated in a healthcare capacity model to assess secondary impacts on the

overall health care delivery to the affected population (Figure 3.1).

Figure 3.1 Social Vulnerability and Health Impacts

3.1.1 Integration of earthquakes on health and health care systems

Figure 3.2 Conceptual Framework showing integration of earthquakes on health and health care systems in a multi-tiered system

18

Figure 3.3 Multi-criteria Framework for Health Impact Analysis

A logic model for health impact has been developed in SYNER-G (Figure 3.2). This

model shows that health impact is not just estimated by casualties after an earthquake,

but controlled by a number of other factors which have the potential to aggravate the

direct impact of the earthquake on health. The framework is shown as a hierarchical

representation in Figure 3.3 which describes different criteria influencing the final health

impact of people in the affected earthquake area in a multi-criteria decision analysis

approach.

3.2 SOCIAL VULNERABILITY FACTORS

3.2.1 Age

In seismic vulnerability analysis age is a very significant factor as the ability to move

and react during an earthquake is depend on the age of the person. Previous studies

have proved that the old people, infant and child are greatly affected by the

earthquake. After Hanshin-Awaji earthquake it has been found that the hospitalization

rate following the earthquake the average age for the patients admitted for the illness

was 60 years (Matsuoka et al, 2004). From many other health problems the old people

who have age over 70 years mainly attract by the heart failure and ischemic heart

disease (Matsuoka et al, 2004, Ramirez et al, 2005). As evidence, the morbidity rates

for patients in Hospital after Hanshin-Awaji earthquake unexpectedly increased at over

60 years of age (Osaki et al, 2004, Matsuoka et al, 2004). It is interesting to note that

the age specific mortality also differ from the country to country. Because of unequal

age structure in different country differ also age specific mortality. For example the life

expectancy in Japan is 81.9 years where the life expectancy in Sierra Leone is 34

years. Sometimes there are also large inequalities within countries (Marmot, 2005).

Furthermore children are very much vulnerable to the posttraumatic stress disorder

(PTSD) and depression in aftermath of an earthquake. A study (Jia et al, 2010) on

2008 Sichuan earthquake has been found a significant higher prevalence of

posttraumatic stress disorder among children. In this case the children who lost their

family members and who felt guilt concerning someone’s death or injury have mainly

affected by the posttraumatic stress disorder. The children who lost family members

are about 6 times more likely to get posttraumatic stress disorder than the children who

have no such experience.

3.2.2 Gender

All are not equally affected by a disaster and there is some variability in response to a

particular disaster i.e. earthquake in terms of gender. Different studies for instance

Armenian et al, 1997 show that the women are more likely to vulnerable. More

specifically the poor or elderly women are more vulnerable or sometimes due to

pregnancy can increase vulnerability. In 1995 after Hanshin-Awaji earthquake about

1948 patients admitted in Hospital for illness where about 54.5% were female and

45.5% were male patients (Matsuoka et al, 2004). In 1993 Latur-Osmanabad

earthquake in India it has been observed that the mortality rates was high among

females (Osaki et al, 2001). Another example is that the female mortality rate was

about 60% in earthquake in Armenia in 1988 (Armenian et al, 1997). The females had

also an elevated risk of traumatic injuries during the 1994 Northridge and California,

1999 Taiwan and 1995 Hanshin-Awaji earthquakes (Ramirez et al, 2005, Seplaki et al,

2006).

3.2.3 Disability

The physically disabled or mental disordered people have a greater risk of earthquake

death (Chou et al, 2004). In 1976 Guatemalan earthquake it has been identified that

the people with physical disabilities have an increasing risk of earthquake deaths

(Ramirez et al, 2005). Normally the persons with mental illness have less capability to

20

take quick protective action during the earthquake and they have also less ability to

withstand the pain of injuries until rescue (Chou et al, 2004). Osaki et al, 2004 shows

that in 1995 Hanshin-Awaji earthquake the physical disability was a very important risk

factor of earthquake death (Osaki et al, 2004).

3.2.4 Social status

The socio status of individual as well as the country also influences the risk of

earthquake injury or death. Earthquake risk increases with the population growth as

well as with poverty. From the database of last 39 years ten most fatal earthquakes it

has found that the low and middle income countries are more vulnerable and also have

more fatalities. Normally those countries have a low quality building, sometimes lack of

spatial planning, high population density, and people living in risk zone which causes at

during earthquake a miserable situation. Another thing is that the poor people become

most vulnerable because they were forced to settle on steep hillsides, valley, flood

prone zone, or to develop their livelihood around terraced agriculture which are

normally the highly vulnerable area for earthquake (Guha-Spir et al, 2011).

3.2.5 Income status/financial resources

Income status of a family or an individual has an important impact on health in the

aftermath of the earthquake. Seplaki et al, 2006 has shown than after 1999 Taiwan

earthquake the individuals who had a low income status before earthquake and had

faced problem to manage his expenses have a higher levels of depressive symptoms

in the aftermath of the earthquake.

3.2.6 Social Networks

Social network can also be considered as an indicator for risk of earthquake death or

injury. A study shows that the person who living alone and use physical handicaps

have a significant risk of death or injury from an earthquake disaster (Osaki et al,

2004). In addition, in the aftermath of 1999 Taiwan earthquake a higher levels of

depressive symptoms has been identified between the socially isolated individuals

(Seplaki et al, 2006). Another thing is to mention that the communication between

victims and medical personals is also important. A hospital cannot function without

proper communication between medical personnel and patients. Sometimes the foreign

crew cannot communicate with the patients without help of a translator. So it might

cause a worse situation (Bar-Dayan et al, 2000).

3.3 ENVIRONMENTAL HEALTH FACTORS

3.3.1 Weather

The post earthquake weather in affected area has a significant impact on health of the

affected area. Many diseases can outbreak in this time because of the unusual weather

condition for example heavy rain, unexpected high or cold temperature etc. which can

occur different acute respiratory diseases (Kun et al, 2010).

3.3.2 Nutrition

Besides many other problems in aftermath of earthquake people confront with the huge

food scarcity people faces food scarcity besides other problems, this may explain the

occurrence of health problem as well as the morbidity. In the developing country the

young generation has a greater mortality tends in earthquake and it happens because

of their nutritional status and functional fitness (Osaki et al, 2004). For an example, in

Haiti before 2010 earthquake the nutrition security of the country was alarming. About

60% of the population was undernourished where one third of under 5 years children

from chronic malnutrition. After earthquake the conditions are much worse which has

put many more children at risk of malnutrition (Basset, 2010). Another example is that

after 2008 Sichuan earthquake because of poor nutrition a large number of affected

victims identified who were progressed to suffer crush syndrome (Ying-kang et al,

2010).

3.3.3 Shelter circumstance

The affected people from an earthquake sometimes live in the temporary shelters. The

environmental condition of those shelters has also an impact on the health condition of

sheltered inhabitant (Kun et al, 2010). Sometimes there are absence of drinking water

supply, poor ventilation, and sanitation problem, no electricity and high population

density etc. those problems of the temporary shelter after earthquake is a big problem

in developing countries. Because of lack of clean drinking water and food it may

outbreak the water related disease like diarrhea, Cholera or because of overcrowding

in shelter or sometimes living in open space causes vector-born disease like Malaria or

Dengue which increase the morbidity after an earthquake spatially in developing

countries (Guha-Sapir, 2009 and Kun et al, 2010).

22

3.3.4 Residential crowding

The residential crowding is a very important factor which can also determine the

number of fatalities. The increasing fatalities from earthquake are not particularly

depending on the frequent of the earthquake but also because of increasing

concentration of large population in an urban centre (Ramirez et al, 2005, Gunn, 1995,

Chou et al, 2004). Furthermore when a hospital built in a densely populated area it may

become useless after the earthquake even though the hospital building may have not

destroyed by the earthquake. Because of debris and destruction around the hospital

building make the rescue, access and communications very difficult (Gunn, 1995).

3.3.5 Pre-disaster health characteristics of the affected area

To understand the post earthquake health issue requires information about the pre

disaster health characteristics of a particular area. But unfortunately in most of the

causes in disaster prone area there is a lack of that information (Guha-Sapir, 2009).

3.4 HEALTH CARE ACCESS

3.4.1 Transport

In emergency situation after earthquake the transport facilities to access the health

care centre may increase the vulnerability as a consequence it may causes more death

toll. The transport links themselves are also become vulnerable to earthquake damage

and disruption (Fawcett et al, 2000). After Benavente earthquake in 1909 (Portugal) the

casualties from stricken areas all were tried to reach the hospital where most hospitals

were located. As a consequence they needed more travel time to reach the hospital

which causes not only more morbidity but also a severe logistic problem in hospitals.

One another side the people mainly from the nearer neighborhood who visited the

smaller hospital capacity area got instant treatment (Fawcett et al, 2000). In 1995

Hanshin-Awaji Earthquake it has found that the one of the important factor of injuries

was the delay in rescue or difficulty of access to medical services (Osaki et al, 2004).

3.5 INFRASTRUCTURAL FACTORS

3.5.1 Geographical location/Area characteristics

The geographical location is also vital determining factor for risk of the fatalities from an

earthquake. A highly agglomerated urban area has higher risk of fatalities than a low

agglomerated urban area though the magnitude scale of earthquake is same

(Armenian et al, 1997). Besides an earthquake cause more severe casualties in higher

income municipalities than in lower income municipalities. It happens because the

higher income municipalities are more likely commercial centre and furnished with

more high-rise buildings. So during the earthquake there is more possibility to collapse

of those high-rise building and as a consequence increase more fatalities (Chou et al,

2004).

3.5.2 Degree of damages to dwellings

The fatalities from an earthquake also depend on the degree of damage to dwellings. A

study on 1995 Hanshin-Awaji earthquake by Osaki et al, 2001 has explained that there

is a higher mortality rate when the victims were lived in a completely destroyed

building.

3.5.3 Building or Dwelling type

The risks of fatalities differ with the different types of building (Chou et al, 2004).

Person who lived in taller buildings during the 1998 Armenian earthquake has been

identified as a higher risk of injuries. In this earthquake it has found that there was a

highest mortality rates for the people who lived in a panel type constructed building

than the other types of construction. It also calculated that the people within a panel

construction type building were at 6 times increased the risk of injury compared to

others types of construction (Armenian et al, 1997). During an earthquake the people

try to escape to outside or seek safety in lower floors of the building. But when the

stairway of the building is not well planned, during an earthquake the occupant may not

escape to outside which may cause more death or injuries (Armenian et al, 1997).

3.5.4 Victims location inside or outside of building during the earthquake

The people outside the building have a low risk of injury and death than the people

being inside the building during the earthquake. From Armenia earthquake in 1988 it

has found that being inside the building increase the risk of injury 2.3 times (Armenian

et al, 1997, Ramirez et al, 2005).

3.5.5 Height of the building

The height of the building is a good predicator of mortality. The study Armenian et al,

1997 shows that high storied building even more than two storied building have a

greater mortality rate than the one storied building. Statistics from Armenia earthquake

24

it has found within multi storey building there was a maximum 60% increase in risk for

injuries than a one storey building (Armenian et al, 1997).

3.5.6 Victims location within a more high stories building

In which floor live the victims are also an important factor for injuries or death of the

individuals during earthquake (Oskai et al, 2001, Gunn, 1995). The location of the

individual at the higher levels in a high stories buildings increase the risk of the injuries

(Armenian et al, 1997).

3.6 SUMMARY

The indicators that representing the vulnerability factors affecting health impact after an

earthquake are not often freely selectable but constrained and directed by social

environmental and infrastructural factors, as above on the basis of available literature

discussed. In the following table (see Table 3.1) has been presented all the factors that

represent the vulnerability to earthquake. The table provides an overview of all selected

factors. In order of nomination quantity, the following 18 factors could be ascertained:

Age, Gender, Disability, Social Networks, Nutrition, Residential crowding, Victims

location within a more high stories building, Shelter circumstance, Transport,

Geographical location/Area characteristics, Building or Dwelling type, Victims location

inside or outside of building during the earthquake, Social Status, Income

status/financial resources, Weather, Pre-disaster health characteristics of the affected

area, Degree of damages to dwellings and Height of the building.

Table 3.1 Nominated indicators representing the vulnerability factors affecting health impact after earthquake disaster

Authors Factors Matsuoka et al, 2004; Ramirez et al, 2005; Osaki et al, 2004; Marmot, 2005; Jia et al, 2010

Age

Armenian et al, 1997; Matsuoka et al, 2004; Osaki et al, 2001; Ramirez et al, 2005; Seplaki et al, 2006

Gender

Soci

al V

ulne

rabi

lity

fact

ors

Chou et al, 2004; Osaki et al, 2001;

Disability

Authors Factors Ramirez et al, 2005; Guha-Spir et al, 2011 Social Status Seplaki et al, 2006 Income status/financial resources Bar-Dayan et al, 2000; Osaki et al, 2004; Seplaki et al, 2006

Social Netzwerks

Kun et al, 2010 Weather Osaki et al, 2004; Basset, 2010; Ying-kang et al, 2010

Nutrition

Kun et al, 2010; Guha-Sapir, 2009

Shelter circumstance

Envi

ronm

enta

l he

alth

fact

ors Ramirez et al, 2005

Gunn, 1995 Chou et al, 2004

Residential crowding

Guha-Sapir, 2009 Pre-disaster health characteristics of the affected area

Hea

lth c

are

acce

ss

Fawcett et al, 2000; Osaki et al, 2004

Transport

Armenian et al, 1997 Chou et al, 2004

Geographical location/Area characteristics

Osaki et al, 2001 Degree of damages to dwellings Chou et al, 2004 Armenian et al, 1997

Building or Dwelling type

Armenian et al, 1997 Ramirez et al, 2005

Victims location inside or outside of building during the earthquake

Armenian et al, 1997 Height of the building

Infr

astr

uctu

ral F

acto

rs

Oskai et al, 2001 Gunn, 1995 Armenian et al, 1997

Victims location within a more high stories building

26

In the above bar chart (see Figure 3.4) it has been presented the number of

nominations of the different factors. For this section of the study it has been reviewed

18 different studies. It has found that at least studies from those 18 have mentioned

age and gender as a risk factor which increase the vulnerability to earthquake.

Disability, Social Networks, Nutrition, Residential crowding, Victims location within a

more high stories building was mentioned three times, Shelter circumstance, Transport,

Geographical location/Area characteristics, Building or Dwelling type, Victims location

inside or outside of building during the earthquake was mentioned from at least two

authors, Social Status, Income status/financial resources, Weather, Pre-disaster health

characteristics of the affected area, Degree of damages to dwellings and Height of the

building was mentioned only one times in reviewed literatures.

Figure 3.4 Number of nominations found for indicators in the 18 studies surveyed.

4 Data Availability

4.1 EUROSTAT CITY STATISTICS - URBAN AUDIT

The Statistical Office of the European Communities, Eurostat, offers a huge range of

data across the 27 member states. (Eurostat1). . From national to local scale they cover

the topics of general and regional statistics, economy and finance, population and

social conditions, industry, trade and services, agriculture and fisheries, external trade,

transport, environment and energy, and science and technology. Statistics of social

conditions are very broad covering information about demographics, health, education

and training, labour market, income, social Inclusion and living conditions, social

protection, household budget, crime and criminal justice, and cultural aspects

(European Commission 2010). Most of the Eurostat data are provided on levels

describing nations, greater regions to large cities. These scales are too small for the

purpose of shelter needs estimations after disasters, requiring the sub- city district

scale for reliable outcome and precise located needs estimations.

Urban Audit focuses on city scale, where data are collected on three levels: The core

cities, larger urban zones (LUZ) including the hinterlands, and sub- city- districts (SCD)

gathering inters- urban discrepancies. Information is covered over nine categories:

Demography, Social Aspects, Economic Aspects, Civic Involvement, Training and

Education, Environment, Travel and Transport, Information Society, and Culture and

Recreation. Eurostat is responsible for coordinating the flow of Urban Audit (European

Commission 2010). The latest data collection took place in 2009 with 322 cities in the

EU27 plus 47 cities from Switzerland, Norway, Croatia, and Turkey. It covers

approximately 20% of each national population. The participation of each capital and

main regional city was intended.

The results of the latest data collection are not yet published thus the model refers to

previous data sets. The Urban Audit data are freely accessible from Eurostat. Data on

SCD scale are most preferable. Table 4.1 shows a compilation of variables that are

available on SCD. These variables serve for the creation of indicators, listed in Table

4.1. Only data on SCD level are considered within the model, and listed in the tables.

Information on this scale are available about the population size, density, and sex and

age distribution, mortality, the residents origin, number of households and household

1http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/city_urban/urban_audit_data_collections

28

size, housing quality, crime rate, unemployment rate, household income, social security

benefiters, education status, and the green space area (European Commission 2010).

On SCD scale, Urban Audit provides 50 variables and, created out of these,

41 indicators. The Norwegian Geotechnical Institute analyzed these data gathered in

the first period of 1999 and found a very incomplete data set. None of the indicators

has been collected for every district and no district is described with all indicators. The

data collection was only completed for 32% as they found (Duzgun, Khazai 2011,

p. 3f). They sort all indicators according to their completeness and the sub- city-

districts according to their data availability, and deleted the most incomplete districts

and indicators. The reduced data set finally contains 20 indicators and 2,820 districts

representing 161 cities, with a completeness of 68%. (Duzgun, Khazai 2011, p. 4f).

These remaining from the analysis are highlighted in Table 4.2. They represent the

most available and most complete Urban Audit data, which will be considered in the

model. It has to be kept in mind that for some districts not all required information will

be available.

Table 4.1: Compilation of Urban Audit Variables, available on Sub-City-Districts

List of Urban Audit Variables Code Label Unit

DE1001V Total Resident Population number DE1002V Male Resident Population number DE1003V Female Resident Population number DE1040V Total Resident Population 0-4 number DE1043V Total Resident Population 5-14 number DE1046V Total Resident Population 15-19 number DE1049V Total Resident Population 20-24 number DE1052V Total Resident Population 25-54 number DE1025V Total Resident Population 55-64 number DE1028V Total Resident Population 65-74 number DE1055V Total Resident Population 75 and over number DE2001V Residents who are Nationals number DE2002V Residents who are Nationals of other EU Member State number DE2003V Residents who are not EU Nationals number DE2005V Residents who are not EU Nationals and citizens of a country with high HDI number DE2006V Residents who are not EU Nationals and citizens of a country with a medium or low HDI number DE2004V Nationals born abroad number DE3001V Total Number of Households (excluding institutional households) number DE3017V Total Resident Population living in households (excluding institutional households) number DE3002V One person households number DE3005V Lone parent households (with children aged 0 to under 18) number

Dem

ogra

phy

DE3008V Lone pensioner (above retirement age) households Total number SA1001V Number of conventional dwellings number SA1012V Households in social housing number

Soc

ial

SA1018V Dwellings lacking basic amenities number List of Urban Audit Variables

30

Code Label Unit SA2016V Total deaths under 65 per year number SA2019V Total deaths per year number SA3001V Total number of recorded crimes within city [country for national data] number

SA3007V Number of domestic burglary number EC1001V Total Economically Active Population number EC1002V Male Economically Active Population number EC1003V Female Economically Active Population number EC1142V Total Economically Active Population 15-24 number EC1010V Residents Unemployed number EC1148V Residents Unemployed 15-24 number

Eco

nom

ic A

spec

ts

EC1151V Residents Unemployed 55-64 number EC3039V Median disposable annual household income euro EC3056V Total Number of Households (relating to the reported household income) number EC3057V Total Number of Households with less than half of the national average disposable annual household income number EC3060V Total Number of Households reliant on social security benefits (>50%) number EC3063V Individuals reliant on social security benefits (>50%) number

CI1009V City Elections: Number of voters turned out number CI1020V Participation rate at national elections ratio

Civ

il in

volv

emen

t

CI1021V Participation rate at city elections ratio TE2025V Number of residents (aged 15-64) with ISCED level 0, 1or 2 as the highest level of education number TE2028V Number of residents (aged 15-64) with ISCED level 3or 4 as the highest level of education number

Trai

ning

an

d ed

ucat

ion

TE2031V Number of residents (aged 15-64) with ISCED level 5 or 6 as the highest level of education number EN5003V Total land area (km2) according to cadastral register km2 EN5012V Green space area (km2) km2

Env

ironm

ent

EN5001V Green space (in hectares) to which the public has access hectares TOTAL 50 variables in 6 categories on SCD level

Table 2: Compilation of Urban Audit Indicators, available on Sub-City-District Highlighted are the indicators being most available over the districts. Sources: European Commission 2010, p. 228-231; and Duzgun, Khazai 2011)

List of Urban Audit Indicators Code Label Numerator Denominator

DE1001I * Total resident population DE1001V - DE1040I Proportion of total population aged 0-4 DE1040V DE1001V DE1003I *Proportion of females to males in total population DE1003V DE1002V

DE1061I Total population change over 1 year DE1001V (t) DE1001V (t-1)

DE1062I * Total annual population change over 5 approx.years DE1001V (t) DE1001V (t-n)

DE2005I * Proportion of Residents who are not EU Nationals and citizens of a country with high HDI DE2005V DE1001V

DE2006I * Proportion of Residents who are not EU Nationals and citizens of a country with a medium or low HDI DE2006V DE1001V

DE3003I * Total number of households DE3001V - DE3004I * Average size of households DE3017V DE3001V DE3002I * Proportion of households that are 1-person households DE3002V DE3001V DE3005I * Prop. of households that are lone-parent households DE3005V DE3001V

Dem

ogra

phy

DE3008I * Prop. households that are lone-pensioner households DE3008V DE3001V SA1001I * Number of dwellings SA1001V - SA1018I * Proportion of dwellings lacking basic amenities SA1018V SA1001V SA1012I * Proportion of households living in social housing SA1012V DE3001V SA2029I Crude death rate per 1000 residents SA2019V*1000 DE1001V SA2030I Crude death rate of male residents per 1000 male residents SA2020V*1000 DE1002V SA2031I Crude death rate of female residents per 1000 female residents SA2021V*1000 DE1003V

Soci

al A

spec

ts

SA2019I Total deaths per year SA2019V

SA2016I Mortality rate for <65 per year SA2016V

DE1040V + DE1043V + DE1046V + DE1052V + DE1025V

SA3001I * Total Number of recorded crimes per 1000 population SA3001V*1000 DE1001V

EC1201I Annual average change in employment over approx. 5 years EC1001V(t)- EC1001V(t-n)

EC1001V -EC1001V(t-n)

EC1010I Number of unemployed EC1010V - EC1020I * Unemployment rate EC1010V EC1001V

Eco

nom

ic

Aspe

cts

EC1148I Proportion of residents unemployed 15-24 EC1148V EC1142V

32

EC1202I Proportion of unemployed who are under 25 EC1148V EC1010V

EC1005I Net activity rate residents aged 15-64 EC1001V-EC1010V

DE1046V + DE1049V + DE1052V + DE1025V

EC1006I Net activity rate residents aged 15-24 EC1142V-

EC1148V DE1046V + DE1049V

EC1007I Net activity rate residents aged 55-64 EC1145V-EC1151V DE1025V

EC3039I * Median disposable annual household income (for city or NUTS 3 region) EC3039V - EC3057I Percent. households with less than half nat.aver.income EC3057V EC3056V EC3055I Percent. households with less than 60% of the national median annual disposable income EC3055V EC3056V EC3060I Proportion of households reliant upon social security EC3060V EC3056V

EC3063I Proportion of individuals reliant on social security EC3063V DE1001V

TE2025I * Prop. of working age population qualified at level 1 or 2 ISCED TE2025V

DE1046V + DE1049V + DE1052V + DE1025V

TE2028I Prop. of working age population qualified at level 3 or 4 ISCED TE2028V

DE1046V + DE1049V + DE1052V + DE1025V

Trai

ning

and

edu

catio

n

TE2031I Prop. of working age population qualified at level 5 or 6 ISCED TE2031V

DE1046V + DE1049V + DE1052V + DE1025V

EN5003I * Total land area (km2) -according to cadastral register EN5003V - EN5001I Green space (in m2) to which the public has access per capita EN5001V*10000 DE1001V EN5012I * Proportion of the area in green space EN5012V EN5003V

Env

ironm

ent

EN5101I * Population density: total resident pop. per square km DE1001V EN5003V TOTAL 41 Indicators in 5 categories on SCD level

4.2 INPUT VARIABLES

Comparing the required factors from the literature survey of vulnerability indicators with

those variables covered through Urban Audit on SCD level offers that seven of 13 are

directly available. These are age, gender, household size, belonging to the minority, income,

occupation status, and educational status. In Table 4.3 the corresponding variables and

indicators of Urban Audit are listed. The weather conditions will be included into the model at

the first decision step. Because this variable does not differ between the districts, it can be

brought easy into the estimations during the model appliance. Due to the incomplete data

set of Urban Audit, not all of the required factors can be integrated.

The factor “Having strong networks or extended family arrangements” is not grasped directly

but will be determined through a proxy. It is assumed that strong social networks need stable

circumstances. In contrast, a rapid population change hinders the development of strong

social networks. The required factor “strong social networks” will be represented over the

“Total annual population change over approx. 5 years” for this model indicating the grade of

solidarity. The “Tenure status” is also not covered through Urban Audit. It is assumed that

the occupancy status is connected to the housing quality. Homeowners who live in their own

property will be more likely to secure the building and do necessary repairs as soon as

possible, while renters have to wait until these improvements will be done from the landlords.

Thus, the factor tenure status will be implemented above the housing quality, a combined

factor of the proportion of dwellings lacking basic amenities and the proportion of households

living in social housing. If people live in social housing or having no services available, they

will be more likely to leave than to stay.

34

Table 4.3 Required variables for the Health Impact model and availability on Urban Audit

This set of vulnerability indicators still lack aspects describing community dynamics and

spatial factors that influence the evacuation behavior. Urban dwellers are living in a built

environment together with a greater social neighborhood. The density of people and

buildings significantly determines the need for assistance during the emergency phase.

Required indicators  Available indicators on URBAN Audit (at SCD)  Decision Code  Indicator  Code  Name  1  2  3  4 AGE  Elderly, 65 years and 

above DE1028V DE1055V 

Total Resident Population 65‐74 Total Resident Population 75 and over 

SEX  Gender (being female)  DE1003I  Female Resident Population  AHH  Household size  

Presence of children DE3004I  

Average size of households 

MIN  Race and ethnicity; Belonging to the minority 

DE2005I  DE2006I 

Prop. of residents who are not EU Nationals and citizens of a country with a high HDI Prop. of residents who are not EU Nationals and citizens of a country with a medium or low HDI 

INC  Income  financial resources 

EC3057V  EC3060V  EC3063V  EC3039I   

Total  Number  of  Households  with  less  than  half  of  the national average disposable annual household income Total  Number  of  Households  reliant  on  social  security benefits (>50%) Individuals reliant on social security benefits (>50%)   Median disposable annual household income (Euro) 

UNE  Occupation status  EC1020I  Unemployment rate  EDU  Skills and education  TE2025V  

 TE2028V   TE2031V  TE2025I 

Number of residents (aged 15‐64) with ISCED level 0, 1or 2 as the highest level of education Number of residents (aged 15‐64) with ISCED level 3or 4 as the highest level of education  Number of residents (aged 15‐64) with ISCED level 5 or 6 as the highest level of education Prop.  of working  age  population  qualified  at  level  1  or  2 ISCED 

SOL  Having strong social networks; extended family (solidarity) 

DE1062I  Total  annual  population  change  over  approx.  5  years  (by sex and age) 

HSQ  Tenure status (housing quality) 

SA1018I SA1012I 

Proportion of dwellings lacking basic amenities Proportion of households living in social housing 

 CAR  Car ownership  n.a.  n.a.   DIS  Disabilities/ health 

status n.a.  n.a. 

 PET  Having pets (no.of dogs) 

n.a.  n.a. 

WEA  Weather  n.a.  Good vs. Bad weather conditions  Additional Variables (NGI 2011) LPH  Proportion of lone 

parent households DE3005I  Prop.  of    households    that  are  lone‐  parent  households 

(with children aged 0 to <18)     

CRM  Crime  SA3001I  Total number of recorded crimes per 1000 population   UPD  Urban population 

density EN5101I EN5012I 

Population density in Urban Audit cities Proportion of the area in green space 

 

General Variables #POP  Total population  DE1001V    Total Resident Population    #BLD  Number of buildings     

The extent of open space in the area is important, which can be used for emergency and

temporary sheltering, as market place or meeting point for information exchange. The more

open space is available in urban areas the less vulnerable people are. They can shelter next

to their homes, and if open space is used as market exchange place, people will be more

likely to stay next to their familiar surroundings.

The population density is a crucial determinant as well. In high dense areas, more people

probably become homeless if buildings collapse and the more people are forced to leave the

crowded area. Especially immobile people will have problems to find shelters close to their

home. A crowded area is characterized by an anonymous society that increases the fear of

crimes, and the shortage of space in shelters. High density forces people to seek

alternatives somewhere else.

Information of open space in the area, and urban population density are covered through

Urban Audit. Combining these two variables to the urban population density (UPD) provides

a factor that explains the desirability and feasibility of leaving home. The crime rate (CRM)

as vulnerability indicator will be included as an own vulnerability indicator. In quarters with

high crime rates before the earthquake, more people will desire to leave the area

independent from other factors. Finally, lone parent households (LPH) with young children

are considered within the model. They will face severe problems leaving their homes without

assistance.

In Table 4.3 all of the considered indicators that will be integrated into the model of Shelter

Need Estimations are listed. Of the originally required variables, Urban Audit provides seven

on SCD. Two of the missing variables can be replaced, and three are removed due to

lacking data availability. The model will be further improved through the additional variables

are lone parent households (LPH), crime rate (CRM), and urban population density (UPD).

Finally, it can be concluded that Urban Audit provides socioeconomic, demographic, and

further data on SCD level covering 369 cities all over Europe what satisfies the demand of

collected variables for the model of Shelter Need Estimations. Even if not all of the originally

required vulnerability indicators have been collected on SCD scale a sufficient set of 12

variables is provided through Urban Audit.

36

5 Conclusions

Research on health impact s from disasters is rapidly growing, however, survey observations

and quantitative data is still largely lacking. Indeed, information about the aftermath of

European earthquakes including detailed vulnerability researches, are very rare. Goal of this

present study was to present a set of indicators that are important in estimating the health

impacts after an earthquake in Europe. Building on a broad literature survey, and in

particular on work completed by the EU FP6 multi-sectoral research project MICRODIS-, this

deliverable has focused on providing background for socio-demographic, environmental,

epidemiological and health behavior indicators which aggravate short- and mid-term health

impacts. These will be used together with outputs of casualty estimation model of the SYNER-G and will be integrated in a healthcare capacity model to assess secondary

impacts on the overall health care delivery to the affected population which is addressed in

Deliverable 4.7. This work will inform a new way for modeling health impacts caused by

earthquake damage which allows for integrating key social impacts on individual health and

on health care systems and for implementing these impacts in quantitative systemic seismic

vulnerability analysis.

Recent statistics and complete data sets are essential to get the best estimation results and

operationalize the proposed indicators. The Urban Audit, database which is collecting the

required input parameters for more than 300 cities all over Europe, was investigated as a

basis for populating the identified indicators. The present study demonstrates that the data

availability of sensitive population statistics on sub-city-district is not sufficient. Even if Urban

Audit is collecting data all over Europe currently in the fourth period, the data sets are still

very incomplete. Thus, it cannot be assumed that the required input parameter for the model

are immediately complete available after disaster. Therefore, a reliable and full set of

information of Urban Audit is very important. Thus, the Urban Audit data has been used as a

basis of harmonization of the data for a Pan-European Approach. The statistical handling of

the data is further explained in deliverable 4.5. Particular conditions with respect to Europe

have to be considered more closely. The population dynamics in Europe are very different.

Main tendency is the growing age and sinking birth rates that leads to negative natural

population growth. Remaining infrastructures degenerate if the conversion of usage or

deconstruction is not yet completed. These vacancies and underuse of infrastructures raise

possible damages of buildings and services. The many elderly have predominantly low

pensions and live in one- or two- person households. Cities suffering rapid population

change due to migration, high percentages of tenants, and high numbers of small

households. Each of these tendencies leads to higher vulnerability due to post- disaster

shelter supply. Earthquakes are more likely than other disasters to affect economically

disadvantaged persons in a negatively direction, due to class related differences in housing

type, quality, and location. Lacking city structures, that fail to plan adequately for the housing

needs of marginalized populations, and minorities exacerbates post- disaster shelter

problems (Bolin and Stanford 1991).

38

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42

Online Sources (latest retrieve: 27.05.2011) BRITISH BROADCASTING CORPORATION

http://www.bbc.co.uk/news/world-asia-pacific-12743417

http://www.bbc.co.uk/news/world-asia-pacific-12771634

http://www.bbc.co.uk/news/world-asia-pacific-12771634

EARTHQUAKE ALARM SYSTEMS, BERKELEY

http://www.elarms.org/turkey/index.php

SPIEGEL ONLINE

http://www.spiegel.de/images/image-193651-galleryV9-kmcx.jpg

DEUTSCHE WELLE

http://www.dw-world.de/dw/article/0,,5195264,00.html

ISTANBUL - EUROPEAN CAPITAL OF CULTURE

http://www.ibb.gov.tr/sites/ks/en-US/0-Exploring-The-City/Location/Pages/PopulationandDemographicStructure.aspx

http://www.ibb.gov.tr/sites/ks/en-US/0-Exploring-The-City/Location/Pages/IstanbulinNumbers.aspx

EUROPEAN- MEDITERRANEAN SEISMOLOGICAL CENTRE

http://www.emsc-csem.org/Files/docs/data/newsletters/newsletter_19.pdf

http://www.emsc-csem.org/Page/index.php?id=199

EUROSTAT

http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/city_urban/urban_audit_data_collections

http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/city_urban

FEMA

http://www.fema.gov/plan/prevent/hazus/hz_iap.shtm

GLEMONE

http://www.glemone.it/archivio/elenco_foto_terremoto.htm

LOS ANGELES TIMES

http://www.latimes.com/search/la-0314-quakeinline11,0,7853878.photo

http://latimesphoto.files.wordpress.com/2011/03/la-0315-japan06.jpg

http://latimesphoto.files.wordpress.com/2011/03/la-0315-japan13.jpg

MID-AMERICA EARTHQUAKE CENTER

http://mae.cee.uiuc.edu/software_and_tools/MAEviz.html

MINERAL RESEARCH AND EXPLORATION GENERAL DIRECTORATE

http://www.mta.gov.tr/v2.0/eng/default.php?id=marmara-earthquake

SYNER-G

http://www.vce.at/SYNER-G/index.htm

TAGESSSCHAU ONLINE

http://www.tagesschau.de/multimedia/bilder/erdbebenspanien126.html

http://www.tagesschau.de/multimedia/bilder/erdbebenspanien126_mtb-1_pos-16.html#colsStructure

THE BOSTON GLOBE

http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/italy_04_08/q28_18569319.jpg

http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/italy_04_08/q08_18577165.jpg

http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/italy_04_08/q21_18590581.jpg

USGS

http://earthquake.usgs.gov/earthquakes/eqarchives/year/

http://earthquake.usgs.gov/earthquakes/world/historical_mag.php

http://earthquake.usgs.gov/learn/today/index.php?month=5&day=26&submit=View+Date

http://neic.usgs.gov/neis/eq_depot/1999/eq_990817/

YANN ARTHUS BERTRAND

http://www.yannarthusbertrand.org/yann2/index2.php?option=com_datsogallery&func=wmark&oid=1805

44

Appendix A

A.1 LIST OF URBAN AUDIT INDICATORS

List of Urban Audit Variables

Code Label Unit Spatial unit LCA annual

DE1001V Total Resident Population number CLSN LCA annual DE1002V Male Resident Population number CLSN annual DE1003V Female Resident Population number CLSN annual DE1040V Total Resident Population 0-4 number CLSN LCA annual DE1043V Total Resident Population 5-14 number CLSN LCA annual DE1046V Total Resident Population 15-19 number CLSN LCA annual DE1049V Total Resident Population 20-24 number CLSN LCA annual DE1052V Total Resident Population 25-54 number CLSN LCA annual DE1025V Total Resident Population 55-64 number CLSN LCA annual DE1028V Total Resident Population 65-74 number CLSN LCA annual DE1055V Total Resident Population 75 and over number CLSN LCA annual DE2001V Residents who are Nationals number CLSN LCA annual DE2002V Residents who are Nationals of other EU Member State number CLSN LCA DE2003V Residents who are not EU Nationals number CLSN LCA DE2005V Residents who are not EU Nationals and citizens of a country with high HDI number CLSN

DE2006V Residents who are not EU Nationals and citizens of a country with a medium or low HDI number CLSN

DE2004V Nationals born abroad number CLSN DE3001V Total Number of Households (excluding institutional households) number CLSN LCA annual

Dem

ogra

phy

DE3017V Total Resident Population living in households (excluding institutional households) number CLSN

DE3002V One person households number CLSN annual DE3005V Lone parent households (with children aged 0 to under 18) number CLSN DE3008V Lone pensioner (above retirement age) households Total number CLSN SA1001V Number of conventional dwellings number CLSN LCA annual SA1012V Households in social housing number CLSN SA1018V Dwellings lacking basic amenities number CLSN SA2016V Total deaths under 65 per year number CLSN SA2019V Total deaths per year number CLSN annual SA3001V Total number of recorded crimes within city [country for national data] number CLSN

Soc

ial

SA3007V Number of domestic burglary number CLSN annual EC1001V Total Economically Active Population number CLSN LCA annual EC1002V Male Economically Active Population number CLSN annual EC1003V Female Economically Active Population number CLSN annual EC1142V Total Economically Active Population 15-24 number CLSN LCA EC1010V Residents Unemployed number CLSN LCA annual EC1148V Residents Unemployed 15-24 number CLSN LCA EC1151V Residents Unemployed 55-64 number CLSN EC3039V Median disposable annual household income euro CLSN LCA EC3056V Total Number of Households (relating to the reported household income) number CLSN

EC3057V Total Number of Households with less than half of the national average disposable annual household income number CLSN annual

EC3060V Total Number of Households reliant on social security benefits (>50%) number CLSN

Eco

nom

ic A

spec

ts

EC3063V Individuals reliant on social security benefits (>50%) number CLSN CI1009V City Elections: Number of voters turned out number CS LCA CI1020V Participation rate at national elections ratio CS LCA

Civ

il in

volv

emen

t

CI1021V Participation rate at city elections ratio CS LCA

TE2025V "Number of residents (aged 15-64) with ISCED level 0, 1or 2 as the highest level of education" number CLSN LCA

Trai

ning

an

d ed

ucat

ion

TE2028V Number of residents (aged 15-64) with ISCED level 3or 4 as the highest level of education number CLSN LCA

46

TE2031V Number of residents (aged 15-64) with ISCED level 5 or 6 as the highest level of education number CLSN LCA

EN5003V Total land area (km2) according to cadastral register km2 CLSN LCA EN5012V Green space area (km2) km2 CLS

Env

ironm

ent

EN5001V Green space (in hectares) to which the public has access hectares CLS TOTAL 50 variables in 6 categories on SCD level

A.2 LIST OF URBAN AUDIT VARIABLES

Code List of Urban Audit Indicators Numerator Denominator Spatial Unit LCA key DE1001I Total resident population DE1001V - C,L,S LCA key DE1040I Proportion of total population aged 0-4 DE1040V DE1001V C,L,S LCA DE1003I Proportion of females to males in total population DE1003V DE1002V C,L,S DE1061I Total population change over 1 year DE1001V (t) DE1001V (t-1) C,L,S LCA key

DE1062I Total annual population change over 5 approx.years DE1001V (t) nSQR(DE1001V) (t-n) C,L,S LCA key

DE2005I Proportion of Residents who are not EU Nationals and citizens of a country with high HDI DE2005V DE1001V C,L,S

DE2006I Proportion of Residents who are not EU Nationals and citizens of a country with a medium or low HDI DE2006V DE1001V C,L,S

DE3003I Total number of households DE3001V - C,L,S LCA DE3004I Average size of households DE3017V DE3001V C,L,S key DE3002I Proportion of households that are 1-person households DE3002V DE3001V C,L,S key DE3005I Prop. of households that are lone-parent households DE3005V DE3001V C,L,S

Dem

ogra

phy

DE3008I Prop. households that are lone-pensioner households DE3008V DE3001V C,L,S SA1001I Number of dwellings SA1001V - C,L,S LCA SA1018I Proportion of dwellings lacking basic amenities SA1018V SA1001V C,L,S SA1012I Proportion of households living in social housing SA1012V DE3001V C,L,S

SA2029I Crude death rate per 1000 residents SA2019V*1000

DE1001V C,L,S

SA2030I Crude death rate of male residents per 1000 male residents

SA2020V*1000

DE1002V

C,L,S

SA2031I Crude death rate of female residents per 1000 female residents SA2021V*1000

DE1003V C,L,S

SA2019I Total deaths per year SA2019V C,L,S

Soci

al A

spec

ts

SA2016I Mortality rate for <65 per year SA2016V

DE1040V + DE1043V + DE1046V + DE1052V + DE1025V

C,L,S

48

Code List of Urban Audit Indicators Numerator Denominator Spatial Unit LCA key

SA3001I Total Number of recorded crimes per 1000 population SA3001V*1000

DE1001V C,L,S

EC1201I Annual average change in employment over approx. 5 years EC1001V(t)- EC1001V(t-n)

nSQR(EC1001V -EC1001V)(t-n) C,L,S LCA

EC1010I Number of unemployed EC1010V - C,L,S LCA EC1020I Unemployment rate EC1010V EC1001V C,L,S LCA key EC1148I Proportion of residents unemployed 15-24 EC1148V EC1142V C,L,S LCA EC1202I Proportion of unemployed who are under 25 EC1148V EC1010V C,L,S LCA

EC1005I Net activity rate residents aged 15-64 EC1001V-EC1010V

DE1046V + DE1049V + DE1052V + DE1025V

C,L,S LCA

EC1006I Net activity rate residents aged 15-24 EC1142V-EC1148V

DE1046V +DE1049V C,L,S

EC1007I Net activity rate residents aged 55-64 EC1145V-EC1151V DE1025V C,L,S

EC3039I Median disposable annual household income (for city or NUTS 3 region) EC3039V - C,L,S

CA

EC3057I Percent. households with less than half nat.aver.income EC3057V EC3056V C,L,S key

EC3055I Percent. households with less than 60% of the national median annual disposable income EC3055V EC3056V C,L,S

EC3060I Proportion of households reliant upon social security EC3060V EC3056V C,L,S

Econ

omic

Asp

ects

EC3063I Proportion of individuals reliant on social security EC3063V DE1001V C,L,S

TE2025I Prop. of working age population qualified at level 1 or 2 ISCED TE2025V

DE1046V + DE1049V +DE1052V +DE1025V

C,L,S LCA key

TE2028I Prop. of working age population qualified at level 3 or 4 ISCED TE2028V

DE1046V + DE1049V + DE1052V + DE1025V

C,L,S LCA

Trai

ning

and

edu

catio

n

TE2031I Prop. of working age population qualified at level 5 or 6 ISCED TE2031V

DE1046V + DE1049V + DE1052V + DE1025V

C,L,S LCA key

ro n EN5003I Total land area (km2) -according to cadastral register EN5003V - C,L,S LCA key

Code List of Urban Audit Indicators Numerator Denominator Spatial Unit LCA key

EN5001I Green space (in m2) to which the public has access per capita EN5001V*10000

DE1001V C,L,S

EN5012I Proportion of the area in green space EN5012V EN5003V C,L,S EN5101I Population density: total resident pop. per square km DE1001V EN5003V C,L,S LCA key

TOTAL 41 Indicators in 5 categories on SCD level