BADAN PUSAT STATISTIK
Jl. Dr. Supomo 6-8 Jakarta 10710 Indonesia Telp. +6221-3841195, 3842508, 3810291 Fax. +6221-3857046 Email:
[email protected]
7th Floor Menara Thamrin Jl. M. H. Thamrin Kav. 3 Jakarta 10250 Indonesia Telp. +6221-3141308, 3907121 Fax. +6221-3904914, 3192702 Website: http://indonesia.unfpa.org
GUIDELINES FOR THE USE OF POPULATION DATA IN DISASTER MANAGEMENT
BNPB
Jl. Ir. H. Juanda No. 36 Jakarta Indonesia Telp. +6221-3442734, 3442985, 3443079 Fax. +6221-3505075 Email:
[email protected]
GUIDELINES FOR THE USE OF POPULATION DATA IN DISASTER MANAGEMENT
BNPB
BADAN PUSAT STATISTIK
GUIDELINES FOR THE USE OF POPULATION DATA IN DISASTER MANAGEMENT
BADAN PUSAT STATISTIK
AUTHORS EXECUTIVE EDITORS Sutopo Purwo Nugroho Razali Ritonga Rosilawati Anggraini
EDITORS
Agus Wibowo Indra Murty Surbakti Ario Akbar Lomban Hermawan Agustina Teguh Harjito Dandi Handiyatmo Dian Oktari Narwawi Pramudhiarta Nuraini
CONTRIBUTING EDITORS Armando Levinson Muhammad Anshory
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WRITERS
Suprapto Ratih Nurmasari Nurul Maulidhini Sri Dewanto Edi Aulia Ismi Savitri Theophilus Yanuarto Trophy Endah Rahayu Parwoto Dwi Trisnani Sri Wahyuni Yogo Atyo Jatmiko
DATA PROCESSORS Apriliani Nurida DA Elfrida Zoraya Dian Daniaty
DESIGNER AND PHOTOGRAPHER Andri Cipto Utomo
Guidelines For The Use Of Population Data In Disaster Management
FOREWORD Praise be to God, the Almighty, for with His mercy and grace we are able to present these Guidelines for the Use of Population Data in Disaster Management. Disaster management is, at its core, about ensuring the best services are provided to disaster victims. Community is the major stakeholder, as ultimately, disaster response is a human-to-human activity. Every effort should be focused toward securing the wellbeing of affected victims. In this respect, population data is crucial to successful humanitarian assistance missions. The indicators and variables provided by population data are critical in planning for the 13 types of disasters that affect areas of Indonesia. From the immediate to 24-hours, 48-hours and 72-hours after a disaster strikes, population data plays a critical role. Collaboration between the National Disaster Management Agency (BNFB) and Statistic Indonesia (BPS), together with the support of UNFPA Indonesia, can help to provide the necessary information to minimize the impact of natural disasters. We hope this publication becomes a useful tool for institutions, agencies and humanitarian workers involved in disaster management, working together to build a resilient nation.
The Authors Team
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INTRODUCTORY REMARKS In a humanitarian crisis we need to make good decisions fast. To ensure good decision making requires timely access to information – information created by appropriate analysis of accurate and valid data. This is especially important in a crisis where the timelines and efficiency of resource allocation is critical. In all phases of a humanitarian crisis we need decisions that are underpinned by accurate data. Pre-disaster, adequate preparedness is essential to a timely and effective response to an emergency including consideration of the data needs and standards. During a disaster, decisions on actions to be taken and resource mobilization require reliable information and data. Post disaster, accurate data is needed to measure the impact of interventions including a valid baseline from which comparisons can be drawn. To date, humanitarian agencies have often reached for the most available information without much concern for its reliability. There has been a lack of coordination between agencies on how data is collected and what data is used. A real need for a standardization of data collection in Indonesia has emerged. In light of this, in 2011 BNPB endorsed a regulation to facilitate a standardization of disaster data, the PERKA no 8 year 2011. The PERKA outlined the data required to be collected by BNPB and BPBD during each phase of a disaster. In reality, implementation of the PERKA has proved difficult as many parameters or variables are not easy to access and there is limited guidance on how to obtain the data. The need for a national guideline on disaster data to support the implementation of the PERKA has become clear.
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Guidelines For The Use Of Population Data In Disaster Management
Toward this end, a guideline on the use of population data for disaster management was developed through collaboration between BNPB-BPS, with technical and funding support from UNFPA. The guideline, outlined in this book, provides guidance on the use of population data during all phases of disaster management – pre-disaster, during disaster and post-disaster. It describes the sources of population data that can be used, methods of data collection, variables or indicators, and how population data can be utilized. The book also documents the experiences of BNPB and other line ministries in the use of population data in each phase of disaster management. We hope that this book – providing guidelines on how to acquire, process and utilize population data – will be a valuable reference for BNPB and other humanitarian actors to ensure a more effective humanitarian emergency preparedness and response that is well- grounded in accurate data.
Jakarta, April 2014 Jose Ferraris UNFPA Representative United Nations Population Fund
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Table of Contents Authors
ii
Foreword
iii
Introductory Remarks
iv
Table of Contents
vi
List of Tables
viii
List of Figures
ix
Acronyms
x
Chapter 1 Introduction
1
1.1 Background
2
1.2 Objectives
3
1.3 Goals
3
1.4 The Use of Population Data In Disaster Management
3
Chapter 2 Population Data in Disaster Management 2.1 Population Data in Each Phase of Disaster Management
6
2.2 Population Data Sources that Can be Used in Disaster Management
8
Chapter 3 Population Data Collection and Use in the Pre-Disaster Phase
vi
5
35
3.1 Data Collection Sources and Methodology in the Pre-Disaster Phase
36
3.2 Population Data Needs and Indicators in the Pre-Disaster Phase
37
Guidelines For The Use Of Population Data In Disaster Management
3.3 The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Pre-Disaster Phase
42
3.4 Population Data Use and Analysis in the Pre-Disaster Phase
43
Chapter 4 Population Data Collection and Use in the Emergency Response Phase
51
4.1 Data Collection Sources and Methodology in the Emergency Response Phase
52
4.2 Population Data Needs and Indicators in the Emergency Response Phase
53
4.3 The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Emergency Response Phase
57
4.4 The Use of Population Data in the Emergency Response Phase
58
Chapter 5 Collection and Use of Population Data in the Post-Disaster Phase
61
5.1 Data Collection Sources and Methodology in the Post-Disaster Phase
62
5.2 Population Data Needs and Indicators in the Post-Disaster Phase
64
5.3 The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Post-Disaster Phase
71
5.4 The Uses of Population Data in the Post-Disaster Phase
71
Chapter 6 Conclusion
77
Bibliography
81
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List of Tables Table 2.1 Table 2.2 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 4.1 Table 4.2 Table 4.3 Table 5.1 Table 5.2 Table 5.3
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Spatial Data Needs for Disaster Management Population Database from BPS Minimum Indicators of Population Data Requirements for Disaster Management Priority Population Data Indicators that should be used in the Pre-Disaster Phase Number of Population Affected by Rokatenda Volcano Eruption Total Female Population by Age Group in Palue Sub-District, Sikka District, East Nusa Tenggara Total Male Population by Age Group in Palue Sub-District, Sikka District, East Nusa Tenggara Total Population of Vulnerable-Aged People in Palue Sub-District, Sikka District, East Nusa Tenggara Number of People Explosed to Earthquake Hazard Zones Total Vulnerable Group Population Exposed to Earthquake Hazards Tables from PERKA No. 8/2011 in the Emergency Response Phase Variables that Should Be Collected in Data Collection Process of IDP’s Statistical Estimation of High-Risk Populations Variables Needed in the Post-Disaster Phase Variables Available from Secondary Data that can be used in the PostDisaster Phase Data Needs during Transition, Recovery and Reconstruction Periods
Guidelines For The Use Of Population Data In Disaster Management
20 23 37 40 44 44 46 46 48 49 54 55 59 64 65 73
List of Figures Figure 2.1
Disaster Management Cycle
6
Figure 2.2
Flow of Data and Information Management
8
Figure 2.3
Home page for dibi.bnpb.go.id
27
Figure 2.4
Population Data at the Provincial Level
28
Figure 2.5
Population Data at the District Level
29
Figure 2.6
Population Data at the Sub-District Level
30
Figure 2.7
Population Data at the Village Level
31
Figure 2.8
Display of DIBI Population Data Graph
32
Figure 2.9
Display of Results Statistics Level Up Village / Village
33
Figure 3.1
Population Density of Palue Island and Mount Rokatenda Disaster Prone Areas
45
Figure 3.2
Indonesian Earthquake Hazard Map
48
Figure 4.1
Data and Information Flow and Management
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Acronyms ASTER
Advanced Space Borne Thermal Emission and Reflection Radiometer
BASARNAS
National Search and Rescue Agency
BIG
Geospatial Information Agency
BMKG
Meteorology, Climatology and Geophysics Agency
BNPB
National Disaster Management Agency
BPBD
Regional Disaster Management Agency
BPS
Statistics Indonesia
CWIQ
Core Welfare Indicators Questionnaire
DAS
Watershed
DDA
Regions in Figures
DHS
Demographic and Health Survey
DIBI
Indonesian Disaster Data and Information
DISHIDROS
Hydrology and Oceanography Service
ESDM
Energy and Mineral Resources
GBV
Gender-Based Violence
GIS
Geographical Information System
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Guidelines For The Use Of Population Data In Disaster Management
ISPA
Acute Respiratory Infection
K/L
Ministries/Institutions
KB
Family Planning (FP)
KK
Family Head
LAPAN
Space Agency
LSM
Non-Governmental Organization (NGO)
MICS
Multiple Indicators Cluster Survey
NASA
National Aeronautics and Space Administration
NTB
West Nusa Tenggara
NTT
East Nusa Tenggara
OSM
Open Street Map
PERKA
Head (of BNPB) Regulation
PODES
Village Potentials
PRSP
Poverty Reduction Strategy Papers
PU
Public Works
PUSDALOPS PB
Center for Operations Control of Disaster Management
PVMBG
Center for Volcanology and Geological Hazard Mitigation
SAKERNAS
National Labor Force Survey
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SDA
Natural Resources
SDKI
Indonesian Demographic and Health Survey (IDHS)
SGBV
Sexual and Gender-Based Violence
SKPD
Regional Working Units
SP
Population Census
SRTM
Shuttle Radar Topography Mission
SUPAS
Intercensal Population Survey
SUSENAS
National Socioeconomic Survey
SWAP
System Wide Action Plan
TB
Tuberculosis
TNI
Indonesian Armed Forces
UKM
Small and Medium Enterprises
UNDAF
United Nations Development Assistance Framework
UNFPA
United Nations Population Fund
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BNPB, BPBD, Armed Forces/Police, BASARNAS and related OPDs perform a joint rehearsal during the display of SRC PB squad of eastern region in Malang, East Java. Source: BNPB
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INTRODUCTION
Guidelines For The Use Of Population Data In Disaster Management
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1.1. Background Indonesia is a disaster prone country from a multitude of perspectives. While the country’s geographical location between two continents and two oceans provides great potential for economic development, it also makes it vulnerable to disasters. Geologically, Indonesia is located on three plates: the Eurasian Plate, the Indo-Australian Plate and the Pacific Plate. Coupled with a number of active volcanoes scattered across the land and seas that make Indonesia rich in mineral reserves, there are also very dynamic geological forces that can lead to potential disasters. Demographically, Indonesia’s large population with ethnic, cultural, and religious diversity – as well as disparities in economic and political conditions – are potential triggers of social conflict. Human behaviour during an emergency situation can create disturbances, ranging from small to national scale conflict. Political and ethnic unrest, armed conflicts with the military and social instability in Indonesia are common. Disaster management requires a structured, focused and integrated approach, built on a strong foundation. However, many responses to disasters so far have not been based on systematic and planned measures. This need can be fulfilled by the presence of disaster risk assessment, which helps to determine the likelihood and magnitude of losses due to existing threats. By knowing the probability and
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magnitude of loss, the planning and integration of disaster management strategies can become more effective. Disaster risk assessment forms the basis of synchronization, and impacts on the effectiveness of disaster management in given areas. Disaster risk assessment can be defined as an integrated mechanism that provides a comprehensive picture of the risks of a disaster in an area, by analyzing the levels of hazards, losses and capacity of the region. Disaster risk assessment can be formulated as follows: Risks of disaster ≈ Hazards * Vulnerability
Capacity
It is important to note that this approach cannot simply be equated as a mathematical formula. Rather, it is used to show the relationship between hazards, vulnerabilities and capacity, and evaluate the disaster risk level of an area. In February 2013, UNFPA facilitated the signing of a Memorandum of Understanding (MoU) between BPS-Statistics Indonesia and the National Disaster Management Agency (BNPB) on the provision of population data for disaster management. Utilizing the 2010 population census (SP) data and the 2011 village potentials
Guidelines For The Use Of Population Data In Disaster Management
(PODES), BNPB will integrate population data into disaster management and risk reduction strategies. At this stage of agreement, BPS
provides data only at the village level, which is the lowest of administrative boundaries.
1.2. Objectives The objectives of these guidelines are: 1. To provide technical guidance for BNPB Head Regulation No. 8 of 2011 on the standardization of disaster data. 2. To provide guidance to humanitarian aid workers in the application of population data for disaster management.
A number of BASARNAS personnel and local people evacuate flood victims in the village of Jleper, Demak, Central Java. Thousands of houses in four villages were flooded as high as 1-1.5 meters when Wulan river dike collapsed. Source: BNPB
1.3. Targets These guidelines are designed to assist government and non-government humanitarian aid workers, at both the individual and institutional
level, in the use of population data for disaster management.
1.4. Use of Population Data in Disaster Management Emergency preparedness spans a variety of fields, from natural events to man-made disasters to the outbreak of disease, such as pandemic influenza. Planning requires careful consideration of external factors and the particular needs of population groups. It is therefore important for disaster prone areas to maintain up-to-date and robust population data.
Population data is usually derived from population and housing censuses and large-scale sampling surveys. With the information obtained from these sources, as well as from other channels, more focused and targeted planning can be undertaken to prepare and analyze the impact of natural or other types of disasters that put the population at risk. No national disaster plan can be successful
Guidelines For The Use Of Population Data In Disaster Management
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without integrating population data into all phases and aspects of emergency preparedness, from disaster relief to recovery and reconstruction. Another important consideration in disaster management is that systems of preparedness and emergency response are generally designed for people with no disabilities, whose escape or rescue would naturally involve walking, running, driving, seeing, hearing, and responding quickly to instructions, warnings, and announcements.
Many other groups, such as children and the elderly, although not disabled, are unable to care for themselves in times of disaster. Agencies in charge of disaster management planning should therefore take into account the diversity of the population, and ensure that all groups are properly accounted for. It is important to remember that in every community, there are vulnerable groups that need special assistance, and they need to be incorporated in the national system of disaster preparedness and emergency response.
Elementary and junior high school students participate in a rehearsal for disaster management in Palu. Source: BNPB
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Guidelines For The Use Of Population Data In Disaster Management
Housing assistance from BNPB for the people of Karangkendal village, Sleman, Yogyakarta, victims of the eruption of Mount Merapi in 2010. Source: BNPB
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Population Data in Disaster Management
Guidelines For The Use Of Population Data In Disaster Management
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2.1. Population Data in Each Phase of Disaster Management In Indonesia, it is the government and local authorities that are responsible for disaster management operations. As defined under Law No. 24 of 2007 on Disaster Management, this constitutes a series of efforts that include mitigation, prevention, response and rehabilitation activities. Population data plays an important role in all phases of disaster management, outlined in the cyclical process below: 1. Pre-disaster: In this phase, an assessment
potential magnitude and distribution of a disaster. 2. Emergency response: This is the phase that usually receives the most attention and resources, as the affected population becomes the focus of government and humanitarian aid workers. Following the occurrence of a disaster, prompt and effective actions are necessary to save lives, protect health, and stabilize the state
There are three main disaster management phases: 1. The pre-disaster phase, consisting of: • The non-disaster situation, and • A potential disaster situation. 2. Emergency response, undertaken in the acute phase of disaster. 3. The post-disaster phase, performed in the aftermath of a disaster.
Figure 2.1 Disaster Management Cycle
of the total population, including vulnerable groups that could potentially be affected, is undertaken, taking into account the
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so that conditions do not worsen. Even in emergencies, assessment is a necessary element of ensuring action is carried out
Guidelines For The Use Of Population Data In Disaster Management
effectively. During this stage, there are typically two types of assessment that are needed: an initial rapid assessment determining the nature and magnitude of the emergency, as well as the possible need for external assistance, and secondly, a detailed sector assessment to plan, implement and coordinate the response. Rapid assessment should provide data on the number of people affected and immediate needs. 3. Early recovery: This phase seeks to accelerate the early recovery of socioeconomic activities and livelihoods in the affected area. It tries to ensure that nothing is missing from post-disaster interventions, and that knowledge of people’s livelihood (such as rural poverty) is taken into account. This phase uses data on the number of people affected, the estimated economic losses due to the disaster and the needs that are required to start the recovery process.
as quickly and efficiently as possible. Population data in the disaster affected area is very important in determining reconstruction plans, such as the need for temporary shelters, houses and schools. Precise and accurate information and planning depends on the availability of comprehensive supporting data. As part of disaster management, data collection and analysis should be conducted quickly and accurately in order to disseminate valid information. Data and information can be collected directly through interviews, or indirectly through sources including the media, the government, voluntary organizations, NGOs, and the community. The flow of data and information management is depicted as follows:
4. Rehabilitation and Reconstruction: This phase involves the rehabilitation and reconstruction – or construction – of public infrastructure, such as restoring the water supply, sanitation and health facilities, schools, roads, housing and shelter. The focus is to return the social and economic life of the community to its normal state
Guidelines For The Use Of Population Data In Disaster Management
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Figure 2.2 Flow of Data and Information Management
2.2. Population Data Sources That Can Be Used In Disaster Management Population data can be obtained from various sources, including the population census, population projections, household surveys, spatial data and regional figures available both online and
offline. Each data source has its strengths and limitations with regard to emergency response planning, which are outlined in the sections below.
2.2.1. Population Census Population censuses contain a complete enumeration of every resident in a country at a given period in time. Population census collects basic demographic characteristics, such as age, gender and residence, as well as socioeconomic and socio-cultural information, grouped by individuals and households. During the data collection process, information about various 8
types of community infrastructure, such as health facilities, schools, churches, village halls, markets and roads, is also collected. According to UN recommendations, each country should conduct a population census once in every ten years.
Guidelines For The Use Of Population Data In Disaster Management
The Strengths of Census: »» Census provides data on the entire population
of a country, grouped by administrative area and residence.
»» Census provides detailed population data
disaggregated into certain categories.
»» Census provides details about the key
elements of population dynamics, such as births, deaths and migration, which form the basis of future population projections.
»» Census provides details on some of the
characteristics of a household, and therefore allows the study of living conditions within a population, such as vulnerability.
»» Analysis of census data often provides an
early warning signal of potential humanitarian crisis situations, such as a very high population density, high dependency on the land, or unconventional population structures.
»» Census mapping collects valuable information
about the location and characteristics of important social infrastructure, such as health facilities, schools, churches, village halls, markets and roads, which are especially useful in situations of humanitarian crisis.
»» Census mapping groups populations within
the boundaries of existing administrative units. In the event of a disaster that affects only a portion of the region, a more realistic estimate of the affected population can be established. »» Most of the surveys conducted in a country
are based on master sampling frames from the latest census.
»» Census results are usually widely published
and disseminated, and used for development planning. They are recognized and official sources of information.
»» Census data is generally readily obtainable.
The Limitations of Census: »» Most countries, especially those vulnerable
to humanitarian crises, do not hold a census every 10 years. Figures may therefore no longer reflect the real situation.
»» Some census data is adjusted prior to
publication, and raw data files may contain information that is different from the information adapted and published. This makes the reconstruction of information for a particular part of the country very difficult.
Guidelines For The Use Of Population Data In Disaster Management
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Personnel of 2010 Population Census. Source: BPS
»» Census data does not provide all the information
that may be required for emergency planning.
»» The information collected during the census
may not be detailed enough, and does not usually reflect details relating to behaviour, aspirations or motivations of a community.
»» Although published census reports are easily
accessible, this information may not be very useful if data is needed on specific parts of a country. Raw data files, which allow the breakdown the data into smaller units, are usually not easily accessible. Most of them are poorly maintained and their quality declines over time.
2.2.2. Village Potential Survey Village Potential (PODES) surveys are conducted once every three years to collect a broad range of demographic and employment information pertaining to housing and environment, education and health services, 10
rural socio-cultural life, entertainment and sports, transportation, communication and information, land use, security, village autonomy, community empowerment, agricultural modules and natural disaster mitigation.
Guidelines For The Use Of Population Data In Disaster Management
PODES surveys are conducted according to village administrative boundaries, the most recent being performed in Indonesia in 2011. The key informant, usually the village head, provides the information collected in the survey, a factor that collectors and users of data should be aware of. The BPS sometimes conducts PODES one year before undertaking a large survey/census to update village data. The Strengths of PODES Survey: »» If stored properly and kept updated, PODES
data can provide important information for disaster assessment and early warning signs. The data provides valuable inputs for monitoring a development in particular regions of a country.
»» The State is an authoritative source of
information on the number and boundaries of administrative units, providing various official documents that are usually important for emergency preparedness and planning.
»» Administrative information is the basis for
the development of other robust information resources, such as geographic information systems, censuses and household surveys.
health facilities and other points of services are vital in determining emergency response scenarios. The Limitations of PODES Survey: »» The coverage and quality of data varies from
one administrative unit and period of time to another, so it can be difficult to assess the actual field situation and trends over time.
»» Access to sources is sometimes difficult,
either because the information is not coordinated at the central level, or because of other constraints such as confidentiality, bureaucracy, or funding from several sources.
»» PODES surveys usually present raw data
and may require a lot of processing before producing synthesized information that may be useful for emergency preparedness and planning.
»» The accuracy level of PODES survey data is
contingent on the officials’ knowledge about the village conditions.
»» Information about the number and condition
of public infrastructure, such as transportation and communication lines, educational and Guidelines For The Use Of Population Data In Disaster Management
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2.2.3. Population Projections Population projections provide estimates of the population at various periods in the future (and even the past), depending on specific assumptions. Population projection methods vary according to levels of sophistication, ranging from a simple mathematical formula to complex cohort component methods that apply calculations based on various assumptions, resulting in more complex and disaggregated data. Population projections come from both national and international sources. At the national level, BPS or other census institutions usually prepare population projections at the end of a data collection project, as do various national organisations. At the international level, the UN Statistics Division and the UN Population Division make projections for all of the world’s countries, using the same methodology and updating them when development partners supply new data. UN agencies even have a tendency to develop alternative population projections, to provide a point of comparison. This is important given that there is often no basis for comparing population projections, as the underlying assumptions and methods applied differ, and are not usually forthcoming. As emergency planning is focused on events that may happen in the future, it relies on population projection figures to scope estimated needs. 12
Based on the results of the 2010 Population Census, BPS has developed Indonesian population projections from 2010 to 2035. These projections are made using a component method, and are based on trends of fertility, mortality, and migration between provinces that are likely to occur during the next 25 years, at both the national and provincial level. The calculation of these population projections is accomplished by using the RUP (Rural Urban Projection). A technical team formed by BPS then discusses the results, which are assessed by officials from BAPPENAS, the National Population and Family Planning Board (BKKBN), the Ministry of Health, Statistics Indonesia, academics and other related agencies. These meetings also act as a reference for population projections that will be adopted in the long-term development plan (PJP). These projections are applied by all government agencies in preparing their respective plans. The results of a population projection are determined by the assumptions that are adopted. Usually, assumptions are based on birth rates, death rates, and migration patterns of the past, taking into account various factors that influence these three components. This information is then supplemented with the views of experts and decision-makers who are knowledgeable about population patterns.
Guidelines For The Use Of Population Data In Disaster Management
The Strengths of Population Projections: »» Population projections provide a basic
estimation of future needs, which can be used for emergency planning.
»» Unlike most census data, there are several
sources of population projections, some of which can be accessed online. These sources also provide detailed data based on the year of projections, and grouped by age and sex. This makes the projection of sub-population’s needs easier.
The Limitations of Population Projections: »» The longer the period of time since the data
for population projections was carried out, the less reliable the information becomes.
»» Most available population projections do
»» Most population projections are made at
the national level, classifying urban, rural and city areas. For proper contingency planning, data is required at the sub-national level, corresponding with projection years. Humanitarian organizations tend to make midterm projections between two time periods, using linear techniques to segregate data by administrative units. This is then projected using patterns observed in the past, or certain theoretical benchmarks.
»» Expertise in calculating population projections
is relatively rare, but various types of software is available. Most institutions, however, calculate projections independently, inputting basic information to project “good” or “not good” results. If the result is the latter, the data inputted will be modified at will, irrespective of the physical constraints in the field.
not provide information on the methods and assumptions used to underpin projections, making it difficult to assess their quality. Most humanitarian agencies rely on the projections that supply the most disaggregated information.
»» Most population projections are based on
five-year intervals, with the data grouped by five-year age ranges. Emergency planning, however, is best designed on an annual basis, with needs usually estimated according to sub-populations, not age ranges.
The launching of Population Projection of Indonesia, Jakarta, 29 January 2014. Source: BAPPENAS
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»» In most developing countries, the ease of
access to projected population figures has contributed to the decline of regular census operations. Some countries are known to use
various types of development frameworks – including PRSP, UNDAF and SWAP – despite the fact that census has not been undertaken for over two decades.
2.2.4. Household Surveys Many countries and development partners rely on national sampling surveys to gather detailed data from households. The result is a simplified overview of the country that is used to reflect administrative units. Household surveys typically span domains including household consumption, poverty and living conditions (NGOs, CWIQ, MICS, etc.), agriculture and food production, nutrition (IDHS), fertility behaviour and other reproductive health dimensions (DHS, MICS, CWIQ), migration, labour force and employment, and activities within the informal sector. The Strengths of Household Surveys: »» Surveys are generally cheaper and can
therefore be conducted much more frequently and regularly than a census. They tend to provide more updated information about a target population.
»» Survey
data provides more detailed information about the behaviour, aspirations and motivations of population groups, which can enhance emergency planning.
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»» Some surveys focus on specific categories
of at-risk groups, which allows for special attention to be given to vulnerable subpopulations in emergency planning.
»» Some survey data can also provide early
warning signals about potential outbreaks and disaster situations.
The Limitations of Household Surveys: »» Not all survey data can be generalized, and
the segregation of results is contingent on the sampling method adopted. In most developing countries, survey results only apply to the second level of the administrative hierarchy (region or province), and can rarely be applied to the third (district) level. This means that emergency planning for smaller administrative units cannot utilise survey results. This constraint is often ignored, however, and indicators are applied to each administrative level without considering their validity.
»» Not all surveys are sample surveys, and not
all survey results can be generalized. An in-
Guidelines For The Use Of Population Data In Disaster Management
depth study of gender-based violence (GBV), for example, can provide tangible evidence of its presence in certain parts of a country during a given period. However, this data cannot be used in emergency planning to predict possible cases of GBV. There are many qualitative studies that provide illuminating data on what happens during humanitarian crisis situations, especially those that focus on conflict, however such data should not be generalised. »» Surveys are usually carried out by ministries,
institutions and development partners at various periods and in different locations
of the country. Often there is no central coordination, and the terms and approaches used are diverse. This makes it difficult to compare the results of one region and time frame with another. »» Most of the implementing agencies tend to
keep their data, and it is usually embedded in national or provincial databases that are difficult to access. Differences in the methods used to collect, process, store and analyze the data also makes it difficult for emergency planning officials, who may be presented with conflicting evidence and data sources.
2.2.5. Region in Figures (DDA) The Region in Figures handbook is an annual publication issued by the district/city and provincial BPS - Statistics Indonesia. The publication provides a general overview of the geographic, administrative, social, agricultural and economic conditions within the regional government of the district/city or province. The DDA is an official publication, and is used at many levels. At the provincial level it is known as Province in Figures, the district publishes it as District in Figures, and the municipality government publishes Municipality in Figures. The district/city government even provides statistical data at subdistrict level known, as Sub-District in Figures.
The DDA is a collection of regional baseline data, collected by primary methods such as local BPS surveys, and from secondary information provided by Government, private agencies or local enterprises. The DDA provides statistical data that helps form a picture of development activities that have been carried out in a particular area. This data is then used to evaluate the results of such activities, so that opportunities and challenges ahead can be identified. The DDA is a useful resource in district/city and provincial planning, and can be used in reviewing and evaluating development policies.
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Each local government publishes the Region in Figures, and presents tables and graphs that describe data in a particular locality. In general, Region in Figures focus on 10 key areas: 1. Geography 2. Governance 3. Population and employment 4. Social welfare 5. Agriculture 6. Industry, energy, mining, and construction 7. Trading 8. Transportation, postal service, communication, and tourism 9. Financial and prices 10. Regional revenue
Knowledge, Attitudes, and Practice (KAP) pilot survey in the city of Padang. Source: BNPB
The Limitations of DDA: »» The accuracy of data contained in the DDA is
The Strengths of DDA: »» The preparation and publication of the DDA
is conducted every year, so the information is usually more updated than other demographic data.
»» The preparation of the DDA involves regional
largely determined by the quality of the data maintained by each SKPD.
»» The preparation of DDA involves the SKPDs,
coordinated by BAPPEDA along with BPS. There is possibly a lack of coordination across these bodies that impacts on the quality of data obtained.
working units (SKPD) that are coordinated by BAPPEDA, along with district/city or provincial BPS. This data is officially accepted by the local government, and used in the evaluation and preparation of local development plans.
16
Guidelines For The Use Of Population Data In Disaster Management
2.2.6. Spatial Data Spatial data is related to geographical reference, and an important medium for development planning and sustainable natural resource management across national, regional and local groupings. The use of spatial data has increased considerably with the development of digital mapping technology, and its utilization in Geographical Information Systems (GIS). GIS incorporates geographical features with tabular data to map, analyze, and assess realworld problems. The technology is centred on geography, with data related in various ways to locations on earth. Spatial data is also often accompanied by attribute data. For example, where spatial data maps the location of a school, attribute data captures information such as the name of the school, the levels of education provided and the students’ capacity. This pairing of data allows GIS to be an effective problemsolving tool. GIS combines layers of territorial information to provide a better understanding of an area and its inhabitants. What information layer is used depends on the purpose or end-use of the information. Currently, GIS is used in almost all professional disciplines, including public health. Mapping the layers of information allows the user to track patterns and draw conclusions, identify at-risk populations, and test the possibility of
intervention. By combining the power of maps and attribute data, BNPB can analyze a variety of emergency scenarios. For example, GIS can be used to estimate the level of infrastructure and environmental damage, which are essential to rehabilitation and construction efforts. Various public and private institutions, such as the Geospatial Information Agency (BIG), the National Disaster Management Agency (BNPB), the Ministry of Transportation, the Ministry of Public Works, the Ministry of Forestry, the Ministry of Agriculture, BAPPENAS, BPS, BPPT, LAPAN and LIPI are contributing to the development and updating of spatial data in Indonesia.
The Strengths of Spatial Data: »» Maps produced by GIS technologies provide
valuable information about the location and characteristics of vital social infrastructure such as health facilities, schools, churches, government offices, markets and roads, which are especially useful in situations of humanitarian crisis.
»» GIS technology enables the creation of detailed
maps in specific areas which, when coupled
Guidelines For The Use Of Population Data In Disaster Management
17
with information regarding the number and location of the population, can be extremely useful in emergency response situations. »» Once created, spatial databases are easier
and cheaper to maintain and access than the basic printed maps. Substantial savings in terms of time and resources are obtainable in the long-term.
The Limitations of Spatial Data: »» Spatial databases require considerable time,
money and human resources, as well as
the special data needed to populate them. Although these one-time expenses are usually compensated by the time and the overall benefits obtained by the project, it is usually not easy to get the necessary funds. »» When spatial databases are created using
geographic or demographic information that is inaccurate, the results may lead to wrong decisions and losses of life during a disaster situation.
»» Advances in technology often require
constant updates in software and hardware to maintain GIS databases. This requires regular monitoring and staff training, but
The use of Spatial Data in the Disaster Monitoring. Source: Disaster Monitoring BNPB (http://geospasial.bnpb.go.id/pantauanbencana) 18
Guidelines For The Use Of Population Data In Disaster Management
some institutions do not have the capacity to prioritise this. »» The replacement of staff over time creates
issues of knowledge transfer, an ongoing challenge in the maintenance and use of GIS.
Spatial Data Needs for Disaster Management: Table 2.1 shows the breakdown of spatial data that is useful in disaster management. The information is broken down into data needs, spatial data, how this is represented, and the data sources.
Website of Geospatial BNPB Source: http://geospasial.bnpb.go.id
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Table 2.1 Spatial Data Needs for Disaster Management DATA NEEDS
SPATIAL DATA
TYPE
SOURCES
Village boundaries Sub-district boundaries District boundaries Administrative Area
Polygon, Lines
BIG, BPS
Points
BIG, navigasi.net, OSM
Points
BIG, navigasi.net, OSM
Points
BIG, navigasi.net, OSM
Provincial boundaries Provincial capitals District capitals Big cities Villages/Sub-villages Road networks Railways Bridges Dams
Infrastructure and Public Facilities
SABO dams Floodgates Levees Schools Hospitals Other health facilities (health centres, subhealth centres, etc.) Places of worship Airports
Infrastructure and Public Facilities
Harbours Terminals Temporary shelters
20
Guidelines For The Use Of Population Data In Disaster Management
DATA NEEDS Infrastructure and Public Facilities Infrastructure and Public Facilities
Population and Land Use
SPATIAL DATA Evacuation points Hotels and lodgings
TYPE
SOURCES
Points
BIG, navigasi.net, OSM
Points
BIG, navigasi.net, OSM
POI Railway stations Buildings Settlements
Polygon
Population density/land scan
Raster Image
Land use
Polygon
BIG, BPS
Watersheds (DAS) Rivers Shorelines Coastal areas Hydrology, Meteorology, and Lakes Climatology Bathymetry (seafloor depth)
Lines, polygon
BIG, OSM, DISHIDROS
Points
BIG, BMKG, ESDM, PU
Lines
BIG, BMKG, ESDM, PU
Daily rainfalls Wind and cloud movement Waves and tides Fire points/hot spots Topography and Geology
High points Earthquake points Volcanoes and mountains Contour lines
Topography and Geology
Faults Active faults Subduction zones
Guidelines For The Use Of Population Data In Disaster Management
21
DATA NEEDS Topography and Geology
Disaster Zoning
Satellite Imagery, Radar, and Aerial Photos
SPATIAL DATA
TYPE
SOURCES
Geology and lithology Soil types
Polygon
Land shapes Disaster hazard (flood, earthquake, tsunami, etc) Maps of areas affected by disaster Resident areas exposed to hazards Points of refugees Disaster command posts Evacuation paths High-resolution satellite imagery Aerial photos SRTM ASTER Digital elevation models Hill Shade Blue Marble
Polygon, Raster Image Points Lines Raster Image
Raster Image
BIG, BMKG, ESDM, PU BNPB, BMKG, ESDM, PU
BNPB LAPAN LAPAN, TNI BIG, LAPAN LAPAN BIG NASA
2.2.7. Merging SP 2010 and PODES 2011 Data Demographic data underpinning emergency circumstances in Indonesia is the product of collaboration between the National Disaster Management Agency (BNPB) and Statistics Indonesia (BPS). This data includes the combined results of the population census (SP) of 2010 and the village potential (PODES) survey data of 2011. These two data sources are considered complementary to one another, as the population 22
census provides detailed information about people living in disaster-prone areas, and PODES survey data focuses on infrastructure and public facilities. The combination of the two sources can be used as a pre-disaster baseline that can guide BNPB in developing appropriate preparedness and risk-reduction strategies. The 2010 Population Census and the 2011 Village Potentials data is presented as follows:
Guidelines For The Use Of Population Data In Disaster Management
Table 2.2 Population Database from BPS NO
VARIABLES/INDICATORS
A.
SOURCES
ADMINISTRATIVE AREA
1.
Province code
SP 2010
2.
Name of province
SP 2010
3.
District code
SP 2010
4.
Name of district
SP 2010
5.
Sub-district code
SP 2010
6.
Name of sub-district
SP 2010
7.
Village code
SP 2010
8.
Name of village
SP 2010
B.
LOCATION, GEOGRAPHIC AND TOPOGRAPHIC CONDITIONS
9.
Village location (peak/slope/valley/plains)
PODES 2011
10.
Slope of land
PODES 2011
11.
Village altitude (m above sea level)
PODES 2011
12.
Village directly adjacent to the sea
PODES 2011
13.
Sea level rises over the last 5 years
PODES 2011
14.
Existence of mangrove forests
PODES 2011
C.
TOTAL POPULATION
15.
Total population (male, female)
SP 2010
16.
Number of households
SP 2010
17.
Total male population by age groups (5-6, 7-9, 10-12, 13-14, 15, 16, 17, 18, 19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65+)
SP 2010
18.
Total female population by age groups (5-6, 7-9, 10-12, 13-14, 15, 16, 17, 18, 19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65+)
SP 2010
19.
Number of farming families
PODES 2011
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NO 20. D.
Number of families whose members are farming labourers
SOURCES PODES 2011
EDUCATION
21.
Number of male population attending school by age groups (7-12, 13-15, 16-17)
SP 2010
22.
Number of male population aged 15 yrs. who have not yet completed primary school
SP 2010
23.
Number of male population aged 15 yrs. and over graduated from primary school
SP 2010
24.
Number of male population aged 15 yrs. and over graduated from junior high school
SP 2010
25.
Number of male population aged 15 yrs. and over graduated from senior high school
SP 2010
26.
Number of male population aged 15 yrs. and over graduated from college
SP 2010
27.
Number of female population attending school by age groups (7-12, 13-15, 16-17)
SP 2010
28.
Number of female population aged 15 yrs. who have not/not yet complete primary school
SP 2010
29.
Number of female population aged 15 yrs. and over graduated from primary school
SP 2010
30.
SP 2010
31.
Number of female population aged 15 yrs. and over graduated from junior high school Number of female population aged 15 yrs. and over graduated from senior high school
32.
Number of female population aged 15 yrs. and over graduated from college
SP 2010
33.
Number of public and private schools (kindergarten, elementary, junior high, senior high, vocational schools, colleges/universities, special schools, Islamic boarding schools, madrasah, seminary)
E.
SP 2010
PODES 2011
HEALTH
34.
Number of health facilities (hospitals, maternity hospitals, polyclinics, health centres, sub health centres, private practices, village health posts, village health clinics, integrated health posts/Posyandu, pharmacies)
PODES 2011
35.
Number of health professionals (general practitioners, dentists, midwives, etc.)
PODES 2011
36.
Number of outbreaks over the last year (diarrhoea, dengue fever, measles, respiratory infections, malaria, avian influenza, tuberculosis, etc.)
PODES 2011
F.
24
VARIABLES/INDICATORS
MARITAL STATUS
37.
Number of female population aged 15-49 years not yet married
SP 2010
38.
Number of female population aged 15-49 years already married
SP 2010
Guidelines For The Use Of Population Data In Disaster Management
NO
VARIABLES/INDICATORS
39.
Number of female population aged 15-49 years divorced/widow
G.
DISABILITY
40.
Number of people with impairment (seeing, hearing, walking, memorizing, taking care of oneself)
H.
SOURCES SP 2010 SP 2010
LITERACY/LANGUAGE PROFICIENCY
41.
Number of people aged 5 years and over who are illiterate
SP 2010
42.
Number of people aged 5 years and over who cannot speak Indonesian
SP 2010
I.
OCCUPATION/LIVELIHOOD
43.
Number of people aged 15 years and over who work (agriculture, industry, trade/ hotel/ restaurant, service)
SP 2010
44.
Number of people aged 15 years and over who do business as an entrepreneur
SP 2010
45.
Number of people aged 15 years and over who do business in the workforce
SP 2010
46.
Main income source of the majority of the population
J.
PODES 2011
CHARACTERISTICS OF THE HOUSE
47.
Number of households with proof of house ownership (Freehold Title/SHM, other certificates, etc.)
SP 2010
48.
Number of households by type of flooring (wood, bamboo, soil, etc.)
SP 2010
49.
Number of households with electricity (PLN meter, PLN without meter, non PLN, not electric)
SP 2010
50.
Number of households with a female head of household
SP 2010
51.
Number of households with one household member aged 60 years and older
SP 2010
52.
Fuel used by most families to cook
K.
PODES 2011 WATER AND SANITATION
53.
Number of households by source of drinking water (bottled, plumbing, pumps, wells, springs, rivers, rainwater, etc.)
SP 2010
54.
Number of households that have their own toilet facilities
SP 2010
55.
Number of households that have their own toilet facilities with septic tanks
SP 2010
Guidelines For The Use Of Population Data In Disaster Management
25
NO 56.
VARIABLES/INDICATORS Places of defecation of most families
L
PODES 2011 COMMUNICATION AND INFORMATION
57.
Number of households with phone facilities (wired, cellular, internet)
58.
Traffic from and to the village
PODES 2011
59.
Type of the widest road surface
PODES 2011
60.
Whether or not the road is traversable by 4-wheel (or more) motor vehicles throughout the year
PODES 2011
61.
Bridges on the main road of the village
PODES 2011
62.
Base Transceiver Station (BTS) or cell phone towers in the village
PODES 2011
63.
Mobile/cell phone signals
PODES 2011
SP 2010
M.
NATURAL DISASTERS (during the last one year)
64.
Number of natural disasters (floods, flash floods, landslides, earthquakes, tsunamis, tidal waves, hurricanes, volcanic eruptions, land and forest fires, droughts)
PODES 2011
65.
Death toll of natural disasters (floods, flash floods, landslides, earthquakes, tsunamis, tidal waves, hurricanes, volcanic eruptions, land and forest fires, droughts)
PODES 2011
66.
Total material loss (millions rupiah) due to natural disasters (floods, flash floods, landslides, earthquakes, tsunamis, tidal waves, hurricanes, volcanic eruptions, land and forest fires, droughts)
PODES 2011
To support data communication, BNPB uses a system called DesInventar, part of the Indonesian Disaster Data and Information (DIBI) system. DIBI integrates demographic and disaster data, allowing the two to be analyzed against each other. The online application can be accessed through the DIBI website (http://dibi.bnpb.go.id), and then by clicking on Population Data menu.
26
SOURCES
Guidelines For The Use Of Population Data In Disaster Management
Figure 2.3 Display: Home dibi.bnpb.go.id
A.
The “View Data” Page
The DIBI homepage displays options for administrative regions including province, district, and sub-district, paired with data sets from the
2010 population census and the 2011 village potentials. Users can select the administration area, or by not selecting an option, view all
Guidelines For The Use Of Population Data In Disaster Management
27
Figure 2.4 Population Data of Provincial Level
provinces. The user must select their data source as either the 2010 population census or the village potentials, and click the OK button. The selected data will appear at the bottom. To obtain the district-level data, the user must select the provincial administrative area. For the sub-district level data, they must select the provincial and 28
district administrative areas. For data at the village level, they must select the provincial, district, and sub-district administrative areas.
Guidelines For The Use Of Population Data In Disaster Management
Figure 2.5 Population Data of District Level
Guidelines For The Use Of Population Data In Disaster Management
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Figure 2.6 Population Data of Sub-District Level 30
Guidelines For The Use Of Population Data In Disaster Management
Figure 2.7 Population Data of Village Level Guidelines For The Use Of Population Data In Disaster Management
31
B.
The “Charts” Page
Administrative areas including province, district and sub-district are displayed on the home page, along with data on types of disaster, disaster impacts, the 2010 population census and 2011 village potentials. Users can select the administration area, or if they want to see all the provinces, can leave the box unchecked.
On the graph page, the users must select either the 2010 population census or the 2011 village potentials, or at least one option in the box of disaster types and one option in the box of disaster variables. The user must then click the OK button, and selected data will appear at the bottom.
Figure 2.8 Display of DIBI Population Data Graph 32
Guidelines For The Use Of Population Data In Disaster Management
C.
The “Statistics” Page
Just like the ‘charts’ page, on ‘statistics’ home page provides options of provincial, district, sub-district administrative regions, as well as
data on types of disaster, disaster impacts, the 2010 population census, and the 2011 village potentials.
Figure 2.9 Display of statistics up to the village level Guidelines For The Use Of Population Data In Disaster Management
33
34
Guidelines For The Use Of Population Data In Disaster Management
President visits an IDP camp in Pujon, Malang. source: BNPB
3
Population Data Collection and Use in the Pre-Disaster Phase
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3.1. Data Collection Sources and Methodology in the Pre-Disaster Phase National scale population data in Indonesia includes the Village Potentials Survey (PODES), the Population Census (SP), the National Socio-economic Survey (SUSENAS), the National Labour Force Survey (SAKERNAS), the Indonesian Inter-censal Population Survey (SUPAS), and the Demographic and Health Survey (IDHS/SDKI). PODES are conducted every three years, based on the administrative boundaries of a village. The surveys gather a broad range of information on areas including population and employment, housing, environment, natural disasters, mitigation, education and health services, socio-cultural life, entertainment and sports, transportation, communication and information, land use, the economy, security, village autonomy and community empowerment programs.
The Head and Principal Secretary of BNPB coordinates with BASARNAS on Jakarta floods data. Source: BNPB 36
Population Census (SP) is a statistical survey conducted once every ten years to gather information on basic demographic characteristics such as age and gender, grouped by residence at the time of enumeration. SP also collects information on socio-economic and sociocultural characteristics of eligible individuals and households. During the census, detailed information about the presence and location of various types of community infrastructure, including health facilities, schools, churches, village halls, markets and roads, is usually collected. SUSENAS has been held every year since 1964. It consists of two questionnaire formats (core and 3 modules) that collect socio-economic information. The resulting data is at the district/city (from the core) and provincial (from the modules) levels. The sample size used in the core questionnaire is approximately 300.1 households, and about 70,000 households for the modules. SAKERNAS provides annual data regarding continuous employment, the unemployed and people who have stopped or moved their work. This data is then grouped at the district/city, provincial and national levels to map changes over time. Another source of population data is the Demographic and Health Survey that collects data on births, deaths, the prevalence of family planning, and health (especially reproductive health).
Guidelines For The Use Of Population Data In Disaster Management
The agreement signed by the BPS and BNPB in February 2013 pertains to summarized data at the village level and therefore, the guidelines set out in this report to integrate population data into BNPB’s disaster risk reduction strategies must reflect this. However, BNPB must also make efforts to obtain data at the census block level, as well as in digital map form. The resulting population data would be very useful in analyzing a variety of different scenarios in case of emergency. This is contingent, of course, on the data being kept
in a system that is both comprehensive and accessible. The integration of village data from the 2010 population census and 2011 PODES into GIS (Geographic Information System) would be an innovative, fast and efficient technique. It would enable data to underpin risk reduction and mitigation policy. Integration requires that each village be placed in its appropriate sub-district, district and province. This would allow GIS to be an effective problem solver through spatial analysis.
3.2. Population Data Needs and Indicators in the Pre-Disaster Phase In this guidebook, population data in the predisaster phase is divided into demographic needs that must be met, and any other data deemed as priority to be integrated in risk reduction strategies. These strategies should be developed
separately in each village, to reflect local conditions and circumstance. Table 3.1 shows the minimum indicators that should be met and used for disaster management.
Table 3.1 Minimum Indicators of Requirements for Population Data For Disaster Management NO
REQUIREMENTS
1
Population Characteristics and Composition
INDICATORS
SOURCES
ESSENTIAL VALUES
Total population by sex and age group
SP 2010
Population distribution needs to be identified prior to the disaster to determine the population at risk.
Sex ratio (male/female)
SP 2010
Women are usually more affected because in most cases, they are responsible for caring for children, the elderly and the disabled. It is also a fact that men are more likely to migrate (internal and external) than women, to seek better employment opportunities. Sex ratios in the region will provide information to disaster relief agencies, allowing them to focus on providing assistance and relief operations.
Guidelines For The Use Of Population Data In Disaster Management
37
NO
REQUIREMENTS
1
Population Characteristics and Composition
Population density
SP 2010
Population density is a very important variable because it provides a visual measure of the population at risk and the concentration of people in disaster-prone areas.
2
Susceptible Group
Number and percentage of the total population aged 60+ by sex
SP 2010
People aged 60+ are classified as a vulnerable population group. Most of the population in this age group will require assistance from other people at the time of evacuation or disaster.
Number and percentage of population aged under 5 years
SP 2010
People age 5 years and under are classified as a vulnerable population group, who will require assistance from other people at the time of evacuation or disaster.
Number and proportion of the population with a disability, by type of disability
SP 2010 and PODES 2011
People with disabilities are one of the most vulnerable groups, and they and their families should be actively involved in disaster prevention efforts, especially in developing evacuation procedures to guarantee their survival. People with disabilities may also require special attention during times of disaster relief.
Number of one-member households aged 60+
SP 2010
Households with one elderly member are classified as a vulnerable group because other household members will have to help them during evacuation or rescue.
Number of households with women as the family heads
SP 2010
Households headed by women are classified as a vulnerable group as women generally have a longer response times than men in disaster situations. Women are usually preoccupied with domestic matters, and will be less focused on disaster preparedness measures.
38
INDICATORS
SOURCES
Guidelines For The Use Of Population Data In Disaster Management
ESSENTIAL VALUES
NO
3
4
REQUIREMENTS
Literacy
Quality of Life
INDICATORS
SOURCES
ESSENTIAL VALUES
Literacy rate of population aged 15-24 years, by gender
SP 2010
Literacy plays a role in understanding the importance of disaster preparedness and mitigation. During a disaster, literate people are also more likely to be proactive, and follow rules and evacuation procedures.
Literacy rates of population aged 15 years and over, by sex
SP 2010
Literacy rates of population aged 5-14, by sex
SP 2010
Literate children can better understand the impact of disaster and are more likely to act positively in the event of a disaster.
Ratio of literate women to literate men, aged 15-24 years
SP 2010
Women are usually burdened with care of children, the elderly and people with disabilities. The better they understand the implications of disaster preparedness, the more likely they are to be proactive.
Proportion of households using solid fuels (wood, coal, coke) for cooking
SP 2010
Proportion of households with sustainable access to good water source
SP 2010
All three of these indicators measure quality of life, and can relate to the level of preparadness a household will have in a disaster. It is likely that more wealthy families will have contingency plans in place in the event of a disaster.
Proportion of households with access to good sanitation
SP 2010
5
Communication
Number of landlines and mobile phones per 100 people
SP 2010
This indicator measures accessibility to information, as well as the quality of life of households.
6
Economy
Productive land area
Land Use Map, Region in Figures
This indicator can be used to identify how much productive land will be affected in the event of a disaster, and the potential losses that may arise if affected.
Contribution of gross regional domestic product (GDP) per sector
Region in Figures
GDP indicators can be used to determine what sectors are sources of income of a region. In the event of a disaster, it can be predicted which sectors are likely to be affected.
Guidelines For The Use Of Population Data In Disaster Management
39
NO
7
REQUIREMENTS
Infrastructure and Public Facilities
INDICATORS
SOURCES
ESSENTIAL VALUES
Number of permanent, semipermanent, and non-permanent houses
PODES 2008
Substandard housing units will be destroyed in the first case of a natural disaster (earthquake or tsunami), and it can be used to estimate the impact of losses if a disaster occurs.
Number of public facilities (education, health, worship)
PODES 2011
Public facilities data can be used to estimate the impact of losses.
Table 3.2 Priority Population Data Indicators that should be Used in the Pre-Disaster Phase NO
1 2 3
Total population by sex and age The sex ratio (male/female) Population distribution (%) in urban/rural areas
4
Annual rate of population change (%) in urban/rural areas
5 6 7 8 9
Percentage of households with pre-school children (ages 0-6) Population estimates from 2010 census data Population density Percentage of family/non-family households Number and percentage of local households consisting of adults aged 60 + Number and percentage of households consisting of single women and children
10 11 12 13 14 15 16 17
40
INDICATORS
SOURCES
SP 2010 SP 2010 SP 2010 SP 2010 with updates from PODES for village and sub-district SP 2010 SP 2010 and previous censuses SP 2010 SP 2010 SP 2010 SP 2010
SP 2010 and cross-age with Number and percentage of unemployed teenagers (aged between 16 and 19) employment data Number and percentage of population under the age of 15 years SP 2010 Number and percentage of population aged 60 + by sex SP 2010 Sex ratio of the population aged 60 + SP 2010 Distribution of population by ethnicity SP 2010 Distribution of population by language used at home SP 2010 Health statistics and statistics from Estimated number of adults living with HIV/AIDS hospitals that treat patients with HIV
Guidelines For The Use Of Population Data In Disaster Management
NO
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
INDICATORS
SOURCES
Number and proportion of persons with disabilities by type of disability SP 2010 Number and proportion of persons with disabilities with more than one type of SP 2010 disability Ministry of Health and data from local Percentage of children age 0-5 who have received recommended vaccines hospital Ministry of Health and data from local Immunization levels hospital PODES, Ministry of Health, and data Type of outbreak In the last three years and number of affected population from local hospital Under five years mortality rate SP 2010 Infant mortality rate SP 2010 Maternal mortality rate SP 2010 SP 2010, Ministry of Health, and data Mortality rate associated with specific diseases (malaria, TB, etc.) from local hospital Total fertility rate SP 2010 Elementary education enrolment, by sex and age (7-12) SP 2010 Portion of girls in elementary education enrolment (age 7-12) SP 2010 Secondary school enrolment by sex SP 2010 Literacy Rate for Age 15-24 Years by Sex SP 2010 Percentage of Population Below Poverty Line SP 2010 Portion of women in the secondary school enrolment SP 2010 Literacy rate for age 15-24 years by sex SP 2010 Adult Literacy Rate by Sex for ages 15 + SP 2010 Literacy rate for 5-14 age group by sex BPS, district level data Ratio of literate women against men for age group 15-24 SP 2010 Median household income SP 2010 Poverty rate BPS, district level data Percentage of population below poverty line BPS, district level data The portion of women in wage employment in the non-agricultural sector SP 2010 Unemployment rate for age group 15-24, by sex and number of population SP 2010 Number and percentage of youth unemployed and not in school (aged 12-17) SP 2010 Adult population (15 +) employed by sex SP 2010 Proportion of population using solid fuels (wood, coal, coke) SP 2010
Guidelines For The Use Of Population Data In Disaster Management
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NO
46 47 48 49 50 51 52 53 54 55 56
INDICATORS
SOURCES
Proportion of population with sustainable access to good water source Proportion of population with access to good sanitation Proportion of households by type of electricity supply Distribution of housing units based on family room size (square meters) Home phone/landline and cell phone subscribers per 100 population Proportion of households with access to the internet Proportion of households that rent their homes Proportion of households that have their own homes Value of house occupied by the owner Number of personal vehicles by type of vehicles (cars, motorcycles, vans, buses) Number of inter-village main roads
SP 2010 SP 2010 SP 2010 SP 2010 SP 2010 SP 2010 SP 2010 SP 2010 SP 2010 Motor vehicles registration PODES
3.3. Roles and Functions of BNPB and Ministries/Institutions Related to Population Data In the Pre-Disaster Phase It is the responsibility of each Ministry/Agency in Indonesia to prepare and plan for a potential disaster. BNPB’s role is to compile demographic data to underpin the disaster baseline system. It must also ensure that this data is easily accessible for all stakeholders in disaster management. The population data compiled by BNPB is sourced from BPS-Statistics Indonesia, the authorized agency on population data. BPS is instrumental in providing the necessary demographic data in disaster management activities. As population data is dynamic, BPS must validate and update it on a regular basis. In regards to national 42
spatial data, Geospatial Information Agency (BIG) is instrumental in providing basic mapping information. Early warning signs for disasters – such as weather conditions, earthquakes, tsunamis, floods, and landslides – should always be made available to disaster stakeholders, including the community. This will help to minimize the number of casualties and property losses. The authorized agency capable of providing such information in Indonesia is the Meteorology, Climatology, and Geophysics Agency (BMKG). The dissemination of information and early warning signs of volcanic
Guidelines For The Use Of Population Data In Disaster Management
eruption is the responsibility of the Center for Volcanology and Geological Hazard Mitigation (PVMBG). Using these early warning systems, the local (BPBDs) and central governments (BNPB) can take the necessary precautions for
disaster preparedness, such as the evacuation of people who live in high-risk areas.
3.4. Population Data Use and Analysis in the Pre-Disaster Phase The use of secondary data in the pre-disaster phase is quite extensive. Population data forms the basis of disaster management activities, such as disaster risk assessment and contingency planning. The population database provided by BPS provides important statistical information such as the total population grouped by age, sex, productive age, vulnerabilities and density. The population of vulnerable groups includes people less than 5 years (0-4 years), children (5-12 years) and the elderly (60+). These population characteristics are important in management planning, as they reflect the number of potential victims and IDPs. Data on the level of education and the number of health workers helps to analyze the human resource capacity on the ground. An understanding of the number of facilities, such as schools that can be used as evacuation centers, is also important in mapping pre-disaster strategies. One scenario that can be used to demonstrate the
use of population data in disaster management is the eruption of Rokatenda volcano in District Palue, Sikka regency, East Nusa Tenggara Province. Using data trends and observation, volcanic activity can be monitored. With early warning from PVMBG, the populations in disaster-prone areas (KRB) and within the radius of danger can be identified. The administrative areas that may be affected by the eruption of Rokatenda volcano include the villages in the Palue sub-district, namely Nitunglea, Lidi, Reruwairere, Maliriwu, Kesokoja, Ladolaka, Tuanggeo, and Rokirole. The following is population data obtained from the online DIBI system. Figure 3.1 shows a combination of demographic and spatial data, which allows the population density in the Mount Rokatenda area to be mapped. This visual depiction enables users to understand what spatial areas are at particular risk. Using this information, BNPB and the local BPBD’s can undertake emergency planning for potential volcanic eruptions.
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43
Table 3.3 Number of Population Affected by Rokatenda Volcano Eruption SEX MALE
FEMALE
TOTAL POPULATION
NITUNGLEA
554
866
1.420
5310061002
LIDI
602
754
1.356
3
5310061003
RERUWAIRERE
497
589
1.086
4
5310061004
MALURIWU
452
642
1.094
5
5310061005
KESOKOJA
573
715
1.288
6
5310061006
LADOLAKA
513
669
1.182
7
5310061007
TUANGGEO
393
558
951
8
5310061008
ROKIROLE
491
685
1.176
NO
IDSP 2010
VILLAGE
1
5310061001
2
The total population of the Palue sub-district is 9553. Based on this data, it is anticipated that a maximum of 9553 could be potentially displaced, given that Palue is a small island on the cone
of Rokatenda volcano. Data on vulnerable population groups can also be obtained from the 2010 Population Census, as follows:
Table 3.4 Total Female Population by Age Group in the Subdistrict of Palue, District of Sikka, East Nusa Tenggara
44
NO
VILLAGE
1
FEMALE POPULATION 0-4
5-6
7-9
10-12
13-14
15
16
17
18
60-64
65+
NITUNGLEA
82
44
46
58
21
8
4
15
3
44
81
2
LIDI
90
44
45
38
14
2
3
6
6
35
87
3
RERUWAIRERE
45
16
26
42
19
9
8
7
5
38
78
4
MALURIWU
43
24
40
30
17
10
6
9
3
38
101
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NO
VILLAGE
FEMALE POPULATION 0-4
5-6
7-9
10-12
13-14
15
16
17
18
60-64
65+
5
KESOKOJA
78
34
43
49
19
7
6
8
2
51
102
6
LADOLAKA
50
26
35
47
10
6
4
3
9
43
98
7
TUANGGEO
40
20
41
37
12
2
2
3
0
28
88
8
ROKIROLE
60
25
41
48
46
17
3
4
3
42
77
Figure 3.1 Population Density of Palue Island and Mount Rokatenda Disaster Prone Areas
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45
Table 3.5 Total Male Population by Age Group in the Sub-district of Palue, District of Sikka, East Nusa Tenggara NO
VILLAGE
MALE POPULATION 0-4
5-6
7-9
10-12
13-14
15
16
17
18
60-64
65+
1
NITUNGLEA
89
31
48
42
22
8
2
4
3
30
42
2
LIDI
95
37
46
50
18
5
7
4
9
35
37
3
RERUWAIRERE
53
22
39
39
21
7
10
4
8
30
57
4
MALURIWU
46
21
36
32
25
12
10
6
4
22
62
5
KESOKOJA
72
37
58
46
15
8
5
12
10
28
60
6
LADOLAKA
55
20
57
38
17
6
5
12
11
28
59
7
TUANGGEO
48
25
44
39
9
3
4
5
2
13
50
8
ROKIROLE
53
24
45
63
40
19
9
9
4
28
41
Using the data above, the total number of vulnerable aged people per village in the sub-
district of Palue, Sikka district, East Nusa Tenggara province, can be calculated.
Table 3.6 Total Population of Vulnerable Aged People in the Subdistrict of Palue, Sikka District, East Nusa Tenggara NO
VILLAGE
1
Toddlers (0-4)
Children (5-12)
Elderly (60+)
TOTAL
Male
Female
Total
Male
Female
Total
Male
Female
Total
NITUNGLEA
89
82
171
121
148
269
72
125
197
637
2
LIDI
95
90
185
133
127
260
72
122
194
639
3
RERUWAIRERE
53
45
98
100
84
184
87
116
203
485
4
MALURIWU
46
43
89
89
94
183
84
139
223
495
5
KESOKOJA
72
78
150
141
126
267
88
153
241
658
6
LADOLAKA
55
50
105
115
108
223
87
141
228
556
TOTAL
511
488
999
939
899
1.838
622
1.031
1.653
4.490
46
Guidelines For The Use Of Population Data In Disaster Management
NO
VILLAGE
7 8
Toddlers (0-4)
Children (5-12)
Elderly (60+)
TOTAL
Male
Female
Total
Male
Female
Total
Male
Female
Total
TUANGGEO
48
40
88
108
98
206
63
116
179
473
ROKIROLE
53
60
113
132
114
246
69
119
188
547
TOTAL
511
488
999
939
899
1.838
622
1.031
1.653
4.490
Using this framework, it can be determined that the total number of vulnerable-aged people in Palue is 4,490, accounting for 47% of the total population. This information can also be overlapped with spatial data. The integration of demographic data with GIS creates an effective and efficient tool in planning for the pre-disaster phase. The presentation of data becomes easier to understand and analyze, and the calculation and visualization of derivative data such as population density can be done easily and quickly. By knowing the size of vulnerable populations in hazard areas, the government can undertake preparedness measures such as prioritizing needs and facilitating the successful evacuation of vulnerable and at-risk groups. Population data can also be used to support decision-making in disaster risk reduction policy. In 2011, BNPB
conducted a national disaster risk assessment up to the provincial level, the results of which showed the number of people exposed to 12 different types of hazard. Using this information, the government could formulate measures to reduce the impact of the disaster. Table 3.7 presents an example of a disaster risk assessment undertaken by BNPB in 2011. It shows the total number of people exposed to an earthquake by hazard class. Table 3.8 displays the number of vulnerable groups exposed by hazard class. Disaster risk assessment can also indicate the location of provinces that have populations exposed to earthquakes. Based on this data, BNPB can prioritize the disaster risk reduction activities in Indonesia. Figure 3.2 shows the map of earthquake hazards in Indonesia, which is determined by the level of PGA (peak ground acceleration).
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47
Table 3.7 Number of People Exposed To Earthquake Hazard Zones in Indonesia Category
HIGH
Absolute Human Exposure Male
Female
Relative Human Exposure Total
Male
Female
Total
3,341,239
3,292,597
6,633,837
2.79
2.79
2.79
MODERATE
71,342,151
70,510,528
141,852,679
59.64
59.75
59.69
LOW
42,787,526
42,033,054
84,820,580
35.77
35.62
35.69
117,470,916
115,836,179
233,307,096
49.25
48.56
97.82
TOTAL
Figure 3.2 Indonesian Earthquake Hazard Map
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Guidelines For The Use Of Population Data In Disaster Management
Table 3.8 Total Vulnerable Group Population Exposed to Earthquake Hazards Jumlah Kelompok Rentan Terpapar Kelas Bahaya Bencana
TINGGI
Balita (< 5thn)
Lanjut Usia (60 thn +)
Penyandang Cacat
Total
740,371
398,219
49,435
1,188,025
SEDANG
13,398,843
11,619,696
1,045,772
26,064,311
RENDAH
8,142,075
5,721,213
554,638
14,417,926
22,281,289
17,739,128
1,649,845
41,670,262
TOTAL
By superimposing earthquake hazard maps with the demographic data, the number of people who live in exposed territory by the level of hazard can be identified.
Areas with high hazard levels and population density should be prioritized in terms of risk reduction and disaster preparedness.
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49
50
Guidelines For The Use Of Population Data In Disaster Management
The Head of BNPB examines the results of data collection and mapping of evacuation places in Ngantang, Malang, E. Java for IDPs from Kelud eruption. source: BNPB
4
Population Data Collection and Use in the Emergency Response Phase
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51
4.1. Data Collection Sources and Methodology in the Emergency Response Phase The ‘emergency response phase’ refers to the series of activities carried out immediately at the time of a disaster, including the rescue and evacuation of casualties and property, ensuring the protection of IDPs, and the recovery of facilities and infrastructure. Humanitarian emergency response activities, which are based on the Humanitarian Charter and Minimum Standards in Humanitarian Response (Sphere Project), help to ensure that the affected population has access to minimum requirements – such as water, sanitation, food, nutrition, shelter and health services – needed to meet basic human rights. Cluster approaches are usually activated in countries where humanitarian crises exceed institutional mandates, or where the needs are large and complex enough in nature to require multi-sectoral emergency intervention by a variety
of stakeholders. In this situation, data to identify the basic services needed for the general public and at-risk groups is extremely useful, but also very challenging for the government and institutions to collect. Precise and accurate information depends on the availability of supporting data that is structured and easy to understand. The collection, analysis and dissemination should be carried out quickly, accurately and completely as part of the disaster response. Disaster data and information can be collected from various sources, including the government, voluntary organizations, NGOs and the community. Data can also be collected directly through interviews, or indirectly such as through various media such as the Internet, television, and printed media. The flow of data and information management is depicted as follows:
Figure 4.1 Flows of Data and Information Management 52
Guidelines For The Use Of Population Data In Disaster Management
In the process of disaster data collection, there are two types of data: dynamic and static. Dynamic data is temporary, meaning that it will undergo changes as a situation plays out, such as the number of IDPs that have been displaced as a result of an emergency. In Indonesia, PUSDALOPS PB or the Emergency Response Command Post are responsible for the collection of dynamic data. Static data is data that is fixed or will remain unchanged. This data is collected by the Centre of Data and Information and Public Relations of BNPB, the Secretariat of provincial BPBD and the Secretariat of district/city BPBD. Static data includes information on the occurrence of disasters, casualties, and damage and loss estimates.
A pregnant woman being evacuated from floods by the BPBD rescue team in Antang, Makassar, South Sulawesi. Source: BNPB
4.2. Population Data Needs and Indicators in the Emergency Response Phase Population data can be used identify the basic services needed by the general public and target groups in emergency response situations. Population data is generally in-line with the Perka (Regulation of BNPB Head) No. 8 of 2011. Data that should be obtained during the emergency response phase is as follows: BNPB provides logistical support to the victims of Way Ela dam collapse, based on data recapitulated by the “POSKO” (post of commands). Source: BNPB
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53
Table 4.1 Tables from Perka No. 8/2011 in the Emergency Response Phase NO
DATA REQUIREMENTS
OCCURRENCE OF DISASTER 1
Type of Disaster
2
Dates of Occurrence
3
Time of Occurrence
4
Location of Disaster (Province/District/Coverage)
5
Geographical Location (Coordinate)
6
Cause of Disaster
7
Description
8
Weather Conditions
CASUALTIES 9
Number of victims (dead, missing, seriously injured, slightly injured, displaced, affected), by sex
10
Data of victims (dead, missing, seriously injured, slightly injured, the location of IDPs, the number of IDPs), by name and sex
DAMAGES 11
Damages (settlement, infrastructure, productive economy, social, cross-sectors)
12
Public facilities that can still be used (access, transport, communications, electricity, clean water, health facilities)
13
Emergency response efforts
14
Resources (human resources, infrastructure, logistics, equipment, funds)
15
Mobilized volunteers (national, international)
16
Acceptance of aids (domestic, foreign, distribution)
17
Potential aftershocks
In addition to the data outlined above, it is also important to collect information on IDPs. The 54
standard form used in data collection is outlined in the Perka No. 8 of 2011. Collection is carried
Guidelines For The Use Of Population Data In Disaster Management
out from each one of the IDPs camps, which is then aggregated at the district/city level. The
following table describes the variables that must be collected in the IDPs data collection process.
Table 4.2 Variables that Should Be Collected in Data Collection Process of IDPs NO
VARIABLES
IDPs’ Place of Origin 1.
Village
2.
Sub-district
3.
District/City
Number of IDPs 4.
Number of IDPs (Male/Female/Total)
5.
Number of IDPs (Male/Female) by the age group: a. < 1 year b. 1 – 5 years c. 6 – 12 years d. 13 – 17 years e. 18 – 60 years f. > 60 years
Number of Vulnerable Groups of IDPs 6.
Pregnant mothers
7.
Lactating mothers
8.
Persons with disabilities
Literacy 9.
IDPs that are able to speak Indonesian
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NO 10.
VARIABLES IDPs that are able to read and write Latin
Education and Employment 11.
Number of working IDPs
12.
Number of IDPs by field of work a. Agriculture, livestock, forestry, and fisheries b. Mining and quarrying c. Processing industry d. Electricity, gas and water supply e. Construction f. Trade, hotels and restaurants g. Transportation and communication h. Finance, real estate and business services i. Services
13.
Number of IDPs by the highest education attainment a. Not completed primary school b. Graduated primary school/MI/equivalent c. Graduated junior high/MTs/equivalent d. Graduated high school/MA/equivalent e. Graduated vocational school f. Graduated Dip I/II g. Graduated Dip III/academy h. Graduated Dip IV/Under Graduate i. Post Graduate (Master/Ph.D)
56
Guidelines For The Use Of Population Data In Disaster Management
NO 14.
VARIABLES Number of IDPs by the religious affiliation a. Islam b. Christian c. Catholic d. Hindu e. Buddha f. Confucius g. Others
4.3. The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Emergency Response Phase During the emergency response phase, BNPB’s role is to analyse the disaster-affected population and the estimated number of IDPs based on available population data. In an emergency response situation, the population in affected locations may be evacuated to a safe place. Search and rescue for casualties is often carried out. These activities are the responsibility of the National Search and Rescue Agency
(BASARNAS). Information regarding the number of casualties is delivered to BNPB as the agency responsible for disaster management. The Ministry of Social Affairs is critical to providing services to people who have been affected by a disaster, including people with special needs and vulnerable groups. The Ministry of Health is instrumental in providing health care personnel, facilities and services.
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57
4.4. The Use of Population Data in the Emergency Response Phase Emergency response situations usually involve the evacuation of people from the affected area. Collecting data on IDPs is difficult, because the data is highly dynamic and can change every day. To assist with this process, information can be supplemented using data from both PODES and the Population Census, to estimate the size of the affected population and their basic needs. In the earlier case of the volcanic eruption of Rokatenda, for example, the affected population was estimated at 9553. Within affected populations are high-risk groups such as infants, pregnant women, and the elderly. In the case of an emergency, it is better
to make statistical estimates based on available demographic data than to try and undertake data collection, which would be very difficult to obtain. Data based on statistical estimates can help to project that: 1. 4% of the population are pregnant women 2. 25% of the population are women on reproductive age. 3. 20% of the population are adult males 4. 20% of pregnant women will experience complications. Based on the above statistical estimation, the following data can be obtained:
The Head of BNPB, accompanied by the Deputy and BPBD staff, visits landslide victims in Cililin, West Java. Source: BNPB
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Table 4.3 Statistical Estimation of High-Risk Populations SEX NO
VILLAGE
1
TOTAL WOMEN WITH PREGNANCY COMPLICATIONS
WOMEN OF CHILDBEARING AGE
ADULT MALES
57
11
355
284
1,356
54
11
339
271
589
1,086
43
9
272
217
452
642
1,094
44
9
274
219
KESOKOJA
573
715
1,288
52
10
322
258
6
LADOLAKA
513
669
1,182
47
9
296
236
7
TUANGGEO
393
558
951
38
8
238
190
8
ROKIROLE
491
685
1,176
47
9
294
235
POPULATION PREGNANT MOTHERS
MALE
FEMALE
NITUNGLEA
554
866
1,420
2
LIDI
602
754
3
RERUWAIRERE
497
4
MALURIWU
5
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60
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The President examines the data collection results and interviews people affected by forest fire smoke in Pekanbaru, Riau. Source: BNPB
5
The Collection and Use of Population Data in the PostDisaster Phase Guidelines For The Use Of Population Data In Disaster Management
61
5.1. Sources and Methodology of Data Collection in the Post-Disaster Phase The post-disaster phase begins after the emergency phase. Post-disaster rehabilitation and reconstruction activities are carried out by improving the environment of affected areas through the restoration of infrastructure and public facilities, supporting community home repair, assisting with psychosocial recovery, providing access to health care, supporting conflict resolution and reconciliation, reinforcing security and order, and revitalizing public services.
population density, and population distribution by area (per village, sub-district, and district). It may also include data on the number of vulnerable and productive age groups, as well as the total labor force. Population data that maps conditions prior to a disaster is also important. Population maps should be made with sufficient scale, such as 1:50,000 for the district/city level, 1:25,000 or 1:10,000 for the sub-district level, and 1:5000 at the village level.
An environmental improvement plan should be underpinned by demographic, social, cultural, economic and infrastructure data. This will help to inform the requirements of rehabilitation, and may include data regarding population growth,
Other disaster management activities in this phase include an assessment of damages. This can be carried out by applying housing, infrastructure, social, economic, and cross-sectoral parameters. Housing parameters include residential housing and neighborhood amenities. Infrastructure parameters cover transportation, energy, post and telecommunications, water and sanitation, infrastructure, and natural resources. Social parameters include health, education, religion, and social institutions. Economic parameters include agriculture, fisheries, animal husbandry, industry, trade, tourism, cooperatives and small to medium enterprises (UKM). Cross-sectoral parameters include environment, governance, order and security, finance and banking.
The Head of BNPB observes residential areas affected by the Sinabung eruption, Karo District, North Sumatra. Source: BNPB 62
Data that may be required to support postdisaster activities includes socio-cultural data at the village, sub-district, district, and provincial
Guidelines For The Use Of Population Data In Disaster Management
level. It may refer to local administration sites, population characteristics such as education, livelihood, health, age and households, the number of houses (permanent/semi-permanent/
non-permanent), the characteristics of the home and the number of residential buildings in a community.
5.1.1. Population Census Post Large-Scale Disaster Large-scale disasters such as the earthquake and tsunami in Aceh in 2004 have altered the conditions of the community and the environment in the province both directly and indirectly. Both short-term and long-term recovery efforts have been made in a comprehensive, systematic and sustainable manner. Planning, monitoring and evaluation, as well as rehabilitation and reconstruction in Aceh, has relied on population and demographic data. Until mid-2005, however, the availability of population and demographic data was low, and did not span 100% of the area due to ongoing conflict. Recognizing this fact, BAPPENAS, BPS, and UNFPA (along with CIDA, AusAID and NZAID as donor partners) organized the Population Census for Aceh Province and Nias Island (SPAN). This post-tsunami census was unprecedented in terms of time and technical and methodological aspects, reflecting the political situation of the region at the time the census was conducted.
The SPAN census was conducted outside of the regular BPS census time frame, which is every 10 years at the year ending in zero. In 2005, the inter-censal population survey (SUPAS) was due to be conducted, but due to the large-scale disaster, the full population census was carried out instead. The techniques and methodologies used in this census were different from the usual approaches adopted by BPS, particularly in terms of local control. BPS mobilized controllers at the local (district/city) level, who were responsible for the implementation of the survey, ranging from the preparation to the data processing stage at the district level. This approach helped to ensure the quality of data and minimize sampling errors. Census results are the only source of comprehensive data that provides information at the district or census block level (lower than the village). Census also allowed for the restoration of the sampling frame in the province, which was affected by the tsunami disaster.
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5.2. Needs and Variables/Indicators of Population Data in the Post-Disaster Phase The need for data in post-disaster situations also includes existing population data from the pre-
disaster phase. Some of the data that may be required is as follows:
Table 5.1 Variables Needed in the Post-Disaster Phase NO
DATA REQUIREMENTS
OCCURRENCE OF DISASTER DATES OF OCCURRENCE PLACES OF DISASTER
1
Province
2
District/City
3
Sub-district
ASSESSMENT OF DAMAGES
4
Settlement (Settlement, Neighbourhood Road, Drinking Water Systems)
5
Infrastructure (Land, Air, Water Transportation; Drainage; Electricity)
6
Productive Economy (Agriculture, Plantation, Animal Husbandry, Fisheries, Commerce, Industry, Tourism)
7
Social (Education, Religious, Health)
8
Cross-Sector (Office, Banking, Environment)
ESTIMATED LOSS ASSESSMENT
9
Estimated Loss
REHABILITATION/RECONSTRUCTION
10
Plan of Action and Funding
11
Accomplishment
SOURCES OF FUNDS
64
12
State Funds
13
Foreign
Guidelines For The Use Of Population Data In Disaster Management
Table 5.2 Variables Available from Secondary Data that can be Used in the Post-disaster Phase NO
VARIABLES/INDICATORS
SOURCES
A.
ADMINISTRATIVE AREAS
1.
Provincial code
SP 2010
2.
Name of province
SP 2010
3.
District code
SP 2010
4.
Name of district
SP 2010
5.
Sub-district code
SP 2010
6.
Name of sub-district
SP 2010
7.
Village code
SP 2010
8.
Name of village
SP 2010
B.
LOCATION, GEOGRAPHICAL AND TOPOGRAPHICAL CONDITIONS
9.
Location of village (peak/slope/valley/overlay)
PODES 2011
10.
Slope of the land
PODES 2011
11.
Village altitude (meters, above sea level)
PODES 2011
12.
Village directly adjacent to the sea
PODES 2011
13.
Sea level rise over the last 5 years
PODES 2011
14.
Existence mangrove forest
PODES 2011
C.
TOTAL POPULATION
15.
Total male population
SP 2010
16.
Total female population
SP 2010
17.
Number of households
SP 2010
18.
Total male population age group 0-4 years
SP 2010
19.
Total male population age group 5-6 years
SP 2010
20.
Total male population age group 7-9 years
SP 2010
21.
Total male population age group 10-12 years
SP 2010
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NO
66
VARIABLES/INDICATORS
SOURCES
22.
Total male population age group 13-14 years
SP 2010
23.
Total male population age group 15 years
SP 2010
24.
Total male population age group 16 years
SP 2010
25.
Total male population age group 17 years
SP 2010
26.
Total male population age group 18 years
SP 2010
27.
Total male population age group 19 years
SP 2010
28.
Total male population age group 20-24 years
SP 2010
29.
Total male population age group 25-29 years
SP 2010
30.
Total male population age group 30-34 years
SP 2010
31.
Total male population age group 35-39 years
SP 2010
32.
Total male population age group 40-44 years
SP 2010
33.
Total male population age group 45-49 years
SP 2010
34.
Total male population age group 50-54 years
SP 2010
35.
Total male population age group 55-59 years
SP 2010
36.
Total male population age group 60-64 years
SP 2010
37.
Total male population age group 65+ years
SP 2010
38.
Total female population age group 0-4 years
SP 2010
39.
Total female population age group 5-6 years
SP 2010
40.
Total female population age group 7-9 years
SP 2010
41.
Total female population age group 10-12 years
SP 2010
42.
Total female population age group 13-14 years
SP 2010
43.
Total female population age group 15 years
SP 2010
44.
Total female population age group 16 years
SP 2010
45.
Total female population age group 17 years
SP 2010
46.
Total female population age group 18 years
SP 2010
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NO
VARIABLES/INDICATORS
SOURCES
47.
Total female population age group 19 years
SP 2010
48.
Total female population age group 20-24 years
SP 2010
49.
Total female population age group 25-29 years
SP 2010
50.
Total female population age group 30-34 years
SP 2010
51.
Total female population age group 35-39 years
SP 2010
52.
Total female population age group 40-44 years
SP 2010
53.
Total female population age group 45-49 years
SP 2010
54.
Total female population age group 50-54 years
SP 2010
55.
Total female population age group 55-59 years
SP 2010
56.
Total female population age group 60-64 years
SP 2010
57.
Total female population age group 65+ years
SP 2010
58.
Total farming families
PODES 2011
59.
Total families whose members are farm labourers
PODES 2011
D.
EDUCATION
60.
Number of Preschools/equiv. (public and private)
PODES 2011
61.
Number of Primary Schools/equiv. (public and private)
PODES 2011
62.
Number of Junior High Schools/equiv. (public and private)
PODES 2011
63.
Number of Senior High Schools/equiv. (public and private)
PODES 2011
64.
Number of Vocational Schools/equiv. (public and private)
PODES 2011
65.
Number of Academy/Colleges/equiv. (public and private)
PODES 2011
66.
Number of Special Education Schools (SLB)
PODES 2011
67.
Number of Muslim Boarding Schools
PODES 2011
68.
Number of Madrasah Diniyah
PODES 2011
69.
Number of Seminars, and the like
PODES 2011
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NO
68
VARIABLES/INDICATORS
SOURCES
E.
HEALTH
70.
Number of Hospitals
PODES 2011
71.
Number of Maternity Hospitals/Maternity-Clinics
PODES 2011
72.
Number of Polyclinics
PODES 2011
73.
Number of Community Health Centres (Puskesmas)
PODES 2011
74.
Number of Subsidiary Health Centres (Pustu)
PODES 2011
75.
Number of Doctors Private Practice
PODES 2011
76.
Number of Midwife Private Practice
PODES 2011
77.
Number of Poskesdes (rural health posts)
PODES 2011
78.
Number of Polindes (rural maternity huts)
PODES 2011
79.
Number of Posyandu (integrated health services posts)
PODES 2011
80.
Number of pharmacies
PODES 2011
81.
Number of male doctors
PODES 2011
82.
Number of female doctors
PODES 2011
83.
Number of dentists
PODES 2011
84.
Number of midwives
PODES 2011
85.
Number of other health personnel (nurses, paramedics, pharmacists, etc.)
PODES 2011
F.
DEFICIENCIES
86.
Number of population with eyesight deficiency by gender
SP 2010
87.
Number of population with auditory deficiency by gender
SP 2010
88.
Number of population with walking deficiency by gender
SP 2010
89.
Number of population with memory deficiency by gender
SP 2010
90.
Number of population with self-care deficiency by gender
SP 2010
G.
LITERACY AND LANGUAGE MASTERY
91.
Number of illiterate population aged 5 years and over
Guidelines For The Use Of Population Data In Disaster Management
SP 2010
NO
VARIABLES/INDICATORS
SOURCES
92.
Number of population unable to speak Indonesian aged 5 years and over
H.
EMPLOYMENT/LIVELIHOOD
93.
Population aged 15 years and over who worked
SP 2010
94.
Population aged 15 years and over who worked in agriculture
SP 2010
95.
Population aged 15 years and over who worked in industry
SP 2010
96.
Population aged 15 years and over who worked in the field of trade/hotel/ restaurant
SP 2010
97.
Population aged 15 years and over who worked in the service sector
SP 2010
98.
Population aged 15 years and over who worked in the field of education services
SP 2010
99.
Population aged 15 years and over who worked in the field of health services
SP 2010
100.
Population aged 15 years and over who worked in business as employers
SP 2010
101.
Population aged 15 years and over who worked in business as labors
SP 2010
102.
The main income source of the majority of the population
I.
SP 2010
PODES 2011
HOUSING CHARACTERISTICS
103.
Household with certificate of proprietary rights (SHM) as proof of ownership in the name of member of household
SP 2010
104.
Household with certificate of proprietary rights (SHM) as proof of ownership not in the name of member of household
SP 2010
105.
Household with other certificate as proof of ownership
SP 2010
106.
Household with other proof of ownership
SP 2010
107.
Households with house flooring made of wood
SP 2010
108.
Households with house flooring made of bamboo
SP 2010
109.
Households with house flooring made of soil
SP 2010
110.
Households with house flooring made of other materials
SP 2010
111.
Households with PLN-metered electricity
SP 2010
112.
Households with non-metered PLN electricity
SP 2010
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NO
VARIABLES/INDICATORS
113.
Households with non-PLN electricity
SP 2010
114.
Households with non-electricity lighting
SP 2010
115.
Households with female head of household
SP 2010
J.
70
SOURCES
WATER AND SANITATION
116.
Households with bottled water as the source of drinking water
SP 2010
117.
Households with tap water supplied to the tap inside the household as the source of drinking water
SP 2010
118.
Households with retailed tap water as the source of drinking water
SP 2010
119.
Households with pumped water as the source of drinking water
SP 2010
120.
Households with protected well water as the source of drinking water
SP 2010
121.
Households with unprotected well water as the source of drinking water
SP 2010
122.
Households with protected spring water as the source of drinking water
SP 2010
123.
Households with unprotected spring water as the source of drinking water
SP 2010
124.
Households with river water as the source of drinking water
SP 2010
125.
Households with rainwater as the source of drinking water
SP 2010
126.
Households with other sources of drinking water
SP 2010
127.
Households has their own toilet facilities
SP 2010
128.
Household has their own toilet facilities with septic tanks
SP 2010
129.
Places for most families to defecate
K.
COMMUNICATION AND INFORMATION
PODES 2011
130.
Households with fixed-line facility
SP 2010
131.
Households with mobile phone facility
SP 2010
132.
Households with fixed-line and mobile phone facilities
SP 2010
133.
Households with internet telephone facility
SP 2010
134.
Traffic from and to the village
Guidelines For The Use Of Population Data In Disaster Management
PODES 2011
NO
VARIABLES/INDICATORS
SOURCES
135.
Type of the widest road surface
PODES 2011
136.
Can 4-wheel (or more) vehicles pass during all year round
PODES 2011
137.
Bridge on the village main road
PODES 2011
138.
Base Transceiver Station (BTS) or cell phone towers in the village
PODES 2011
139.
Cell phone/mobile phone signals
PODES 2011
5.3. The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Post-Disaster Phase BNPB’s role in the post-disaster phase is to analyze the disaster-affected population, to conduct an inventory of damaged public facilities and to coordinate the ministries/institutions in rehabilitation and reconstruction efforts. The Ministry of Public Works is responsible for enumerating and rebuilding public facilities
damaged by the disaster. The Ministry of Social Affairs is responsible for assisting IDPs to return to their homes, as well as relocate those who have been left without housing or remain in the danger zone. The Ministry of Health provides health services and facilities.
5.4. The Use of Population Data in the Post-Disaster Phase After a disaster, the situation of an area may have changed greatly, which will need to be reflected in post-disaster data. There may have been massive loss to lives, homes, farms, and properties, which will bring about a series of changes to existing demographic and socioeconomic data. Disasters can also cause the loss of national expertise that is vital to the proper functioning of statistical systems, making data-gathering a more difficult
exercise. Data that is collected during recovery and reconstruction efforts is needed for short-term and long-term planning. During this period, humanitarian and development agencies collaborating with national authorities lay the groundwork for rehabilitation programs, which will ultimately lead to more sustainable development
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71
programs. The data requirements for the key sectors vary, depending on whether it reflects recovery, reconstruction or transition efforts. This is shown in Table 5.3. During the transition phase, existing conditions do not easily allow for the collection of detailed demographic or socioeconomic information. Even additional security cannot guarantee an organization will be safe while collecting data. Using a post-crisis needs assessment approach or a quick survey to obtain the most current information about the number, location and needs of vulnerable groups is therefore the most effective and efficient way of gathering valuable information. This can include a basic overview
of infrastructure needs and the capacity of national institutions and existing partners. Such information is vital to: 1. A ssessing and prioritizing the urgent national needs for transition into the recovery phase. 2. U nderstanding the strategies for repatriation, disarmament, demobilization, rehabilitation and resettlement. 3. D esigning advocacy strategies for resource mobilization to implement emergency projects and programs, particularly as humanitarian aid is becoming increasingly scarce.
“Dharma Wanita Persatuan” (Women’s Association) of BNPB provides post-earthquake relief in Central Aceh. Source: BNPB 72
Guidelines For The Use Of Population Data In Disaster Management
Table 5.3 Data Needs during Transition, Recovery and Reconstruction TRANSITION
RECOVERY/RECONSTRUCTION
Identification of the number of people affected by the crisis
Identification of population, age, number, structure and distribution
Identification of the population that requires resettlement Socio-demographic characteristics of the population (IDPs, (especially IDP returnees, including troops from the internally displaced persons, local communities, etc.) previously warring parties, internally displaced persons, and people living in the local community) Human capacity by sector
Demographic determinants of the population (fertility, mortality, migration, relationships, etc.)
Identification of systems of management and coordination
Identification of current fertility behaviour (sexuality, marriage rates, procreation, contraception use, etc.)
Urgent reproductive health needs
Socio-demographic impacts on the population
Infrastructure that requires rehabilitation (schools, hospitals, health centres, etc.)
Existing skills in all sectors of the economy, including physicians, statisticians, nurses, teachers, etc.
Sexual and gender-based violence cases
Levels of poverty, landlessness, food distribution, housing and household amenities
Identification of specific population groups (youth, children, the elderly, the sick, orphans, etc.)
The number and distribution of sub-populations groups (children, adolescents, women of reproductive age)
For the purpose of recovery and reconstruction, authorities at the national level and development partners require more detailed demographic data to design policies and programs. This forms the basis for sustainable development, with the underlying ethos of “Building back better” than before. This kind of data and information can also help to measure the impact of a crisis or disaster on the population, increasing the accuracy at which vulnerable categories can be identified. It can also provide guidance to prevent or manage similar situations in a better way in the future.
Population data in the post-disaster phase can be used for: »» Databases that calculate disaster impacts and
losses.
»» The identification of specific population groups
such as children, adolescents and the elderly, which can be used in the preparation of specific post-disaster relief programs.
»» Databases for the number of public facilities,
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73
such as hospitals, health centres, schools and places of worship, which can be used to determine the level of losses caused by the disaster. This data can also be used to estimate rehabilitation and reconstruction efforts. »» Population data, including existing medical
resources in the affected area, which can be used as a reference for the health sector in response and rehabilitation. »» Socio-economic characteristics that can be
used for the preparation of programs that are in harmony with the livelihoods of the local people.
Renovation of houses, post-eruption of Kelud volcano Source: http://www.divif2kostrad.com 74
Guidelines For The Use Of Population Data In Disaster Management
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75
76
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6
President Yudhoyono visits IDP’s in Pujon, Malang, E. Java. Source: BNPB
Conclusion
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77
Organizing disaster response requires sound, focused and integrated planning. Unfortunately, disaster management in Indonesia Is still lacking of systematic and planned intervention, therefore often there are overlaps or even important steps that are inadvertently skipped. Disaster management ranges across a variety of fields, from planning responses to manmade disasters, to preparing for the outbreak of disease. Disaster management requires careful consideration of external factors, and the needs of particular population groups. Population data is therefore critical for disaster-prone areas. Without integrating it into disaster management plans, it will be impossible to make effective plans and policies for disaster management, and to determine the impact of losses that will inform recovery efforts. Based on the need for population data in disaster management, the National Disaster Management Agency (BNPB) has entered into
Victims of the Way Ela dam disaster report to BNPB command post to record property losses Source: BNPB 78
collaboration with Statistics Indonesia (BPS) for the provision of population data for disaster management activities. Integrating this data with existing statistical information will facilitate greater accessibility to all stakeholders, made available through the Indonesian Disaster Data and Information website - DIBI (http://dibi.bnpb. go.id). Population data sources will include the 2010 census (SP 2010) and potential village of 2011 (PODES 2011). Important population data in the pre-disaster phase includes the size of the population, grouped by derivatives including age, vulnerability and density. These characteristics will form the basis of response plans when disaster strikes, as they relate to the number of victims and IDP’s. Data on the level of education and the number of health workers will help to form a picture of the human resource capacity of an area. Mapping the availability of education facilities that can be used as evacuation sites in the advent of a disaster is also vital in the pre-disaster phase. Based on the Humanitarian Charter and Minimum Standards in Humanitarian Response (Sphere Project), humanitarian emergency response mechanisms help to ensure the affected population has access to basic standards of water, sanitation, food, nutrition, shelter and health services. This raises the demand for data to identify needs within the community and target groups. As collecting data in a state of
Guidelines For The Use Of Population Data In Disaster Management
emergency is very difficult, the best alternative is to use pre-disaster population data as a baseline for analysis. In the post-disaster phase, the disaster may have triggered population movements on a large scale, resulting in a loss of property, the cessation of farming activities, and fatalities. During the transition phase, existing circumstances make it difficult to provide detailed demographic and infrastructure-related information. Further, existing conditions do not easily allow for the collection of detailed demographic or socioeconomic information. Using a post-crisis needs assessment approach or a quick survey to obtain the most current information about the number, location and needs of vulnerable groups is therefore the most effective and efficient way of gathering valuable information. This can also help to obtain basic information about the capacity of national institutions and the existing partners in supporting efforts.
BNPB interviews people at post-placement habitation after the eruption of Merapi. Source: BNPB
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Bibliography BNPB
Head Regulation No. 08. Standardization of Data Disaster, 2011.
BNPB
Head Regulation No. 02. The General Guidelines for Disaster Risk Assessment, 2012.
BNPB
Head Regulation No. 09. Standard Operation Procedures of Quick Response Team, National Disaster Management Agency, 2008.
BNPB
Head Regulation No. 11. Guidelines for Post-Disasters Rehabilitation and Reconstruction, 2008.
BNPB
Head Regulation No. 15. Guidelines for Post-Disaster Assessment, 2011.
BNPB
Head Regulation No. 17. The General Guidelines for the Implementation of Post-Disasters Rehabilitation and Reconstruction, 2010.
UNFPA
Guidelines on Data Issues in Humanitarian Crisis Situations, 2010.
KEMENTERIAN KESEHATAN RI
Technical Guidelines for Health Crisis Management, 2010.
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81
BADAN PUSAT STATISTIK
Jl. Dr. Supomo 6-8 Jakarta 10710 Indonesia Telp. +6221-3841195, 3842508, 3810291 Fax. +6221-3857046 Email:
[email protected]
7th Floor Menara Thamrin Jl. M. H. Thamrin Kav. 3 Jakarta 10250 Indonesia Telp. +6221-3141308, 3907121 Fax. +6221-3904914, 3192702 Website: http://indonesia.unfpa.org
GUIDELINES FOR THE USE OF POPULATION DATA IN DISASTER MANAGEMENT
BNPB
Jl. Ir. H. Juanda No. 36 Jakarta Indonesia Telp. +6221-3442734, 3442985, 3443079 Fax. +6221-3505075 Email:
[email protected]
GUIDELINES FOR THE USE OF POPULATION DATA IN DISASTER MANAGEMENT
BNPB
BADAN PUSAT STATISTIK