GUIDELINES FOR THE USE OF POPULATION DATA IN DISASTER MANAGEMENT

BADAN PUSAT STATISTIK Jl. Dr. Supomo 6-8 Jakarta 10710 Indonesia Telp. +6221-3841195, 3842508, 3810291 Fax. +6221-3857046 Email: [email protected] 7th...
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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

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Foreword

iii

Introductory Remarks

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Table of Contents

vi

List of Tables

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List of Figures

ix

Acronyms

x

Chapter 1 Introduction

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1.1 Background

2

1.2 Objectives

3

1.3 Goals

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1.4 The Use of Population Data In Disaster Management

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Chapter 2 Population Data in Disaster Management 2.1 Population Data in Each Phase of Disaster Management

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2.2 Population Data Sources that Can be Used in Disaster Management

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Chapter 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

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3.2 Population Data Needs and Indicators in the Pre-Disaster Phase

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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

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3.4 Population Data Use and Analysis in the Pre-Disaster Phase

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Chapter 4 Population Data Collection and Use in the Emergency Response Phase

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4.1 Data Collection Sources and Methodology in the Emergency Response Phase

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4.2 Population Data Needs and Indicators in the Emergency Response Phase

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4.3 The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Emergency Response Phase

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4.4 The Use of Population Data in the Emergency Response Phase

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Chapter 5 Collection and Use of Population Data in the Post-Disaster Phase

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5.1 Data Collection Sources and Methodology in the Post-Disaster Phase

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5.2 Population Data Needs and Indicators in the Post-Disaster Phase

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5.3 The Roles and Functions of BNPB and Ministries/Institutions Related to Population Data in the Post-Disaster Phase

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5.4 The Uses of Population Data in the Post-Disaster Phase

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Chapter 6 Conclusion

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Bibliography

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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

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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

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Figure 2.2

Flow of Data and Information Management

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Figure 2.3

Home page for dibi.bnpb.go.id

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Figure 2.4

Population Data at the Provincial Level

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Figure 2.5

Population Data at the District Level

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Figure 2.6

Population Data at the Sub-District Level

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Figure 2.7

Population Data at the Village Level

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Figure 2.8

Display of DIBI Population Data Graph

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Figure 2.9

Display of Results Statistics Level Up Village / Village

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Figure 3.1

Population Density of Palue Island and Mount Rokatenda Disaster Prone Areas

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Figure 3.2

Indonesian Earthquake Hazard Map

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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

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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

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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.

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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.

Guidelines For The Use Of Population Data In Disaster Management

15

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

Guidelines For The Use Of Population Data In Disaster Management

19

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

Guidelines For The Use Of Population Data In Disaster Management

23

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

29

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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35

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

41

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

Guidelines For The Use Of Population Data In Disaster Management

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).

Guidelines For The Use Of Population Data In Disaster Management

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

48

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.

Guidelines For The Use Of Population Data In Disaster Management

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

Guidelines For The Use Of Population Data In Disaster Management

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

Guidelines For The Use Of Population Data In Disaster Management

55

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

58

Guidelines For The Use Of Population Data In Disaster Management

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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59

60

Guidelines For The Use Of Population Data In Disaster Management

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.

Guidelines For The Use Of Population Data In Disaster Management

63

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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65

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

Guidelines For The Use Of Population Data In Disaster Management

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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67

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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69

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

Guidelines For The Use Of Population Data In Disaster Management

75

76

Guidelines For The Use Of Population Data In Disaster Management

6

President Yudhoyono visits IDP’s in Pujon, Malang, E. Java. Source: BNPB

Conclusion

Guidelines For The Use Of Population Data In Disaster Management

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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79

80

Guidelines For The Use Of Population Data In Disaster Management

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

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