52

PART 2

INTERNALDISPLACEMENT DATA:FROM CHALLENGE TOOPPORTUNITY

GRID
2019

Internally displaced people
receive aid at a distribution
site, Gedeo Zone, Ethiopia.
Photo: IOM/Olivia Headon,
September 2018

53

As the number of IDPs worldwide continues to rise andcrises become increasingly protracted, it is ever more
important that all actors working to address internal
displacement have comprehensive and accurate dataand evidence. We currently have a good sense of thenumbers of IDPs from country to country, but muchremains unknown about the scale, patterns, impacts
and drivers of the phenomenon, particularly in urban
areas. There are a number of unanswered questions
about the extent to which displaced people are able toachieve durable solutions.
There are many good reasons to invest in quality dataand evidence. It helps to prioritise the investment of
resources and inform programming, policymaking
and advocacy. It establishes a better understandingof internal displacement, which helps to forecast
and contextualise trends. This in turn informs better
responses and contingency and long-term planning.
It also sheds light on the costs and impacts of thephenomenon on individuals, communities and states,
which helps to make the case for risk reduction andpromote the accountability of governments and otheractors to vulnerable people.
Improved data and evidence may also challenge assumptions and orthodoxies about the nature of displacement
crises, including those in urban areas, which remain
poorly understood. Solid baseline data and evidence atthe national and global level is a precondition for measuring the success of efforts to protect and assist IDPs
and help them achieve durable solutions. Using data tobetter understand the phenomenon over time, including
displacement risk, is also paramount to informing theachievement of sustainable development targets andthe monitoring of progress to that end.

To be considered comprehensive and robust, internal
displacement data should cover the causes, triggers,
patterns and impacts of displacement in all its forms andregardless of scale, including its duration and severity. It
is also vital that such data is recorded using methodologies that track population flows based on events andwithout the use of minimum thresholds for collection
and analysis, in order to fully capture and differentiate
between small-scale disasters or localised conflicts andmajor events that trigger mass displacement.
Data should be disaggregated by geographic, demographic, temporal and socioeconomic characteristics
which also cover host communities to ensure thatvulnerable groups are identified and no one is left
behind.
Significant advances have been made, including thedevelopment and use of innovative technologies andmethodologies that will be discussed below. Partnerships at the national and international level need to bestrengthened and government agencies’ capacity torecord displacement data improved. Greater collaboration would also help to explore the untapped potentialof data to generate evidence and insights about thewhole spectrum of human mobility and its links with
development challenges and opportunities.
Along with political will, adequate resources and appropriate capacities, a clear and comprehensive pictureof the severity and scale of internal displacement will
enable those working to address the phenomenon
to take appropriate measures to mitigate the risk of
future displacement. It would also help donors and thehumanitarian and development community to prioritise
their spending and national and local governments totake evidence-based decisions.

PART 2

WHY DOES INTERNAL DISPLACEMENTDATA MATTER?

54

COORDINATION AND COLLABORATION:
THE INTERNAL DISPLACEMENTDATA ECOSYSTEMWHO USES INTERNALDISPLACEMENT DATA AND
WHAT FOR?
A broad range of stakeholders with diverse roles
produce and publish internal displacement data fordifferent reasons. They include governments, UN agencies, local and international NGOs and research organisations. Some focus on data collection, others support
its sharing or analysis and some perform a combinationof functions.
Internal displacement data is often collected or analysedas part of wider exercises, or can be extrapolated from
reports that focus primarily on other issues such as
housing or protection. It often does not to cover the fullscope of displacement crises, whether because of access
restrictions, funding shortfalls, security issues or otherfactors. Nor is the data that is collected always madepublic to increase transparency and accountability.
Data users at the local, national, regional and global
level can be divided into the following broad categories:|| Governments, donors and institutional policymakers
use data to design laws and policies, decide where
to invest resources for prevention, planning andresponse, and to track progress over time

GRID
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|| Research bodies use data to analyse displacement
trends, patterns and impacts, collate and aggregate
figures, and to establish understanding of emerging
or under-explored aspects of internal displacement|| Civil society organisations use data to raise awareness, and advocate for policy changes, resource
allocation and more effective programming|| Technology initiatives use data to identify betterways to quantify displacement and understand its
impacts|| Media use data and evidence for reporting and to
inform the public
The main functions performed by various actors at thenational and global level in the data ecosystem areoutlined below. Some specialise in specific functions, butmany engage across a number of them (see Figure 9).
|| Primary data collection: this is the process by which
actors gather specific information on IDPs andother populations affected by crises, using methods
including key informant interviews, surveys andvarious forms of registration|| Data aggregation: once primary data has beencollected, it is consolidated with other data to facilitate analysis, whether for geographic, sectoral,
temporal or thematic purposes|| Operational (including coordination) actors use
data to inform responses to internal displacement
crises, collate and aggregate figures, and help
monitor trends and identify risks to inform future
programming|| Data analysis: once data has been collected and/or
aggregated, it is evaluated and scrutinised to informand support policymaking, operational decisions,
reporting and research|| Displaced persons can use evidence to inform theirown decision making, and to advocate for better
service delivery, inclusive solutions and respect for
their civil, human and political rights|| Data repositories: these are storage platforms thathost and preserve data according to specific categories, and which are used to facilitate analysis,
reporting and research

55|| Research: the use of primary or aggregated data
to produce qualitative and quantitative studies that
explore specific lines of inquiry and answer specificquestions|| Technical support and capacity building: the provision of expert advice and training to governments
and other stakeholders to build or reinforce theircapabilities in areas such as profiling and other datacollection methods
A number of processes, initiatives, networks and frameworks focus on humanitarian or development data,and include internal displacement as a component of
their work. But only a handful focus solely on internal
displacement data.

ARRIVING AT BETTER
INTERNAL DISPLACEMENTDATA
The displacement data ecosystem is complex, particularly given that actors often perform more than one
function. Different stakeholders also experience andprioritise challenges in different ways, depending on
their focus (e.g. humanitarian or development) and thelevels at which they operate (e.g. local, national, regional
or global). Some challenges involve institutional overlap

or the potential proliferation of initiatives, but the mainones in obtaining comprehensive and quality data andevidence – which emerge repeatedly during formal andinformal discussions – relate largely to a lack of common
standards, ineffective coordination and limited sharing
and interoperability.

Common standards
There is conceptual and legal guidance on internal
displacement, starting with the Guiding Principles
and encompassing various resources such as the InterAgency Standing Committee (IASC) Framework on
durable solutions for internally displaced persons, its
Operational Guidelines on the Protection of Persons in
Situations of Natural Disasters, and the Brookings Institution’s publication Addressing Internal Displacement:
A Framework for National Responsibility.266
Translating this guidance into practical common standards for data collection and analysis has, however,proved challenging. There is not yet agreement on major
questions such as how to systematically measure theend of displacement, and what the best methodologies
are for accurately capturing its different stages and characteristics. This significantly hampers the availability andcompilation of quality, comparable data and evidence
at the national and global level.
Processes such as the Expert Group on Refugee and
IDP Statistics (EGRIS), established by the United Nations
Statistical Commission (UNSC) at its 47th session in2016, are starting to address some of these questions
as they relate to official statistics. The group includes
various governments, international experts and organisations including the UN Refugee Agency (UNHCR), theFigure 9: Functions in the internal displacement data ecosystem
Data analysis: Local NGOs, research bodies, governments, clusters,
UNHCR, IOM, REACH, UNICEF, WFP, IDMC, PDD, ACAPSRed Cross and Red Crescent Societies and other INGOs

Analysis

Aggregation
Collection
Technical support &capacity buildingcharRepository

se

o
Co

Re

i
rd

t
na

i

on

Data repository: Governments, IDMC, HDX, IOM,UNHCR-WB joint data centre, WB Open Data Initiative
Data aggregation: Regional bodies, governments, OCHA, UNHCR,
IOM, REACH, IDMC
Data collection: Governments, UNHCR, IOM, OCHA, NRC, REACH,
UNICEF, WFP, Red Cross and Red Crescent Societies, local NGOs
Data coordination: Governments, OCHA, clusters
Technical support and capacity building: JIPS, IDMC, INGOs,
UN agencies, clusters, WB
Research: IDMC, ACAPS, UN agencies, INGOs, research bodies,governments

This graphic is not intended to be an exhaustive listing of organisations contributing to each category.

PART 2|| Coordination: the process of bringing together thework of different actors that collect and aggregate
data to share information and, where relevant, aligntheir efforts

56
World Bank, IOM, the Joint IDP Profiling Service (JIPS),
and IDMC among others. It has been developing two
sets of guidelines for national statistics offices, the International Recommendations on Refugee Statistics (IRRS)
and International Recommendations on IDP Statistics(IRIS).267 Building government capacity and developing
this type guidance is important, and EGRIS’s recommendations will be an extremely valuable resource once
finalised in 2020.
In many displacement situations, however, governments still lack the capacity and support, or have little
incentive, to collect data. Major data gaps are currently
largely filled by humanitarian and development agencies, which collect the bulk of internal displacement
data. But their operational planning and responses
require information that differ substantively from official statistics.
IASC’s guidelines to improve common data collection to
inform disaster preparedness and response, mentioned
above, outline the common datasets needed for
responses to humanitarian emergencies. This governance model lays out accountabilities and responsibilities
in data management, technical standards and recommendations to improve data quality and interoperability.268 To support the guidance, IASC has developedand endorsed operational datasets, which OCHA has
disseminated. These articulate and share baseline data
across sectors for mapping and other information andplanning purposes.
IASC’s durable solutions framework is also supported bya library that builds on the IASC framework to develop
a set of indicators and guidance to durable solutions
analysis in internal displacement contexts.269 Such analyses can support national authorities and other stakeholders to develop joint evidence-based responses todisplacement crises.
However to further improve the quality of the evidencebase on internal displacement, national and international actors will still need to agree on clear definitionsof basic concepts and develop common metrics andindicators. They could also build on existing indicatorsfrom other fields as proxies, in order to monitor andanalyse different forms of displacement.

GRID
2019

Coordination
There are examples of good coordination on data collection. But many displacement crises happen in countries
where governments have limited capacity, or in somecases the political will to collect data. In the international
community, while it is the role of the humanitarian
coordinator’s role to lead and coordinate responses
during crises, no single agency has the sole mandate tomonitor and lead the response to internal displacement.
Even where the Cluster System or other consortia
operate, coordination within and across them can bechallenging, creating data collection and aggregation
issues that ultimately affect the quality of evidence.
When national authorities lead the collection or aggregation of data, typically in response to disasters, gaps
in coverage and the fragmentation of datasets acrossministries or levels of government are also challenges.
Lack of coordination also is an issue internationally,
which can create confusion about the scope of institutional mandates. This can lead to overlapping initiatives
and ultimately the publication of less comprehensive
and reliable data than would have been the case if
coordination were more effective.
In order to avoid duplication and identify potential
complementarities, new and existing data initiatives
and processes should be linked and aligned as muchas possible. Opportunities to collaborate on broader
thematic research that goes beyond data would also
help to establish a stronger evidence base on internal
displacement.

Data sharing and interoperability
Interoperable data is drawn from different sources but
can be jointly analysed or compared to help consolidate
numbers and create more holistic contextual information to support analysis, decision-making and accountability. In practice, however, different organisations
define and monitor population movements and collectdata in different ways. There may even be differences
within organisations in how populations, their movements and their needs are defined. This means the data
produced tends not to be interoperable. At the national
level, this can undermine the effectiveness of programming and impedes critical insights into trends, patterns
and linkages between different population movements.
The lack of interoperability also affects efforts to aggregate and analyse data at the global level, including

57
PART 2for forecasting. To address the issue, organisations willneed to share information more regularly, consistently
and systematically, with due regard for data protection
and privacy concerns. They will also need to identify thedifferent methodologies they use to collect data moreclearly to strengthen analyses and identify potential
duplication, overlap and gaps in coverage.

Box 1.	 HXL: making datasets interoperable and useful
One of the main challenges data users face is the sheer amount available across different platforms. One way toaddress this is by labelling, storing and sharing displacement data in a way that allows for easier, faster and more
efficient access and use.
Humanitarian Exchange Language (HXL, pronounced HEXel) aims to do just that.270 It is a new kind of standard,
designed to complement rather than replace existing humanitarian data processes. Supported by a range of partners and convened by OCHA, it is intended to reduce duplication, improve interoperability, semi-automise datapreparation and make use of existing data visualisation applications using common tags and practices.
IOM, for example, uploads, updates and shares its Displacement Tracking Matrix (DTM) data on the Humanitarian
Data Platform (HDX) as part of its standard operating procedure. It also adds HXL hashtags to improve processing
and sharing. Common tags include administrative division, geographical information, population, sector, needs
and incident/event. This helps to harmonise and enhance data quality and usefulness.271
Adding a row of HXL hashtags to a spreadsheet greatly improves interoperability. For example, #country+name
identifies a column containing country names, #adm1+name always identifies a column containing top-levelgeopolitical subdivision names and #affected+idps+ind identifies a column containing the number of individual
IDPs. Because the hashtags are standardised, differences in column ordering or even the number of columns nolonger pose a problem.272#HXL

Country

Top-Level Geopolitical Subdivision Number of IDPs

#country+name#adm1+name#affected +idps +ind

BurundiAdmin1

Nombre de PDICentral African Republic

ADM1_NAME

IDP_ind

Libya

ADM 1 Geodivision (EN)

IDPs in Baladiya (IND)

Madagascar

Admin 1

Total No# of IDPs Ind#

Nigeria

State of Displacement

Number of IDP

IDMC releases all its publicly available datasets with HXL tags to make it easier to promote, disseminate and share.
Tagging also facilitates data visualisation using platforms such as HDX and its tools.273

58

CRITICAL DATA GAPS AND WAYS TOOVERCOME THEM
IDMC collects data from a wide range of sources,
including UN organisations, national governments, the
Red Cross and Red Crescent movement, international
and local NGOs and media sources (see Figure 10).
These organisations are engaged in a broad spectrumof humanitarian, development, human rights and otherwork. They also differ widely in terms of regional andnational scope, and the types of movement and metrics
they report.
Seventy-five per cent of the data that IDMC uses tocompile its global estimates for displacement associatedwith disasters comes from national governments. It
relies largely on UN and humanitarian agencies for dataon displacement associated with conflict and violence.
IDMC uses the data provided by different sources to
produce the best estimates possible of internal displacement at the national and global level (see Figure 11). In
doing so we have identified some critical gaps in datacollection that prevent a comprehensive assessment of
the scale, nature, trends and impacts of the phenomenon. Still, progress has been made in some areas, andnew technologies and approaches hold the promise ofsignificant advances in the future.

Figure 10: IDMC workflow
Monitoring
Internal displacement

INCONSISTENTMETHODOLOGIESThe lack of consistent methodologies for data collection
and analysis creates many challenges. When different
stakeholders use different methodologies to collectdisplacement data in the same country, it may leadto conflicting estimates of the number of IDPs, newdisplacements or returns. This is turn has the potential to cause confusion for policymakers, donors andorganisations on the ground, and to undermine effective interventions and the prioritisation of resources to
respond to internal displacement crises. In some crises,
constraints on humanitarian access to areas where IDPs
are present impede or severely limit data collection
efforts. In others, more effort is required to ensure theadded value of simultaneous data collection in similar
locations.
This issue also makes aggregating and comparing dataat the regional and global level more difficult, particularly if no adequate explanation for differing methodologies is given that might allow for comprehensive
analysis. This in turn hampers efforts to understandthe regional dynamics of crises. Collecting displacement
data will by necessity continue to involve a number ofstakeholders. Addressing this issue requires the development of clear, harmonised and consistent approaches
to data collection for these actors.

Compiling,triangulating, aggregating
and curating internal displacement data
(structured and unstructured data)

Conflictand violence

Assessments, surveys
registration data, media andsatellite imagery analysis*

Organized bydisplacement
term

IDPs, evacuees,
forced to flee*

Disasters
Housing
destruction

Development
projects* The list of terms used in this graphic is not exhaustive.

GRID
2019

Validation of data
and analysis

Publication of global figuresin the GIDD

in coordination withprimary data collectors,
governments and otherrelevant actors(Global Internal Displacement Database)

59|| Conflict and violence

United Nations
Sub-Saharan Africa

Clusters and Consortias
East Asia & Pacific

Media
Civil SocietySouth Asia

Local Authority
International NGOs

Middle East & North Africa
Europe & Central Asia
Americas

Government
National/Regional Disaster Authority
International Organisations
Community Leaders
Red Cross/Red Crescent Movement
Non-State Armed Group|| Disasters

National/Regional Disaster Authority
East Asia & Pacific

Local Authority
Americas
Government
South Asia

Media
Clusters and Consortias

Sub-Saharan Africa
Europe & Central Asia
Middle East & North Africa

United Nations
Red Cross/Red Crescent Movement
Civil SocietyInternational Organisations
International NGOs
Community Leaders
Private Sector

Note: IDMC distinguishes between the the source, where the information comes from, and where it is published. Sources provide their own figures for IDPs.

PART 2

Figure 11: Sources of IDMC’s estimates for displacement associated with conflict and violence, and disasters, by region

60
SPOTLIGHTDATA TRIANGULATION
What is it, and why is it useful?
The main purpose of triangulating data is to increase
its credibility and validity.274 IDMC uses triangulation tovalidate datasets from various sources that describe thesame phenomenon. Doing so becomes even more relevant given today’s fast-moving news cycle, including theproliferation of “fake news”, and the fact that anyone
can present unverified information, potentially leading
to significant discrepancies in what is reported andconfusion about true displacement numbers. Around70 per cent of the information on disasters recorded in
IDMC’s database in 2018 was collected for triangulation purposes.
The value of triangulation is demonstrated by IDMC’s
method of estimating displacement associated withdisasters in Afghanistan. IOM and OCHA each work
closely with local humanitarian organisations to produce
two comprehensive datasets on disaster damage. OCHA
recorded 235 disaster incidents in the first six months of
2018, and IOM 304 incidents. The datasets overlapped
geographically. OCHA’s covered 24 of Afghanistan’s 34
provinces, and IOM’s covered 26. Twenty-three provinces were covered by both. The two datasets differ inthe terminology they use to classify disaster events or
damaged and destroyed housing, which highlights theneed to synchronise and develop common definitions
and metrics.
IDMC used the two datasets to analyse displacement
triggered by floods in May 2018. Using OCHA’s dataon housing destruction as a proxy for the number ofpeople displaced yielded a figure of 24,589. IOM’s
data on housing destruction produced an estimate of
12,090 people. Additional IOM data on affected people
living with host families, in open spaces and informal
settlements suggested that 44,884 people had been
displaced. IDMC compared the data taking into accountdifferences in definition and coverage, and the potential
for double counting. It also triangulated the data withinformation from media sources to arrive at a final figureof 46,380 people displaced.

GRID
2019

Triangulation is also useful in compiling the best estimates for new displacements associated with conflict.
Ethiopia had the highest figure worldwide in 2018, and
IDMC used data from 16 sources that reported internal
displacement during the year. IOM’s DTM reports wereused as the basis for triangulation, and calculations weremade using the sum of positive variations between
reports at site level for the whole year. The DTM reports,
which covered only nine out of eleven regions in the
country, were published bi-monthly and people’s movements were highly dynamic. This meant that relying
only on DTM data risked missing a significant numberof short-term or repeated displacements. Using only
IOM DTM reports, IDMC calculated about 1.5 million
new displacements.
To overcome this issue, IDMC used other sources
including OCHA, national and regional government
reports, The Directorate-General for European Civil
Protection and Humanitarian Aid Operations (ECHO),
other types of IOM DTM reports and assessments,and local and international media to produce a morecomprehensive estimate. This method allowed to reach
2.9 million new displacements, which is almost twice thenumber based only on IOM DTM bi-monthly reports. In
order to avoid double counting and errors, the dates,
type of movement, triggers, contextual information,
geographical and temporal coverage and access to IDPs
were considered to determine which caseloads should
be included or excluded from the calculation. Thismethod ensured that a significant number of underreported displacements were not left out, and at thesame time minimised the risk of double counting.
Given the increasing amount of information, news anddata available, such triangulation exercises will remain
important and necessary tools in IDMC’s monitoring.

61

A number of challenges remain when it comes to identifying IDPs’ place of origin, destination and current
location at a specific point in time. The general lackof data disaggregated by sex, age, disability and othercharacteristics is also a significant issue that countries
need to address if they are to support IDPs in achieving
durable solutions and report progress against global
frameworks.275 Data disaggregation gaps currently tend
to be filled with qualitative and anecdotal evidence.
Data disaggregated demographically, geographicallyand temporally would also make a significant differencein shaping responses, informing policy and assessing
progress in reducing displacement.
Displacement is a highly dynamic phenomenon, making
it difficult to monitor over time, and a number of factors
make doing so more complicated still. A significant
proportion of IDPs live in dispersed settings with oramong host families and communities, where they are
harder to locate let alone monitor. Some IDPs may bedisplaced a number of times, while others undertake
pendular movements between their places of origin andrefuge. Families may also split up, with some members
remaining in displacement while others return home.
In addition, capturing pendular displacement is further
complicated by the fact that it is often missed by assessments which are more prone to capture data on individuals in camps or camp-like settings.
IDMC aims to gather and report information disaggregated by geographical area, including by urban and rural
locations, but even when such data is available it can beinaccurate and is not always broken down by location.
Knowing what triggers people’s displacement is also
vital to fully understand the phenomenon and thetype of response needed. To address this issue, IDMC
revised its hazard classification and developed a newconflict typology in 2018 with the aim of improving its
reporting, and clarifying and expanding its data model
to better capture how and why people move.

Data disaggregated by sex, age
and other characteristics
Data disaggregation is essential to ensure all vulnerable groups and their needs are properly captured andaddressed. Different groups will require tailored interventions that respond to the circumstances of theirdisplacement depending on their income, age, genderand location. It is challenging, however, to obtaincomprehensive data on key metrics disaggregated bysex, age and other characteristics such as socioeconomic
status, ethnicity, and disability.
In 2018, IDMC obtained data on displacement associated with conflict disaggregated by sex or age for 12
out of 57 countries and territories, or 21 per cent, andby age for 8, or 23 per cent. Some of the data received
for other countries was also disaggregated in this way,
but the datasets either did not cover the whole country
or the whole year. Further, despite greater efforts andimprovements over the past decade in collecting dataon displaced children, there is very little information
available about their age and sex, where they comefrom, where they are going, why they moved, whether
they moved with their families or alone, how they faredalong the way or what their specific vulnerabilities andneeds are.276
Understanding these characteristics, the push and pull
factors that lead to their displacement and its impacts is
key to designing responses that address diverse protection and assistance needs and to better understanding
the implications of displacement for communities andstates.

Geolocation and geographicalreference issues
Obtaining data disaggregated by specific location
continues to be a challenge. One of the most notablegaps in displacement monitoring is the lack of georeferenced information. Local data collectors often havea more accurate idea of where people move to, but
gaining access to such information at the global level isdifficult. This means that most of the data made available to IDMC is not geolocated at the site level. At best,
only the names of towns and provinces are mentioned.

PART 2DATA DISAGGREGATION:
WHO IS DISPLACED, WHERE
AND WHY?

62
In some conflicts, governments or non-state actors
obstruct the systematic collection of data, or security
risks may be too high to undertake such exercises. Data
may also not be made available because of protection
protocols, and investments in data collection tend to
decrease after the emergency phase of a crisis. This may
mean that IDPs in hard-to-reach areas or protracted
situations fall off the radar. Having accurate estimatesof the scale of population movements, including rural
to urban, urban to urban, intra-urban and urban to rural
movements, would also help to fill the stubborn data
gap on the number of displaced people living in cities(see Urban displacement spotlight, p.63).
To establish a more accurate picture of where displacement happens, IDMC applies different methods togeoreference the phenomenon. In doing so, it gives
due consideration to the ethical implications of using
such data. The aim is not to track individuals, but rather
to understand broader displacement trajectories during
and after crises to inform improved responses and help
estimate future movements.

The main method used was reverse geocoding, in which
coordinates are automatically allocated based on location names mentioned in the data sources. Automatedcoordinates are generated using the centre of administrative areas, which reflect an approximate location ofwhere displacement occurred. If information is provided
at the provincial level, reverse geocoding will only point
to the centre of the province in question. Municipallevel information allows more accurate locations to be
determined.
Using reverse geocoding in this way is useful in understanding where displacement happens, but mostsources’ data tends not to identify the origin and destination points of peoples’ movements, which makes it
difficult to understand specific displacement patterns.
This is particularly the case for fast-moving emergencies,
hard-to-reach areas and less visible protracted displacement caseloads.

In 2018, IDMC was able to geolocate the origin or destination of some displacement flows. Figure 12 shows
the approximate geolocation of displacement reports
related to both conflict and disasters during the year.

Figure 12: Geolocated displacement events monitored by IDMC in 2018

Note: 11,511 data entries in the IDMC database by cause of displacement recorded in 2018

GRID
2019

63
SPOTLIGHT

URBAN DISPLACEMENTFirst steps to paint a global picture

By some estimates, between 60 and 80 per cent of
IDPs live in cities and “out-of-camp settings”.277 Thereis, however, no strong evidence to support such assertions, and a number of factors make understanding thetrue scale and characteristics of urban displacement
particularly challenging.
There is a lack of common definitions and methodologies to classify urban and rural areas.278 Different
definitions emphasise different factors including demographics, social dynamics, infrastructure, the availability
and provision of services and the way land use and thebuilt environment are structured.279 Nor does displacement data that includes information about the type ofsettlement, whether it be a camp, out-of-camp settingor spontaneous site, necessarily specify an urban orrural location.
Camps and camp-like settlements facilitate data collection because IDPs are gathered together in one place,unlike urban areas where they tend to live in dispersed
settings among the local population, which makes themmore difficult to identify. Some may also choose to stayunder the radar to preserve their anonymity because of
potential threats to their security.

IDMC was able to address some of these challenges forthe first time in 2018, and disaggregate displacement
associated with conflict by urban and rural location.
The EU’s Global Human Settlement Layer (GHSL) wasused as a basis for conducting the analysis. GHSL is a
global dataset that assesses degrees of urbanisation
using census data from national statistical institutes
and satellite observations.280 It provides multi-temporalgeospatial data, presented in grids of one square kilometre, enabling a globally consistent and comparable
classification of rural and urban areas.281 By overlaying
information on displacement sites with GHSL, it waspossible to disaggregate the data.282
More than 41.3 million people were living in internal
displacement as result of conflict and violence as of theend of 2018. Information on displacement sites was
obtained for 19.8 million, or 48 per cent of the total. Of
55 countries where conflict displacement was identified,
information on IDPs’ specific location was available for
12. Within these 12, specific caseloads were selected
where good quality geolocalised data was available, forexample from site-level assessments. IDMC’s analysis
concluded that 52 per cent of IDPs were living in urban
settings in these twelve countries (see Figure 13).

Figure 13: Disaggregation of stock figures for 12 countries by rural and urban settings using GHSL as reference

41.3 mPeople living in
displacement as
result of conflict andviolence in 55 countries
as of 31 December 2018

48% of thesedatasets havedisaggregatedinformation(e.g. GPS coordinates)

48%

Living inrural areas

52%

Living inurban areasin 12 countries*

80%
72%
70%
68%
44%
56 %
42%
58%
28%
72%
28%
72%
24%
76%
17% 83%
99%
100 %

20%
28%
30%
32%

Syria
Nigeria
Iraq
Palestine
Cameroon
Dem. Rep. Congo
Yemen
Sudan
Afghanistan
Myanmar
Libya
Chad* Percentages refer to the portion of the national IDP caseload that has geospatial information. In the case of Yemen, Chad, Iraq, Syria, Afghanistan, Sudan and Dem.
Rep. Congo, more than the 90% of the caseload is published with coordinates. In other countries this proportion is lower: Libya 85%, Nigeria 55%, Cameroon 37%
and Palestine 0.2%.

64
Iraq provides an interesting example. The use of data
provided by IOM’s DTM, all of which was geolocated,helped to paint a more accurate picture of urban
displacement. The analysis shows that 70 per centof IDPs were living in urban settings as of the end of
2018. Sixty-two per cent were living with host families
or in rented accommodation.283 Ninety-six per cent of
displacement sites were also within ten kilometres ofan urban area. At an even more granular level, Figure
14 illustrates urban displacement in Mosul as revealedby overlaying data with the GHSL layer.
This exercise was a first step toward filling the significant
data gaps on urban displacement. Clearly, when primarycollectors geolocate and share their data, a consistentmethodology can be applied to illustrate the scale ofthe phenomenon. As long as this kind of information is
not consistently collected or shared, however, it will bedifficult to paint a global picture. In its absence, policies
and programmes to support IDPs and host communities
and to help cities cope with displacement will not be aseffective as they might.

Figure 14: Iraq: IDPs’ location by distance from anurban settlement

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2019

65

IDMC collects and analyses data on two main metrics– stocks and new displacements, as well as on solutions. Stocks are the number of people displaced at a
certain moment in time, new displacements also include
repeated movements, and solutions include returns,
local integration, resettlement and “partial or unverified solutions” (see Part 1). IDMC uses a number ofproxy data sources to paint a more complete pictureof internal displacement events. A significant part of
IDMC’s analysis relies on data and reporting that does

Mobile phone data
The number of mobile phone users in the world is
expected to pass five billion in 2019, and anonymous
mobile phone data offers a way to bridge data gaps
and better monitor IDPs’ movements with due regard
for privacy and data protection concerns. The use ofmobile technology in the humanitarian sector is notnew, but it has been used primarily to support emergency operations and disaster relief.284 Its use beyond
the emergency phase helps to understand population movements before, during and after a disasteror conflict event, including their duration (see Box 2).

Box 2.	Using mobile phone data to track displacement in Papua New
GuineaPapua New Guinea experienced a 7.5 magnitude earthquake on 26 February 2018, which was followed by significant
aftershocks in the subsequent days and weeks.285 As many as 465,000 people are thought to have been affectedby the disaster, which left around 247,000 in need of immediate humanitarian assistance. Around 58,000 people
were internally displaced.
A study conducted in the aftermath of the earthquake showed the benefits of using anonymous mobile phonedata to track the displacement it triggered. The data proved particularly useful in assessing how far and in which
direction people had fled, and how their movements evolved over time. Because mobile phone data is uniquely
detailed and traceable, it can provide information that is not easily gleaned from other sources at such scale. On
the downside, only the movements of mobile phone users can be analysed, and it is not possible to assess people’s
specific vulnerabilities.
Papua New Guinea has a mobile penetration rate of 54 per cent, and Digicel is the main service provider with amarket share of more than 90 per cent. The study, which was carried out with the collaboration of the government, the UN country team and Digicel, ensured users’ privacy was protected by following guidelines for the useof mobile phone data published in 2014 in response to the Ebola outbreak in West Africa.
The data was analysed from several angles, first by modelling the relationship between the activity of transmission towers and their distance from the earthquake’s epicentre. The logs of calls and messages sent and received
via the towers also reveal population movements that can be tracked over time. Analysis over longer periods canprovide insight into return movements.
The study also combined mobile phone data with information from humanitarian sources including OCHA andIOM DTM. It found that the humanitarian community had targeted its assistance effectively, showing the data’s
added value in monitoring crisis responses and disaster management.
The Papua New Guinea study illustrates the significant value of anonymous mobile phone data in tracking displacement flows, including when it is used in conjunction with other forms of data collection and assessments determining displaced people’s specific needs.

PART 2OTHER METHODS OF
TRACING DISPLACEMENT
FLOWS

not focus specifically on displacement, which can posechallenges in terms of accuracy.

66

Young Senegalese who returned home from Algeria,Mali, and Libya interview one another about theirmigration experiences. The initiative is part of IOM’s
Migrants as Messengers project, which uses peer-topeer messaging to fill gaps in information and ensure
people are better informed about the risks of irregularmigration. Photo: IOM/Julia Burpee, June 2017

Aerial and satellite imagery
analysis
Satellite and aerial imagery is useful in assessing
displacement via proxy indicators such as housing
damage and destruction or the extent of flooding. The
method is particularly suited to urban settings, where
the ready availability of images can also help to trackreconstruction processes and estimate the duration of
displacement.
For example, IDMC conducted a satellite imagery analysis exercise for its previous global report in close collaboration with the UN’s Operational Satellite Applications
Programme (UNOSAT). It aimed to assess displacement
associated with development projects by tracking
the number of dwellings inundated as a result of theconstruction of a dam in Indonesia. Similar methodologies were applied to estimate housing damage anddestruction in southern Turkey, because no other formof data on the conflict in the region was available.286

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This method does not come without challenges,
however, such as the expense of scaling up given thecost of the images, and the complexity of the analysis.
The human validation currently required is also resource
intensive, and may lead to misinterpretation and errors.
That said, new technologies such as aerial and drone
imagery produce high-resolution images, and recent
advances in artificial intelligence permit the extractionof information with very high accuracy, even if image
quality is compromised. This can help to overcome some
limitations from satellite imagery noted above. IDMC
continues to work with its partners to explore innovative
ways of filling data gaps. This includes a study of disasterdisplacement in Dar es Salaam (Tanzania), where drone
imagery is complemented by local data collection on
the ground (see Box 3).

67PART 2

Box 3. The Ramani Huria project: community mapping to assess
displacement
Ninety-five per cent of global urban growth over the next 20 years will take place in developing countries.287 As
cities grow, disaster displacement risk is also expected to increase, but a number of factors make assessing it at
the local level difficult.288 They include:|| A lack of up-to-date population data|| A lack of up-to-date urban plans|| A lack of data on populations’ exposure, vulnerability and resilience|| A lack of disaggregated information on possible vulnerability to specific hazards|| A lack of national spatial data infrastructures that provide access to information, such as exposure, vulnerability
or risk models
To overcome these challenges, participatory tools such as community mapping have been developed to report on
events that take place at the local level. This type of crowdsourcing, which is combined with geographical data,
has been constantly improving as communication technologies and tools evolve.
One example of this approach is Ramani Huria, Swahili for “open map”, a project in flood-prone areas of Dar es
Salaam. Dar es Salaam is one of the fastest growing cities in Africa. Its population is expected to exceed 10 million
by 2040, making it a megacity. However urbanisation is largely unplanned and flooding occurs regularly during
two annual rainy seasons, the “long rains” between March and May and the “short rains” between October andDecember.289
Around 70 per cent of the city’s residents live in informal settlements which are particularly vulnerable to flooding
given their poor infrastructure and drainage and a lack of solid waste management.290 Data and information about
the impact of large-scale floods are fairly easy to come by, but not for smaller, more localised events in vulnerable
areas, despite the fact that local residents are affected. The Ramani Huria project is supported by the World Bankand DFID, and implemented by the Humanitarian OpenStreetMap Team. It has addressed the need for up-to-dateand detailed data on flood hazards, vulnerability and exposure since 2015. Through the engagement and participation with local communities, authorities, leaders, civil society and academia, it produces community maps using
open geospatial technologies and tools supported by the collection of high-resolution drone imagery.291

Drainage mapping
being conductedat the Hananasif
Ward in Kinondoni District,Tanzania. Photo:World Bank/
Chris Morgan,
September 2017

68

DETERMINING
THE DURATION OFDISPLACEMENTWith a few exceptions, it remains difficult to estimate
for how long people are displaced. This is a major gap
that is far from being filled, but it requires attention,
especially as internal displacement becomes protracted.
As explained above, IDMC collects and analyses dataon two main metrics – stocks and new displacements,
as well as on solutions.

Measuring the duration of
displacement associated withdisasters
The main obstacle to determining the end of displacement following disasters is that systematic data collection stops long before IDPs have achieved a durable
solution. Data tends to be collected only during theimmediate recovery phase to guide responses. The
emergency phase is also usually covered by media
reporting. IDMC has previously found that for morethan half of the largest disasters recorded since 2008,
displacement data was collected for less than a month.
This makes attempting to assess whether IDPs haveachieved a durable solution following many disasters
extremely complex and resource intensive.
As a result, it has been difficult to compile an end-ofyear estimate of the total number of people living insituations of internal displacement as a result of disasters at a given moment in time (disaster stock figure).
Without this the aggregate global estimates of thenumber of people living in displacement, including
those quoted in previous GRIDs, are incomplete. For
example, UNHCR adds its global number of refugees
to IDMC’s conflict stock figure to arrive at a global
displacement figure that is often published or cited by
the media and policymakers, but without a disasterstock figure it constitutes a considerable underestimate.
This gap also encourages the framing of displacement
as associated exclusively with conflict, when in fact it
is a much broader and more complex phenomenon.
The absence of a global disaster stock figure also feeds
the mistaken assumption that people who flee disasters are only displaced for short periods, when in fact

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2019

there are many examples of such displacement lastingyears and even decades. This in turns means policy
and operational responses tend not to address major
questions such as how to achieve durable solutions to
disaster displacement. Without time-series data it is alsodifficult to measure and track the social and economic
impacts on individuals, communities and economies.
IDMC was able to estimate a global stock figure fordisaster displacement for the first time in 2018. Moreand better data from partners was obtained, andmodels to fill gaps and infer the number of people
displaced by disasters over time were applied. The estimate for 2018 was of just over 1.6 million people still
living in displacement as a result of disasters which took
place in 2018. This is a highly conservative estimate, as
it does not include people displaced by disasters prior
to 2018. More work will need to be done to refine themodel in 2019 and beyond (see Box 4).
Estimating a stock figure for disaster displacement
will not only clarify how many people are still living in
displacement at the end of a given year. It will help toreveal those who until now have been off the radar andso are unlikely to have been reached as part of disasterrecovery processes or with initiatives to support theachievement of durable solutions. It will also help toassess the mid to long-term impacts of displacement
on societies and economies.292
More broadly, comprehensive monitoring and reportingon the duration and end of disaster displacement will filla critical gap, which in turn will help the humanitarianand development sectors to plan and implement more
effective and evidence-based responses.

Returning home, but under which
conditions?
A core element of the Guiding Principles is that IDPs
achieve durable solutions by returning to their habitualplace of residence, integrating locally or resettling elsewhere in the country or further afield. To be considered a durable solution, this must happen voluntarily, insafety and in dignity and involve overcoming all vulnerabilities associated with displacement. Such outcomesare particularly complex to achieve, and also to measure.

69
PART 2

Box 4.	 Estimating a disaster stock figure for 2018

Figure 15: A conceptual illustration of IDMC’s
disaster stock estimation

Figure 16: Estimating the decay rate fordisaster displacements

Magnitude of displacement

Figure 15 shows a conceptual illustration of how the number of people living in displacement as a result of disasters
at the end of 2018 was estimated. The time-series curves for the stocks are represented by the blue dotted lines.
The stock for disasters on a given date equals the sum of the values of each curve on that date.

Stock figure

Event 1

Event 2

1.2

Event n1.0
0.8

1.6 million
Stockpeople remaining, displacedby disasters in 2018

0.6
0.4

Un

“

“Universal” model curvefor returns

Sep

Oct

Nov

31 DecEstimation of stock
cumulating remaining
displacement as of
31 December

0.2
0.0
0

ive

20

rsal”

model Uncertainty

40

60

Time from onset [days]

80

100

Note: The time series for the 100 events are approximated byexponential functions. The average of the time series is used to
fit the “universal” model, depicted by the thick black line. The
grey interval either side depicts the uncertainty in the prediction.

IDMC applied a two-step approach based on data gathering and modelling to estimate the number of people
living in displacement as a result of disasters as of the end of 2018. A data pool from a variety of sources wascompiled, based primarily on around 100 disaster events recorded in its database during the year. Each time series
describes the evolution over time of the number of people displaced in a specific location.
These time series were used to model the remaining displacement for each event. Due to the lack of data, themodel does not distinguish between different types or location of disasters, and is applied to all of the events.
A simple model was used because it provided a good fit with the observational data and was easy to interpret.
IDMC may construct specific models for hazard types or countries in the future as more time series data becomes
available.
IDMC derived a closed mathematical expression for the model. By using the most recent stock figure it had foreach of the events that took place in 2018, it applied the model and estimated a global stock figure of 1,601,150
individuals. Taking into account the uncertainty of the model, the number could be twice as high.
IDMC believes this to be an underestimate because the learned curve is heavily skewed toward capturing evacuations, which dominated the time-series pool used for training. This type of displacement tends to last for a relatively short period of time.
The same curve is also applied to all events, regardless of whether the reported figures correspond to evacuations
or displacements. This implicit assumption is an important source of modelling error because the same decay rate
is applied even to time series in which the reported displacement was inferred from reported destruction of housing. Again, given these issues, the true figure could be twice as high.
This methodology is a first step toward estimating a global stock figure for disaster displacement. IDMC will refine
the model by using additional and more representative data, testing and validating it against observational dataand employing more complex modelling methods. Taken together, these improvements will help to estimate how
the number of people displaced by disasters evolves over time more accurately.

70
The IASC’s framework establishes a set of criteria forwhat would constitute a durable solution.293 They are:|| Safety and security|| Adequate standard of living|| Access to livelihoods|| Restoration of housing, land and property|| Access to documentation|| Family reunification|| Participation in public affairs|| Access to effective remedies and justice.
Putting the framework into practice, however, has
proved a challenge. It is supported by the Durable
Solutions Indicators Library that provides useful guidance for durable solutions analyses, but translating suchanalyses into numerical, universal and comparable data
is difficult, particularly when trying to aggregate at the
global level.294 It is hard, for example, to measure vulnerabilities numerically and so to assess progress towardachieving durable solutions. EGRIS is, however, in theprocess of developing guidance and recommendations
for measuring the end of displacement statistically, and
it is hoped that this will provide more concrete guidance.
Nor are many reported returns followed up on over time,making any assessment of their sustainability extremely
difficult. When return movements are reported prematurely or go unverified, people may be “taken off thebooks” before they have been able to achieve a durable
solution, which means their needs are unlikely to be
met. This happens, for example, when IDPs are reported
as returnees simply because they have left a camp, or
because they have gone back to their places of origin
despite their homes having been damaged or destroyed.
In order to capture such movements, IDMC has started
recording them as “partial and unverified solutions”(see Part 1).

ASSESSING THE IMPACTS
AND THE SEVERITY OFDISPLACEMENT
The number of people living in displacement offers only
partial insight into IDPs’ reality. Their needs and theimpacts of their displacement at the individual, local andnational level differ from one situation to another. They
may vary widely based on the duration of displacement,
pre-existing capacities and resources and current levels

GRID
2019

of vulnerability. There are, for example, roughly equal
numbers of IDPs in Colombia and Syria, but their needs
are vastly different. Effective and targeted responses
require a fuller understanding of the impacts and experiences of displacement beyond the numbers.

Economic impacts ofdisplacement
Displacement affects economies in many overlappingand interconnected ways. Its impacts may be felt directly
or indirectly, in the short or long term and at the local
or national level. Some are tangible, for instance whencrops and livestock are lost. Others, such as months oflost education, are intangible. Impacts may occur at
the time of displacement, when IDPs have to pay fortransport and temporary lodgings, or later when they
have to accept a lower-paid job in the saturated labour
market of their host area. As part of IDMC’s research
into the economic impacts of internal displacement,
we have developed a new methodology to assess the
direct costs, showing that internal displacement could
be costing countries across the world a total of nearly$13 billion each year.295
We were able to estimate the direct economic impact
of displacement associated with flooding in Somalia in2018 at around $19 million for 287,000 people displaced
from April to August. We also estimated the impact of
displacement associated with drought between January
2017 and August 2018 at $500 million for 1.2 million
IDPs, and of displacement associated with conflict overthe same period at $110 million for 423,000 IDPs. The
latter figures represent 4.7 and 1 per cent of Somalia’sannual average GDP respectively.296
A better understanding of the longer-term economic
impacts of displacement is also needed to inform thedevelopment sector about where and how to engage
in displacement crises (see Box 5).

71

IDMC’s estimates account for the costs associated with IDPs’ housing, health, education and security needs, and fortheir loss of livelihoods. They focus on direct and immediate costs and losses for which quantitative data is publicly
available at the global level. Information such as the funding required to provide food to a given number of IDPs
serves as proxy for the cost associated with nutritional needs that result from displacement.
The estimates do not account for the longer-term consequences of internal displacement, nor its impacts on hosts,
communities of origin and other affected groups. As such, they should be considered underestimates of its overall
financial burden.

Dimension

MetricWhat is included

Housing
Data sources: Humanitarian Response Plan and
Humanitarian Needs Overview by OCHA

Cost of shelters or
temporary accommodations|| providing emergency and transitional shelter solutions,
including subsidies for rents or repairs|| delivering needs-based lifesaving non-food items|| providing water, sanitation and hygiene services|| coordinating and managing shelters and camps

Livelihoods

Loss of income|| loss of income from work

Cost of providing
temporary education|| restoring educational activities for children of primaryand secondary school age|| ensuring healthy and secure learning environments,
including in some cases psychological support to children

Cost of providing
food assistance|| providing life-saving food assistance|| improving food production|| preventing and treating malnutrition of children aged
under five, and pregnant and lactating women

Cost of providing
healthcare in
emergency situations|| providing emergency and essential primary/secondary
health services|| preventing and responding to outbreaks and communicable diseases|| providing immunisation coverage for children aged
under five

Cost of ensuring
security in hostareas|| reinforcing and providing protection to IDPs|| preventing and responding to human rights violations|| protecting children and women

Data sources: World Development Indicators
and PovcalNET by the World Bank, Displacement Tracking Matrix by IOM

Education
Data sources: Humanitarian Response Plan and
Humanitarian Needs Overview by OCHA

HealthData sources: Humanitarian Response Plan and
Humanitarian Needs Overview by OCHA

Security
Data sources: Humanitarian Response Plan and
Humanitarian Needs Overview by OCHAPART 2

Box 5. Assessing the economic impacts of displacement

72

Toward a better understandingof displacement severity andvulnerability
Understanding how IDPs’ vulnerabilities differ from one
situation to another, irrespective of scale, is importantin painting a comprehensive picture of the severity ofthe phenomenon and informing effective and targeted
responses and planning. IDPs in the Afghan city ofGhazni, for example, were caught in the crossfire offighting between the Taliban and government forcesin 2018. Those sheltering in civilian protection sites inSouth Sudan are generally shielded from such conflict,but women and girls are at high risk of sexual andgender-based violence. IDPs in Mexico’s Guerrero state
are exposed to criminality and extortion.
These contrasting experiences of security are just one
component of displacement severity. Access to housing,
services, livelihoods, documentation, family reunification, public affairs and justice are also factors.297 IDPs
in makeshift shelters in Tanganyika and South Kivu in
DRC, for example, are exposed to severe weather andfire hazards, while those in collective centres in Ukraine’s
government-controlled areas of Donetsk and Luhansk
regions close to the Line of Contact receive free accommodation but are reportedly at risk of eviction. Similar
contrasts exist in terms of access to services. The costof medicines is the main barrier to healthcare for IDPs
in Donetsk and Luhansk, while for those in Tanganyika
and South Kivu it is scarce and poorly equipped facilities.
Assessing the severity of displacement and differing
vulnerabilities associated with disasters is also challenging, mainly because of the absence of reliable dataon its duration and the different coping capacities ofindividuals, communities and states. Some people areable to return shortly after a disaster strikes, but manyremain displaced for months or even years and find it
difficult to access effective mechanisms to restore or
rebuild their homes, land and property.
For each disaster event recorded, IDMC tries to collectas much information as possible on housing destruction,
whether as a proxy for displacement or for triangulation purposes. Housing destruction is a good proxy toestimate the magnitude of displacement. Also, as highlighted in the section above on measuring the disasterstock, the duration of displacement could be used as
an indicator of people’s vulnerability.

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2019

An illustrative example can be found in India, where
tropical cyclone Titli struck the states of Odisha andAndhra Pradesh in October 2018 and caused significant
destruction, particularly in coastal districts. In the aftermath of the disaster, the authorities in Andhra Pradesh
recorded the extent of damage by housing type.
Indian families traditionally build different types of
housing depending on their location and economic
resources. “Pukka houses” are relatively solid structures built with durable materials such as concrete,
bricks and timber. They tend to withstand the impactsof cyclones reasonably well, but may be vulnerable toearthquakes. “Kutcha houses” are built with cheaperand less durable materials such as mud, thatch andbamboo, which makes them vulnerable to most natural
hazards. When kutcha houses are destroyed, they tend
to be replaced by pukka houses.298
By using the authorities’ classification of damage
according to the two housing types, IDMC was able
to establish a better and more nuanced understandingof Titli’s impacts on the local population. People living
in kutcha houses were four times more affected byhousing destruction than those living in pukka houses.
Those whose pukka houses were destroyed, however,are likely to have remained displaced for longer time.
IDMC will continue to develop this qualitative assessmentof the severity of displacement to enable meaningfulcomparisons between countries, and to help monitor
their progress toward resolving the phenomenon.

HARD-TO-DETECTDISPLACEMENT
Some types of displacement are particularly difficult toassess because of missing data or the complexity of theirdrivers and triggers. These include displacement associated with development projects, criminal violence, slowonset hazards such as drought and sea level rise, andoverlapping factors. Data limitations also impede efforts
to determine how many IDPs become refugees, andmigrants return to a life of internal displacement. The
result is an incomplete understanding of the displacement continuum and its dynamics.
Many reports of displacement associated with disasters
provide data on evacuation orders, but not necessarily

73

The most visible and tangible aspect of displacement associated with disasters is having to evacuate from one’s
home. This may take the form of self-evacuations or as ordered by local authorities. The challenge with accounting
for displacement associated with mandatory evacuation orders is that the orders cover more people than areaccounted for in temporary shelters.
At the height of tropical cyclone Prapiroon in Japan, for example, as many as two million people were ordered toevacuate, but fewer than 31,000 were recorded in shelters (see Japan spotlight, p.30).299 Two factors accountfor at least some of such discrepancies. First, not all of those ordered to evacuate may comply, meaning that the
number of people covered by evacuation orders is likely to overestimate the scale of displacement. Second, many
evacuees may choose to stay with family and friends, in hotels or elsewhere, meaning the number of people inshelters is likely to underestimate the scale of displacement.
People’s reasons for not following evacuation orders are complex and multifaceted. They range from practical challenges for the elderly and those with disabilities, to a lack of timely and accessible information and the perception
and communication of disaster risk. These factors may also combine with people’s desire to protect their home,
pets, livestock and other assets.300 Socioeconomic factors may also be an influence. Some households may not beable to afford to self-evacuate, and the same people may also be vulnerable in other ways such as being locatedon flood plains, living in mobile homes or lacking reliable transport.301

on how many people obey them. This makes the scaleof disaster displacement hard to detect, given that many
people do not to follow mandatory evacuation orders,taking the risk of staying put (see Box 6).
Displacement associated with slow-onset disasters andenvironmental degradation is also complex to monitor.
It is difficult to distinguish from internal migration andpainting a comprehensive picture is challenging because
it encapsulates a wide range of phenomena, drivers,
triggers, impacts and movement types. More concrete
examples and evidence of how displacement occurs in
different slow-onset situations are needed to inform
more solid risk assessments and the evaluation of appropriate policy responses.
To overcome these challenges, IDMC has begun to
explore modelling and the interconnectivity of systems
to understand the complexity of slow-onset displacement. A similar approach is being used to better understand the interconnected factors driving displacement
associated with criminal violence in the Northern
Triangle of Central America (see Figure 17, p.74).302

PART 2

Box 6. Why do some people refuse to leave?

74
Figure 17: Interconnectivity of systems
Shelter

Infrastructure

Crops /
livestock

Water

Physical
capitalSoil

NaturalcapitalIncomeNetworks

Remittances

Financial
capitalSocialcapitalPresent
wellbeing

Savings

Relationships

HumancapitalHealthExpected
human
capital

Expectedfinancial
capital

Expected
physical
capital

Expectedsocialcapital

Expected
naturalcapital

Expected
wellbeingPastwellbeing

Land

Political
capitalCorruption /coercion
Governance /freedomPhysicalsecurity
Skills /education

Perceived
wellbeing
Expected
political
capital

Expected
wellbeingelsewhere

Gap inexpected
wellbeing

Networks
elsewhere
Savings

Motivation
to flee

Ability
to flee

HealthInfrastructure

Displacement
Population

Based on system dynamics and agent-based modelling
approaches, IDMC is also mapping the ways in which
policy responses and long-term investments determine
displacement risk. This helps to understand the circumstances in which displacement is likely to occur and why.
This approach will enable IDMC to model scenarios fordisplacement risk in different slow-onset situations andwith different policy interventions.

ACCOUNTING FOR
FUTURE RISK
Many governments and operational actors recognise
the need to understand future displacement risk. Thereis increasing demand for displacement risk models andforecasting tools that are able to estimate the scale andseverity of future displacement and reveal its underlying

GRID
2019

Displaced
population

drivers. However, the development and improvement ofthese tools is still catching up with the demand.
Using probabilistic approaches to estimate displacement, however, requires highly localised and detailed
information, and many governments lack the dataneeded to validate risk models and conduct full risk
assessments. More capacity building is needed before
models can be adapted to specific needs and the results
applied to inform policy development and investment
planning.
IDMC released its unique global disaster displacement
risk model in 2017.303 The first iteration, based on a
global model developed by the UN Office for Disaster
Risk Reduction (UNISDR) calculates the average numberof people likely to be displaced every year by suddenonset hazards (earthquake, tsunami, floods, cyclonicwinds and storm surge).304 It calculates the probability

75
Probability of
being destroyed=
Displacement risk

X

Probability of
being damaged

X

Probability of not
being affected

HazardExposure

Vulnerability

Phenomenon that may
cause displacement(e.g. Earthquake)

People and buildings
in hazard-prone areas

How different buildings react
to different hazard intensities

and intensity of hazards, and exposure and vulnerability components to estimate disaster displacement risk(see Figure 18). The results are based on the likelihoodof housing destruction as a proxy for displacement, andsuggest that an average of around 14 million people arelikely to be displaced globally in any given year in the
future.305
Because the model excludes those displacements associated with pre-emptive evacuations – which is particularly relevant for countries with strong disaster preparedness capacity such as Bangladesh, China, Cuba, thePhilippines and Viet-Nam – the estimation of risk isinherently conservative. The model is likely to be a closer
fit for countries with less disaster preparedness capacity.
IDMC has since worked closely with the Swiss Federal
Institute of Technology in Zurich (ETHZ) to improvethe model’s ability to predict flood displacement risk.
Increasing the resolution of the exposure layer fromfive square kilometres to one allowed a more granularassessment of the people and assets exposed to floods.
This, coupled with a re-run of hazard scenarios using
the latest technologies, produced a more accurate estimate that suggests the number of people at risk ofdisplacement by floods is significantly higher. Better
resolution of the model also allowed the disaggregationof displacement flood risk figures by urban and rural
locations (Flood displacement risk spotlight, p.84).
The evidence disaster displacement risk modelling
produces can be used to inform national and more
local disaster risk reduction (DRR) policies and investments, and to identify areas where large numbers ofpeople risk losing their home and being displaced. It
also helps to identify the required capacity for evacuation centres and the amount of assistance needed tosupport displaced people.

IDMC’s model also provides a benchmark for measuring progress toward DRR, including against international frameworks such as the Sendai Framework for
Disaster Risk Reduction, and the 2015 Paris Agreement
on climate change. It can also be adapted to support
operations in real time by indicating the number andlocation of damaged and destroyed homes caused bymodelled or observed hazards.

THE WAY FORWARD
The ongoing challenges associated with internal
displacement data are clearly interlinked. A systemic
response which develops common standards andimproves cooperation, coordination and data interoperability is critical, if governments and other actors areto fully understand, prevent and address the phenomenon through better policymaking, planning and risk
reduction.
New technologies and approaches have the potential to
overcome some of the specific challenges in obtaining
comprehensive, timely, accurate and disaggregated dataon a range of displacement situations. Used with due
regard for ethical questions of privacy and data protection, they could help to fill significant knowledge gaps.
Armed with stronger data and evidence, governments
and other stakeholders will be better equipped tounderstand and plan for future risk and to monitor
progress toward targets under frameworks such as theSustainable Development Goals, the Sendai Framework
on Disaster Risk Reduction and the Paris Agreement.
More importantly, they would also be in a better position to address the needs of IDPs.

PART 2

Figure 18: How is displacement risk calculated?