IOM Displacement Tracking Matrix (DTM): Intelligence Source Guide

DTM is how IOM counts displaced people at administrative and site level in active crises, with a public API for admin-level figures. The numbers are key-informant estimates produced in rounds, not registrations, and reading them as a census is the standard error.

At a glance

Source	IOM Displacement Tracking Matrix (DTM)
Category	Conflict, Crime & Human Security › Humanitarian & Displacement
Homepage	https://dtm.iom.int/
Machine interface	`https://dtm.iom.int/data-and-analysis/dtm-api`
Format	`JSON`
Access	Open — no account required
Disciplines	Environmental Intelligence
Mission domains	Border Security & Migration, Food & Agricultural Security, Healthcare & Drug Security

IOM system tracking mobility and displacement of populations; public API for admin-level displacement figures. — as catalogued in the platform’s own source registry.

The Displacement Tracking Matrix is the system IOM uses to track population mobility during and after crises. It began as a field tool in a single operation and now runs in a large number of countries, staffed by national enumerators and coordinated with national authorities and the humanitarian cluster system. It has four main components. Mobility Tracking produces baseline assessments: teams work down administrative levels, interviewing key informants — local authorities, community leaders, camp managers, health workers — to estimate how many displaced people, returnees and in some contexts migrants are present in each location, along with their reasons for displacement and broad conditions. Site assessments extend that into individual displacement sites with indicators on shelter, water, health, food and protection. Flow Monitoring counts and samples people moving through transit points, producing route-level data on volumes, demographics, intentions and vulnerabilities. Registration operates at individual or household level for specific caseloads and its data is not public. Surveys cover intentions, returns and durable-solutions indicators. Output comes as country dashboards, periodic reports, downloadable datasets and, more recently, a documented public API that serves displacement figures at country and subnational administrative levels. Assessments are conducted in numbered rounds at intervals that vary by country and by funding, rather than continuously.

Almost every other displacement figure you will encounter is an aggregate produced at global level from national reporting, published annually, and already a year old when it appears. DTM is the layer underneath: subnational, produced by people on the ground, updated in rounds measured in weeks or months, and disaggregated to administrative units small enough to plan against. That resolution is the whole point. A national IDP total tells you a crisis exists; a district-level figure with reason for displacement and arrival timing tells you where people went, in what direction, and whether the movement is still happening — which is what determines logistics, service siting and early warning. For humanitarian and ENVINT work it is the primary quantitative input to needs assessment, and it feeds the international displacement figures that most people cite without knowing where they came from. It also carries information no aggregate can: the site-level indicators expose service gaps as conditions rather than as counts, the flow monitoring surveys capture route-level vulnerability including exposure to exploitation, and the reasons-for-displacement field distinguishes conflict from disaster from other drivers at a granularity that matters enormously for both response and attribution. The trade-off is precision. DTM buys timeliness and resolution by using key-informant estimation rather than enumeration, and everything about how you use it follows from that choice.

Who publishes it, and why that matters

IOM is a UN agency with a member-state governance structure and an operational, project-funded business model. DTM is not a core-funded global programme; it is a set of country operations, each financed by donors for a period, which is the single most important thing to understand about its coverage. A DTM operation exists in a country because someone paid for it, continues while they keep paying, and stops when they stop — and when it stops, the data series ends without any statement that the underlying displacement ended. Humanitarian funding has been volatile in recent years and reductions in major donor contributions have had visible effects on assessment frequency and country coverage across the sector, so check the current operational status of a country's DTM before treating a gap in the data as a finding. IOM's incentives are operational rather than academic: DTM exists to inform response, and its methodological choices consistently favour producing a usable number quickly over producing a defensible number slowly. That is the correct trade for its purpose and the wrong one for several purposes you might have. IOM also works with and often at the invitation of national governments, which shapes what can be counted, what terminology is permissible, and occasionally whether an assessment can happen at all in a contested area.

Provenance is the first question to ask of any dataset and the one most often skipped. Who collects it, what their incentive is, whether they publish a methodology, and whether they correct the record when they get something wrong all bear directly on how much weight a finding drawn from it can carry.

What a record actually contains

The fields you will be working with, what each one means, and whether it is something you can pivot on. Read the meanings carefully — more analysis is wrecked by misreading a field than by failing to find one, and a field that looks like an observation is often an inference.

Field	Type	What it means	Pivot value
`admin0_pcode`	string	Country identifier, normally an ISO code, anchoring every record to a national context. Straightforward, and the only geographic field you can rely on being stable across rounds.	Country dashboards, national response plans, cross-source joins with refugee and conflict data.
`admin1_pcode`	string	First subnational level — province, region, state — using the humanitarian place-code convention rather than an international standard. Codes are maintained nationally and do occasionally change when administrative boundaries are redrawn.	Subnational aggregation, cluster planning, joining to conflict-event and food-security datasets that use the same codes.
`admin2_pcode`	string	Second subnational level, typically district. This is the resolution at which most public DTM data is served and the level at which planning actually happens. Some operations publish a third level and some do not.	District-level needs analysis, logistics planning, correlation with health and market data.
`round_number`	int	The assessment round the figure comes from. Rounds are discrete exercises, not a continuous series, and their spacing is irregular and funding-dependent. Two consecutive rounds may be three weeks apart or eight months apart.	Temporal alignment; the essential key for any change analysis and the field most often ignored.
`reporting_date`	timestamp	The date the round's data refers to, which is distinct from when it was collected and from when it was published. The gap between them is often weeks and is the main source of apparent contradiction with other sources.	Timeline reconstruction; alignment with conflict events and seasonal factors.
`idp_individuals`	int	Estimated number of internally displaced individuals present in the location. An estimate derived from key-informant interviews and, in some operations, from household counts — not a registration and not a census, whatever its apparent precision suggests.	Needs quantification, per-capita service planning, comparison with returnee and host-population figures.
`idp_households`	int	Estimated displaced households. The relationship to the individual figure depends on an assumed or locally measured household size, which varies by country and sometimes by round, so the two fields are not independent measurements.	Shelter and non-food-item planning; sanity-checking the individual estimate against a plausible household size.
`returnees`	int	People assessed as having returned to a location of origin. Return is not the same as durable solution, and a returnee figure says nothing about whether housing, services or safety exist at the destination.	Durable-solutions analysis, reconstruction planning, comparison with intention-survey results.
`displacement_reason`	enum	The categorised driver — conflict, disaster, or other categories that vary by operation. This is a key-informant characterisation of a mixed reality, and in practice most displacement has more than one cause.	Attribution analysis, climate and conflict displacement separation, alignment with international reporting categories.
`site_type`	enum	For site assessments: planned camp, spontaneous settlement, collective centre, host community and similar. Determines which service standards apply and how visible the population is to any other data system.	Protection risk assessment, service-gap analysis, satellite corroboration of site extent.
`arrival_period`	string	When the displaced population arrived, at whatever granularity the operation records. This is what converts a stock figure into something you can read as a flow, and it is the field that distinguishes a new emergency from an accumulated caseload.	Event correlation, early-warning triggers, distinguishing new displacement from protracted presence.
`needs_indicators`	array	Site-level assessments of shelter adequacy, water access, health service availability, food security and protection concerns, generally as ordinal or categorical judgements rather than measurements.	Cluster-specific response planning; identification of sites where a specific service is absent rather than merely strained.
`data_source`	enum	How the figure was produced — key informant, direct observation, registration, or partner data. Reliability varies by an order of magnitude across these and the field is frequently dropped by downstream users.	Confidence weighting; deciding which figures can carry an operational decision.
`coverage_status`	enum	Whether a location was assessed, partially assessed, or inaccessible during the round. Inaccessible areas are where the worst conditions usually are, so this field carries more analytical weight than most of the counts.	Missingness analysis, access-constraint mapping, honest presentation of what the totals exclude.

Coverage — and what is not in it

DTM operates in crisis and post-crisis contexts, which means coverage is wide but deliberately partial: it exists where there is displacement and where an operation is funded, and it does not attempt to cover stable countries. In practice that has meant sustained operations across the Sahel and Lake Chad basin, the Horn of Africa, Sudan and South Sudan, the Great Lakes, parts of the Middle East and North Africa, South and Central Asia, South East Asia, the Caribbean and, since 2022, large-scale work in Ukraine and its neighbours, alongside route-based flow monitoring on major migration corridors. Within a country, coverage is defined by administrative geography and by access: assessed units are enumerated, inaccessible units are flagged or absent. Temporal coverage is round-based and irregular. A high-priority emergency may see rapid rounds weeks apart, while a protracted situation may be assessed twice a year or less, and a country whose funding lapses simply stops appearing. Historical depth varies enormously by operation — some have more than a decade of rounds, others a handful — and older rounds are frequently available only as reports rather than as data. The public API and the humanitarian data platform between them expose a substantial subset of the admin-level figures; the full richness, particularly site-level indicators and flow-monitoring survey detail, remains in country datasets and reports.

Known blind spots

Absence of evidence here is not evidence of absence. These are the conditions under which IOM Displacement Tracking Matrix (DTM) will not show you something that is nevertheless real:

Areas that are inaccessible for security, political or logistical reasons are not assessed, and those are systematically the areas with the worst conditions. Every national total therefore under-counts in a direction you can predict but not quantify.
Displaced people who blend into host communities, particularly in cities, are far harder for key informants to see than people in identifiable sites, so urban displacement is chronically under-represented relative to camp populations.
Rounds are discrete and irregularly spaced. A change between rounds may reflect movement, or a change in assessed coverage, or a methodological revision, and the data alone frequently cannot tell you which.
The operation exists only while it is funded. A series ending is a statement about donor budgets, not about displacement ending, and treating a gap as a return to normality is a serious and common misreading.
Key-informant estimation carries the biases of the informants: local authorities may have incentives to inflate or suppress figures, and community leaders see their own community better than they see marginal groups within it.
Cross-border movement is covered by flow monitoring at specific points rather than comprehensively, so a corridor without a monitoring point is invisible regardless of how much movement it carries.
Definitions of displaced, returnee and migrant vary between operations and sometimes between rounds within an operation, so figures are not automatically comparable across countries even when the field names match.
Individual-level registration data exists but is not public and will not become public, so nothing in the open layer supports analysis of specific people, households or protection cases.
Government relationships shape what can be assessed and how it can be described. In some contexts particular populations, causes or terminology are politically constrained, and the constraint is rarely visible in the published data.

Write the blind spot into the product. A statement that something “was not observed in IOM Displacement Tracking Matrix (DTM)” is defensible; a statement that it “did not happen” is not, and the difference is what survives cross-examination.

Access, licensing and what you may do with it

Access model: Open — no account required

There are three practical routes. The country pages on the DTM site carry dashboards, reports and downloadable datasets, and are the richest source but require per-country navigation and manual extraction. The humanitarian data exchange platform hosts a large volume of DTM datasets in tabular form, which is usually the fastest way to obtain historical rounds in bulk. The documented public API serves displacement figures at country and administrative levels for participating operations, which is the right route for a systematic ingest and the one that survives contact with automation. Terms of use apply to all three and you should read them rather than assume; the API's authentication requirements and coverage have changed since it was introduced, so confirm the current arrangement against the documentation rather than against a blog post. Site-level and flow-monitoring detail is frequently published only in reports, so a complete picture for a given country usually requires combining the API with document extraction. For anything below the published aggregate — individual records, protection case data, unpublished rounds — the answer is that it does not exist as an access route, and requests for it will be declined on data-protection grounds.

Licence

DTM data is generally published for humanitarian use with attribution, and the datasets on the humanitarian data platform carry explicit licence statements that you should read per dataset rather than assume across the portfolio, because they are not uniform. IOM's data-protection framework governs everything upstream of publication and is the reason the public layer is aggregated: individual and household records collected during registration are personal data belonging to people in vulnerable situations, and they are not released. The practical obligations on you are attribution, non-misrepresentation, and not attempting re-identification or fine-grained geolocation of displaced populations from published aggregates. Commercial reuse is not automatically permitted and varies by dataset; if you are building a product on DTM data, resolve the licence question per country dataset and, where you are aggregating many, approach IOM directly. Confirm current terms before relying on any of this commercially — the publication arrangements have evolved as the API has matured.

Rate limits and fair use

The API is a public service run by a humanitarian agency, not a commercial platform with headroom to spare. Pull incrementally by country and round rather than sweeping the whole catalogue, cache aggressively, and schedule collection at a cadence that matches the data's actual update rhythm — for most countries that is monthly or slower, and nothing is gained by hourly polling of a dataset that changes three times a year. Identify your client. If you need bulk historical data, take it from the humanitarian data platform rather than paginating through an API for records that are not changing. Where a country operation is in an acute emergency phase and publishing rapid rounds, a daily check is reasonable for that country and unnecessary for the rest.

Licensing changes, and it changes without warning. A dataset that was free for research this year may not be free for commercial or evidential use next year. Confirm the current terms before you build a dependency on it, and record the terms you relied on alongside the data — the licence in force at the time of collection is part of the provenance.

Collecting it

How IOM Displacement Tracking Matrix (DTM) is actually pulled, in the order you would set it up. Prefer the bulk or export interface over per-item lookups wherever one exists: it is kinder to the publisher, faster for you, and gives a reproducible snapshot rather than a series of point-in-time answers you cannot reconstruct later.

Method	Format	Cadence	Notes
Public API	JSON	per round; poll monthly for most countries	The systematic ingest route for admin-level displacement figures. Confirm current authentication and coverage against the documentation, which has changed since launch.
Humanitarian data platform datasets	CSV	per round	Bulk historical rounds in tabular form, often with richer disaggregation than the API. The right route for backfill and for site-level data where it is published.
Country dashboards and reports	HTML	per round	Where methodology notes, coverage statements and site-level indicators live. The numbers are not interpretable without these, and they are not in the API.
Flow monitoring outputs	bulk	varies by corridor	Route-level counts and survey results including vulnerability indicators. Published separately from mobility tracking and frequently overlooked by users who only ingest IDP stocks.
Methodological documentation	HTML	periodic	IOM's framework describing what each component measures and how. Read once properly; it prevents most of the errors that follow from treating estimates as enumerations.

Ingesting it into the platform

Every step below is idempotent and cursor-based: interrupt one and it resumes from where it stopped rather than duplicating rows or losing progress. Collection is recorded per source, so a feed that quietly stops publishing shows up as a stale timestamp instead of silently thinning your coverage.

Register per country operation — Add DTM in `sources.php` as a set of per-country collection targets rather than one global feed, so `collect.php` reflects that each operation has its own cadence, its own funding status and its own failure modes.
Key every record to round and reporting date — Use `import.php` to store round number, reporting date, collection date and publication date as distinct fields. Collapsing these into one timestamp destroys your ability to align DTM with anything else and is the commonest ingest mistake.
Preserve place codes verbatim and map separately — Keep the original administrative codes as supplied and build the mapping to your own geography as a labelled layer. Codes change when boundaries are redrawn, and an unrecorded mapping turns an administrative reform into an apparent population movement.
Carry coverage status through to presentation — Propagate the assessed, partially assessed and inaccessible flags into every aggregate and every map. A national total that silently omits inaccessible districts is a wrong number, and the fix is structural rather than editorial.
Store estimates as estimates — Record the data-source field and attach an uncertainty characterisation at ingest. Key-informant estimates and registration counts should not be able to flow into the same downstream calculation without the difference being visible.
Build round-to-round change as a derived object — Compute inter-round changes explicitly, with the coverage difference between the two rounds attached. A change figure without its coverage delta is not interpretable and should not be creatable in your schema.
Join to conflict, climate and food security data — Correlate admin-level displacement against conflict events, hazard data and food-security classifications in `correlate.php` and `matrix.php`, aligning on the same place codes and on comparable time windows rather than on nominal dates.
Surface without re-identification risk — Present results on `country-dashboard.php` and `vulnerable-populations.php` at the administrative resolution DTM published, and do not attempt to interpolate, geocode or downscale site locations to a precision the source did not provide.

Registered sources and their last-collected state are listed in sources.php, and the scheduled chain that keeps them current is in automation.php.

How it is wrong, and how to tell

Every dataset is wrong in characteristic ways. Knowing which ways is the difference between using a source and being used by one, and it is the part of source evaluation most often skipped because it is the part that takes work.

Judged against its purpose, DTM is a well-run and unusually transparent system: the methodology is documented, the components are distinguished, coverage limitations are stated in the reports, and the operations are staffed by people with direct access to the populations being counted. Judged as a statistical product, it is an estimate system with real and acknowledged uncertainty, and the published figures carry no confidence intervals despite being presented to the unit. The reliability of any given number depends heavily on the component that produced it — a registration count is a different object from a key-informant baseline estimate, and the data-source field tells you which you have. Corroboration is possible and worth doing: satellite imagery of site extent, partner agency figures, health facility and food distribution data, and market indicators all provide independent checks, and in most well-run operations they broadly agree at the order-of-magnitude level. Where DTM is weakest is in the joints — between rounds, between operations, and across definitional changes — and that weakness is structural rather than a failure of execution. Treat the number as the best available estimate produced quickly under difficult conditions, which is exactly what it is and what it was built to be.

Characteristic false positives

Reading the figures as enumerations. They are mostly key-informant estimates, and their apparent precision to the individual is an artefact of arithmetic rather than a claim about accuracy.
Interpreting a change between rounds as movement. Coverage changes, methodology revisions and administrative boundary changes all produce the same signature in the data, and only the round documentation distinguishes them.
Treating a series that stops as a crisis that ended. Operations stop when funding stops, and the resulting silence in the data is a budget event that looks exactly like a resolution.
Summing figures across countries or across components. Different operations use different definitions and different components count different things, so a cross-national total is an assembly of incompatible objects.
Adding stock figures to flow figures, or double-counting people who appear in both a mobility-tracking baseline and a flow-monitoring count. This is the most consequential arithmetic error in displacement analysis and it produces totals that are badly wrong.
Assuming national totals include inaccessible areas. They generally do not, which means the reported figure and the real one diverge most in precisely the places where the humanitarian need is greatest.
Mapping displacement at a resolution finer than the administrative unit reported. Interpolating or point-locating displaced populations from admin-level data creates false precision that can endanger people if it circulates.
Comparing DTM figures directly with refugee statistics or with global displacement estimates. Those count different legal categories, are compiled on different cycles, and in the case of global estimates often incorporate DTM itself, so agreement is circular and disagreement is expected.

None of these make the source unusable. They make it a source that requires corroboration before an assertion built on it goes into a product, which is true of every source and admitted by few.

Ageing

Ageing is round-driven and highly variable. In an acute emergency, a figure two weeks old can be materially wrong because the population it describes has moved again; in a protracted displacement situation, a figure from eight months ago may still be the best available and remain broadly accurate. The correct handling is to store the reporting date and the round with every value, to display them together, and to refuse to present a number without both. The site-level condition indicators age faster than the population counts, because service provision changes as partners arrive and depart. Coverage statements age fastest of all: access changes with the security situation, and last round's inaccessible district may be assessed this round, which will produce a large apparent increase that is entirely an artefact of access. What never ages is the methodological documentation, which is why reading it once is worth more than repeatedly re-checking figures. A stale record from this source looks like a displacement figure on a dashboard with no round, no date and no coverage note — which describes a great deal of the secondary reporting built on DTM.

What this source feeds

A source is only worth what it lets you conclude. These are the disciplines that collect through it, the mission domains it serves and the data points it yields — every one is a tag, so you can follow any thread from here into the rest of the library.

Collected by these intelligence disciplines

Environmental Intelligence

Serves these mission domains

Yields these data points

How each sector uses IOM Displacement Tracking Matrix (DTM)

The same dataset is worked very differently depending on who you are, what authority you hold, and what you are ultimately producing. A military analyst is supporting a commander’s decision; a journalist is meeting a publication standard; an NGO caseworker is protecting a person. The records are shared — the constraints, thresholds and outputs are not.

🎖 Military and defence

For planning in humanitarian assistance, disaster response and stability operations, DTM is the closest thing to an authoritative subnational picture of where non-combatant populations actually are, which is directly relevant to movement planning, protection of civilians assessment and the siting of any assistance activity. Use it for the population geography and not for the security picture, and be aware that the association is sensitive: IOM's access depends on being seen as independent of military actors, and an operation whose data is visibly used for targeting loses that access and with it the data. Treat the humanitarian civil-military coordination framework as binding rather than advisory when working with this material.

🕵 National intelligence

The analytical value is in early warning and in inference about areas you cannot observe directly. Displacement is a leading indicator of conflict escalation, state collapse, food insecurity and cross-border pressure, and DTM provides it at administrative resolution with reason-for-displacement attribution. The coverage-status field is separately valuable as an access proxy: a district that becomes inaccessible to a humanitarian assessment is telling you something about control on the ground. Handle the sourcing with care — this is humanitarian data, and analytical products that appear to instrumentalise it can create real risk for the enumerators who collected it.

👮 Law enforcement

Relevance runs through the migration and exploitation corridors rather than through the displacement counts. Flow-monitoring surveys along major routes capture the conditions in which trafficking and smuggling occur, including recruitment patterns, payment arrangements and reported abuse, at a resolution that supports understanding the route as a system rather than as a set of incidents. That is background for investigations, not evidence in them. Nothing in DTM identifies individuals, and any expectation that it might is misplaced.

🔍 Private investigation and corporate security

The realistic uses are corporate: assessing whether an operation, a supply route or a workforce catchment is exposed to displacement-driven disruption, and understanding the labour-market conditions that make exploitation likely in a region where a client operates. Displaced populations are a recognised vulnerability for recruitment into exploitative labour, and the site-level and route-level data indicate where that pressure is concentrated. Use it as context for a due-diligence programme; it will not resolve any question about a specific person or facility.

📰 Journalism and OSINT media

This is where the numbers in displacement coverage actually come from, and reporting that cites DTM properly — with the round, the date and the coverage caveat — is immediately better than reporting that cites a global aggregate. The strongest stories are usually about the gaps: districts that could not be assessed, operations that stopped when funding lapsed, or the divergence between an official government figure and a DTM estimate for the same area. Do not present admin-level figures on a map at a finer resolution than they were collected; the false precision is both wrong and, in some contexts, unsafe for the people depicted.

🌍 NGO, humanitarian and human rights

For humanitarian organisations DTM is infrastructure rather than a source: it underpins needs assessment, cluster planning, targeting and appeal documentation, and most operational decisions in a displacement response trace back to it. The professional discipline is to read the round methodology before using a figure in a proposal, to state coverage limitations in your own documents rather than inheriting a clean-looking number, and to feed corrections back to the country operation when your field presence contradicts an assessment. DTM improves when partners tell it what it got wrong, and that feedback loop is underused.

🎓 University and research

DTM has become a standard data source for research on forced migration, climate-related mobility and conflict displacement, and it supports genuine subnational panel analysis in contexts where no other data exists. The methodological requirements are stringent and often skipped: round irregularity must be modelled rather than interpolated away, coverage must enter the analysis as a variable rather than as an assumption, and definitional changes between rounds must be identified from the documentation rather than inferred from the numbers. Work that treats DTM as a balanced panel of enumerated counts produces results that are largely artefacts of the assessment schedule.

Playbook: working IOM Displacement Tracking Matrix (DTM) end to end

A repeatable sequence from first pull to finished product. Each phase states what you are trying to establish, not merely what to click — the objective is a defensible chain of reasoning, not a completed checklist.

Phase 1 — Read the methodological framework first

Before touching any figure, read IOM's own description of what mobility tracking, flow monitoring, registration and surveys each measure. These are four different instruments producing four different kinds of number, and using them interchangeably is the root cause of most published errors involving this source.

Phase 2 — Establish the operation's status and history

For each country in scope, find out when the operation started, whether it is currently active, how many rounds exist and how they are spaced. This tells you what analysis is even possible and prevents you from interpreting a funding gap as a displacement trend.

Phase 3 — Ingest with round, date and coverage attached

Every value you store carries its round number, reporting date and coverage status. Build the schema so that a figure cannot exist without them. Most downstream errors with DTM are recoverable if these three fields survived ingest and unrecoverable if they did not.

Phase 4 — Pin the administrative geography

Capture the place codes exactly as published and record which boundary vintage they refer to. Administrative reorganisation is common in the countries DTM works in, and an unnoticed boundary change will present itself as a mass movement of people between districts.

Phase 5 — Separate stocks from flows explicitly

Keep mobility-tracking baseline populations and flow-monitoring transit counts in different tables with different semantics. They answer different questions, they overlap in unknown ways, and adding them is the single most consequential arithmetic error available in displacement analysis.

Phase 6 — Compute change with its coverage delta

When comparing rounds, calculate both the change in the figure and the change in which locations were assessed. Present them together. A district appearing for the first time is not an influx, and a district dropping out is not a return.

Phase 7 — Corroborate the largest movements independently

For any change big enough to drive a decision, seek a second signal: satellite imagery of site extent, partner agency figures, health or food distribution data, market prices, or conflict-event records for the same window. Agreement raises confidence substantially; disagreement usually reveals a coverage or definitional issue rather than an error in either source.

Phase 8 — Model the inaccessible areas rather than ignoring them

Identify which units were not assessed and estimate, qualitatively and explicitly, what direction that biases the total. Do not impute values silently. The honest statement — that the figure excludes districts where conditions are probably worst — is more useful than a filled-in number and is defensible in a way that imputation is not.

Phase 9 — Bring in the flow-monitoring vulnerability data

Where the corridor is relevant, the route-level survey material on exploitation, recruitment and abuse is far richer than the displacement counts and is routinely ignored by users who only ingest stocks. It connects displacement work to trafficking and protection analysis and it is the part of DTM with the least competition.

Phase 10 — Align time windows before joining anything

When correlating with conflict events, hazard data or food-security classifications, align on the period the DTM round refers to rather than on its publication date. The lag between them is often weeks and misalignment will produce a confident finding that displacement preceded its own cause.

Phase 11 — Present at the published resolution and no finer

Map and report at the administrative level the data was collected at. Do not interpolate to points, do not geocode site names to coordinates you derived elsewhere, and do not smooth. False precision about where displaced people are is not merely inaccurate; in some contexts it is dangerous to them.

Phase 12 — Re-check operational status before every refresh

Each time you update, verify that each country operation is still running. A silent series is the most common way this source misleads a system that has been left to collect on a schedule, and it takes one check to distinguish a stable situation from an absent one.

The platform ships this as a step-checked workflow in playbooks.php, so progress is recorded against a case rather than held in someone’s head.

What to pair it with

No single source carries a finding. These are the datasets that corroborate, extend or contradict this one — and a source that contradicts is worth more than one that agrees, because it is the only thing that will tell you when you are wrong.

Source	Relationship	What it adds
Humanitarian Data Exchange	extends	Hosts a large volume of DTM datasets in tabular form alongside conflict, health and food-security data using the same administrative codes, which makes it the natural joining ground.
Internal Displacement Monitoring Centre	corroborates	Compiles global internal displacement estimates, drawing partly on DTM. Useful for the global picture, and specifically not an independent check where DTM is one of its inputs.
IDMC displacement database	extends	Stock and new-displacement figures by country and year, with the flow-versus-stock distinction handled explicitly — the distinction most often botched by users of raw DTM data.
ReliefWeb	extends	Situation reports and analysis from across the humanitarian system, which supply the narrative context explaining why a round's figures moved.
ACAPS	corroborates	Independent humanitarian needs analysis, useful as a second opinion on severity and on access constraints in the same locations.
Joint IDP Profiling Service	extends	Methodological work on profiling displaced populations, which is the reference for understanding what different displacement data collection methods can and cannot support.
US State Department TIP Report	extends	Country-level governance assessment covering whether protection systems exist for displaced and migrant populations exposed to exploitation.
Polaris typology research	extends	Business-model detail on exploitation that gives analytical shape to the vulnerability indicators captured in flow-monitoring surveys.

Legal, ethical and operational constraints

The published layer is aggregated humanitarian data with licence terms that vary by dataset and should be checked individually. The serious constraints are ethical and protection-related rather than contractual. Displaced people are among the most vulnerable populations any dataset describes, and information about where they are can be used to find them — by armed actors, by governments seeking to prevent or compel movement, by traffickers and by hostile communities. IOM's data-protection framework exists for that reason and is why individual-level registration data is not released and will not be. Your obligations follow: do not attempt re-identification, do not increase the spatial precision of published figures, do not combine DTM with other datasets in a way that would locate an identifiable group, and think carefully before publishing site-level detail in an active conflict. In several jurisdictions the legal status of displaced populations is itself contested, and characterising a group as displaced, returnee or migrant can have consequences for their rights and treatment, which is why the terminology in the data is sometimes constrained. Where your use is commercial, resolve licensing explicitly rather than assuming that free access implies free reuse.

Operational security

Querying a public humanitarian API is low-exposure in itself, but the analytical output is not. A product mapping displaced populations in an active conflict is valuable to actors who mean them harm, and once it circulates you cannot recall it. Assume that anything you publish about displacement geography will be read by every party to the conflict, and design the resolution and the framing accordingly. Association is the second exposure: humanitarian access depends on perceived independence, and analysis that visibly links DTM data to military, intelligence or enforcement purposes can jeopardise the operation that produced it and the safety of national staff who collected it. That is a real and documented risk, not a theoretical one. Internally, treat draft displacement analyses concerning contested areas as sensitive, restrict distribution, and avoid naming specific sites, informants or enumerating organisations in anything that will travel outside your organisation.

Two rules that hold regardless of jurisdiction. Collection that is lawful is not automatically proportionate, and a dataset assembled for one purpose does not carry consent for another. Where the records concern identifiable people, the question is not only whether you may hold the data but whether holding it serves the purpose you are accountable for.

Is it earning its place?

Sources accumulate. Feeds get added during an incident and are never reviewed again, and a decade later the pipeline is carrying dead weight that nobody dares remove. These are the measures that show whether IOM Displacement Tracking Matrix (DTM) is contributing anything, and they are worth baselining now so the answer is available later.

Whether every displacement figure in your system carries round, reporting date and coverage status, verified by inspecting what a user actually sees rather than what the schema permits.
The proportion of inter-round change calculations that include a coverage delta, which should be all of them.
How many country operations in your collection are currently active versus silent, and whether anyone checked in the last quarter.
Whether stock and flow figures are physically separated in your data model, tested by attempting to sum them and confirming that the attempt fails.
How often an independent signal — imagery, partner data, market or health indicators — was sought for changes large enough to drive a decision.
Whether administrative boundary vintages are recorded, checked by looking for unexplained step changes in district-level series.
Whether any output has ever mapped displacement at finer resolution than the source published, which should be zero and should be treated as a protection incident rather than a formatting error.
Whether flow-monitoring vulnerability data has been used at all, which distinguishes a serious user of this source from one who only wanted a headline number.

Beware of volume. Indicator counts rise easily and say almost nothing. Unique contribution — findings this source produced that no other source in your stack would have — is the measure that matters, and it is usually far lower than anyone expects.

Tradecraft notes

The distinctions that separate a competent analyst from a fast one:

The four DTM components are four different instruments. Knowing which one produced a number tells you more about its reliability than any confidence interval would, and the data-source field is right there in the record.
Coverage is the most analytically important field in the dataset and the one most often discarded at ingest. An unassessed district is not an empty district, and a total that omits it is not a total.
A series that stops is a funding event until proven otherwise. Check the operation's status before constructing any narrative about a crisis resolving.
Never add stocks to flows. People counted in a baseline assessment and people counted at a transit point overlap in unknown proportions, and the sum is a number that describes nothing.
Administrative boundary changes masquerade as population movements. Record the boundary vintage with the place code and you will catch this; omit it and you will publish a migration that never happened.
The reporting date, the collection date and the publication date are three different things, and the gaps between them are where apparent contradictions with other sources come from. Align on what the round refers to, not on when you received it.
Key informants have incentives. Local authorities may inflate figures to attract assistance or suppress them to deny a problem, and neither bias is random. Corroborate the numbers that matter.
The flow-monitoring surveys are the least-used and most information-dense part of the source, carrying route-level detail on exploitation and vulnerability that nothing else in the open landscape provides.
Precision is not accuracy. A figure reported to the individual is the output of arithmetic on estimates, and quoting it to that precision in your own writing transfers an unearned confidence to your reader.

Questions analysts actually ask

Are DTM figures counts of individuals or estimates?

Mostly estimates. Mobility tracking derives figures from key-informant interviews and structured assessment rather than from enumeration, and only the registration component produces individual-level counts — and that data is not public. The apparent precision of the published numbers is a product of arithmetic, not of measurement, and should not be carried into your own writing.

The figure for a district doubled between rounds. Did that many people arrive?

Possibly, but check three other explanations first: whether the district was fully assessed in both rounds, whether the methodology or definitions changed, and whether administrative boundaries moved. All three produce the same signature, and the round documentation is usually explicit about which occurred.

A country's data stopped updating. Has the displacement ended?

Almost certainly not. DTM operations are donor-funded per country, and a series ending most often means the funding did. Verify the operation's current status before drawing any conclusion, and be aware that recent volatility in humanitarian funding has caused exactly this pattern across multiple countries.

Can I add DTM figures to refugee statistics for a total displacement number?

Not safely. They count different legal categories under different definitions on different cycles, and global displacement aggregates often already incorporate DTM, so you risk double-counting the same people. If you need a combined figure, use a compiler that has done the reconciliation and cite its methodology rather than performing the addition yourself.

Why do DTM figures differ from the government's?

Because the two are produced by different methods with different coverage and, frequently, different incentives. Government figures may derive from registration for assistance, may exclude certain populations for political reasons, or may cover areas DTM cannot access. The divergence is often the most interesting thing in the comparison, and it should be reported rather than resolved by picking a favourite.

Is there an API and do I need a key?

There is a documented public API serving displacement figures at country and administrative levels for participating operations. Its authentication requirements and coverage have changed since it was introduced, so check the current documentation rather than an older description, and read the terms of use before building on it.

Can I get site coordinates to map displacement precisely?

You should not try. Published data is served at the administrative resolution it was collected at, and increasing that precision — by geocoding site names, interpolating, or joining to other sources — creates a product that can be used to locate vulnerable people. That is the specific harm IOM's data-protection framework exists to prevent.

What is the most underused part of DTM?

The flow-monitoring surveys. They capture route-level information on movement drivers, intentions and exposure to exploitation and abuse, which is far richer than the displacement stock counts and has almost no equivalent in the open landscape. Most users ingest the IDP numbers and never look at it.

How should I handle the areas that could not be assessed?

Explicitly. State which units were inaccessible, state that the total excludes them, and state the likely direction of the bias, which is almost always toward under-count in the worst-affected places. Do not impute values silently; an honest gap is more useful and more defensible than a filled-in number nobody can audit.

Standards, formats and interoperability

What this source speaks natively, and what it has to be translated into before a partner can consume it. Work that arrives in a recognised format is easier to defend, easier to hand over and easier to automate against:

Humanitarian place-code conventions for administrative geography, which are what allow DTM to join to conflict, health and food-security datasets at subnational level.
The IASC cluster coordination system, which structures how DTM outputs feed sectoral response planning.
IOM's data protection principles, which govern collection and determine why individual-level data is not published.
The Guiding Principles on Internal Displacement, which supply the definitional basis for who counts as internally displaced.
IPC and equivalent food-security classification systems, which are the natural companion dataset at the same administrative resolution.
Humanitarian data exchange metadata and licensing conventions, which govern how DTM datasets are described and reused.
Durable-solutions indicator frameworks, which structure the survey components on return and reintegration.

References

Primary documentation and authoritative references for this source. Publishers revise and retire material, so treat the retrieval date as part of the citation and re-check before relying on any of it in a formal product.

Displacement Tracking Matrix — International Organization for Migration. The system itself: country operations, dashboards, reports and downloadable datasets.
DTM API — International Organization for Migration. Documentation for the public interface serving admin-level displacement figures. Read it for current coverage, authentication and terms rather than relying on secondary descriptions.
Humanitarian Data Exchange — UN OCHA. Bulk access to DTM datasets alongside the conflict, health and food-security data that share its administrative codes.
IOM data on the Humanitarian Data Exchange — UN OCHA. IOM's dataset collection, which is usually the fastest route to historical rounds in tabular form.
Internal Displacement Monitoring Centre — IDMC. Global internal displacement estimates and the flow-versus-stock framing that raw DTM data does not enforce.
IDMC displacement database — IDMC. Country-year stock and new-displacement figures, useful for placing a DTM round in a longer national context.
ReliefWeb — UN OCHA. Situation reports and operational analysis providing the narrative context behind a round's movement.
ACAPS — ACAPS. Independent humanitarian needs and access analysis, a useful second opinion on severity and on why an area could not be assessed.
Joint IDP Profiling Service — JIPS. Methodological reference on displacement profiling, and the best available explanation of what different collection methods can support.
International Labour Organization forced labour work — ILO. The labour-exploitation framework relevant to the vulnerability indicators captured in flow-monitoring surveys.

Link integrity: every reference above was verified with a live request when this page was generated. Where a publisher had moved or withdrawn a document, the link was repointed at a preserved copy in the Internet Archive and marked as archived. Anything with no reachable copy anywhere had its link removed rather than left to rot — the source is still credited, it simply cannot be linked.

Put it into practice

The Quantus Intel threat intelligence platform operationalises this source: it collects DTM per country operation rather than as one feed, stores every figure with its round, reporting date and coverage status so an inter-round change cannot be computed without its coverage delta, keeps stocks and flows physically separate, and joins admin-level displacement to conflict and food-security data on shared place codes in `correlate.php` and `country-dashboard.php`.. Browse the full source catalogue, or follow any tag above into the rest of the library.