The International Organization for Migration (IOM) is the leading inter-governmental organization in the field of migration and works closely with governmental, intergovernmental and non-governmental partners. It is dedicated to promoting humane and orderly migration for the benefit of all and works to help ensure the orderly and humane management of migration, to promote international cooperation on migration issues, to assist in the search for practical solutions to migration problems and to provide humanitarian assistance to migrants in need, including refugees and internally displaced people.
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(Unless otherwise noted, all datasets are shared by IOM and consist of a single DTM round.)
HDX dataset | Date | Notes |
|---|---|---|
2016-04-30 | ||
2015-10-31 | Raw DTM (with some sensitive fields removed). Much-more granular than the other examples, with 175 columns, and structured as a survey. This lets us see what the DTM originally looks like, before it’s digested into the high-level reports that are more typically shared publicly. Includes ADM1, ADM2, “Payam”, and “Village”, as well as site id and lat/lon. Lists number of IDPs and households. | |
2015-11-25 | ||
2015-04-30 | ||
2016-01-10 | ||
2016-01-20 | Multiple rounds. | |
2015-12-30 | Multiple rounds. | |
2016-01-28 | Multiple rounds. | |
2015-11-30 | ||
2016-03-01 | ||
2015-11-03 | ||
2015-11-30 | ||
2014-09-14 | Shared by OCHA Iraq. | |
2014-08-07 | Shared by OCHA Iraq. | |
2014-08-24 | Shared by OCHA Iraq. | |
2014-09-01 | Shared by HDX team. Multiple rounds. | |
2014-11-25 | Shared by OCHA Iraq. | |
2014-07-02 | Shared by OCHA Iraq. | |
2015-05-20 | Shared by OCHA ROSA. | |
2015-03-06 | Shared by OCHA ROSA. | |
2015-03-06 | Shared by OCHA ROSA. | |
2015-05-31 | Shared by OCHA Mali. |
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Yemen: r4_DTM_Master_List_Apr2016.xlsx
This has both survey and aggregated level data . The aggregated data is in sheet in different sheets. Sheet "Returnee hybrid data Locationlv" which has a single line header:
| AreaAssessmentID | AssessmentRound | Governorate | GovernoratePCode | DistrictEN | DistrictPCode | RetIDPsFromDistrict_HH | OfficialPlaceEN | PCode | Returnee_ConflictHH | Returnee_DisasterHH | Returnee_Accessibility | Returnee_WomenPer | Returnee_MenPer | Returnee_FemChildPer | Returnee_MaleChildPer | Latitude | Longitude |
Sheet "IDPs Raw data" has this single line header:
| AreaAssessmentID | Assessed Governorate | Governorate PCode | Assessed District | District PCode | Interview Date | Assessment Round | Site Name | Site Name A | Site PCode | Latitude | Longitude | Site Type | HH IDPs in District | Individual IDPs in District | Arrival Year | Arrival Month in 2015 | Site Estimated Conflict HHs | Site Estimated Disaster HHs | Site Estimated IDPs HHs | Family Size | Accessibility | Women % | Men % | Female Children % | Male Children % | Women Estimeted # | Men Estimeted # | Female Children Estimeted # | Male Children Estimeted # | Camps | Using rented accomodation | With host families who are relatives (no rent fee) | With host families who are not relatives (no rent fee) | Using schools, Health facilities, religious building | Using private or public building | In informal settlement (grouped families) in urban areas | In informal settlement (grouped families) in rural areas | Out of settlement (isolated families) |
Common Fields
From the headers above, there are certain fields which are common to all. These are:
- Latitude
- Longitude
- Location Name (ward, district etc.)
- Families/Households
- Type of shelter (although this is represented in different ways eg. individual columns for each type or one column with the type)
Conclusion
Given the lack of any consistency between the DTM Master List spreadsheets, writing a one size fits all automated data checker and cleaner for all of them is challenging. It would involve placing a great deal of "intelligence" into the cleaning program with the possibility that errors are introduced during cleaning for example, by accidentally matching the wrong column heading when making an algorithm to match a very diverse range of names. It may be possible to write cleaners per country but the effort involved would be large.
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