Import Nigeria eHealth Kano Health Facilities

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The goal is to import 1,087 health facilities collected by eHealth Africa for the Kano state. Some 538 health facilities were uploaded in the past, but not including some information that would be useful, so they will be deleted while they are replaced by the new ones.


  1. Preparation, discussion - due to start the 28th of May.
  2. Import - expected to start any time after the community has solved any issues, doubts or concerns about this import.

Import Data

Data description

The datasets are drawn and generated by eHealth Africa in the course of their mapping activities in Northern Nigeria. The organisation has performed data collections in the entire area and remote tracing using aerial imagery. For the Kano state, a Q&A has been performed to double-check the accuracy of this data.

The original dataset consists of 1,087 health facilities entries, in .csv format.

The dataset contains several field attributes, like the name of the facility, the health facility type and the ward, LGA and state to which it belongs.


ODbL Compliance verified: YES
eHealth Africa has given full authorization for the use of their data with the standard authorization document of the Humanitarian OpenStreetMap Team (HOT). A scan of the document can be found here.

Import Type

The import will be done manually through one job in the HOT Tasking Manager (TM), having for each task of the job only the health facilities nodes that lie within the task tile, in a similar way as it was done for the Central African Republic UNICEF import. For each task, we will first check the eHealth nodes to be imported, and second we will assess the data already in the OSM database against the eHealth Africa one for the merging of the eHealth data into the OSM database. The OSM mappers who will contribute to this import job will follow a detailed workflow to accomplish this.

Data Preparation

Data Reduction & Simplification

The data is originally in csv format, in only one file for the whole Kano state. Some internal eHealth Africa codes were deleted for being not relevant for the import.

Tagging Plans

Each health facility will have the following tags:

eHealth Africa key OSM tag Observations
All objects amenity=hospital
All objects
StateName addr:state=*
LGAName addr:district=* Local Government Area (LGA)
WardName addr:municipality=* Ward
WardName, LGAName, StateName addr:full=*
HealthFacility_Name name=* With the transformation script, the abbreviations in the name (like H.P.) are converted to the full version (Health Post).
HealthFacility_Type health_facility:type=* Health Post will be translated by dispensary, Primary Health Centre by health_centre and General Hospital by hospital. The 9 health facilities that were tagged as Other have been searched and tagged manually.

Changeset Tags

We will use the following changeset tags:

Data Transformation

Data is in csv format. We just process the csv file and convert it to osm format with a gawk script.

Data Merge Workflow

Team Approach

Import will be undertaken by experienced OSM mappers, using a import specific OSM user account, following a workflow and working through a HOT Task Manager job similar to the ones set for the import of UNICEF health facilities, schools and water resources in Central African Republic.


The import will be discussed in the import list, in the Talk-Ng list and in the HOT list.


You can see the workflow here.

Reverse plan

In case of any trouble, JOSM reverter will be used.


The location of the eHealth nodes is generally correct, but following the already mentioned workflow, we will place the nodes in the exact position. For example, for a hospital compound, we will place the node more or less at the centre of it, in case is is not centered.

Around a month ago, some 540 health facilities were uploaded to the OSM database, but unfortunately they were lacking some interesting data that are in the eHealth dataset (they are tagged only with amenity=hospital and name=*), and the name use abbreviations like P.H.C., instead of the more correct full version (Primary Health Centre). So the approach will be to delete those nodes when encountered and replace them with the new ones of this import.

If other health facilities are encountered, they will be compared with the import ones and merge the data in the best possible way, keeping all info that the old ones may have. In case of doubt, a fixme tag will be placed, and the issue reported through the comment of the task (tile) of the TM job.