Import Nigeria eHealth Kano Health Facilities
Goals
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.
Schedule
- Preparation, discussion - due to start the 28th of May.
- 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.
Background
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 | source=ehealthafrica.org | |
| 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:
- comment=eHealth Africa Kano health facilities import, #hotosm-task-566
- created_by=JOSM/version
- source=ehealthafrica.org
- source:date=2014
- import=yes
- url=http://wiki.openstreetmap.org/wiki/Import_Nigeria_eHealth_Africa_Kano_Health_Facilities
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.
References
The import will be discussed in the import list, in the Talk-Ng list and in the HOT list.
Workflow
You can see the workflow here.
Reverse plan
In case of any trouble, JOSM reverter will be used.
Conflation
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.