Import Nigeria eHealth Borno Health Facilities

Goals

The goal is to import 508 health facilities collected by eHealth Africa for the Borno State, Nigeria. Although most of them are in Borno (484 nodes), a few of these nodes lie in border areas of the neighbour states of Adamawa (18 nodes), Gombe (5 nodes) and Yobe (1 node).

Schedule

1. Preparation, discussion: This import is similar to the Kano State and to the Bauchi State health facilities imports, but includes more information.
2. Import - expected to start as soon as community has solved any issues.

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 areas that weren't off-limits due to the NE Nigeria ongoing crisis. Moreover, a Q&A has been performed to double-check the accuracy of this data.

The original dataset consists of 509 health facilities entries, in .osm format, of which one was deleted as being a duplicated node without information, so being the total 508 nodes.

The dataset contains several field attributes already present in the Kano and Bauchi state imports, like the name of the facility, the health facility type, the ward, LGA and state to which it belongs and the ownership. But this one includes more information, like specialities and services provided, numbers of doctors and nurses, dates of surveys, etc.

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 project in the HOT Tasking Manager (TM), having for each task of the project 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.

Apart from this, and with their regular OSM accounts, these users will add road access to each facility and, in case the place where it is located is unnamed, they will also add a place node with the name of the place extracted from the facility addr:full=*, adding the source=ehealthafrica.org (health facility) tag to that place node.

Data Preparation

Data Reduction & Simplification

The data is originally in osm format, in only one file.

Tagging Plans

Each health facility will have the following tags:

eHealth Africa key	OSM tag	Comments
All objects	medical_system:western=yes
All objects	source=ehealthafrica.org
All objects	addr:full=*	It will be community, ward, lga, BO
CLASS=*	Ignored.	Not relevant
GPS_part_3=*	Ignored.	Not relevant
GPS_part_4=*	Ignored.	Not relevant
Latitude=*	Ignored.	Not relevant
Longitude=*	Ignored.	Not relevant
OBJECTID=*	Ignored.	Not relevant
antenatal_=* 3 values: TRUE, FALSE and NA	health_specialty:obstetrics:antenatal=yes/no/unknown resp.	If they offer antenatal services
c_section_=* 3 values: TRUE, FALSE and NA	health_specialty:obstetrics:caesarean_section=yes/no/unknown resp.	If they carry out caesarean section for child birth
child_heal=* 3 values: TRUE, FALSE and NA	health_specialty:paediatrics=yes/no/unknown resp.	If there is a Paediatrician that treats infants
community=*	Together with unique_lga=* and ward=* will produce addr:full=*	place in which the health facility is located
date_of_su=*	source:date=YYYY-MM-DD	Date of survey
emergency_=* 2 values: TRUE and FALSE	health_specialty:emergency_medicine=yes/no resp.	It means if they accept emergencies (first aid) or not
facility_i=*	Ignored.	Not relevant. It's an eHealth Africa internal code
facility_n=*	name=*	Name of the facility. Names will be corrected for spelling, inconsistencies, etc, during the import
facility_t=Basic Health Centre / Primary Health Clinic	amenity=doctors + health_facility:type=health_centre
facility_t=Basic Health Centre or Primary Health Clinic	amenity=doctors + health_facility:type=health_centre
facility_t=Clinic	amenity=clinic + health_facility:type=clinic
facility_t=Dispensary	amenity=doctors + health_facility:type=dispensary
facility_t=District / General Hospital	amenity=hospital + health_facility:type=hospital
facility_t=District Hospital / Comprehensive Health Centre	amenity=hospital + health_facility:type=hospital
facility_t=General Hospital	amenity=hospital + health_facility:type=hospital
facility_t=Health Post	amenity=doctors + health_facility:type=office
facility_t=Primary Health Center	amenity=doctors + health_facility:type=health_centre
facility_t=Primary Health Centre (PHC)	amenity=doctors + health_facility:type=health_centre
facility_t=Specialist Hospital	amenity=hospital + health_facility:type=hospital + health_specialty:unknown=partial
facility_t=Teaching / Specialist Hospital	amenity=hospital + health_facility:type=hospital + health_specialty:unknown=partial
family_pla=* 3 values: TRUE, FALSE and NA	health_specialty:social_paediatrics=yes/no/unknown resp.	If they offer family planning services and advice
formhub_ph=*	Ignored.	Health facility photo file name
improved_s=*	Ignored.
improved_w=*	Ignored.
malaria_tr=* 3 values: TRUE, FALSE and NA	health_specialty:tropical_medicine=partial/no/unknown + disease:malaria=yes/no/unknown resp.	If they give malaria treatment
management=public	operator:type=public
management=private	operator:type=private
management=faith_based	operator:type=private
management=NA	operator:type=unknown
maternal_h=* 3 values: TRUE, FALSE and NA	health_specialty:obstetrics:postnatal=yes/no/unknown resp.	If they offer treatment to mothers
num_chews_=*	Ignored.	Not relevant
num_doctor=*	staff_count:doctors=* (staff_count:doctors=unknown in case of NA)	Number of doctors
num_nursem=* + num_nurses=*	staff_count:nurses=* being the sum of both quantities. If both values are NA, then staff_count:nurses=unknown	Number of male and female nurses
phcn_elect=* 3 values: TRUE, FALSE and NA	power_supply=yes/no/unknown resp.	If it is connected to public electricity
sector=health	Ignored.
skilled_bi=*	Ignored.	Not relevant
survey_id=*	Ignored.	Not relevant
unique_lga=*	Together with community=* and ward=* will produce addr:full=*	Includes the Local Government Area (LGA) and the state in which the health facility is located
vaccines_f=* 3 values: TRUE, FALSE and NA	health_service:prevention:vaccination=yes/no/unknown resp.	If they provide vaccination service
ward=*	Together with community=* and unique_lga=* will produce addr:full=*	Ward in which the health facility is located

Changeset Tags

We will use the following changeset tags:

• comment=eHealth Africa Borno health facilities import, #hotosm-project-946
• created_by=JOSM/version
• source=ehealthafrica.org
• import=yes
• url=https://wiki.openstreetmap.org/wiki/Import_Nigeria_eHealth_Borno_Health_Facilities

Data Transformation

Data is in osm format. We just process the osm file and convert it to a new osm file with an awk script.

Data Merge Workflow

Team Approach

Import will be undertaken by experienced OSM mappers, using an import specific OSM user account, following a workflow and working through a HOT Task Manager project 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 first in the Talk-Ng list and the HOT list, and then in the import 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 it is not centered.

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, the issue will be reported through the comment of the task (tile) of the TM job, and the user that uploaded the old health facility node would be eventually contacted if needed.