MartinWahnschaffe
commented
6 years ago Sources
- Openstreetmap data (e.g. geofabrik): https://download.geofabrik.de/africa.html geofabrik shapefiles are customized and only the payed version contains administrative boundaries Steps to import into a PostGIS database: https://wiki.openstreetmap.org/wiki/PostGIS
- ESRI
- WikiData
- DHIS2: https://docs.dhis2.org/2.27/en/developer/html/webapi_organisation_units.html
Administrative Divisions - Regions, Districts, Communities
- OSM Nigeria seems to contain states, LGAs and wards (admin_level 4, 6 and 8). The data is not clean and incomplete though (3x Rivers, 2x Ondo, 10 regions missing)
- OSM Ghana only has regions and a few districts (14). No sub-districts at all
- ESRI "World Adminstrative Divisions" seems to have region shapefiles only (https://www.arcgis.com/home/item.html?id=f0ceb8af000a4ffbae75d742538c548b)
- DHIS2 Query https://play.dhis2.org/2.30/api/organisationUnits.geojson?level=2 Results contain region name and geometry. Level depends on the country
- Wiki Data Query https://query.wikidata.org/ Very powerful and good data quality, but queries are a bit tricky. Entries also contain OSM relation ID Example: https://query.wikidata.org/#SELECT%20%3FNigeria%20%3FUntereinheit__administrative_Einheit_%20%3FUntereinheit__administrative_Einheit_Label%20%3FNigeriaLabel%20WHERE%20%7B%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22%5BAUTO_LANGUAGE%5D%2Cen%22.%20%7D%0A%20%20%0A%20%20OPTIONAL%20%7B%20%3FNigeria%20wdt%3AP150%20%3FUntereinheit__administrative_Einheit_.%20%7D%0A%20%20%3FNigeria%20wdt%3AP17%20wd%3AQ1033.%0A%20%20%3FNigeria%20wdt%3AP31%20wd%3AQ6256.%0A%7D
Conclusion
From my point of view DHIS 2 and Wiki Data seem to be the best source for administrative divisions. The import will still need configuration details and a check from a user - the best we can get is something semi-automatic.
Points of Interest and Health Facilities
- WikiData is not a good source - only few entries
- ESRI: couldn't find a proper source
- DHIS2 Query https://play.dhis2.org/2.30/api/organisationUnits.geojson?level=4 Health facilities only. Level depends on the country
- OSM Nigeria is also not complete - probably because most health facilities aren't tagged in any way. Using 'hospital','clinic','doctors' on amenity and building gives ~3.000 entries (compared to >20.000 in our db). See data below
- The OSM tile layer already displays the POIs. They are not interactive, though and only become visible when zooming in close enough It may be a good idea to switch to the humanitarian layer for a better visualization
Conclusion
DHIS2 seems to be the best source for health facilities. OSM still has the most data on a global level, but it's not complete and not sufficient for the purpose of importing health facilities.
Example: OSM POI of Nigeria
| count | amenity |
|---|---|
| 141071 | undefined |
| 2607 | hospital |
| 1239 | place_of_worship |
| 601 | school |
| 489 | fuel |
| 391 | bank |
| 334 | doctors |
| 208 | clinic |
| 206 | public_building |
| 184 | restaurant |
| 128 | bar |
| 121 | vending_machine |
| 94 | fast_food |
| 88 | marketplace |
| 80 | pharmacy |
| 65 | police |
| 63 | bus_station |
| 59 | college |
| 34 | waste_basket |
| 33 | community_centre |
| 31 | atm |
| 29 | shelter |
| 28 | waste_disposal |
| 27 | university |
| 26 | ferry_terminal |
| 24 | pub |
| 23 | parking |
| 21 | parking_entrance |
| 20 | cafe |
| 20 | post_office |
| 16 | drinking_water |
| 14 | car_wash |
| 14 | embassy |
| 14 | toilets |
| 14 | kindergarten |
| 13 | courthouse |
| 13 | cinema |
| 11 | townhall |
| 10 | ice_cream |
| 10 | library |
SORMAS is relying on administrative divisions and health facilities to map cases and other elements to their region. It comes with the needed geo data for Nigeria and Madagascar. An important question is how this data can be imported into the system for other countries – ideally it would be able to connect to a third-party system that provides the needed data. In order to find out whether this is possible we need to evaluate possible sources for the following types of data: