{"description":{"idno":"SEN_2020_HRWIM_GEO_v01_M","language":"ENG","characterSet":{"codeListValue":"utf-8"},"hierarchyLevel":"dataset","contact":[{"individualName":"Kamwoo Lee","organisationName":"World Bank, Development Data Group (DECDG), Data Analytics and Tools unit (DECAT)","contactInfo":{"address":{"elctronicMailAddress":"klee16@worldbank.org"}},"role":"author"}],"dateStamp":"2021-06-10T12:00:00","metadataStandardName":"ISO 19115:2003\/19139","dataSetURI":"http:\/\/nada-demo.ihsn.org\/index.php","spatialRepresentationInfo":[{"vectorSpatialRepresentation":{"topologyLevel":"geometryOnly","geometricObjects":[{"geometricObjectType":"point","geometricObjectCount":30131}]}}],"referenceSystemInfo":[{"code":"4326","codeSpace":"EPSG"}],"identificationInfo":{"citation":{"title":"High-resolution Wealth Index Map, Senegal, 2020","date":[{"date":"2020-08-31","type":"creation"},{"date":"2020-09-01","type":"released"},{"date":"2021-09-10","type":"distribution"}],"edition":"v.1","editionDate":"2021-06-10","identifier":[{"code":"SEN_2020_HRWIM_GEO_v01_M"}],"citedResponsibleParty":[{"individualName":"Kamwoo Lee","organisationName":"World Bank, Development Data Group (DECDG), Data Analytics and Tools unit (DECAT)","contactInfo":{"address":{"elctronicMailAddress":"klee16@worldbank.org"}},"role":"author"}],"presentationForm":["mapDigital","tableDigital","imageDigital"],"series":{"name":"High-resolution Wealth Index Maps in Sub-Saharan Africa"},"otherCitationDetails":"Lee, K., & Braithwaite, J. (2020). High-Resolution Poverty Maps in Sub-Saharan Africa. arXiv preprint https:\/\/arxiv.org\/abs\/2009.00544."},"abstract":"High-resolution Wealth Index Maps in Sub-Saharan Africa have been created using machine learning techniques and several publicly available geospatial data including satellite images, OpenStreetMap (OSM), UN OCHA settlements dataset (OCHA), High Resolution Population Density Maps (HRSL), WorldPop Gridded population dataset, NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), and Database of Global Administrative Areas (GADM). This dataset contains information on buildings, roads, points of interest (POIs), night-time luminosity, population density, and estimated wealth index for 1-square mile inhabited places identified by the underlying datasets. 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This dataset is not a result of a World Bank project. The World Bank does not represent, endorse, or guarantee its accuracy and accepts no liability for any loss or damage arising from inaccuracies or omissions."],"accessConstraints":["unrestricted"],"useConstraints":["unrestricted"]}}],"extent":{"geographicElement":[{"geographicBoundingBox":{"southBoundLatitude":12.342567805000044245389290153980255126953125,"westBoundLongitude":-17.5188110909999750219867564737796783447265625,"northBoundLatitude":16.67051744300005111654172651469707489013671875,"eastBoundLongitude":-11.3680645319999484854633919894695281982421875},"geographicDescription":"Senegal"}]},"spatialRepresentationType":["vector"],"language":["ENG","FRA"],"characterSet":[{"codeListValue":"utf-8-sig"}]},"distributionInfo":{"distributionFormat":[{"name":"Shapefile","specification":"Esri vector data storage format"},{"name":"text\/csv","specification":"RFC4180 - Common Format and MIME Type for Comma-Separated Values (CSV) Files"}],"distributor":[{"organisationName":"World Bank, 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Method-wise, a new learning mechanism was devised to combine an XGBoost model and a convolutional neural network (CNN) model to estimate wealth index from both geospatial features and satellite images. Data-wise, training data was collected from 6 data sources:\n- OpenStreetMap (OSM)\n- Demographic and Health Survey (DHS)\n- UN OCHA settlements dataset (OCHA)\n- Visible Infrared Imaging Radiometer Suite Nighttime Lights (VNL)\n- Daytime satellite images through Google Static Maps API (Google Maps)\n- High-Resolution Settlement Layer (HRSL) and the WorldPop gridded population dataset (WorldPop).","processStep":[{"description":"<b>1. Identifying inhabited places:<\/b> <br>\nThis step combines OCHA, OSM, HRSL and WorldPop to create a harmonized 1-sqmi-level settlement dataset.\nIt first gets information on inhabited places from the OCHA dataset if it's available. Then it adds OSM places where the place tag is 'city', 'town', 'village', 'hamlet', or 'isolated_dwelling'. If the distance from an OSM place to an OCHA place is less than 300 m (around 1000 feet), it assumes that they are the same place and adds supplemental info to the OCHA place. If the minimum distance from an OSM place to any OCHA places is more than 300 m (around 1000 feet), it adds the OSM place as a separate inhabited place.\nIn order not to overlook numerous unnamed places and many smaller communities within big cities, this step has an additional process. It divides each of the smallest admin regions into a grid of 10 km^2 (around 4-sq-mi) squares and finds a 1-sq-mi area that has the largest population within a grid. If the population is larger than 100 and there is no listed place in UN OCHA and OSM datasets within 2 km around the HRSL place, it is added as a separate inhabited place.","dateTime":"2020-08-31","processor":[{"individualName":"Kamwoo Lee","role":"author"}],"source":[{"description":"OCHA","sourceCitation":{"title":"SenegalSettlements","citedResponsibleParty":[{"organisationName":"United Nations Office for the Coordination of Humanitarian Affairs","contactInfo":{"onlineResource":{"linkage":"https:\/\/data.humdata.org\/"}}}]}},{"description":"OSM","sourceCitation":{"title":"GEOFABRIK dump","citedResponsibleParty":[{"organisationName":"Geofabrik GmbH","contactInfo":{"onlineResource":{"linkage":"https:\/\/www.geofabrik.de\/data\/download.html"}}}]}},{"description":"HRSL","sourceCitation":{"title":"High Resolution Population Density Maps","citedResponsibleParty":[{"organisationName":"Facebook Connectivity Lab and Center for International Earth Science Information Network \u2013 CIESIN \u2013 Columbia University","contactInfo":{"onlineResource":{"linkage":"https:\/\/data.humdata.org\/dataset\/highresolutionpopulationdensitymaps"}}}]}},{"description":"WorldPop","sourceCitation":{"title":"WorldPop gridded population estimate","citedResponsibleParty":[{"organisationName":"WorldPop","contactInfo":{"onlineResource":{"linkage":"https:\/\/www.worldpop.org\/methods\/populations"}}}]}}]},{"description":"<b>2. Extracting buildings, roads, and points of interest (POIs) information for each inhabited place:<\/b> <br>\nThis step extracts information on man-made structures and services from OSM.\nBuilding info: Total building area, and the number of buildings within 1 km radius surrounding the GPS coordinates of each inhabited place.\nRoad info: Total length of roads and number of junctions within 1 km radius surrounding the GPS coordinates of each inhabited place. Distance to the closest road and junctions from the GPS coordinates of each inhabited place.\nPOI info: The number of and distance to 110 types of service locations. POIs: bar, bbq, biergarten, cafe, drinking water, fast food, food court, ice cream, pub, restaurant, college, driving school, kindergarten, language school, library, toy library, music school, school, university, bicycle parking, bicycle repair station, bicycle rental, boat rental, boat sharing, bus station, car rental, car sharing, car wash, vehicle inspection, charging station, ferry terminal, fuel, grit bin, motorcycle parking, parking, parking entrance, parking space, taxi, atm, bank, bureau de change, baby hatch, clinic, dentist, doctors, hospital, nursing home, pharmacy, social facility, veterinary, arts centre, brothel, casino, cinema, community centre, fountain, gambling, nightclub, planetarium, public bookcase, social centre, stripclub, studio, swingerclub, theatre, animal boarding, animal shelter, baking oven, bench, childcare, clock, conference centre, courthouse, crematorium, dive centre, embassy, fire station, give box, grave yard, gym, hunting stand, internet cafe, kitchen, kneipp water cure, marketplace, monastery, photo booth, place of worship, police, post box, post depot, post office, prison, public bath, ranger station, recycling, refugee site, sanitary dump station, sauna, shelter, shower, telephone, toilets, townhall, vending machine, waste basket, waste disposal, waste transfer station, watering place, water point.","dateTime":"2020-08-31","processor":[{"individualName":"Kamwoo Lee","role":"author"}],"source":[{"description":"OSM","sourceCitation":{"title":"GEOFABRIK dump","citedResponsibleParty":[{"organisationName":"Geofabrik GmbH","contactInfo":{"onlineResource":{"linkage":"https:\/\/www.geofabrik.de\/data\/download.html"}}}]}}]},{"description":"<b>3. Extracting population density information for each inhabited place:<\/b> <br>\nThis step extracts estimated population within 1-square mile, 25km2, 100km2 areas around each inhabited place.","dateTime":"2020-08-31","processor":[{"individualName":"Kamwoo Lee","role":"author"}],"source":[{"description":"HRSL","sourceCitation":{"title":"High Resolution Population Density Maps","citedResponsibleParty":[{"organisationName":"Facebook Connectivity Lab and Center for International Earth Science Information Network \u2013 CIESIN \u2013 Columbia University","contactInfo":{"onlineResource":{"linkage":"https:\/\/data.humdata.org\/dataset\/highresolutionpopulationdensitymaps"}}}]}},{"description":"WorldPop","sourceCitation":{"title":"WorldPop gridded population estimate","citedResponsibleParty":[{"organisationName":"WorldPop","contactInfo":{"onlineResource":{"linkage":"https:\/\/www.worldpop.org\/methods\/populations"}}}]}}]},{"description":"<b>4. Extracting nighttime luminosity information for each inhabited place:<\/b> <br>\nThis step extracts nighttime luminosity within 1-square mile, 25km2, 100km2 areas around each inhabited place.","dateTime":"2020-08-31","processor":[{"individualName":"Kamwoo Lee","role":"author"}],"source":[{"description":"VNL","sourceCitation":{"title":"Annual VIIRS Cloud Mask - Outlier Removed - Nighttime Lights","citedResponsibleParty":[{"organisationName":"National Oceanic and Atmospheric Administration (NOAA)","contactInfo":{"onlineResource":{"linkage":"https:\/\/www.ngdc.noaa.gov\/eog\/viirs\/download_dnb_composites.html"}}}]}}]},{"description":"<b>5. Training XGBoost model and CNN model:<\/b> <br>\nThis step trains\/estimates\/validates wealth index for each inhabited place.\nFirst, an XGBoost model is trained on the buildings, roads, POIs, night-time luminosity, and population density features. It estimates wealth levels for all inhabited places.\nSecond, a customized CNN model is trained on the estimated wealth index from the XGBoost model and day-time satellite images downloaded from Google Static Map. It estimates the 4 probabilities that each populated place is a rich, upper-middle, lower-middle, or poor area.\nThird, the featurized information (4 probabilities) from the CNN model is added to the XGBoost model as an additional inputs resulting in better estimates.\nFourth, the CNN model is retrained on the better estimates from the XGBoost model.\nThe whole process creates a cycle where the two models improve each other by providing better training data with every iteration.\nThe wealth index dataset is the result of the 7th iteration.","dateTime":"2020-08-31","processor":[{"individualName":"Kamwoo Lee","role":"author"}],"source":[{"description":"DHS","sourceCitation":{"title":"Demographic and Health Surveys","citedResponsibleParty":[{"organisationName":"United States Agency for International Development (USAID)","contactInfo":{"onlineResource":{"linkage":"https:\/\/dhsprogram.com\/"}}}]}},{"description":"Google Maps","sourceCitation":{"title":"Daytime satellite images through Google Static Maps API","citedResponsibleParty":[{"organisationName":"Google","contactInfo":{"onlineResource":{"linkage":"https:\/\/maps.googleapis.com"}}}]}}]}]}}],"feature_catalogue":{"name":"High-resolution Wealth Index Maps in Sub-Saharan Africa - Feature Catalogue","featureType":[{"typeName":"inhabited places","definition":"1-square mile inhabited areas identified by UN OCHA settlement data, OSM populated places, HRSL, and WorldPop","carrierOfCharacteristics":[{"memberName":"lat","definition":"Latitude","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"degree","valueType":"xs:decimal"},{"memberName":"lon","definition":"Longitude","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"degree","valueType":"xs:decimal"},{"memberName":"info_source","definition":"Source of information","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OCHA_place_name","definition":"place name in the OCHA settlement dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OCHA_lat","definition":"latitude in the OCHA settlement dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OCHA_lon","definition":"longitude in the OCHA settlement dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OCHA_{extra info}","definition":"country-specific extra information in the OCHA settlement dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OSM_place_name","definition":"place name in the OSM dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OSM_lat","definition":"latitude in the OSM dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OSM_lon","definition":"longitude in the OSM dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OSM_id","definition":"unique number identifying a node in OSM dataset","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"OSM_place_type","definition":"type of inhabited place","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string","listedValue":[{"label":"city","definition":"the largest settlement or settlements within a territory, including national, state and provincial capitals, and other major conurbations."},{"label":"town","definition":"an important urban centre that is larger than a village, smaller than a city, and not a suburb. Towns normally have a good range of shops and facilities which are used by people from nearby villages."},{"label":"village","definition":"a smaller distinct settlement, smaller than a town, with few facilities available, with people traveling to nearby towns to access these."},{"label":"hamlet","definition":"an isolated settlement, typically with less than 100-200 inhabitants, although this may vary by country."},{"label":"isolated_dwelling","definition":"the smallest kind of human settlement. They are outside other settlements (this does not mean that they are outside administrative boundaries) and form by themselves a settlement."}]},{"memberName":"GADM_GID_0","definition":"Preferred unique ID for country. ISO 3166-1 alpha-3 country code when available","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_NAME_0","definition":"Country Name in English","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_GID_i","definition":"Preferred unique ID at level i","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_NAME_i","definition":"Official name in latin script","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_VARNAME_i","definition":"Variant name. Alternate names in usage for the place, separated by pipes |","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_NL_NAME_i","definition":"Non-Latin name. Official name in a non-latin script","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_HASC_i","definition":"Hierarchical Administrative Subdivision Codes. A unique ID from Statoids","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_CC_i","definition":"Country code. Uniqe ID used within the country","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_TYPE_i","definition":"Administrative type in local language","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_ENGTYPE_i","definition":"Administrative type in English (following commonly used translations)","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_VALIDFR_i","definition":"Valiid From. Date from which data is known to have started. default: Unknown. Format is YYYY-MM-DD or YYYY-MM or YYYY","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_VALIDTO_i","definition":"Valid To. Date at which data is no longer valid. default: Present or Current. Format is YYYY-MM-DD or YYYY-MM or YYYY","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"},{"memberName":"GADM_REMARKS_i","definition":"Comments about edits, relevant to history","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:string"}]},{"typeName":"buildings, roads, POIs","definition":"area, distance, count information on buildings, roads, and points of interest for each inhabited place. Data is collected from OSM.","carrierOfCharacteristics":[{"memberName":"lat","definition":"Latitude","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"degree","valueType":"xs:decimal"},{"memberName":"lon","definition":"Longitude","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"degree","valueType":"xs:decimal"},{"memberName":"total_building_area","definition":"sum of all building areas within 1 km radius","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"square kilometer","valueType":"xs:decimal"},{"memberName":"total_building_count","definition":"number of buildings within 1 km radius","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:int"},{"memberName":"distance_to_closest_road","definition":"distance to the closest road (any type)","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"kilometer","valueType":"xs:decimal"},{"memberName":"total_road_length","definition":"sum of all roads' lengths within 1 km radius","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"kilometer","valueType":"xs:decimal"},{"memberName":"distance_to_closest_paved_road","definition":"distance to the closest paved road","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"kilometer","valueType":"xs:decimal"},{"memberName":"total_paved_road_length","definition":"sum of paved roads' lengths within 1 km radius","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"kilometer","valueType":"xs:decimal"},{"memberName":"distance_to_closest_junction","definition":"distance to the closest junction. A junction is defined as a node where two or more ways cross, or three or more ways begin.","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"kilometer","valueType":"xs:decimal"},{"memberName":"total_junction_count","definition":"number of junctions within 1 km radius","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:int"},{"memberName":"total_{POI}_count","definition":"number of 110 types of service locations. 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POIs: bar, bbq, biergarten, cafe, drinking water, fast food, food court, ice cream, pub, restaurant, college, driving school, kindergarten, language school, library, toy library, music school, school, university, bicycle parking, bicycle repair station, bicycle rental, boat rental, boat sharing, bus station, car rental, car sharing, car wash, vehicle inspection, charging station, ferry terminal, fuel, grit bin, motorcycle parking, parking, parking entrance, parking space, taxi, atm, bank, bureau de change, baby hatch, clinic, dentist, doctors, hospital, nursing home, pharmacy, social facility, veterinary, arts centre, brothel, casino, cinema, community centre, fountain, gambling, nightclub, planetarium, public bookcase, social centre, stripclub, studio, swingerclub, theatre, animal boarding, animal shelter, baking oven, bench, childcare, clock, conference centre, courthouse, crematorium, dive centre, embassy, fire station, give box, grave yard, gym, hunting stand, internet cafe, kitchen, kneipp water cure, marketplace, monastery, photo booth, place of worship, police, post box, post depot, post office, prison, public bath, ranger station, recycling, refugee site, sanitary dump station, sauna, shelter, shower, telephone, toilets, townhall, vending machine, waste basket, waste disposal, waste transfer station, watering place, water point","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"kilometer","valueType":"xs:decimal"}]},{"typeName":"night-time luminosity","definition":"Annual Visible and Infrared Imaging Suite (VIIRS) Cloud Mask - 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Estimated wealth level on the International Wealth Index scale","cardinality":{"lower":1,"upper":1},"valueMeasurementUnit":"NA","valueType":"xs:decimal"}]}]}},"tags":[{"tag":"Machine learning"},{"tag":"OpenStreetMap"},{"tag":"Satellite images"},{"tag":"Sub-Saharan Africa"},{"tag":"Wealth estimation"}],"schematype":"geospatial"}