20260813092817001.0FALSECopyMultiple "D:\outputs\cod\consolidated_data_v9\older\GRID3_COD_v9_0_unpublished_20260810.gpkg\main.GRID3_COD_health_areas_unpublished_20260810 FeatureClass" D:\outputs\cod\consolidated_data_v9\GRID3_COD_health_areas_v9_0.gdb GRID3_COD_health_areas_unpublished_20260810 "main.GRID3_COD_health_areas_unpublished_20260810 FeatureClass GRID3_COD_health_areas_unpublished_20260810 #"Rename D:\outputs\cod\consolidated_data_v9\GRID3_COD_health_areas_v9_0.gdb\GRID3_COD_health_areas_unpublished_20260810 D:\outputs\cod\consolidated_data_v9\GRID3_COD_health_areas_v9_0.gdb\GRID3_COD_health_areas_v9_0 FeatureClassCalculateField GRID3_COD_health_areas_v9_0 pays "République démocratique du Congo" PYTHON3 "import re def clean_spaces(column): return re.sub(" +", " ", column).strip()" TEXT NO_ENFORCE_DOMAINS002GRID3_COD_health_areas_v9_012.19166631.277003-13.4590355.3920031file://\\CIESIN-CBNYL34-\C$\Users\mlukang\Downloads\CODv9\GRID3_COD_health_areas_v9_0.gdb-20260813T173101Z-1-001\GRID3_COD_health_areas_v9_0.gdbLocal Area NetworkGeographicGCS_WGS_1984Angular Unit: Degree (0.017453)<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.5.0'><WKT>GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4326]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.983152841195215e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4326</WKID><LatestWKID>4326</LatestWKID></GeographicCoordinateSystem>150000000500020260813135001002026081314244700ISO19139datasetexternalfc35357071b3fbb77098f751c60aeff50bde2225694aa386633d321aad49d310Center for Integrated Earth System Information (CIESIN)info@ciesin.columbia.eduTrueCenter for Integrated Earth System Information (CIESIN)20260813File Geodatabase Feature ClassGRID3 COD - Health Areas v9.02026-08-13T00:00:00v9.0Dataset citation: Center for Integrated Earth System Information (CIESIN), Columbia University, Ministère de la Santé Publique, Hygiène et Prévention, Democratic Republic of the Congo, and GRID3. 2026. GRID3 COD - Health Areas v9.0. New York: Columbia University. https://doi.org/10.7916/hv5g-p227. Accessed [DAY MONTH YEAR].Center for Integrated Earth System Informationinfo@ciesin.columbia.eduCenter for International Earth Science Information Network (CIESIN)Democratic Republic of the CongoCODhealth areasThe GRID3 COD - Health Areas v9.0 dataset consists of health area boundaries with name, health zone, and other attributes for all the twenty-six provinces in the Democratic Republic of the Congo (COD). <DIV STYLE="text-align:Left;"><DIV><DIV>The GRID3 COD - Health Areas v9.0 dataset consists of spatial point data with attribute information. The current version supersedes the <A href="https://data.grid3.org/datasets/GRID3::grid3-cod-health-areas-v8-0/about" target="_blank" STYLE="text-decoration:underline;">GRID3 COD - Health Areas v8.0</A>. The following changes were made:<UL><LI>The data for Haut-Lomami, Haut-Katanga, Ituri, Kwango, Lomami, Maniema and Tanganyika provinces were updated.</LI><LI>Health area names were updated.</LI></UL>Dataset citation:Center for Integrated Earth System Information (CIESIN), Columbia University, Ministère de la Santé Publique, Hygiène et Prévention, Democratic Republic of the Congo, and GRID3. 2026. GRID3 COD - Health Areas v9.0. New York: Columbia University. https://doi.org/10.7916/hv5g-p227. Accessed [DAY MONTH YEAR].Contacts and data queries: The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu</DIV></DIV></DIV>CIESIN and its co-authors thank the following institutions that provided input data and/or assistance with data production: Acasus. Agence Nationale d'Ingénierie Clinique et du Numérique de la Santé (ANICNS). Bluesquare. Bureau Central du Recensement (BCR). Caritas. Centers for Disease Control and Prevention (CDC). Direction d'Etudes et Planification (DEP). Direction des Soins de Santé Primaires (DSSP). Division du Système National d'Informations Sanitaires (DSNIS). Division Provinciale de la Santé (DPS) de Bas-Uele, Equateur, Haut-Katanga, Haut-Lomami, Haut-Uele, Ituri, Kasaï, Kasaï-Central, Kasaï-Oriental, Kinshasa, Kongo-Central, Kwango, Kwilu, Lomami, Lualaba, Mai-Ndombe, Maniema, Mongala, Nord-Kivu, Nord-Ubangi, Sankuru, Sud-Kivu, Sud-Ubangi, Tanganyika, Tshopo, et Tshuapa. Ecole de Santé Publique de Kinshasa (ESPK). Gates Foundation. Gavi, the Vaccine Alliance. Geospatial Evaluation and Observation Lab (geoLab), College of William & Mary. Global Affairs Canada (GAC). Global Good. IMA World Health, Initiative Régionale de Documentation et d'Accompagnement Communautaire au Développement (IRDAC Sarl). International Federation of Red Cross and Red Crescent Societies (IFRC). International Medical Corps (IMC). Médecins Sans Frontières (MSF). Ministère de l’Environnement et Développement Durable (MEDD). Ministère de la Santé publique, Hygiène et Prévention, Novel-T. Open Street Map (OSM). PATH. Programme Elargi de Vaccination (PEV). Programme National de Lutte contre le Paludisme (PNLP). Référentiel Géographique Commun (RGC). Soins de Santé Primaires en Milieu Rural (SANRU). The International Organization for Migration (IOM). United Nations Children Fund (UNICEF). United Nations Development Programme (UNDP). United Nations Office for Project Services (UNOPS). Denmark and CO United Nations Office for the Coordination of Humanitarian Affairs (OCHA). United Nations Organization Stabilization Mission in the Democratic Republic of the Congo (MONUSCO). University of California, Los Angeles (UCLA) Health Research and Training Program. VillageReach. World Health Organization (WHO) - HQ, AFRO, CO. World Resources Institute (WRI). Funding for this work was provided by GRID3 under grants INV-044979 GRID3 - Phase 2 Scaling, and FAE/GRID3/001/2024 Soutien à la mise en oeuvre des activités du Projet de Vaccination des Enfants Zéro Dose du Fonds d’Accélération de l’Équité (FAE) en RDC. Democratic Republic of the Congo, CODSettlements<DIV STYLE="text-align:Left;"><DIV><DIV>Terms of useUsers are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.Copyright 2026. The Trustees of Columbia University in the City of New York.Data licenseThe data and accompanying document are licensed under a Creative Commons Attribution 4.0 International License, CC BY 4.0 (http://creativecommons.org/licenses/by/4.0) and specified in legal code (http://creativecommons.org/licenses/by/4.0/legalcode). Known Data Limitations and Disclaimer:The spatial accuracy of the health areas dataset depends on both the quality of the input data and the correctness of edits applied throughout the validation process. Temporal mismatches may exist between the point datasets and the satellite imagery used for quality checks. Spelling errors and naming inconsistencies may also occur due to colloquial variations in how locations are referenced in the field.Boundaries in Kwango Province for three health zones (Boko, Kenge, and Kimbao) were generated using limited data inputs (no primary data collection took place in the province; GRID3 did not have access to the most recent PNLP data - the only dataset available dated from 2018 ; in addition, only partial health-zone microplans were obtained from the authorities) and may therefore contain unintended errors; users are advised to apply caution when working with these features.Due to ongoing insecurity in the Nord-Kivu and Sud-Kivu provinces, secondary data collection efforts are generally constrained and therefore less systematic and comprehensive. Enumerators involved in various data collection activities cannot safely access all areas of the provinces. As a result, some data inputs may be incomplete or potentially biased, which may affect the reliability of boundary-related outputs in these highly unstable contexts.Table 12 of the Data Release Notes (PDF) summarizes the number of missing health areas by province based on the DHIS2 database downloaded in February and July 2026. While health zone boundaries remain relatively stable, the number of health areas has changed substantially over the past few years. These discrepancies are partly explained by the dynamic redefinition of health area boundaries, which aims to reduce their size and reconfigure their spatial extent. This dataset should be considered operational; it has not been fully validated by government authorities. Its production is part of ongoing work, and additional edits and refinements are expected in future releases.CIESIN, Columbia University, and its co-authors follow procedures designed to ensure that data disseminated by the project are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact CIESIN, info@ciesin.columbia.edu. CIESIN, Columbia University, its co-authors, and their sponsors do not guarantee the accuracy, reliability, or completeness of any data provided. We provide these data without warranty of any kind whatsoever, either expressed or implied, and shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided.</DIV></DIV></DIV>See Data Release Notes (PDF) for more information5.392003112.19166631.277003-13.459035externalfc35357071b3fbb77098f751c60aeff50bde2225694aa386633d321aad49d310Center for Integrated Earth System Information (CIESIN)info@ciesin.columbia.eduTrueCenter for Integrated Earth System Information (CIESIN)Microsoft Windows 10 Version 10.0 (Build 26200) ; Esri ArcGIS 13.5.0.57366externalfc35357071b3fbb77098f751c60aeff50bde2225694aa386633d321aad49d310Center for Integrated Earth System Information (CIESIN)info@ciesin.columbia.eduTrueCenter for Integrated Earth System Information (CIESIN)<DIV STYLE="text-align:Left;">Terms of useUsers are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.Copyright 2026. The Trustees of Columbia University in the City of New York.Data licenseThe data and accompanying document are licensed under a Creative Commons Attribution 4.0 International License, CC BY 4.0 (http://creativecommons.org/licenses/by/4.0) and specified in legal code (http://creativecommons.org/licenses/by/4.0/legalcode). Known Data Limitations and Disclaimer:The spatial accuracy of the health areas dataset depends on both the quality of the input data and the correctness of edits applied throughout the validation process. Temporal mismatches may exist between the point datasets and the satellite imagery used for quality checks. Spelling errors and naming inconsistencies may also occur due to colloquial variations in how locations are referenced in the field.Boundaries in Kwango Province for three health zones (Boko, Kenge, and Kimbao) were generated using limited data inputs (no primary data collection took place in the province; GRID3 did not have access to the most recent PNLP data - the only dataset available dated from 2018 ; in addition, only partial health-zone microplans were obtained from the authorities) and may therefore contain unintended errors; users are advised to apply caution when working with these features.Due to ongoing insecurity in the Nord-Kivu and Sud-Kivu provinces, secondary data collection efforts are generally constrained and therefore less systematic and comprehensive. Enumerators involved in various data collection activities cannot safely access all areas of the provinces. As a result, some data inputs may be incomplete or potentially biased, which may affect the reliability of boundary-related outputs in these highly unstable contexts.Table 12 of the Data Release Notes (PDF) summarizes the number of missing health areas by province based on the DHIS2 database downloaded in February and July 2026. While health zone boundaries remain relatively stable, the number of health areas has changed substantially over the past few years. These discrepancies are partly explained by the dynamic redefinition of health area boundaries, which aims to reduce their size and reconfigure their spatial extent. This dataset should be considered operational; it has not been fully validated by government authorities. Its production is part of ongoing work, and additional edits and refinements are expected in future releases.CIESIN, Columbia University, and its co-authors follow procedures designed to ensure that data disseminated by the project are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact CIESIN, info@ciesin.columbia.edu. CIESIN, Columbia University, its co-authors, and their sponsors do not guarantee the accuracy, reliability, or completeness of any data provided. We provide these data without warranty of any kind whatsoever, either expressed or implied, and shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided.</DIV>9720EPSG6.2(3.0.1)GRID3_COD_health_areas_v9_0Feature Class9720OBJECTIDOBJECTIDOID800Internal feature number.EsriSequential unique whole numbers that are automatically generated.SHAPEShapeGeometry000Feature geometry.EsriCoordinates defining the features.payspaysString214748364700iso3iso3String214748364700provinceprovinceString214748364700prov_uidprov_uidString214748364700antenneantenneString214748364700zonesantezonesanteString214748364700zs_uidzs_uidString214748364700as_uidas_uidString214748364700grid3idgrid3idString214748364700datedateString214748364700edit_paredit_parString214748364700asnom_altasnom_altString214748364700source_acronymsource_acronymString214748364700sourceidsourceidString214748364700airesanteairesanteString214748364700Shape_LengthShape_LengthDouble800Length of feature in internal units.EsriPositive real numbers that are automatically generated.Shape_AreaShape_AreaDouble800Area of feature in internal units squared.EsriPositive real numbers that are automatically generated.SimpleFALSE9720TRUEFALSE