20260813093008001.0FALSECopyMultiple "D:\outputs\cod\consolidated_data_v9\older\GRID3_COD_v9_0_unpublished_20260810.gpkg\main.GRID3_COD_health_zones_unpublished_20260810 FeatureClass" D:\outputs\cod\consolidated_data_v9\GRID3_COD_health_zones_v9_0.gdb GRID3_COD_health_zones_unpublished_20260810 "main.GRID3_COD_health_zones_unpublished_20260810 FeatureClass GRID3_COD_health_zones_unpublished_20260810 #"Rename D:\outputs\cod\consolidated_data_v9\GRID3_COD_health_zones_v9_0.gdb\GRID3_COD_health_zones_unpublished_20260810 D:\outputs\cod\consolidated_data_v9\GRID3_COD_health_zones_v9_0.gdb\GRID3_COD_health_zones_v9_0 FeatureClassCalculateField GRID3_COD_health_zones_v9_0 pays "République démocratique du Congo" PYTHON3 "def clean_text(val): if isinstance(val, str): # 1. Strip leading/trailing whitespace val = val.strip() # 2. Replace multiple spaces with a single space import re val = re.sub(r' +', ' ', val) # 3. Return None (ArcGIS standard for Null) if empty or only spaces if not val or val.isspace(): return None return val" TEXT NO_ENFORCE_DOMAINS002GRID3_COD_health_zones_v9_012.19166631.277003-13.4590355.3920031file://\\CIESIN-CBNYL34-\C$\Users\mlukang\Downloads\CODv9\GRID3_COD_health_zones_v9_0.gdb-20260813T173200Z-1-001\GRID3_COD_health_zones_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>150000000500020260813142900002026081314395300ISO19139datasetexternalfc35357071b3fbb77098f751c60aeff50bde2225694aa386633d321aad49d310Center for Integrated Earth System Information (CIESIN)info@ciesin.columbia.eduTrueCenter for Integrated Earth System Information (CIESIN)20260813File Geodatabase Feature ClassGRID3 COD - Health Zones 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 Zones v9.0. New York: Columbia University. https://doi.org/10.7916/06qw-9y10. Accessed [DAY MONTH YEAR].externalfc35357071b3fbb77098f751c60aeff50bde2225694aa386633d321aad49d310Center for Integrated Earth System Information (CIESIN)info@ciesin.columbia.eduTrueCenter for Integrated Earth System Information (CIESIN)Democratic Republic of the CongoCODhealth zoneThe GRID3 COD - Health Zones v9.0 dataset consists of health zone boundaries with name and other related 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 Zones v.9. 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-zones-v8-0/about" target="_blank" STYLE="text-decoration:underline;">GRID3 COD - Health Zones v8.0</A>. The following changes were made:<UL><LI>The data for Haut-Katanga, Haut-Lomami, Ituri, Kwango, Lomami, Maniema, and Tanganyika provinces were updated</LI><LI>Health zone 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 Zones v9.0. New York: Columbia University. https://doi.org/10.7916/06qw-9y10. 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, CODhealth zone<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 (https://creativecommons.org/licenses/by/4.0) and specified in legal code (https://creativecommons.org/licenses/by/4.0/legalcode).Known Data Limitations and Disclaimer:The spatial accuracy of the health zone data depends on both the quality of the input datasets and the correctness of edits applied throughout the validation process. In general, field-collected data were assumed to represent the most accurate information available. Temporal mismatches may exist between the point datasets and the satellite imagery used for quality assurance. Spelling errors or naming inconsistencies may also occur due to colloquial variations in how locations are referenced in the field.Boundaries for three health zones in the Kwango Province (Boko, Kenge, and Kimbao) were generated using limited data inputs (no primary data collection took place in these health zones ; 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.The health zone boundaries produced by GRID3 aim to represent operational health service areas using a bottom-up approach, grounded in the realities faced by health workers and informed by service delivery activities such as bed net distribution campaigns. These boundaries reflect the practical limits of health areas—where services are actually delivered—rather than formal administrative divisions. Consequently, the resulting health zone boundaries do not systematically align with official administrative boundaries. For example, while the administrative boundary in the south of Maniema is formally defined by the 5th parallel south, the GRID3 health zone and provincial boundaries are derived from empirical field data collected and therefore do not follow a straight, geometrically defined line. Similar discrepancies exist along the southern boundary of Kinshasa with Kongo Central, Mai-Ndombe, and Équateur provinces.Although this dataset includes all 519 health zones present in the latest DHIS2 download, it should be considered operational. It has not yet undergone full validation by government authorities.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 13.5.0.57366<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 (https://creativecommons.org/licenses/by/4.0) and specified in legal code (https://creativecommons.org/licenses/by/4.0/legalcode).Known Data Limitations and Disclaimer:The spatial accuracy of the health zone data depends on both the quality of the input datasets and the correctness of edits applied throughout the validation process. In general, field-collected data were assumed to represent the most accurate information available. Temporal mismatches may exist between the point datasets and the satellite imagery used for quality assurance. Spelling errors or naming inconsistencies may also occur due to colloquial variations in how locations are referenced in the field.Boundaries for three health zones in the Kwango Province (Boko, Kenge, and Kimbao) were generated using limited data inputs (no primary data collection took place in these health zones ; 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.The health zone boundaries produced by GRID3 aim to represent operational health service areas using a bottom-up approach, grounded in the realities faced by health workers and informed by service delivery activities such as bed net distribution campaigns. These boundaries reflect the practical limits of health areas—where services are actually delivered—rather than formal administrative divisions. Consequently, the resulting health zone boundaries do not systematically align with official administrative boundaries. For example, while the administrative boundary in the south of Maniema is formally defined by the 5th parallel south, the GRID3 health zone and provincial boundaries are derived from empirical field data collected and therefore do not follow a straight, geometrically defined line. Similar discrepancies exist along the southern boundary of Kinshasa with Kongo Central, Mai-Ndombe, and Équateur provinces.Although this dataset includes all 519 health zones present in the latest DHIS2 download, it should be considered operational. It has not yet undergone full validation by government authorities.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>519EPSG6.2(3.0.1)GRID3_COD_health_zones_v9_0Feature Class519OBJECTIDOBJECTIDOID800Internal feature number.EsriSequential unique whole numbers that are automatically generated.ShapeShapeGeometry000Feature geometry.EsriCoordinates defining the features.payspaysString214748364700iso3iso3String214748364700provinceprovinceString214748364700prov_uidprov_uidString214748364700antenneantenneString214748364700zonesantezonesanteString214748364700zs_uidzs_uidString214748364700datedateString25500edit_paredit_parString25500sourceidsourceidString214748364700source_acronymsource_acronymString214748364700grid3idgrid3idString25500Shape_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.SimpleFALSE519TRUEFALSE