This spatial data set provides information pertaining to the known land use and disturbance history for lands within the March 2018 administrative boundary of the North Unit of Badlands National Park, South Dakota. Land use and disturbance history presented here are not a comprehensive record of all potential land uses and disturbances but rather a record of known and documented land uses and disturbances based on the best available information. Additional land use and disturbance information may exist but due to time and budget constraints may not have been discovered during the research and development of this data set. The information in this data set was gathered through a variety of sources including but not limited to communication with National Park Service staff, historical documents, land patent records, online information searches, aerial imagery, historical photographs, and spatial data repositories. Data are presented as polygon features, each with a unique area number, its total area (in acres) and the percent of the park the area covers. Polygons were delineated based on existing GIS layers in park records, or, when these were not available, they were digitized using ESRI Arc Map 10.5.1 in conjunction with USDA Natural Resource Conservation Service NAIP orthoimagery based on written descriptions of locations (e.g., Township and Range Survey System) or maps in information sources. History of each polygon is described for one or more of five land use or disturbance types: cultivation, structures, excavation, grazing, and other disturbance. Each land use or disturbance type has six attribute fields. The first field indicates if there is evidence of the land use or disturbance type in the polygon. "Yes" indicates there is evidence and a

Feature layer containing authoritative parcel polygons for Sioux Falls, South Dakota. The City of Sioux Falls is responsible for the geometry of each parcel and shares parcel attribute responsibilities with Minnehaha County and Lincoln County. Note: The legal assessment date is November 1st of every year. Please see Minnehaha County or Lincoln County for more information. Click here for Land Use Activity Code descriptions

This data set depicts federal lands having restrictions on access or activities -- that is, lands mangaed by the National Park Service, Defense Department, or Energy Department -- in western North America. The data set was created by reformatting and merging state- and province-based ownership data layers originally acquired from diverse sources (including state GAP programs, USBLM state offices and other sources). For each original dataset 3 additional fields, "Pub_Pvt", "CA_OWN", and "SOURCE" were added and populated based on the specific ownership information contained in the source data. The original coverages were then merged based on the "CA_OWN" field. Finally, NPS, DOD, and DOE lands were selected out of the ownership layer. All work was completed in AcMap 8.3. This product and all source data are available online from SAGEMAP: http://sagemap.wr.usgs.gov.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Application containing parcel, address, and zoning information for Sioux Falls, South Dakota.The City of Sioux Falls Parcel Finder provides access to interactive parcel and address information such as parcel id, owner name, legal description, land use, building photos, zoning, preliminary information, and more. In addition, Parcel Finder has the following features: Search by address, intersection, county parcel id, city parcel id, and owner name. Ability to select features. Selected features can be exported to a csv, or other file types. Layers in the layer list can be turned on and off, and reordered. The layer list, by default, contains the address layer that can be turned on to label the house/building number. Add data from the City of Sioux Falls data repository. Add data featuring Demographic and Lifestyle topics. Measuring tools are back! Drawing tools, allowing you to customize your map, suitable for printing. Expanding printing options.

🇺🇸 United States English Application containing parcel, address, and zoning information for Sioux Falls, South Dakota.The City of Sioux Falls Parcel Finder provides access to interactive parcel and address information such as parcel id, owner name, legal description, land use, building photos, zoning, preliminary information, and more. In addition, Parcel Finder has the following features: Search by address, intersection, county parcel id, city parcel id, and owner name. Ability to select features. Selected features can be exported to a csv, or other file types. Layers in the layer list can be turned on and off, and reordered. The layer list, by default, contains the address layer that can be turned on to label the house/building number. Add data from the City of Sioux Falls data repository. Add data featuring Demographic and Lifestyle topics. Measuring tools are back! Drawing tools, allowing you to customize your map, suitable for printing.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

The Statewide Parcel Project was funded by the 66th Legislative Assembly, appropriating funds through House Bill 1021. During the 2019-2021 Biennium, AppGeo aggregated parcel boundary and tax roll data from 51 counties and developed parcel boundary data for Adams and Benson counties.Beginning in the 2021-2023 Biennium the parcel dataset will be maintained on a regular basis using funding provided by the 67th Legislative Assembly and leveraging the existing North Dakota GIS Hub infrastructure. Data maintenance will consist of parcel boundary and tax roll data being submitted by counties and their vendors and/or harvested by the GIS Hub. The frequency of data updates will vary by county, ranging from monthly to yearly. To obtain the most recent data for a county, that county should be contacted.IMPORTANT: If you wish to download this entire dataset rather than stream the data as a web service, we suggest you use the 'Download Zipped fGDB' link. The other download options may take several minutes to generate.IMPORTANT: Please see the metadata for Limitations of UseOTHER NOTES:Rural parcels are the focus of the Statewide Parcel Program. In the future, city parcels may be filled in where currently missing.There is a one-to-many relationship between the parcels feature class and the tax roll table, based on the UniqueGISID attribute in each dataset.What appears to be missing parcel boundaries may be due to things such as:State and federal lands - refer to the GIS Hub Portal for these datasetsCities - some cities are included in the data submitted by the counties and their vendors, some are notInvalid geometry - issues with the data are flagged and stored in the 'Invalid Geometry' feature class

These data are part of a larger USGS project to develop an updated geospatial database of mines, mineral deposits and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, are currently being digitized on a state-by-state basis from the 7.5-minute (1:24,000-scale) and the 15-minute (1:48,000 and 1:62,500-scale) archive of the USGS Historical Topographic Maps Collection, or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. To date, the compilation of 500,000-plus point and polygon mine symbols from approximately 67,000 maps of 22 western states has been completed: Arizona (AZ), Arkansas (AR), California (CA), Colorado (CO), Idaho (ID), Iowa (IA), Kansas (KS), Louisiana (LA), Minnesota (MN), Missouri (MO), Montana (MT), North Dakota (ND), Nebraska (NE), New Mexico (NM), Nevada (NV), Oklahoma (OK), Oregon (OR), South Dakota (SD), Texas (TX), Utah (UT), Washington (WA), and Wyoming (WY). The process renders not only a more complete picture of exploration and mining in the western U.S., but an approximate time line of when these activities occurred. The data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. The data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.

This dataset is a digital version of the MAPA DE VEGETAO DO BRASIL (IBGE, 1988), which was digitized at the U.S. Geological Survey's (USGS) EROS Data Center, Sioux Falls, South Dakota. Data are classed into three different classifications: a 59 major vegetation classification map, a 13-class generalization classification map, and a 6-class overprint classification map. Generalized classes were determined from the original subheadings on the original published map which was used use to digitize the data set. Overprint classes are used to describe areas which are best described by a combination of two classes, one from the major classification and the other from the overprint classification. Overprint classes were originally displayed with special symbols printed over major classification shades on the original map. For instance, a series of stipple marks or hash marks may have been printed on top of major class shades to represent areas which had characteristics from the major classification as well as the overprint classification. The overprint classifications are also referred to as subclasses in the original data. Additional information about this data can be obtained from The Woods Hole Research Center, Woods Hole, Massachusetts via their URL at "http://terra.whrc.org/science/tropfor/setLBA.htm".

Digital images of these data are also available from the EOS-WEBSTER Image Gallary. Please see the Data Tab at the following URL: "http://eos-earthdata.sr.unh.edu/". These images can be downloaded as JPEGs and used directly in a document or printed.

These data were modified, as described in documentation provided when data are ordered from EOS-WEBSTER, from the original data. Original data were downloaded from the Woods Hole Research Center Website ("http://terra.whrc.org/science/tropfor/setLBA.htm"). Original author of these data is cited as:

This version of the map was made available by and digitized by: Dr. Norman Bliss, Principal Scientist Land Sciences Section Eros Data Center Sioux Falls, South Dakota 57198

This image service contains high-resolution land cover data for the states of Nebraska, South Dakota, and North Dakota. These data are a digital representation of land cover derived from 1-meter aerial imagery from the USDA National Agriculture Imagery Program (NAIP.) The year of NAIP used for each state was 2014.Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification. These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap. These data can be downloaded by county at the Forest Service Research Data Archive. Nebraska: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0038 South Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0068 North Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0067 A Kansas dataset was also developed using the same methods and is located at: Kansas data download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0052 Kansas map service: https://data-usfs.hub.arcgis.com/documents/high-resolution-tree-cover-of-kansas-2015-map-service/exploreThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This spatial data set provides information pertaining to the known land use and disturbance history for lands within the March 2018 administrative boundary of Wind Cave National Park, South Dakota. Land use and disturbance history presented here are not a comprehensive record of all potential land uses and disturbances but rather a record of known and documented land uses and disturbances based on the best available information. Additional land use and disturbance information may exist but due to time and budget constraints may not have been discovered during the research and development of this data set. The information in this data set was gathered through a variety of sources including but not limited to communication with National Park Service staff, historical documents, land patent records, online information searches, aerial imagery, historical photographs, and spatial data repositories. Data are presented as polygon features, each with a unique area number, its total area (in acres) and the percent of the park the area covers. Polygons were delineated based on existing GIS layers in park records, or, when these were not available, they were digitized using ESRI Arc Map 10.5.1 in conjunction with USDA Natural Resource Conservation Service NAIP orthoimagery based on written descriptions of locations (e.g., Township and Range Survey System) or maps in information sources. History of each polygon is described for one or more of five land use or disturbance types: cultivation, structures, excavation, grazing, and other disturbance. Each land use or disturbance type has six attribute fields. The first field indicates if there is evidence of the land use or disturbance type in the polygon. "Yes" indicates there is evidence and a

The Web-enabled Landsat Data (WELD) project is collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and academic partner South Dakota State University Geographic Information Science Center of Excellence. It is funded by NASA's Making Earth System Data Records for Use in Research Environments, with significant USGS cost sharing.

This data set contains raw unregistered Landsat digital data covering most of Alaska. Data obtained from EROS Data Center in Sioux Falls, South Dakota. Data acquired from 1980 and is ongoing. Some Landsat scenes date back to 1972. The data set currently has 585 records with a growth chart at 5-10 records per year. The amount of storage required varies by medium used or full scene or subscene selection; the file structure is sequential. Spatial referencing of data is by 57 x 59 meter grid cell size-MSS data. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and custom formats are available. Limited documentation is available. The data is organized in 7 1/2 ' or 15 ' quads. Data is used for false color composites, land cover analysis, geologic analysis, hydrogeologic analysis, land use planning, basis for update of topographic maps, production of image maps.

Originally produced by the Farm Security Administration, these are georeferenced aerial images from Morton County, North Dakota. Historic print images housed at the Mandan, North Dakota ARS Long-Term Agricultural Research facility were digitized, georeferenced, and processed for use in both professional and consumer level GIS applications, or in photo-editing applications. The original images were produced by the Farm Security Administration to monitor government compliance for farm land agreements. Current applications include assessing land use change over time with regard to erosion, land cover, and natural and man-made structures. Not for use in high precision applications. Resources in this dataset:Resource Title: 1938_AZY_3_89. File Name: 1938_AZY_3_89_0.zipResource Description: Contains IIQ, JPG, OVR, XML, AUX, and TIF files processed in ArcMap / ArcGIS that can be used in ArcGIS applications, or in other photo or geospatial applications. Resource Title: 1938 Mosaic Index. File Name: 1938_mosaic_index_1.zipResource Description: This is the index key for the 1938 Mandan aerial images from Morton County, ND. To find the geographic location for each uploaded 1938 image, consult this map. File titles are arranged as follows: Year_Area_Roll_Frame. The mosaic map displays Roll_Frame coordinates to correspond to these images. Contains TIF, OVR, JPG, AUX, IIQ, and XML files. Resource Title: 1938_AZY_5_113. File Name: 1938_AZY_5_113_2.zipResource Description: Contains IIQ, JPG, OVR, XML, AUX, and TIF files processed in ArcMap / ArcGIS.

This resource is a repository of the annual subsurface drainage (so-called "Tile Drainage") maps for the Bois de Sioux Watershed (BdSW), Minnesota and the Red River of the North Basin (RRB), separately. The RRB maps cover a 101,500 km2 area in the United States, which overlies portions of North Dakota, South Daokta, and Minnesota. The maps provide annual subsurface drainage system maps for recent four years, 2009, 2011, 2014, and 2017 (In 2017, the subsurface drainage maps including the Sentinel-1 Synthetic Aperture Radar as an additional input are also provided). Please see Cho et al. (2019) in Water Resources Research (WRR) for full details.

Map Metadata (Proj=longlat +datum=WGS84) Raster value key: 0 = NoData, masked by non-agricultural areas (e.g. urban, water, forest, or wetland land) and high gradient cultivated crop areas (slope > 2%) based on the USGS National Land Cover Dataset (NLCD) and the USGS National Elevation Dataset 1 = Undrained (UD) 2 = Subsurface Drained (SD)

Preferred citation: Cho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55. https://doi.org/10.1029/2019WR024892

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu)

State Government Tax Collections, Property Taxes in North Dakota was 5818.00000 Thous. of $ in January of 2024, according to the United States Federal Reserve. Historically, State Government Tax Collections, Property Taxes in North Dakota reached a record high of 5818.00000 in January of 2024 and a record low of 1133.00000 in January of 1988. Trading Economics provides the current actual value, an historical data chart and related indicators for State Government Tax Collections, Property Taxes in North Dakota - last updated from the United States Federal Reserve on November of 2025.

This product is a GeoTIFF image illustrating the Normalized Difference Vegetative Index (NDVI) across the conterminous United States, as well as subsets for the states of North Dakota, South Dakota, Montana, Wyoming, and Idaho. In order to provide a product that is easily interpreted by our users, the Upper Midwest Aerospace Consortium (UMAC) acquires a weekly NDVI product and a Departure from NDVI average product from the EROS Data Center and converts these products into a GeoTIFF image. This GeoTIFF image illustrates for the user the relative greenness of an area and the comparison of a particular week's index to the average of that week historically. The values in the image are no longer linked to real data values, but are rather simply color coded for illustrative purposes. The dataset covers the year 2000 with some gaps.

This series of three-period land use land cover (LULC) datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources (exception is Tchad at 4 kilometers). To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification†(Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop ArcMap software. Citation: Trochain, J.-L., 1957, Accord interafricain sur la définition des types de végétation de l’Afrique tropicale: Institut d’études centrafricaines.