Roads in Rwanda from the Digital Chart of the World

DIVA-GIS's admin0 file of Rwanda

This dataset visualizes health facilities throughout Rwanda, including Hospitals (District, Provincial, Referral, and National Referral), Health Posts (primary and secondary), and Health Centres.Data was sourced from the HDX website, found here: https://data.humdata.org/dataset/health-facilities-in-sub-saharan-africa?force_layout=desktop. This dataset was initially published in the Journal Nature in 2019. That publication can be found here: https://www.nature.com/articles/s41597-019-0142-2#Sec7This data set is just one of the many datasets on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Rwanda data available from WorldPop here.

This feature layer provides access to OpenStreetMap (OSM) point data of medical facilities for Africa, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes amenity features defined as a query against the hosted feature layer where the amenity value is any of 'hospital', 'clinic', 'doctors', or 'pharmacy'.In OSM, amenities are useful and important facilities for visitors and residents, such as hospitals and clinics. These features are identified with an amenity tag. There are thousands of different tag values used in the OSM database. In this feature layer, unique symbols are used for the most common amenity tags used for medical facilities.Zoom in to large scales (e.g. Neighborhood level or 1:20k scale) to see the amenity features display. You can click on a feature to get the name of the amenity. The name of the amenity will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this medical facilities layer displaying just one or two amenity types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. amenity is hospital), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri will publish a few such layers (e.g. Places of Worship, Schools, and Parking) that are ready to use, but not for every type of amenity.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

This template is used to compute urban growth between two land cover datasets, that are classified into 20 classes based on the Anderson Level II classification system. This raster function template is used to generate a visual representation indicating urbanization across two different time periods. Typical datasets used for this template is the National Land Cover Database. A more detailed blog on the datasets can be found on ArcGIS Blogs. This template works in ArcGIS Pro Version 2.6 and higher. It's designed to work on Enterprise 10.8.1 and higher.References:Raster functionsWhen to use this raster function templateThe template is useful to generate an intuitive visualization of urbanization across two images.Sample Images to test this againstNLCD2006 and NLCD2011How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual representation of urban sprawl across two images. Applicable geographiesThe template is designed to work globally.

This project file contains row research data and result data that have been used for the paper entitled "GIS-based multi-criteria analysis for Arabica coffee expansion in Rwanda" by Innocent Nzeyimana, Alfred E. Hartemink, Violette Geissen. http://dx.doi.org/10.6084/m9.figshare.1128594- See more at: http://figshare.com/preview/_preview/1128594#sthash.QkGK7m8Y.dpuf

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

Start Points Of The Rwanda Road Network, June 2016. The Points Define Start Of Classified National And District Roads)
This dataset falls under the category Planning & Policy.
It contains the following data: Start Points for National Roads and District Roads Class 1. The Road Network data is has been published in June 2016 and is maintained by the Rwanda Transport Development Agency RTDA. RTDA is part of the Ministry of Infrastructure in Rwanda. Other data from the Ministry is available on the Rwanda Infrastructure Geoportal.. The data can be accessed using the following URL / API Endpoint: https://services5.arcgis.com/8CmSuQoUmWaF7pX3/arcgis/rest/services/Road_start_point/FeatureServer
This dataset was scouted on 02/06/2022 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. See URL for data access and license information.

Rwanda Road Network - Land Marks)
This dataset falls under the category Planning & Policy.
It contains the following data: These Land Marks are part of the Rwanda Road Network. The Road Network data is has been published in June 2016 and is maintained by the Rwanda Transport Development Agency RTDA. RTDA is part of the Ministry of Infrastructure in Rwanda. Other data from the Ministry is available on the Rwanda Infrastructure Geoportal.. The data can be accessed using the following URL / API Endpoint: https://services5.arcgis.com/8CmSuQoUmWaF7pX3/arcgis/rest/services/Road_land_mark/FeatureServer
This dataset was scouted on 02/06/2022 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. See URL for data access and license information.

Rwanda lakes dataset as obtained from NISR.

This globe map is made from a flat map of the world on Mercator's projection. Explorers' routes are shown and the interiors of Africa and Australia are largely unknown at the time.More information on this map...

Map with solar irradiation and PV power potential in Rwanda. The GIS data stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png). The Global Solar Atlas is continuously updated. Provided GIS data layers include long-term yearly average of: (1) PVOUT – Photovoltaic power potential kWh/kWp GHI – Global horizontal irradiation kWh/m2 DIF – Diffuse horizontal irradiation kWh/m2 GTI – Global irradiation for optimally tilted surface kWh/m2 OPTA – Optimum tilt to maximize yearly yield ° DNI – Direct normal irradiation [kWh/m2]

Created as part of the USGS’s Africa Ecosystems Mapping project, the Africa Surficial Lithology layer maps the geology of Africa into 20 classes based bedrock type and the distribution of unconsolidated surface material. The data available through this layer map the key geological features that affect the distribution of plants and ecosystems in Africa. Dataset SummaryThis layer provides access to a 100m cell sized raster compiled from multiple sources. The data covers Africa, Madagascar, and other Islands near Africa. It was published by the U.S. Geological Survey and The Nature Conservancy in 2009.Link to source metadata What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data are available here. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks

Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF) sector.

This scene highlights layers for Johannesburg, South Africa available in ArcGIS to support your work in 3D. Use these layers in conjunction with your own layers to create new scenes focused on a specific topic or area of interest to you.What's in this scene? Terrain: Includes a global 3D terrain layer to provide elevation context. Your layers are placed in relationship to this terrainBasemap: Includes one of the ArcGIS Basemaps regularly used in in your mapping workScene Layers: Includes a layer of 3D buildings to help understand your data within the context of the built environment. The layer is a file type optimized for rendering in 3D.Create your own sceneOpen this item using the Open in Scene Viewer buttonChoose basemap: Select one of the ArcGIS basemaps from the Basemap GalleryAdd your own unique layersCreate slides to direct users to interesting places in your scene - See MoreSave and share the results of your work with others in your organization and the publicFor more see these helpful videosMashup 3D Content Using ArcGIS OnlineAuthor Web Scenes Using ArcGIS Online

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled so that the applications can restrict the displayed scenes to a specific date range. This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.For more information on each of the individual layers, see http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ; http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ; http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6

Since 2011, Esri Rwanda Ltd. is collecting points of Interest about Accommodation, Shopping, Tourism and Transport, Cooperatives, Restaurants, Landmarks, Monuments etc.The database contains some 6000 points, mainly in Rwanda, some in Burundi, DRC and Uganda. A special focus lies on the City of Kigali. Data collection continues and the database is updated on regular basis.This is a layer copy of Kaspar's Feature Service and it is used in this POI Web Map and this Web App with a Symbology based on the Symbol attribute.

Data for medium and high voltage transmission lines in Rwanda. The data were compiled for
the AICD study led by the World Bank. A variety of sources were consulted, including regional power pool documents
and maps from World Bank project documents.

Land Cover maps were developed for Green Houses gases Inventories to provide baseline data for Land use, land-use change and forestry (LULUCF) sector.