BDT GeoCatalogue
BDT GeoCatalogue (ITU-BDT-DNS-FNS)
Continents, countries, sea regions of the world.
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Dictionnarie Données - Infrastructure TICs
Dicionário de Dados de Infraestructura TIC
Connectivity Analysis Training - Sample Data
Disaster Connectivity Map (DCM) Connectivity is a map layer of performance, that compiles quality of service (Qos) measurements received via third party providers from software probes installed on end-user devices. The measurements received from SpeedChecker are ping (Ms), download speed (Mbps) and upload speed (Mbps), which are processed into point grid cells of 100-km, 10-km, 1-km, and 100-m which are displayed at different zoom levels depending on scale that the map is zoomed to. The default style (‘point_download’) displays download speed from the ‘download’ attribute field, but changing the style to the ‘ping’ (‘point_ping’) or ‘upload’ (‘point_upload’) fields will display these indicators. DCM Connectivity is a rolling worldwide baseline map layer of measurement data, in which all data received is added cumulatively and the median average for each measurement is recalculated each time it is updated. When the DCM is in active mode, the DCM Connectivity map layer is updated hourly. This map layer does not have a time dimension or time field, so cannot be filtered by time.
Diccionario de Datos Infraestructura TIC
Data Dictionaries for ICT infrastructure data
The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country’s economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population’s susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km. This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.
Mobile Coverage Explorer 2021 - Operator submission (MCE) 4G Network Coverage (Global, National - 260m)
Published by Collins Bartholomew in partnership with Global System for Mobile Communications (GSMA), the Mobile Coverage Explorer is a raster data representation of the area covered by mobile cellular networks around the world. Data_MCE global coverage has been sourced from the network operators and created from submissions made directly to Collins Bartholomew or to GSMA, from operators who provide roaming detail for inclusion in the GSMA’s Network Coverage Maps web application (https://www.gsma.com/coverage). The dataset is provided at Global and National level. Global datasets contain the merged global coverages with the following file naming convention. MCE_Global<Technology>_<DataRelease>, where technology is ‘4G’. At country level the rasters depict the network coverage in each country for each technology. Therefore a country is not defined by its border extent but rather by the coverage supplied by network operators with licences in that country. In the file naming convention, countries are identified using their ISO 3166-1 code as follows: MCE_<ISO2Code><Technology>_<DataRelease>. The cell value in the MCE rasters are as follows: 1 - strong signal 2 - variable signal strength This data is supplemented with coverage created from OpenCellID (please see link in the associated resources of this metadata).
Copyright in the Coverage Data belongs to Collins Bartholomew and the GSMA only. All screen images derived or generated from the Coverage Data should incorporate the following acknowledgement: Coverage Data © Collins Bartholomew and GSMA 2021. The Coverage Data is for research use only, and may not be used for commercial purposes; for clarification, the use of the data to support research that is partially funded by a corporate source does not constitute a commercial purpose. The Coverage Data may be represented in research results as static images, tables or text as necessary to convey research findings, and all screen images derived or generated from the Coverage Data should incorporate the following acknowledgement: Coverage Data © Collins Bartholomew and GSMA 2020. The data may not be redistributed in its raw form.
Data_MCE data is provided directly by operators to GSMA or Collins Bartholomew. However, not every operator has submitted data and so there are inevitable gaps in coverage. The following table shows the percentage number of operators in the latest release that have submitted their coverage data compared to the total number of operators who are members of the GSMA with licenses for each technology. Technology -- % Operator Submissions 4G -- 29% In 2020, 55 operators submitted new data for inclusion in the MCE datasets. Operators are asked to submit strong and variable signal strengths as defined below: Network Type -- Strong/Indoor -- Variable/Outdoor 4G (LTE) -- Greater than -- -105 dBm -105 to -120 dBm However, the data received from operators often does not include signal strength information or does not follow the above guidelines. Therefore, whilst the rasters retain the strong and variable distinctions, this inconsistency must be understood by the user. The cell values in the MCE rasters are as follows: 1 – strong signal strength 2 – variable signal strength Information on the individual networks included in the MCE and OCI data folders is provided as an Excel spreadsheet called Mobile Coverage Explorer Metadata.xlsx. The columns in the spreadsheet are as follows: OPERATOR_ID – a unique numerical identifier assigned to each network operator; NAME – the name of the network operator; CNTRY_ISO2 - the ISO 3166-1 country code used to describe the territory that the mobile network covers; COUNTRY - country name, in English, according to Collins Bartholomew’s naming policy; MCE_<Technology> - A date indicates that this network is included in the MCE coverage data. The date is when the operator’s coverage data was last updated. The format is DD/MM/YYYY. OCI_<Technology> - A ‘Y’ indicates that this network is included in the OCI coverage data. If an operator is not listed in the spreadsheet then they have not submitted coverage data to Collins Bartholomew and are not identified in the OpenCellID database.
ITU Transmission Networks (BBmaps) - Range to Fibre Node
Range to a Fibre Notes (Access Points) layers from the ITU Broadband Mapping programme (BBmaps) and research The layer displays 10, 25 and 50 km of distance to a Fibre Node. This is used to calculate the Broadband Capacity Indicator 7 (Percentage of population within reach of transmission networks) The population living within reach of transmission networks is calculated from network nodes (points) rather than routes (lines), because nodes are access points to the network. This is the equivalent of a motorway: a motorway may pass through areas of high and low population density, but the only means of accessing the motorway are at junctions
Scenarios of major production systems in Africa
CLIMAFRICA – Climate change predictions in Sub-Saharan Africa: impacts and adaptations
Future Networks and Spectrum Management Division