We use real-time data on vessel movements—Automatic Identification System (AIS) signals of vessels—as our primary data source.Source:Arslanalp, S., Koepke, R., & Verschuur, J. Tracking Trade from Space: An Application to Pacific Island Countries. IMF Working Paper No. 2021/225. https://www.imf.org/en/Publications/WP/Issues/2021/08/20/Tracking-Trade-from-Space-An-Application-to-Pacific-Island-Countries-464345Concepts:Ports: Full list of ports we cover and associated additional information can be found here. Port Calls: a port call is defined when a ship enters the port boundary. Port calls with a turnaround time of less than 5 hours and no draft change between the current and next port are excluded to filter out vessels in transit (i.e., vessels that visit a port for reasons other then loading and discharging cargo, such as anchoring, refueling or provisioning). Trade Estimates: as described in the paper, we use the vessel information (length, width, draft, capacity, block coefficient) to estimate the payload (or utilization rate) of the vessel when entering and leaving the port boundary. The change in the vessel payload (in percentage points) multiplied by the vessel’s deadweight tonnage (the maximum carrying capacity) in metric tons is the resulting trade flow (either import or export) in metric tons.Ship Categories: all the indicators are available by 5 main ship categories: container, dry bulk, general cargo, ro-ro and tanker.Variables: date: all port call dates are expressed in Coordinated Universal Time (UTC), a standard used to set all time zones around the world. year: as extracted from date. month: month 1-12 extracted from date. day: day 1-31 extracted from date. portid: port id. Full list of ports and associated additional information can be found here. portname: port name. country: country the port resides in. ISO3: ISO 3-letter country code of the port. portcalls_container: number of container ships entering the port at this date. portcalls_dry_bulk: number of dry bulk carriers entering the port at this date. portcalls_general_cargo: number of general cargo ships entering the port at this date. portcalls_roro: number of ro-ro ships entering the port at this date. portcalls_tanker: number of tankers entering the port at this date. portcalls_cargo: total number of ships (excluding tankers) entering the port at this date. This is the sum of portcalls_container, portcalls_dry_bulk, portcalls_general_cargo and portcalls_roro. portcalls: total number of ships entering the port at this date. This is the sum of portcalls_container, portcalls_dry_bulk, portcalls_general_cargo, portcalls_roro and portcalls_tanker. import_container: total import volume (in metric tons) of all container ships entering the port at this date. import_dry_bulk: total import volume (in metric tons) of all dry bulk carriers entering the port at this date. import_general_cargo: total import volume (in metric tons) of all general cargo ships entering the port at this date. import_roro: total import volume (in metric tons) of all ro-ro ships entering the port at this date. import_tanker: total import volume (in metric tons) of all tankers entering the port at this date. import_cargo: total import volume (in metric tons) of all ships (excluding tankers) entering the port at this date. This is the sum of import_container, import_dry_bulk, import_general_cargo and import_roro. import: total import volume (in metric tons) of all ships entering the port at this date. This is the sum of import_container, import_dry_bulk, import_general_cargo, import_roro and import_tanker. export_container: total export volume (in metric tons) of all container ships entering the port at this date. export_dry_bulk: total export volume (in metric tons) of all dry bulk carriers entering the port at this date. export_general_cargo: total export volume (in metric tons) of all general cargo ships entering the port at this date. export_roro: total export volume (in metric tons) of all ro-ro ships entering the port at this date. export_tanker: total export volume (in metric tons) of all tankers entering the port at this date. export_cargo: total export volume (in metric tons) of all ships (excluding tankers) entering the port at this date. This is the sum of export_container, export_dry_bulk, export_general_cargo and export_roro. export: total export volume (in metric tons) of all ships entering the port at this date. This is the sum of export_container, export_dry_bulk, export_general_cargo, export_roro and export_tanker.How to Cite? These datasets are based on raw AIS data from the United National Global Platform and estimates by the PortWatch team based on the methodology described in the paper. The recommended citation is: “Sources: UN Global Platform; IMF PortWatch (portwatch.imf.org).”About AIS Data The UN has made available satellite-based AIS data through the UN Global Platform (UNGP) to national and international agencies that are members to the UN-CEBD (UN, 2021). The platform contains live data and global archive data from December 1, 2018. AIS data at the UNGP are provided by Spire, which collects AIS messages from two different satellite constellations, with more than 65 AIS equipped satellites. Spire complements this information with data collected by FleetMon through terrestrial receivers. There are several challenges with using AIS data. First, ships can turn off their AIS transponder to avoid being detected. Strictly speaking, this is not legal and is mostly limited to fishing vessels conducting illegal fishing or oil tankers circumventing international sanctions, usually in international waters. It is not common for container and other cargo ships (which is the focus of our study) entering a country’s port. In fact, in most jurisdictions, port authorities make it mandatory for ships entering a port to keep their AIS transponders on at all times for the safety all vessels in the port. Second, the AIS data do not have information about the ship’s carrying capacity (i.e., deadweight tonnage) and maximum draft. To fill this gap, we use ship registry databases from FleetMon and IHS Markit (the latter is available from the UNGP), with information for around 120,000 vessels. Finally, a potentially more serious challenge with AIS data is that some information is entered manually and, hence, may have human errors. This is expected as AIS was intended originally for safety at sea, not for producing statistics. For our purposes, the key issue is that the crew may not always update the draft information after a ship leaves the port. The draft is the vertical distance between the waterline and the bottom of the ship’s hull and is a measure of the payload of the vessel. However, our algorithm uses techniques to address this issue. Particularly, the missing information can be backtracked or imputed in most cases, given the wealth of information in the AIS data (Arslanalp, Koepke, Verschuur, 2021). The AIS was originally developed by the International Maritime Organization (IMO) in 2004 as an outcome of amendments to the International Convention SOLAS (Safety of Life at Sea) in 2002. It is a self-reporting system, which allows vessels to periodically broadcast their identity, navigation, position data and other characteristics. The AIS has been made compulsory for all international commercial ships with gross tonnage of 300 or more tons (i.e., virtually all commercial ships) and all passenger ships regardless of size. There are three main types of information in AIS messages. AIS broadcasts voyage-related information (including ship location, speed, course, heading, rate of turn, destination, draft, and estimated arrival time), static information (including ship ID, ship type, ship size and dimensions), and dynamic information. Dynamic information such as the positional aspects (latitude and longitude) is automatically transmitted, depending on the vessels’ speed and course. The signals can be picked up by satellite or terrestrial receivers. For ships in open seas, however, the signals can only be picked up by satellite receivers as terrestrial receivers typically cover only about 15–20 nautical miles from the coast. For island states, satellite data tend to be much more reliable as the coverage of terrestrial receivers can be low (or nonexistent) for these smaller countries. Terrestrial receivers are useful for congested ports where congestion may make it difficult for satellites to capture all emitted messages. Additional information on AIS data can be found in Arslanalp et al. (2019), Verschuur et al. (2020), and the UN’s AIS Handbook. References: Arslanalp, S., Koepke, R., & Verschuur, J. Tracking Trade from Space: An Application to Pacific Island Countries. IMF Working Paper No. 2021/225. https://www.imf.org/en/Publications/WP/Issues/2021/08/20/Tracking-Trade-from-Space-An-Application-to-Pacific-Island-Countries-464345 AIS Handbook https://unstats.un.org/wiki/display/AIS/AIS+Handbook

Un Global Trading Limited Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Original site: https://fieldmaps.io/data/population

Population statistics using data from WorldPop Unconstrained Individual Countries to create a complete global coverage population raster. Results are aggregated to humanitarian edge-matched boundaries, adjusted so that ADM0 totals match those of the 2024 projections in the United Nations World Population Prospects.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

Concepts:Chokepoint: the full list of chokepoints we cover and associated additional information. Vessel Count: we use real-time data on vessel movements—Automatic Identification System (AIS) signals of vessels— to calculate the yearly average number of ships passing through the chokepoint, over the timeframe 2019-2024. These yearly averages are available by 5 main ship categories (container, dry bulk, general cargo, ro-ro and tanker) plus the total. Top Industries: dominant traded industries based on the volume of goods estimated to flow through the chokepoint. Note that this is not based on official statistics.Variables: portid: chokepoint unique id. portname: chokepoint name. lat: latitude of the chokepoint location.lon: longitude of the chokepoint location.vessel_count_total: yearly average number of all ships transiting through the chokepoint. Estimated using AIS data between 2019-2024. The total is calculated over the sum of vessel_count_container, vessel_count_dry_bulk, vessel_count_general_cargo, vessel_count_roro and vessel_count_tanker.vessel_count_container: yearly average number of containers transiting through the chokepoint. Estimated using AIS data between 2019-2024. vessel_count_dry_bulk: yearly average number of dry bulk carriers transiting through the chokepoint. Estimated using AIS data between 2019-2024. vessel_count_general_cargo: yearly average number of general cargo ships transiting through the chokepoint. Estimated using AIS data between 2019-2024. vessel_count_roro: yearly average number of Ro-Ro ships transiting through the chokepoint. Estimated using AIS data between 2019-2024. vessel_count_tanker: yearly average number of tankers transiting through the chokepoint. Estimated using AIS data between 2019-2024. industry_top1: first dominant traded industries based on the volume of goods estimated to flow through the chokepoint.industry_top2: second dominant traded industries based on the volume of goods estimated to flow through the chokepoint.industry_top3: third dominant traded industries based on the volume of goods estimated to flow through the chokepoint.How to Cite?These datasets are based on raw AIS data from the United National Global Platform and estimates by the PortWatch team based on the methodology described in the paper. The recommended citation is: “Sources: UN Global Platform; IMF PortWatch (portwatch.imf.org).”

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

To help countries identifying the main systemic ports of interest, we have classified ports into those that are globally systemic (i.e., important for global supply-chains), regionally systemic (e.g., essential for regional trade integration), and domestically systemic (e.g., important for domestic supply-chains).In particular, global systemically important ports (G-SIPs) are the world’s largest ports that cumulatively handle more than 50 percent of global trade value. Regional systemically important ports (R-SIPs) are the ports that handle at least five percent of the imports of at least four countries. Domestic systemically important ports (D-SIPs) are the ports that cumulatively handle more than 80 percent of a country’s imports.Concepts:Port: the full list of ports we cover and associated additional information. Vessel Count: we use real-time data on vessel movements—Automatic Identification System (AIS) signals of vessels— to calculate the yearly average number of ships flowing through the port, over the timeframe 2019-2024. These yearly averages are available by 5 main ship categories (container, dry bulk, general cargo, ro-ro and tanker) plus the total. Top Industries: dominant traded industries based on the volume of goods estimated to flow through the port. Note that this is not based on official statistics. The 13 economic industries are based on the International Convention on the Harmonized Commodity Description and Coding System (HS Convention) and align to the 21 HS Sections as per the table below.NameHS SectionAnimal & Animal Products1Vegetable Products2Prepared Foodstuffs & Beverages3+4Mineral Products5Chemical & Allied Industries6Plastics, Rubber, Leather7+8Wood & Wood Products9+10Textiles & Footwear11+12Stone & Glass13+14Metals15Machinery & Electrical Equipment16+18Vehicles & Equipment17Miscellaneous19+20+21Port's Share of Economy's Maritime Trade: based on AIS-derived imports and exports at the port-level with respect to country totals during 2019-24.Variables: portid: port unique id. portname: port name. lat: latitude of the port location.lon: longitude of the port location.vessel_count_total: yearly average number of all ships calling at the port. Estimated using AIS-derived portcalls data between 2019-2023. The total is calculated over the sum of vessel_count_container, vessel_count_dry_bulk, vessel_count_general_cargo, vessel_count_roro and vessel_count_tanker.vessel_count_container: yearly average number of containers calling at the port. Estimated using AIS-derived portcalls data between 2019-2024.vessel_count_dry_bulk: yearly average number of dry bulk carriers calling at the port. Estimated using AIS-derived portcalls data between 2019-2024.vessel_count_general_cargo: yearly average number of general cargo ships calling at the port. Estimated using AIS-derived portcalls data between 2019-2024.vessel_count_roro: yearly average number of Ro-Ro ships calling at the port. Estimated using AIS-derived portcalls data between 2019-2024.vessel_count_tanker: yearly average number of tankers calling at the port. Estimated using AIS-derived portcalls data between 2019-2024.industry_top1: first dominant traded industries based on the volume of goods estimated to flow through the port.industry_top2: second dominant traded industries based on the volume of goods estimated to flow through the port.industry_top3: third dominant traded industries based on the volume of goods estimated to flow through the port.share_country_maritime_import: based on AIS-derived imports volume at the port-level with respect to country totals during 2019-24.share_country_maritime_export: based on AIS-derived exports volume at the port-level with respect to country totals during 2019-24.How to Cite?These datasets are based on raw AIS data from the United National Global Platform and estimates by the PortWatch team based on the methodology described in the paper. The recommended citation is: “Sources: UN Global Platform; IMF PortWatch (portwatch.imf.org).”

The UN Biodiversity Lab is an online platform that allows policymakers and other partners to access global data layers, upload and manipulate their own datasets, and query multiple datasets to provide key information on the Aichi Biodiversity Targets and nature-based Sustainable Development Goals.

The core mission of the UN Biodiversity Lab is three-fold: to build spatial literacy to enable better decisions, to use spatial data as a vehicle for improved transparency and accountability, and to apply insights from spatial data across sectors to deliver on the Convention on Biological Diversity and the 2030 Agenda for Sustainable Development.

We document here the occurrences of invasive species uploaded to the InvBasa platform. This platform was created for the registration of alien and invasive species occurrences in Colombia. The project was born as a response to the problematic introduction of Basa fish (Pangasianodon hypophthalmus)to the Magdalena river basin in Colombia. Researchers from the Instituto de Ciencias Naturales (ICN) and Fundación Humedales documented this problematic and together with the Biodiversity Informatics Program of the ICN, developed the platform and mobile app. Invbasa´s goals are to register direct observations of alien species in the field by citizen scientists.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

This layer contains airports locations. This dataset brings together various public sources such as OpenStreetMap or ourairports.com with WFP logistics information. It is updated regularly with inputs from WFP aviation unit but also from many partners through the Logistics Cluster and the Logistics Capacity Assessment (LCA: dlca.logcluster.org). The information is compiled at a global level by the Emergency and Preparedness Geospatial Information Unit at the World Food Programme (WFP) Headquarters in Rome, Italy.

This dataset is at a global scale and is updated country by country. The last update date can be retrieved from the data of the country of interest.

Feel free to contribute to this dataset by contacting hq.gis@wfp.org.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.