Abstract

The AfDB's Africa Infrastructure Knowledge Program

The Africa Infrastructure Knowledge Program (AIKP) is a successor program to the Africa Infrastructure Country Diagnostic (AICD) which grew out of the pledge by the G8 Summit of 2005 at Gleneagles to increase substantially ODA assistance to Africa, particularly the infrastructure sector, and the subsequent formation of the Infrastructure Consortium for Africa (ICA). This was against the background that sub-Saharan Africa (SSA) suffers from a weak basic infrastructure base, and that this was a key factor in the SSA region not realizing its full potential for economic growth, international trade, and poverty reduction.

Since 2010, the African Development Bank (AfDB) has taken over leadership for managing the infrastructure database and knowledge work under its Africa Infrastructure Knowledge Program (AIKP). The AIKP builds on the AICD but has a longer-term perspective to provide a platform for: (i) regular updating of the infrastructure database on African countries; (ii) defining and developing analytic knowledge products to guide policy and funding decisions and to inform development policy and program management activities; and (iii) building infrastructure statistical capacity in the region. The AIKP is therefore intended to provide a sustainable framework for generating reliable and timely data on the various infrastructure sectors to guide policy design, monitoring and evaluation and to improve efficiency and delivery of infrastructure services.

The aikp collect a comprehensive data on the infrastructure sectors in Africa-covering power, transport, irrigation, water and sanitation, and information and communication technology (ICT), also the institutional and fiscal issues that cut across infrastructure performance and spending. The institutional issues relate to national level reforms and regulations as well as provider level governance structures in the utility infrastructure sector (energy, water, telecommunications), while the fiscal issues relate to spending and financing of infrastructure.

Geographic coverage

All African Countries

Analysis unit

Pays

Kind of data

Données administratives [adm]

Mode of data collection

Interview de groupe [foc]

Research instrument

Data collection is organized around a series of data templates that are made available for download online or distributed by the Statistical Department of the African Development Bank (AfDB-SD). these templates are organised by sector: Fiscal template: - Fiscal Data Template A: Jurisdictional responsibilities in infrastructure service delivery -national level - Fiscal Data Template B: Special funds financing infrastructure service delivery -national level - Fiscal Data Template C: Basic Budgetary Institutions -national level - Fiscal Data Template D: Budget Cycle, national level - Fiscal Data Template E. Macroeconomic parameters for budgetary context of infrastructure spending - Fiscal Data Template F. Functional and economic classification of government expenses - Fiscal Data Template G. Financial data of public operators Institutional template: - Institutional Data Template A: Reform variables - national level - Institutional Data Template B: Regulation variables - national level - Institutional Data Template C: Governance variables - utility level Power template: - Power Data Template A: National Level Institutions - Power Data Template B: National Level Data Variables - Power Data Template C: Utility Level Data Variables WSS template: - WSS Data Template A: National Level Institutions - WSS Data Template B: Utility Level Data Variables ICT template: - ICT Data Template A: National Level Institutions - ICT Data Template B: National Level Data Variables - ICT Data Template C: National Level Data Variables - ICT Data Template D: Utility Level Data Variables - ICT Data Template E: Operator level - Main national fixed line service provider - ICT Data Template F: Operator level - Largest mobile operator - ICT Data Template G: Operator level - Largest Internet Service Provider Roads template: - Roads Data Template A: Institutional variables – national level - Roads Data Template B: Technical variables – link by link Rails template: - Railways Data template A: Integrated national railway - Railway Data template B: Rail infrastructure company - Railway Data template C: Train operating company - Data template D: Binational railway - Data template E: Dedicated minerals railway Ports template: - Ports Data Template A: Institutional variables - national level - Ports Data Template B: Data variables - ports level Air template: Air Transport Template A: Collection from CAA or Main International Airport

Subtypes:Standard: A typical gravity main.Inverted Siphon: A gravity main that siphons stormwater to flow under an obstruction.Other: All other types of gravity mains.Attributes: Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes per operations of the Los Angeles County Flood Control District are listed below:AttributeDescriptionASBDATEThe date the design plans were approved "as-built" or accepted as "final records".CROSS_SECTION_SHAPEThe cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.DIAMETER_HEIGHTThe diameter of a round pipe or the height of an underground box or open channel.DWGNODrain Plan Drawing Number per LACFCD NomenclatureEQNUMAsset No. assigned by the Department of Public Works' (in Maximo Database).MAINTAINED_BYIdentifies, to the best of LAFCD's knowledge, the agency responsible for maintaining the structure.MOD_DATEDate the GIS features were last modified.NAMEName of the individual drainage infrastructure.OWNERAgency that owns the drainage infrastructure in question.Q_DESIGNThe peak storm water runoff used for the design of the drainage infrastructure.SOFT_BOTTOMFor open channels, indicates whether the channel invert is in its natural state (not lined).SUBTYPEMost feature classes in this drainage geometric nature contain multiple subtypes.UPDATED_BYThe person who last updated the GIS feature.WIDTHWidth of a channel in feet.

Number of provincially, territorially, regionally and municipally owned roads for all provinces and territories. Values are presented in kilometres.

Capital expenditures on infrastructure assets according to the function, or purpose, of the spending. Public ownership includes the assets that are majority-owned by the governments in Canada (federal, provincial, territorial, regional and municipal). Annual data beginning from 2018.

Subtypes:Closed: A culvert which flows under pressure.Open: A culvert which does not flow under pressure.Attributes: Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes per operations of the Los Angeles County Flood Control District are listed below:AttributeDescriptionASBDATEThe date the design plans were approved "as-built" or accepted as "final records".CROSS_SECTION_SHAPEThe cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.DIAMETER_HEIGHTThe diameter of a round pipe or the height of an underground box or open channel.DWGNODrain Plan Drawing Number per LACFCD NomenclatureEQNUMAsset No. assigned by the Department of Public Works' (in Maximo Database).MAINTAINED_BYIdentifies, to the best of LAFCD's knowledge, the agency responsible for maintaining the structure.MOD_DATEDate the GIS features were last modified.NAMEName of the individual drainage infrastructure.OWNERAgency that owns the drainage infrastructure in question.Q_DESIGNThe peak storm water runoff used for the design of the drainage infrastructure.SOFT_BOTTOMFor open channels, indicates whether the channel invert is in its natural state (not lined).SUBTYPEMost feature classes in this drainage geometric nature contain multiple subtypes.UPDATED_BYThe person who last updated the GIS feature.WIDTHWidth of a channel in feet.

At the beginning of 2019, Toyota had recorded the largest amount of total electric vehicle charging infrastructure patent families filed in the United States. The Japanese automaker boasted some 154 patent families, ahead of the American Qualcomm and Ford.

In the time period between 2000 and 2019, Saudi Aramco was awarded with gas midstream infrastructure projects in the Middle East worth 15.8 billion U.S. dollars. The total value of all midstream infrastructure projects in the Middle East and North Africa was worth over 59 billion U.S. dollars.

As a share of the country’s GDP, China’s average infrastructure spending in 2022 was nearly ** times higher than that of the United States. Indeed, at *** percent of its GDP, China's investments were significantly higher than anywhere else in the world. By comparison, investments in Central & Eastern Europe - the CEE region - were relatively higher than those in their Western European counterparts. Infrastructure construction and development The construction industry plays a significant role in most economies. The reason for that is that public investment into essential infrastructure enables the economy to function properly and be well connected. Without transportation and energy infrastructure, which were the two types of infrastructure with the highest construction spending in the U.S., or telecommunication networks, such as 5G base stations, many industries could not perform their activities. Infrastructure needs Despite the importance of infrastructure for the wellbeing of communities, infrastructure investment is sub par in many countries across the world. As of 2020, projected infrastructure spending was estimated to be unable to fulfill spending needs in the United States, where the aging infrastructure is in dire need of repair. Although as seen here, China was the country with the highest investment in infrastructure relative to its GDP, as of 2019, it also has higher projected infrastructure needs than most regions.

A list if all County facilities and related data about County-owned buildings. This information is generated by the County's Facility Condition Index System. An important number in the dataset is the Facility Condition Index (FCI) number. A building in good condition should have a FCI of less than .05.

The set contains the Register of datasets, for each set specified identification number,name,resource formats

Indonesia Infrastructure Finance Company: Equity: General Reserve data was reported at 3,724.622 IDR bn in Jul 2023. This stayed constant from the previous number of 3,724.622 IDR bn for Jun 2023. Indonesia Infrastructure Finance Company: Equity: General Reserve data is updated monthly, averaging 1,182.983 IDR bn from Jan 2014 (Median) to Jul 2023, with 115 observations. The data reached an all-time high of 3,724.622 IDR bn in Jul 2023 and a record low of 80.520 IDR bn in Jun 2014. Indonesia Infrastructure Finance Company: Equity: General Reserve data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI018: Financial System Statistics: Infrastructure Finance Company Sector.

Indonesia Infrastructure Finance Company: Assets: Cash data was reported at 0.125 IDR bn in Jul 2023. This records an increase from the previous number of 0.125 IDR bn for Jun 2023. Indonesia Infrastructure Finance Company: Assets: Cash data is updated monthly, averaging 0.125 IDR bn from Jan 2014 (Median) to Jul 2023, with 115 observations. The data reached an all-time high of 0.178 IDR bn in Nov 2018 and a record low of 0.052 IDR bn in Mar 2017. Indonesia Infrastructure Finance Company: Assets: Cash data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI018: Financial System Statistics: Infrastructure Finance Company Sector.

The set contains the Register of datasets owned by the Department of Housing and Communal Infrastructure of the KMR (KCSA). Each set contains an identification number, name, resource formats, hyperlinks to the dial page and other metadata.

Finland Road Infrastructure Investment: Euro data was reported at 1,677,000,000.000 EUR in 2022. This records an increase from the previous number of 1,662,000,000.000 EUR for 2021. Finland Road Infrastructure Investment: Euro data is updated yearly, averaging 906,000,000.000 EUR from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 1,789,000,000.000 EUR in 2020 and a record low of 429,000,000.000 EUR in 1996. Finland Road Infrastructure Investment: Euro data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Finland – Table FI.OECD.ITF: Transport Infrastructure, Investment and Maintenance: OECD Member: Annual. [STAT_CONC_DEF] Capital expenditure on new road infrastructure or extension of existing roads, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, toll collection installations, etc.) as opposed to road vehicles. [COVERAGE] Data should include both government and private investment, unless otherwise specified. [COVERAGE] Data refer to investment carried out by State and municipalities assuming that investment carried out by municipalities is made on roads. Data include investment in urban roads, but not in private roads.

In the fourth quarter of 2024, the most popular vendor in the cloud infrastructure services market, Amazon Web Services (AWS), controlled ** percent of the entire market. Microsoft Azure takes second place with ** percent market share, followed by Google Cloud with ** percent market share. Together, these three cloud vendors account for ** percent of total spend in the fourth quarter of 2024. Organizations use cloud services from these vendors for machine learning, data analytics, cloud native development, application migration, and other services. AWS Services Amazon Web Services is used by many organizations because it offers a wide variety of services and products to its customers that improve business agility while being secure and reliable. One of AWS’s most used services is Amazon EC2, which lets customers create virtual machines for their strategic projects while spending less time on maintaining servers. Another important service is Amazon Simple Storage Service (S3), which offers a secure file storage service. In addition, Amazon also offers security, website infrastructure management, and identity and access management solutions. Cloud infrastructure services Vendors offering cloud services to a global customer base do so through different types of cloud computing, which include infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Further, there are different cloud computing deployment models available for customers, namely private cloud and public cloud, as well as community cloud and hybrid cloud. A cloud deployment model is defined based on the location where the deployment resides, and who has access to and control over the infrastructure.

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric power generating facilities, (8) electric power transmission lines, (9) liquefied natural gas terminals, (10) oil and gas pipelines, (11) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic/petroleum province), (12) cumulative production, and recoverable conventional resources (by oil- and gas-producing nation), (13) major mineral exporting maritime ports, (14) railroads, (15) major roads, (16) major cities, (17) major lakes, (18) major river systems, (19) first-level administrative division (ADM1) boundaries for all countries in Africa, and (20) international boundaries for all countries in Africa.

This statistic displays the responses of surveyed mid-sized businesses to the question: 'Which of the following are the biggest challenges your organization experienced when migrating infrastructure to the cloud?' in the United Kingdom (UK) in 2015. The most frequently cited challenge when migrating infrastructure to the cloud (with 53 percent of respondents) was 'gaining understanding of new technology/vendors & if/how relevant they are to my organization'.

On November 15, 2021, President Biden signed the Bipartisan Infrastructure Law (BIL), which invests more than $13 billion directly in Tribal communities across the country and makes Tribal communities eligible for billions more. For further explanation of the law please visit https://www.congress.gov/bill/117th-congress/house-bill/3684/text. These resources go to many Federal agencies to expand access to clean drinking water for Native communities, ensure every Native American has access to high-speed internet, tackle the climate crisis, advance environmental justice, and invest in Tribal communities that have too often been left behind. On August 16, 2022, President Biden signed the Inflation Reduction Act into law, marking the most significant action Congress has taken on clean energy and climate change in the nation’s history. With the stroke of his pen, the President redefined American leadership in confronting the existential threat of the climate crisis and set forth a new era of American innovation and ingenuity to lower consumer costs and drive the global clean energy economy forward. More information on this can be found here: https://www.whitehouse.gov/cleanenergy/inflation-reduction-act-guidebook/. This dataset illustrates the locations of Bureau of Indian Affairs projects funded by the Bipartisan Infrastructure Law and Inflation Reduction Act in Fiscal Year 2022, 2023, and 2024. The points illustrated in this dataset are the locations of Bureau of Indian Affairs projects funded by the Bipartisan Infrastructure Law and Inflation Reduction Act in Fiscal Year 2022 and 2023. The locations for the points in this layer were provided by the persons involved in the following groups: Division of Water and Power, DWP, Ecosystem Restoration, Irrigation, Power, Water Sanitation, Dam Safety, Branch of Geospatial Support, Bureau of Indian Affairs, BIA.GIS point feature class was created by Bureau of Indian Affairs - Branch Of Geospatial Support (BOGS), Division of Water and Power (DWP), Ecosystem Restoration, Irrigation, Bureau of Indian Affairs (BIA), Tribal Leaders Directory: https://www.bia.gov/service/tribal-leaders-directory/tld-csvexcel-dataset, The Department of the Interior | Strategic Hazard Identification and Risk Assessment Project: https://www.doi.gov/emergency/shira#main-content

The Los Angeles County Storm Drain System is a geometric network model representing the storm drain infrastructure within Los Angeles County. The long term goal of this network is to seamlessly integrate the countywide drainage infrastructure, regardless of ownership or jurisdiction. Current uses by the Department of Public Works (DPW) include asset inventory, operational maintenance, and compliance with environmental regulations.

GIS DATA DOWNLOADS: (More information is in the table below)

File geodatabase: A limited set of feature classes comprise the majority of this geometric network. These nine feature classes are available in one file geodatabase (.gdb). ArcMap versions compatible with the .gdb are 10.1 and later. Read-only access is provided by the open-source software QGIS. Instructions on opening a .gdb file are available here, and a QGIS plugin can be downloaded here.

Acronyms and Definitions (pdf) are provided to better understand terms used.

ONLINE VIEWING: Use your PC’s browser to search for drains by street address or drain name and download engineering drawings. The Web Viewer link is: https://dpw.lacounty.gov/fcd/stormdrain/

MOBILE GIS: This storm drain system can also be viewed on mobile devices as well as your PC via ArcGIS Online. (As-built plans are not available with this mobile option.)

More About these Downloads All data added or updated by Public Works is contained in nine feature classes, with definitions listed below. The file geodatabase (.gdb) download contains these eleven feature classes without network connectivity. Feature classes include attributes with unabbreviated field names and domains.

ArcMap versions compatible with the .gdb are 10.1 and later.

Feature Class Download Description

CatchBasin In .gdb Catch basins collect urban runoff from gutters

Culvert In .gdb A relatively short conduit that conveys storm water runoff underneath a road or embankment. Typical materials include reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Typical shapes are circular, rectangular, elliptical, or arched.

ForceMain In .gdb Force mains carry stormwater uphill from pump stations into gravity mains and open channels.

GravityMain In .gdb Underground pipes and channels.

LateralLine In .gdb Laterals connect catch basins to underground gravity mains or open channels.

MaintenanceHole In .gdb The top opening to an underground gravity main used for inspection and maintenance.

NaturalDrainage In .gdb Streams and rivers that flow through natural creek beds

OpenChannel In .gdb Concrete lined stormwater channels.

PumpStation In .gdb Where terrain causes accumulation, lift stations are used to pump stormwater to where it can once again flow towards the ocean

Data Field Descriptions

Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes are listed here per LACFCD operations.

Attribute Description

ASBDATE The date the design plans were approved “as-built” or accepted as “final records”.

CROSS_SECTIN_SHAPE The cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.

DIAMETER_HEIGHT The diameter of a round pipe or the height of an underground box or open channel.

DWGNO Drain Plan Drawing Number per LACFCD Nomenclature

EQNUM Asset No. assigned by the Department of Public Works’ (in Maximo Database).

MAINTAINED_BY Identifies, to the best of LAFCD’s knowledge, the agency responsible for maintaining the structure.

MOD_DATE Date the GIS features were last modified.

NAME Name of the individual drainage infrastructure.

OWNER Agency that owns the drainage infrastructure in question.

Q_DESIGN The peak storm water runoff used for the design of the drainage infrastructure.

SOFT_BOTTOM For open channels, indicates whether the channel invert is in its natural state (not lined).

SUBTYPE Most feature classes in this drainage geometric nature contain multiple subtypes.

UPDATED_BY The person who last updated the GIS feature.

WIDTH Width of a channel in feet.

The global sales of High-Performance Message Infrastructure are estimated to be worth USD 1.77 Billion in 2025 and are anticipated to reach a value of USD 6.08 billion by 2035. Sales are projected to increase at a compound annual growth rate (CAGR) of 13.1% over the forecast period from 2025 to 2035. The revenue generated by High-Performance Message Infrastructure in 2024 was USD 25780.0 million. The market is expected to exhibit a year-over-year (Y-o-Y) growth of 7.2% in 2025.

Attributes	Key Insights
Estimated Size, 2025	USD 1.77 Billion
Projected Size, 2035	USD 6.08 billion
Value-based CAGR (2025 to 2035)	13.1%

Semi Annual Market Update

Particular	Value CAGR
H1, 2024	12.5% (2024 to 2034)
H2, 2024	12.9% (2024 to 2034)
H1, 2025	13.1%(2025 to 2035)
H2, 2025	13.7% (2025 to 2035)

Country-wise Insights

Country	Value CAGR (2025 to 2035)
USA	11.5%
Germany	12.4%
UK	12.9%
China	13.0%
india	14.4%

Category-wise Insights

Drive Type	Share (2025)
hardware	47.8%

Industry	CAGR (2025 to 2035)
Telecommunication	13.2%