This dataset provides values for MAPS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

GEM's Global Socio-Economic Vulnerability Maps

The Global Social Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-human-vulnerability) is a composite index that was developed to measure characteristics or qualities of social systems that create the potential for loss or harm. Here, social vulnerability helps to explain why some countries will experience adverse impacts from earthquakes differentially where the linking of social capacities with demographic attributes suggests that communities with higher percentages of age-dependent populations, homeless, disabled, under-educated, and foreign migrants are likely to exhibit higher social vulnerability than communities lacking these characteristics. Other relevant factors that affect the social vulnerability of populations include in-migration from foreign countries, population density, an accounting of slum populations, and international tourist arrivals.

The Global Economic Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-economic-vulnerability) is a composite index that was designed primarily to measure the potential for economic losses from earthquakes due to a country’s macroeconomic exposure. This index is also an appraisal of the ability of countries to respond to shocks to their economic systems. Relevant indicators include the density of exposed economic assets such as commercial and industrial infrastructure. Metrics used to measure the ability of a country to withstand shocks to its economic system include reliance on imports/exports, government debt, and purchasing power. The economic vulnerability category also considers the economic vitality of countries since the economic vitality of a country can be directly related to the vulnerability and resilience of its populations. The latter includes measurements of single-sector economic dependence, income inequality, and employment status.

The Recovery/Reconstruction Potential Map (viewable here: https://maps.openquake.org/map/sv-global-recovery-and-reconstruction) is closely aligned with the concept of disaster resilience. Enhancing a country’s resilience to earthquakes is to improve its capacity to anticipate threats, to reduce its overall vulnerability, and to allow its communities to recover from adverse impacts from earthquakes when they occur. The measurement of recovery and reconstruction potential includes capturing inherent conditions that allow communities within a country to absorb impacts and cope with a damaging earthquake event, such as the density of the built environment, education levels, and political participation. It also encompasses post-event processes that facilitate a population’s ability to reorganize, change, and learn in response to a damaging earthquake.

Criteria for indicator selection
To choose indicators contextually exclusive for use in each map, the starting point was an exhaustive review of the literature on earthquake social vulnerability and resilience. For a variable to be considered appropriate and selected, three equally important criteria were met:

- variables were justified based on the literature regarding its relevance to one or more of the indices.
- variables needed to be of consistent quality and freely available from sources such as the United Nations and the World Bank; and
- variables must be scalable or available at various levels of geography to promote sub-country level analyses.

This procedure resulted in a ‘wish list’ of approximately 300 variables of which 78 were available and fit for use based on the three criteria.

Process for indicator selection
For variables to be allocated to an index, a two-tiered validation procedure was utilized. For the first tier, variables were assigned to each of the respective indices based on how each variable was cited within the literature, i.e., as being part of an index of social vulnerability, economic vulnerability, or recovery/resilience. For the second tier, machine learning and a multivariate ordinal logistic regression modelling procedure was used for external validation. Here, focus was placed on the statistical association between the socio-economic vulnerability indicators and the adverse impacts from historical earthquakes on a country-by country-basis.

The Global Significant Earthquake Database provided the external validation metrics that were used as dependent variables in the statistical analysis. To include both severe and moderate earthquakes within the dependent variables, adverse impact data was collected from damaging earthquake events that conformed to at least one of five criteria: 1) caused deaths, 2) caused moderate damage (approximately 1 million USD or more), 3) had a magnitude 7.5 or greater 4) had a Modified Mercalli Intensity (MMI) X or greater, or 5) generated a tsunami. This database was chosen because it considers low magnitude earthquakes that were damaging (e.g., MW >=2.5 & MW<=5.5) and contains socio-economic data such as the total number of fatalities, injuries, houses damaged or destroyed, and dollar loss estimates in USD.

Countries not demonstrating at least a minimal earthquake risk, i.e., seismicity <0.05 PGA (Pagani et al. 2018) and <$10,000 USD in predicted average annual losses (Silva et al. 2018) were eliminated from the analyses so as not to include countries with minimal to no earthquake risk. A total study area consists of 136 countries.

This world map shows the member economies of Asia-Pacific Economic Cooperation (APEC).

Abstract

The Partnership for Economic Inclusion (PEI) Landscape Survey 2019 - 2020 aimed to provide a comprehensive inventory of ongoing economic inclusion programs, or those that are in the development pipeline. For the purpose of the PEI Landscape Survey 2019 - 2020, the PEI management team (PEIMT) defined economic inclusion programs as multidimensional interventions that support and enable households to achieve sustainable livelihoods and increase their incomes and assets, while building human capital and promoting social inclusion.

To map the universe of economic inclusion programs, the PEIMT reviewed the World Bank financing portfolio as well as external sources. The first stage of the World Bank portfolio scan involved manually reviewing ongoing and pipeline programs from the Social Protection and Jobs (SPJ) Global Practice, listed in the World Bank Operations Portal, across all geographical regions. To determine whether a program focused on economic inclusion, the PEIMT reviewed each program's development objective and the component description included in its Project Appraisal Document (PAD) or, when a PAD was not available, its Project Information Document (PID), Project Paper (PP), or Project Information and Integrated Safeguards Data Sheet (PSDS).

Kind of data

Administrative records data [adm]

Sampling procedure

To map the universe of economic inclusion programs, the PEIMT reviewed the World Bank financing portfolio as well as external sources. The first stage of the World Bank portfolio scan involved manually reviewing ongoing and pipeline projects from the Social Protection and Jobs (SPJ) Global Practice, listed in the World Bank Operations Portal, across all geographical regions. To determine whether a program focused on economic inclusion, the PEIMT reviewed each project's development objective and the component description included in its Project Appraisal Document (PAD) or, when a PAD was not available, its Project Information Document (PID), Project Paper (PP), or Project Information and Integrated Safeguards Data Sheet (PSDS).

As a second stage, in order to validate each economic inclusion program and to speed up the mapping process, the PEIMT worked with the Text and Data Analytics (TDA) team from the Development Economics (DEC) department of the World Bank. Using a predefined set of keywords , the TDA team applied advanced text analytics to projects' summaries as well as to their PADs, PIDs, PPs, or PSDSs. They applied this technique to a total sample of approximately 1,200 projects (both active and pipeline) across all geographical regions under these Global Practices: Urban Resilience and Land; Social Development; Social Protection and Jobs; Finance, Competitiveness and Innovation; and Agriculture and Food. The team then ranked projects based on the number of keywords found. Any project that had at least one keyword could be considered an economic inclusion project. The PEIMT then compared the TDA-assisted selection with the manual selection for the SPJ projects and found that the results were accurate in correctly excluding projects. The TDA-assisted selection, however, also included far more projects than the manual review did.

To finalize the mapping of World Bank-financed economic inclusion projects, the PEIMT team manually reviewed the TDA-assisted selection of economic inclusion projects for the remaining Global Practices. The team assessed the relevance of a project based on project summaries, the types of words identified through the TDA techniques, and the frequency with which keywords came up in the project documents. In some cases, when a summary did not provide enough information, the PAD was reviewed to make a final decision. Overall, the TDA methods allowed the PEIMT to trim the number of projects for review by half. In total, the PEIMT identified 149 World Bank economic inclusion projects (representing 92 individual government programs in 57 countries ). Surveys were sent to these 92 unique identified programs, and responses were received back from 77 of them. The mapping of World Bank-supported projects was updated in June 2020 through a full manual review of nearly 50 projects from the Environment and Natural Resources Global Practice, which resulted in 17 additional projects and a total of 166 economic inclusion projects supported by the World Bank.

To map projects outside of World Bank operations, the PEIMT used the PEI's 2017 survey dataset to identify projects that were still ongoing as well as partners, including governments, NGOs, regional organizations, multilaterals, and other development partners involved in economic inclusion programming. Organizations were approached to self-identify programs that met a prescribed set of criteria, which had been developed based on the working definition of economic inclusion programs. Since the 2017 survey captured mostly non-government programs, in order to map other relevant economic inclusion interventions the PEIMT scanned several databases and inventories of social protection and productive inclusion programs, including ECLAC's database of labor and productive inclusion programs in Latin America and the Caribbean and Manchester's Social Assistance database. The number of projects identified outside of the World Bank portfolio totaled 146, from which 140 responses were expected and 127 responses were received.

Mode of data collection

Internet [int]

Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data was reported at 0.014 USD mn in Jan 2025. This records an increase from the previous number of 0.014 USD mn for Dec 2024. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data is updated monthly, averaging 0.012 USD mn from Apr 2022 (Median) to Jan 2025, with 34 observations. The data reached an all-time high of 0.028 USD mn in Aug 2023 and a record low of 0.005 USD mn in Mar 2023. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH147: Foreign Trade: by HS 8 Digits: Import: HS49: Printed Books, Newspapers, Pictures, and Other Products of Printing Industry, Manuscripts, Typescripts, and Plans.

This map contains Gross Domestic Product - the total value of goods produced and services provided - by country, per capita in 2016, expressed in 2016 US Dollars. Expressing the GDP in "per capita" terms allows for better comparisons across countries. Total GDP is available in an accompanying map. GDP as a measure has been largely criticized as an incomplete measure of productivity and wealth, as it does not take into account production in the informal economy, quality of life, degradation to the environment, or income distribution. However, GDP is an internationally comparable measure, used in everything from banks setting interest rates to political campaign speeches.Source: World Bank, World Development Indicators.

The average for 2023 based on 193 countries was -0.07 points. The highest value was in Liechtenstein: 1.61 points and the lowest value was in Syria: -2.75 points. The indicator is available from 1996 to 2023. Below is a chart for all countries where data are available.

An exclusive economic zone (EEZ) is a sea zone prescribed by the United Nations Convention on the Law of the Sea over which a sovereign state has special rights over the exploration and use of marine resources, including energy production from water and wind. This maritime boundary is designed to be used with other marine boundaries in order to help determine areas of trade, commerce and transportation. The 200 NM zone is measured, country-by-country, from another maritime boundary, the baseline (usually but not in all cases the mean low-water mark, used is not the same thing as the coast line. For each country, obtain the official list of the baseline points from the United Nations under Maritime Space.The exclusive economic zone stretches much further into sea than the territorial waters, which end at 12 NM (22 km) from the coastal baseline (if following the rules set out in the UN Convention on the Law of the Sea). Thus, the EEZ includes the contiguous zone. States also have rights to the seabed of what is called the continental shelf up to 350 NM (648 km) from the coastal baseline, beyond the EEZ, but such areas are not part of their EEZ. The legal definition of the continental shelf does not directly correspond to the geological meaning of the term, as it also includes the continental rise and slope, and the entire seabed within the EEZ. The chart below diagrams the overlapping jurisdictions which are part of the EEZ. When the (EEZ) boundary is between countries which are separated by less than 200NM is settled by international tribunals at any arbitrary line. Many countries are still in the process of extending their EEZs beyond 200NM using criteria defined in the United Nations Convention on the Law of the Sea. Dataset Summary The data for this layer were obtained from https://www.marineregions.org/published here. Link to source metadata.Preferred Citation: Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 12. Available online at https://www.marineregions.org/. https://doi.org/10.14284/632This layer is a feature service, which means it can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.

Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data was reported at 122,018.721 Metric Ton in Jun 2018. This records a decrease from the previous number of 162,431.279 Metric Ton for May 2018. Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data is updated monthly, averaging 177,625.943 Metric Ton from Oct 2010 (Median) to Jun 2018, with 93 observations. The data reached an all-time high of 274,650.833 Metric Ton in May 2012 and a record low of 64,030.941 Metric Ton in Dec 2010. Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data remains active status in CEIC and is reported by Map Ta Phut Industrial Port Office. The data is categorized under Global Database’s Thailand – Table TH.TA023: Port Statistics: Map Ta Phut Industrial of Thailand.

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.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

This dataset represents the polygons of the Exclusive Economic Zones (EEZ) of the world, in a high resolution: the coastline is based on GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database) The data set of the Exclusive Economic Zones can be used in many applications. In biogeography for example, it is possible to create for instance species distribution lists per country.

Mapping Our World Using GIS is a 1:1 set of instructional materials for teaching basic concepts found in middle school world geography. Each module consists of multiple files.

        Economists generally classify a country as “developing” or “developed” by determining the percentage

of gross domestic product (GDP) engaged in each of three sectors of the economy — agriculture, industry, and services. A country with a high percentage of its GDP in agriculture is categorized as developing, while a country with a high percentage of its GDP in services and industry is categorized as developed.

        In this activity, you will use maps of percentages of GDP in the three sectors to explore patterns of

development around the world. You will also examine two other economic indicators — energy use and GDP per capita — and compare the maps of GDP in economic sectors to the maps of GDP per capita and energy use. You will evaluate whether or not the economic sector criteria are good indicators of a country’s economic status.

  The Mapping Our World collection is at: http://esriurl.com/MOW. All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

This layer contains Gross Domestic Product (GDP) Per Capita - the total value of goods produced and services provided, divided by the total population in each country, from 1960 to 2016, expressed in 2016 US Dollars. Expressing the GDP in "per capita" terms allows for better comparisons across countries. Total GDP is available in an accompanying layer. GDP as a measure has been largely criticized as an incomplete measure of productivity and wealth, as it does not take into account production in the informal economy, quality of life, degradation to the environment, or income distribution. However, GDP is an internationally comparable measure, used in everything from banks setting interest rates to political campaign speeches.Source: World Bank, World Development Indicators.

Thailand Cargo Volume: OP: Glow Co. Ltd (GLOW) data was reported at 0.000 Metric Ton in Jun 2018. This stayed constant from the previous number of 0.000 Metric Ton for May 2018. Thailand Cargo Volume: OP: Glow Co. Ltd (GLOW) data is updated monthly, averaging 0.000 Metric Ton from Oct 2010 (Median) to Jun 2018, with 93 observations. The data reached an all-time high of 0.000 Metric Ton in Jun 2018 and a record low of 0.000 Metric Ton in Jun 2018. Thailand Cargo Volume: OP: Glow Co. Ltd (GLOW) data remains active status in CEIC and is reported by Map Ta Phut Industrial Port Office. The data is categorized under Global Database’s Thailand – Table TH.TA023: Port Statistics: Map Ta Phut Industrial of Thailand.

The average for 2020 based on 165 countries was 1.26 percent. The highest value was in Eritrea: 12.36 percent and the lowest value was in Iceland: 0 percent. The indicator is available from 1991 to 2020. Below is a chart for all countries where data are available.

France Ex: FOB: Maps & Technical Drawings, Photographic Plates & Films data was reported at 2.000 EUR mn in Sep 2018. This stayed constant from the previous number of 2.000 EUR mn for Aug 2018. France Ex: FOB: Maps & Technical Drawings, Photographic Plates & Films data is updated monthly, averaging 2.000 EUR mn from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 12.000 EUR mn in Sep 2001 and a record low of 1.000 EUR mn in Mar 2018. France Ex: FOB: Maps & Technical Drawings, Photographic Plates & Films data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.JA003: Trade Statistics: by Industry: NAF rev 2.

France Im: CIF: Maps & Technical Drawings, Photographic Plates & Films data was reported at 2.000 EUR mn in May 2018. This records a decrease from the previous number of 4.000 EUR mn for Apr 2018. France Im: CIF: Maps & Technical Drawings, Photographic Plates & Films data is updated monthly, averaging 4.000 EUR mn from Jan 2000 (Median) to May 2018, with 221 observations. The data reached an all-time high of 17.000 EUR mn in Dec 2002 and a record low of 1.000 EUR mn in Apr 2014. France Im: CIF: Maps & Technical Drawings, Photographic Plates & Films data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.JA003: Trade Statistics: by Industry: NAF rev 2.

Imports: Sitc Basic Heading: Vol: Maps & Hydrographic,Similar Charts of All Kinds (Including Wall Maps, Topographical Plans & Globes), Printed, Not in Book Form data was reported at 4,274.000 kg in Mar 2025. This records an increase from the previous number of 215.000 kg for Feb 2025. Imports: Sitc Basic Heading: Vol: Maps & Hydrographic,Similar Charts of All Kinds (Including Wall Maps, Topographical Plans & Globes), Printed, Not in Book Form data is updated monthly, averaging 1,961.000 kg from Jan 1997 (Median) to Mar 2025, with 333 observations. The data reached an all-time high of 29,567.000 kg in Jul 1998 and a record low of 1.000 kg in May 1999. Imports: Sitc Basic Heading: Vol: Maps & Hydrographic,Similar Charts of All Kinds (Including Wall Maps, Topographical Plans & Globes), Printed, Not in Book Form data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.SITC: Basic Heading: Section 08: Imports: Volume.

According to Cognitive Market Research, the global HD Map for Autonomous Vehicle Market size will be USD 1624.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 31.50% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 601.18 million in 2025 and will grow at a compound annual growth rate (CAGR) of 29.3% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue with a market size of USD 471.19 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 389.95 million in 2025 and will grow at a compound annual growth rate (CAGR) of 33.5% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue with a market size of USD 61.74 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.5% from 2025 to 2033.
Middle East had a market share of around 4.00% of the global revenue and was estimated at a market size of USD 64.99 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.8% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 35.75 million in 2025 and will grow at a compound annual growth rate (CAGR) of 31.2% from 2025 to 2033.
Commercial Vehicles is the fastest growing segment of the HD Map for Autonomous Vehicle Market industry

Market Dynamics of HD Map for Autonomous Vehicle Market

Key Drivers for HD Map for Autonomous Vehicle Market

Rising adoption of autonomous and semi-autonomous vehicles fuel the market demand

The global shift toward autonomous and semi-autonomous vehicles is significantly propelling the demand for HD maps. With increasing consumer inclination toward advanced driver-assistance systems (ADAS) and partial automation features such as lane centering, adaptive cruise control, and self-parking, automakers are integrating high-definition maps into vehicle systems for enhanced precision. HD maps support sensor fusion and real-time decision-making by providing centimeter-level accuracy, which is essential for safe navigation. As Level 2 and Level 3 vehicles become more mainstream, particularly in North America, Europe, and East Asia, the dependency on detailed geospatial data is expanding rapidly. According to the 2025 report by the World Economic Forum in partnership with Boston Consulting Group, about 4% of newly sold personal vehicles in 2035 are projected to be equipped with Level 4 autonomous capabilities. This growth trajectory is further supported by regulatory encouragement and pilot testing of autonomous technologies, creating a solid foundation for market expansion.

https://reports.weforum.org/docs/WEF_Autonomous_Vehicles_2025.pdf

Increasing demand for real-time navigation and safety features boosts the industry

The rising demand for real-time navigation and vehicle safety systems is a core driver of the HD map market. Traditional maps fall short in offering the dynamic data required for autonomous driving, such as lane-level information, road gradients, and obstacle detection. HD maps bridge this gap by providing enriched geospatial content that complements vehicle sensors, enabling safer and more reliable driving decisions. Consumers are increasingly valuing safety-centric features, and automakers are responding with integrated solutions that rely on continuously updated and context-aware HD maps. These maps enhance situational awareness in complex traffic scenarios, support predictive navigation, and reduce the risk of collisions. As real-time responsiveness becomes a critical factor in vehicular operations, the demand for HD mapping solutions continues to surge.

Restraint Factor for the HD Map for Autonomous Vehicle Market

High cost of map creation and frequent updates limit market growth

One of the primary constraints hindering the widespread adoption of HD maps is the high cost associated with their creation and maintenance. Generating centimeter-level accurate maps requires advanced hardware such as LiDAR, high-resolution cameras, and precise GPS systems, all of which involve substantial capital expenditure. Moreover, these maps must be frequently updated to reflect real-time changes in road conditions, traffic patterns, construction zones, and signage. The continual need for accurate, up-to-date data further increases ...