View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

United States Import Value Index data was reported at 126.779 2015=100 in 2021. This records an increase from the previous number of 103.958 2015=100 for 2020. United States Import Value Index data is updated yearly, averaging 51.384 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 126.779 2015=100 in 2021 and a record low of 11.319 2015=100 in 1982. United States Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

The Centers for Disease Control Social Vulnerability Index shows which communities are especially at risk during public health emergencies because of factors like socioeconomic status, household composition, racial composition of neighborhoods, or housing type and transportation. The CDC SVI uses 15 U.S. census variables to identify communities that may need support before, during, or after disasters. Learn more here. The condition is the overall ranking of four social theme rankings where lower values indicate high vulnerability and high values indicate low vulnerability. Quintiles for this condition were determined for all the Census tracts in King County. Quintile 1 is the most vulnerable residents, Quintile 5 is the least vulnerable residents. Data is released every 2 years following the American Community Survey release in December of the year following the Survey. The most recent data for 2018 was downloaded from the ATSDR website.

OverviewThis dataset assesses countries' progress on the 17 Sustainable Development Goals (SDGs), adopted by UN Member States in 2015. Derived from the annual Sustainable Development Report, it provides structured data for analyzing global and national SDG performance, trends, and challenges.PurposeThe primary purpose of this dataset is to facilitate in-depth analysis, research, and policy-making related to sustainable development. It enables users to track progress, identify areas requiring greater attention, compare national performances, and support evidence-based interventions for achieving the 2030 Agenda for Sustainable Development.Data CoverageGeographical Scope: Covers all 193 United Nations Member States.Temporal Coverage: Data is available annually since the adoption of the SDGs in 2015, up to the latest published report year.Goals and Indicators: Encompasses all 17 Sustainable Development Goals, with data presented across numerous indicators (approximately 125 unique indicators) used to measure progress under each goal. GOAL 1: No PovertyGOAL 2: Zero HungerGOAL 3: Good Health and Well-beingGOAL 4: Quality EducationGOAL 5: Gender EqualityGOAL 6: Clean Water and SanitationGOAL 7: Affordable and Clean EnergyGOAL 8: Decent Work and Economic GrowthGOAL 9: Industry, Innovation and InfrastructureGOAL 10: Reduced InequalityGOAL 11: Sustainable Cities and CommunitiesGOAL 12: Responsible Consumption and ProductionGOAL 13: Climate ActionGOAL 14: Life Below WaterGOAL 15: Life on LandGOAL 16: Peace and Justice Strong InstitutionsGOAL 17: Partnerships to achieve the GoalData Points and MetricsThe dataset includes various metrics for each country, SDG, and indicator:SDG Index Scores: Overall scores reflecting a country's aggregate performance across all SDGs.Individual SDG Scores/Performance: Scores and ratings for each of the 17 individual SDGs, indicating how well a country is performing on specific goals.Indicator-Level Data: Raw values for the underlying indicators that comprise the SDG scores.Trends: Trends indicating whether a country is on track, moderately improving, stagnating, or decreasing on specific goals and indicators.Traffic Light System Ratings: A color-coded rating (green, yellow, orange, red) indicating a country's status on achieving each SDG.Data Sources and MethodologyThe data is compiled by independent experts and draws from a wide range of official and non-official data sources, including international organizations (e.g., World Bank, WHO, UNESCO), research institutions, and national statistical offices. The methodology for calculating the SDG Index and individual goal scores involves normalization, aggregation, and imputation techniques to ensure comparability across countries and over time. Full methodological details are typically provided in accompanying documentation (e.g., Codebook and Methodology Report) available with the downloadable dataset.Potential UsesAcademic research and statistical analysis on sustainable development.Policy formulation and review by governments and international bodies.Monitoring and evaluation of SDG implementation.Educational purposes and public awareness campaigns.Development of visualizations and interactive dashboards.Access and DownloadThe complete dataset, along with the full report, codebook, and methodological explanations, is typically available for free download from the official Sustainable Development Report website. Users are encouraged to refer to the source website for the most up-to-date versions and supporting documentation.

The real-time index database market is experiencing robust growth, driven by the increasing demand for immediate insights from large volumes of data across diverse sectors. The market's expansion is fueled by the proliferation of IoT devices generating massive real-time data streams, the need for faster decision-making in competitive environments, and the rise of sophisticated analytics applications requiring rapid data access. Cloud-based solutions dominate the market due to their scalability, cost-effectiveness, and ease of deployment, attracting both individual users and large enterprises. However, concerns around data security and latency in cloud-based systems present some restraints. The on-premises segment, while smaller, continues to cater to businesses with stringent data sovereignty requirements or those managing exceptionally sensitive information. Key players like Elastic, Amazon Web Services, Apache Solr, Splunk, and Microsoft are shaping the market landscape through continuous innovation and competitive offerings. Geographic distribution reflects the concentration of technological infrastructure and data generation, with North America and Europe currently leading the market, followed by the Asia-Pacific region showing significant potential for future growth. The market's Compound Annual Growth Rate (CAGR) suggests a consistent upward trajectory, indicating continued investment and market expansion throughout the forecast period. The competitive dynamics are marked by a mix of established players and emerging entrants. Established players leverage their existing infrastructure and customer bases, while new entrants focus on niche areas and innovative solutions. The market is also witnessing increased adoption of hybrid models combining cloud and on-premises solutions to balance cost-efficiency, security, and performance. Future growth will depend on technological advancements, particularly in areas like distributed ledger technology and edge computing, which will enhance the real-time capabilities and scalability of index databases. Furthermore, the increasing focus on data governance and regulatory compliance will also influence market adoption and shape the development of future solutions. The market is anticipated to witness a sustained period of growth, fueled by the ever-growing demand for real-time data analytics and insights across various sectors and regions.

1) Data Introduction • The AI Global Index Dataset is a comprehensive index that benchmarks 62 countries based on the level of AI investment, innovation, and implementation, including seven key indicators (human resources, infrastructure, operational environment, research, development, government strategy, commercialization) and general information by country (region, cluster, income group, political system).

2) Data Utilization (1) AI Global Index Dataset has characteristics that: • This dataset consists of a total of 13 columns with 5 categorical variables (regions, clusters, etc.) and 8 numerical variables (scores for each indicator), covering 62 countries. • The seven key indicators are classified into three pillars: △ implementation (human resources/infrastructure/operational environment) △ innovation (R&D) △ investment (government strategy/commercialization), and assess each country's overall AI ecosystem capabilities in multiple dimensions. (2) AI Global Index Dataset can be used to: • Global AI leadership pattern analysis: Correlation analysis between seven indicators can identify AI strengths and weaknesses by country and perform group comparisons by region and income level. • Machine learning-based predictive model: It can be used for data science education and application, such as country-specific index prediction through regression analysis or classification of AI development types through clustering.

Singapore led the Index of Economic Freedom in 2024, with an index score of 83.5 out of 100. Switzerland, Ireland, Taiwan, and Luxembourg rounded out the top five. Economic Freedom Index In order to calculate the Economic Freedom Index, the source takes 12 different factors into account, including the rule of law, government size, regulatory efficiency, and open markets. All 12 factors are rated on a scale of zero to 100 and are weighted equally. Every country is rated within the Index in order to provide insight into the health and freedom of the global economy. Singapore's economy Singapore is one of the four so-called Asian Tigers, a term used to describe four countries in Asia that saw a booming economic development from the 1950s to the early 1990. Today, the City-State is known for its many skyscrapers, and its economy continue to boom. It has one of the lowest tax-rates in the Asia-Pacific region, and continues to be open towards foreign direct investment (FDI). Moreover, Singapore has one of the highest trade-to-GDP ratios worldwide, underlining its export-oriented economy. Finally, its geographic location has given it a strategic position as a center connecting other countries in the region with the outside world. However, the economic boom has come at a cost, with the city now ranked among the world's most expensive.

Based on two decades of radial velocity (RV) observations using Keck/High Resolution Echelle Spectrometer (HIRES) and McDonald/Tull, and more recent observations using the Automated Planet Finder, we found that the nearby star HR 5183 (HD 120066) hosts a 3 M_J_ minimum mass planet with an orbital period of 74_-22_^+43^ yr. The orbit is highly eccentric (e~0.84), shuttling the planet from within the orbit of Jupiter to beyond the orbit of Neptune. Our careful survey design enabled high cadence observations before, during, and after the planet's periastron passage, yielding precise orbital parameter constraints. We searched for stellar or planetary companions that could have excited the planet's eccentricity, but found no candidates, potentially implying that the perturber was ejected from the system. We did identify a bound stellar companion more than 15000 au from the primary, but reasoned that it is currently too widely separated to have an appreciable effect on HR 5183 b. Because HR 5183 b's wide orbit takes it more than 30 au (1") from its star, we also explored the potential of complimentary studies with direct imaging or stellar astrometry. We found that a Gaia detection is very likely, and that imaging at 10 {mu}m is a promising avenue. This discovery highlights the value of long-baseline RV surveys for discovering and characterizing long-period, eccentric Jovian planets. This population may offer important insights into the dynamical evolution of planetary systems containing multiple massive planets.

Ireland: Fragile state index, 0 (low) - 120 (high): The latest value from 2024 is 18.6 index points, a decline from 19.5 index points in 2023. In comparison, the world average is 64.56 index points, based on data from 176 countries. Historically, the average for Ireland from 2007 to 2024 is 22.1 index points. The minimum value, 18.6 index points, was reached in 2024 while the maximum of 26.5 index points was recorded in 2012.

Topographic Position Index (TPI) is a topographic position classification identifying upper, middle and lower parts of the landscape. This dataset includes a mask that identifies where topographic position cannot be reliably derived in low relief areas. The TPI product was derived from Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. A masked version of the TPI product was derived using the slope relief classification product. The TPI data are available at 1 arc-second and 3 arc-second resolution. The 3 arc-second resolution dataset was generated from the 1 arc-second TPI product and masked by the 3” water and ocean mask datasets.

The Social Vulnerability Index (SoVI) 2006-10 measures the social vulnerability of U.S. counties to environmental hazards. The index is a comparative metric that facilitates the examination of the differences in social vulnerability among counties. SoVI is a valuable tool for policy makers and practitioners. It graphically illustrates the geographic variation in social vulnerability. It shows where there is uneven capacity for preparedness and response and where resources might be used most effectively to reduce the pre-existing vulnerability. SoVI also is useful as an indicator in determining the differential recovery from disasters.The index synthesizes 27 socioeconomic variables, which the research literature suggests contribute to reduction in a community's ability to prepare for, respond to, and recover from hazards. SoVI data sources include primarily those from the United States Census Bureau.The data are compiled and processed by the Hazards and Vulnerability Research Institute at the University of South Carolina. The data are standardized and placed into a principal components analysis to reduce the initial set of variables into a smaller set of statistically optimized components. Adjustments are made to the components' cardinality (positive (+) or negative (-)) to insure that positive component loadings are associated with increased vulnerability, and negative component loadings are associated with decreased vulnerability. Once the cardinalities of the components are determined, the components are added together to determine the numerical social vulnerability score for each county.SoVI 2006-10 marks a change in the formulation of the SoVI metric from earlier versions. New directions in the theory and practice of vulnerability science emphasize the constraints of family structure, language barriers, vehicle availability, medical disabilities, and healthcare access in the preparation for and response to disasters, thus necessitating the inclusion of such factors in SoVI. Extensive testing of earlier conceptualizations of SoVI, in addition to the introduction of the U.S. Census Bureau's five-year American Community Survey (ACS) estimates, warrants changes to the SoVI recipe, resulting in a more robust metric. These changes, pioneered with the ACS-based SoVI 2005-09 carry over to SoVI 2006-10, which combines the best data available from both the 2010 U.S. Decennial Census and five-year estimates from the 2006-2010 ACS.

S&P 500 index data including level, dividend, earnings and P/E ratio on a monthly basis since 1870. The S&P 500 (Standard and Poor's 500) is a free-float, capitalization-weighted index of the top ...

Between March 4 and March 11, 2020, the S&P 500 index declined by ** percent, descending into a bear market. On March 12, 2020, the S&P 500 plunged *** percent, its steepest one-day fall since 1987. The index began to recover at the start of April and reached a peak in December 2021. As of December 29, 2024, the value of the S&P 500 stood at ******** points. Coronavirus sparks stock market chaos Stock markets plunged in the wake of the COVID-19 pandemic, with investors fearing its spread would destroy economic growth. Buoyed by figures that suggested cases were leveling off in China, investors were initially optimistic about the virus being contained. However, confidence in the market started to subside as the number of cases increased worldwide. Investors were deterred from buying stocks, and this was reflected in the markets – the values of the Dow Jones Industrial Average and the Nasdaq Composite also dived during the height of the crisis. What is a bear market? A bear market occurs when the value of a stock market suffers a prolonged decline of more than 20 percent over a period of at least 2 months. The COVID-19 pandemic caused severe concern and sent stock markets on a steep downward spiral. The S&P 500 achieved a record closing high of ***** on February 19, 2020. However, just over 3 weeks later, the market closed on *****, which represented a decline of around ** percent in only 16 sessions.

Russia Import Value Index data was reported at 157.494 2015=100 in 2021. This records an increase from the previous number of 124.154 2015=100 for 2020. Russia Import Value Index data is updated yearly, averaging 99.370 2015=100 from Dec 1995 (Median) to 2021, with 27 observations. The data reached an all-time high of 176.806 2015=100 in 2013 and a record low of 20.483 2015=100 in 1999. Russia Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Russian Federation – Table RU.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

China's main stock market index, the SHANGHAI, rose to 3876 points on September 1, 2025, gaining 0.46% from the previous session. Over the past month, the index has climbed 8.16% and is up 37.87% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

About Transportation Services Index

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.

For charts and discussion on the relationship of the TSI to the economy, see our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

Dive into Market Research Intellect's Index Fund Market Report, valued at USD 5.0 trillion in 2024, and forecast to reach USD 10.0 trillion by 2033, growing at a CAGR of 8.5% from 2026 to 2033.

The U.S. Social Vulnerability Index Grids, Revision 01 data set contains gridded layers for the overall Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) using four sub-category themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) based on census tract level inputs from 15 variables for the years 2000, 2010, 2014, 2016, 2018, and 2020. SVI values range between 0 and 1 based on their percentile position among all census tracts in the U.S., with 0 representing lowest vulnerability census tracts and 1 representing highest vulnerability census tracts. SEDAC has gridded these vector inputs to create 1 kilometer spatial resolution raster surfaces allowing users to obtain vulnerability metrics for any user-defined area within the U.S. Utilizing inputs from CIESIN's Gridded Population of the World, Version 4 (GPWv4) Revision 11 data sets, a mask is applied for water, and optionally, for no population. The data are provided in two different projection formats, NAD83 as a U.S. specific standard, and WGS84 as a global standard. The goal of the SVI is to help identify vulnerable commUnities by ranking them on these inputs across the U.S.

This layer shows the total crime index in the U.S. in 2017 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indicesThe values are all referenced by an index value. The index values for the US level are 100, representing average crime for the country. A value of more than 100 represents higher crime than the national average, and a value of less than 100 represents lower crime than the national average. For example, an index of 120 implies that crime in the area is 20 percent higher than the US average; an index of 80 implies that crime is 20 percent lower than the US average.Additional Esri Resources:Esri DemographicsU.S. 2017/2022 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

The main stock market index of United States, the US500, rose to 6464 points on September 1, 2025, gaining 0.06% from the previous session. Over the past month, the index has climbed 2.13% and is up 16.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.