This resource represents the Urban Green Index analysis for Milan during the period 032022. The Urban Green Index is a metric used to evaluate the distribution and density of green spaces within urban environments. This analysis provides a detailed spatial representation of green areas, including parks, gardens, street trees, and other vegetated zones, across the city.

About this dataset

Context

The dataset tabulates the Index population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Index across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Index was 157, a 1.29% increase year-by-year from 2022. Previously, in 2022, Index population was 155, an increase of 1.31% compared to a population of 153 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Index decreased by 3. In this period, the peak population was 211 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Index is shown in this column.
• Year on Year Change: This column displays the change in Index population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Index Population by Year. You can refer the same here

This dataset contains the Compound Topographic Index (CTI) for North America from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The CTI data were developed and distributed by processing units. There are 13 processing units for North America. The distribution files have the number of the processing unit appended to the end of the zip file name (e.g. na_cti_3_2.zip contains the CTI data for unit 3-2). The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

Abstract

Mali is a Sahelian country, landlocked and structurally vulnerable to food insecurity and malnutrition. The economy is heavily dependent on the primary sector: agriculture, livestock, fishing and forestry account for 68.0% of the active population1 . This sector is itself dependent on exogenous factors, mainly climatic, such as recurrent droughts. In 2018, the prevalence of food insecurity at the national level was 19.1%, of which 2.6% was severely food insecure. The most affected regions were Kidal, Gao, Timbuktu, Mopti and Kayes. The Global Food Crisis Network Partnership Programme baseline studies are designed to feed into the overall monitoring, evaluation, accountability and learning programme of each project. In this regard, the baseline study has short, medium and long-term objectives.

Geographic coverage

Regional coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The EAC-I 2014 has been designed to have national coverage, including both urban and rural areas in all the regions of the country except Kidal. The domains were defined as the entire country, district of Bamako, other urban areas, and rural areas; and in the rural areas: agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 51 explicit sample strata were selected. The target population was drawn from households in all regions of Mali except Kidal which was not accessible for security reasons. Kidal also has very low population density.The sample was chosen through a random two stage process: - In the first stage, 1070 enumeration areas (EAs) were selected with Probability Proportional to Size (PPS) using the 2009 Census of Population as the base for the sample, and the number of households as a measure of size. - In the second stage o 3 households were selected with equal probability in each of the rural EAs o 9 households were selected with equal probability in each of the urban EAs The total estimated size of the sample for the survey was 4,218.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Please refer to the Questionnaires for the value labels of the variables.

Cleaning operations

Data Entry & Data cleaning Data entry was performed at the CPS/SDR using a CSPro application designed by an international consultant recruited by the LSMS team. The data entry program allows three types of data checks: (1) range checks; (2) intra-record check to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies pertinent between the different modules of the questionnaire. Data entry for the first visit was done from August 11th, 2014 to November 30th, 2014 and, from February 9th 2015 to March 27th, 2015 for the second visit. Data cleaning was done from May 2015 to July 2015. Data cleaning was done in a CSPro application. The data cleaning focused on more intra-record and inter-record checks.

Required data to replicate the Ecoacoustic Index Sensitivity Analysis (linked below)

Comprehensive comparison of Artificial Analysis Intelligence Index vs. Output Speed (Output Tokens per Second) by Model

Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.

NBR = (NIR – SWIR) / (NIR + SWIR)

Sentinel-2 NBR = (B08 - B12) / (B08 + B12)

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.

NDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA

As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Comparison of Represents the average of coding benchmarks in the Artificial Analysis Intelligence Index (LiveCodeBench & SciCode) by Model

Abstract

The Uganda 2016 Resilience Index Measurement and Analysis (RIMA) measures the food security and resilience in Karamoja, North-East, Uganda. In 2015, three United Nations (UN) agencies – the United Nations Children’s Fund (UNICEF), the Food and Agriculture Organization of the United Nations (FAO), and the World Food Programme (WFP) – developed a resilience strategy for Karamoja. This Joint Resilience Strategy (JRS) represents a commitment and collaborative focus for UNICEF, FAO, and WFP’s efforts to build resilience in the Karamoja region. The overall goal of the JRS is to improve the food security and nutrition status of the region during the period from 2016 to 2020. This JRS identifies the need for the three agencies to develop a common approach to measuring resilience in the context of Karamoja, which have thus adopted FAO’s Resilience Index Measurement and Analysis-II (RIMA II) approach to measure resilience to food insecurity there.

Geographic coverage

Regional Coverage

Analysis unit

Households

Universe

Households in Karamoja region.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the household survey is composed in total of 2 380 households. The sampling strategy is stratified according to the following five strata: (1) target households, which are those reached by the JRS in 12 parishes of the Moroto and Napak districts; (2) direct spillover households, which are those located in the remaining parishes of the Moroto and Napak districts and are not involved in the JRS; (3) indirect spillover households, which are those located in the two districts where the JRS is not actually operating (Kotido and Nakapiripirit) but where other UN projects are ongoing; (4) the ‘different ethnicity’ group, which includes those households located in two districts (Abim and Amudat) populated with ethnic groups that are different from the Karamojong;21 (5) and the pure control group, comprised of households located in the Kaabong district, which have the same ethnic group and socioeconomic conditions, mostly pastoralism, as the target group, but which are not involved in the JRS.

Mode of data collection

Computer Assisted Personal Interview [capi]

Analysis of ‘Home Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/PythonforSASUsers/hpindex on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Federal Housing Finance Agency House Price Index (HPI) is a broad measure of the movement of single-family house prices. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. The technical methodology for devising the index, collection, and publishing the data is at: http://www.fhfa.gov/PolicyProgramsResearch/Research/PaperDocuments/1996-03_HPI_TechDescription_N508.pdf

Content

Contains monthly and quarterly time series from January 1991 to August 2016 for the U.S., state, and MSA categories. Analysis variables are the aggregate non-seasonally adjusted value and seasonally adjusted index values. The index value is 100 beginning January 1991.

Acknowledgements

This data is found on Data.gov

Inspiration

Can this data be combined with the corresponding census growth projections either at the state or MSA level to forecast 24 months out the highest and lowest home index values?

--- Original source retains full ownership of the source dataset ---

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can DAX Index Revolutionize Data Analysis?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. Index values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2. NDWI = (GREEN – NIR) / (GREEN + NIR) Sentinel-2 NDWI = (B03 - B08) / (B03 + B08) These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. NDWI files are generated for the following Sentinel-2 granules: • T30UWE • T30UXF • T30UWF • T30UXE • T31UCV • T30UYE • T31UCA As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Abstract

This dataset corresponds to the baseline survey for an emergency livelihoods and food security support project to strengthen the resilience of crisis-affected populations. Data was collected from 1376 households, including 675 beneficiaries and 701 non-beneficiaries. The objective is to know the situation of households before the project in terms of various indicators of food consumption, sources of income, access to basic services, ownership of assets (productive and non-productive), agricultural and fisheries production, adoption of innovative techniques, social safety nets, exposure to shocks and coping strategies. The questionnaire is based on the Short RIMA questionnaire, which collects the minimum amount of information necessary to calculate the resilience capacity index using the RIMA methodology developed by FAO.

Geographic coverage

Prefectures of Bangui, Ombella M'poko, Lobaye, Ouham, Haute Kotto, Sangha Mbaéré, Mambéré Kadéi, Nana Mambéré, Nana Gribizi, Kémo and Ouaka

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline survey targets the 12 prefectures with project villages. For the treatment group, the target population of the study is the project beneficiary households. The sample was drawn from the selected beneficiaries. To provide a comparison group, non-beneficiary villages in the same intervention prefectures were targeted. Households in the comparison group were selected from outside the beneficiary villages to minimize contamination effects. The sample was distributed to reflect the weight of beneficiaries per prefecture. A total of 1376 households were interviewed, including 675 beneficiary households and 701 non-beneficiary households. The population surveyed among the beneficiaries was almost equally divided between the three types of intervention (cash transfers, direct distribution and vouchers).

Mode of data collection

Computer Assisted Personal Interview [capi]

Dataset

This dataset was created by MoMasoud

Contents

This dataset contains the Compound Topographic Index (CTI) grid for the Asian continent from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The CTI data were developed and distributed by processing units. There are 19 processing units for Asia. The distribution files have the number of the processing unit appended to the end of the zip file name (e.g. as_cti_3_2.zip contains the CTI data for unit 3-2).The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

Abstract

Mauritania, like many countries in the Sahel, regularly face recurrent plagues such as droughts, floods, bird invasions, off-season rains, as well as, regional security issues. Drought, for example, is a common phenomenon in the south of Mauritania, which favors food insecurity and malnutrition, and significantly reduces household resilience while increasing their vulnerability to future shocks. Apart from the fact that only 0.5 percent of the land is suitable for agriculture, Mauritania consists of reliefs and very large, fragile agroecological complexes which are also faced with the effects of climate change.

In recent years, food crises and nutritional factors have been regularly observed due to structural causes which has poverty as its common denominator. These crises, as well as, climatic factors have a negative consequence on natural resources and reduce the resilience of livelihoods, thereby generating a loss in productivity and poor governance of natural resources. The concept of resilience generally defines the capacity of individuals, households, communities and countries to absorb shocks and adapt to a changing environment, while transforming the institutional environment in the long term. Thus, it is necessary to set up interventions that will have an impact on adaptability and risk management over time, in order to strengthen the resilience of vulnerable households.

For more than 10 years, FAO has measured the household resilience index in different countries, using a tool developed for this purpose; Resilience Index Measure and Analysis (RIMA). RIMA analysis requires household data, covering the different aspects of livelihood; activities (productive and non-productive), social safety nets, income, access to basic services (such as schools, markets, transportation etc.) and adaptive capacity. Following the two RIMA surveys carried out in 2015 and 2016 during the lean season and the post-harvest period, this survey was carried out in 2017 to determine the resilience index in all regions of the country.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling size used in the household survey was determined by the FAO - ESA statistical team based on the results of the General Census of Population and Housing (RGPH) 2013, Permanent Survey on Household Living Conditions (EPCV) 2014 and the results of Resilience Index Measurement and Analysis (RIMA) surveys conducted in 2015 and 2016. A total sample of 3,560 households was selected. A 2 stage, simple random sampling method was employed to select the sampled households, distributed among the different rural and urban areas of the country.

The first stage sampling frame consists of an exhaustive list of Census Districts (CD) from the mapping of the RGPH carried out in 2013. An average CD has a population of about 1,000 people (approximately 200 households). The frame has been reorganized into 25 strata, corresponding to the total number of districts in the country, each subdivided into two environments, except Nouakchott which constitutes the 25th stratum. Drawing units called primary units are made up of census districts in the sampling frame at the level of each stratum.

The second stage sampling frame consists of the list of households in each CD sampled. This database was updated after a preliminary count which takes place shortly before the actual data collection in order to reduce the risks linked to the mobility of households. A total of 20 households were drawn from each CD counted.

Out of the 3,560 sampled households, 2,826 were interviewed.

Sampling deviation

Some teams encountered several difficulties related mainly to access, due to the collection period (winter). Also, the methodology used i.e carrying out census of districts before drawing sample households caused a delay in data collection and therefore the time provided was not sufficient to ensure collection at all level of the sampled census districts.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data collection operation was performed using tablets. The program entered, designed by the statistical office has been tested and all constraints/controls necessary to ensure data quality have been integrated into the program.This program has been shared and tested before training. Also, consistency procedures have been incorporated into the program to minimize collection errors and ensure harmonization and consistency between different sections of the questionnaire.

In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

Response rate

The response rate was 79.4%.

Data appraisal

A 5-day training was provided by the FAO team in collaboration with the team from the national statistical office on the RIMA-national questionnaire. This training was done to examine the questionnaire and explain to the different participants the meaning of all the questions asked. During this training, a practical session on the tablets was provided by the statistical team in order to allow the data collection agents understand the handling and testing of the questionnaire. At the end of this training, a pilot survey was organized in some districts of Nouakchott. This survey revealed errors in the collection program which were corrected before field teams were deployed for data collection.

The data collection in the field lasted 1 month and 10 days. In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra NCEA project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-1 Analysis Ready Data.

RVI and RVIv files are generated for Sentinel-1 orbit 132 (ascending) every 12 days.

Indices have been generated using the Defra and JNCC Sentinel-1 and Sentinel-2 ARD for the granules and scenes described above. As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.

Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

For more information on the consumer price indexes, see:

• (1) Bureau of Economic Analysis. “CPI Detailed Report.” 2013
• (2) Handbook of Methods
• (3) Understanding the CPI: Frequently Asked Questions

This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

How to use this dataset

• Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
• Study the influence of Consumer Price Index For All Urban Consumers: All Items on Title:
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The real-time index database market is experiencing robust growth, driven by the increasing demand for immediate insights from large volumes of streaming data across diverse industries. The market's expansion is fueled by the need for faster data processing and analysis, particularly in applications requiring real-time decision-making, such as fraud detection, cybersecurity threat response, and algorithmic trading. Cloud-based solutions are dominating the market due to their scalability, cost-effectiveness, and ease of deployment, attracting both individual developers and large enterprises. While on-premises deployments still hold a segment of the market, the shift towards cloud is undeniable. Key players like Elastic, Amazon Web Services (AWS), Apache Solr, Splunk, and Microsoft are fiercely competing, constantly innovating to offer enhanced features and performance. The market is geographically diverse, with North America and Europe currently holding significant shares, although rapid growth is anticipated in regions like Asia-Pacific, driven by increasing digitalization and adoption of advanced analytics. The overall market is estimated to be valued at $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 18% between 2025 and 2033, indicating significant future potential. Factors like rising data volumes, increasing need for real-time analytics across diverse sectors, and enhanced data security measures are key drivers, while challenges including data complexity, integration issues, and cost considerations are potential restraints to market expansion. The market segmentation reveals a significant proportion of enterprise users adopting real-time index databases, highlighting the critical role of these technologies in streamlining business operations and improving decision-making capabilities within larger organizations. While individual users contribute to the market, the enterprise segment is a key engine for growth. Future growth will likely be shaped by technological advancements, including the development of more efficient indexing algorithms and enhanced support for diverse data formats. Furthermore, strategic partnerships and mergers & acquisitions will play a crucial role in reshaping the competitive landscape and fostering innovation within the real-time index database market.

Graph and download economic data for Chain-Type Quantity Index for Real GDP: All Industry Total in the United States (USQQGSP) from Q1 2005 to Q1 2025 about quantity index, GSP, industry, GDP, and USA.