The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

This table shows the tract-level change in population for all Tennessee census tracts between two 5-year American Community Survey releases: 2007-2011 and 2012-2016. Each tract is also ranked to indicate its change in growth compared to all other census tracts in the state; the largest growth being ranked one and tracts with the largest declines ranked lowest.A test of statistical significance is included. Tracts with statistically significant changes in population at the 90% confidence level are noted in the 'Statistically Significant' field (STAT_SIGNIFICANT) as being "TRUE". Tracts with population change that could fall within the survey's margin of error are categorized as "FALSE".

Analysis of ‘Final Report of the Asian American Quality of Life (AAQoL)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/feb17efd-fa23-4e28-8acb-993def19d8a3 on 28 January 2022.

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

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

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

United States Database Security Market was valued at USD 7.6 billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 19.2% through 2029.

County Boundary for Pitt County North Carolina - This dataset only contains one polygon representing the Pitt County boundary. This dataset is maintained in collaboration between Pitt County Tax Administration and Pitt County Management Information Systems. For specific questions regarding the data you may contact the Pitt County MIS department at 252-902-3800 OR contact Pitt County Tax Administration at 252-902-3400.Pitt County is a county located in the U.S. state of North Carolina. As of the 2010 census, the population was 168,148, making it the seventeenth-most populous county in North Carolina. The county seat is Greenville. Pitt County comprises the Greenville, NC Metropolitan Statistical Area. As one of the fastest growing centers in the state, the county has seen a population boom since 1990.

As per world agriculture statistics India is the world's largest producer of many fresh fruits like banana, mango, guava, papaya, lemon and vegetables like chickpea, okra and milk, major spices like chili pepper, ginger, fibrous crops such as jute, staples such as millets and castor oil seed. India is the second largest producer of wheat and rice, the world's major food staples.

India is currently the world's second largest producer of several dry fruits, agriculture-based textile raw materials, roots and tuber crops, pulses, farmed fish, eggs, coconut, sugarcane and numerous vegetables. India is ranked under the world's five largest producers of over 80% of agricultural produce items, including many cash crops such as coffee and cotton, in 2010. India is one of the world's five largest producers of livestock and poultry meat, with one of the fastest growth rates, as of 2011.

One report from 2008 claimed that India's population is growing faster than its ability to produce rice and wheat.[20] While other recent studies claim that India can easily feed its growing population, plus produce wheat and rice for global exports, if it can reduce food staple spoilage/wastage, improve its infrastructure and raise its farm productivity like those achieved by other developing countries such as Brazil and China.

Data collected from Ministry of Agriculture and Farmers Welfare of India

Reflects housing density depicting where humans and their structures meet or intermix with wildland fuels.Colorado is one of the fastest growing states in the Nation, with much of this growth occurring outside urban boundaries. This increase in population across the state will impact counties and communities that are located within the Wildland Urban Interface (WUI). The WUI is described as the area where structures and other human improvements meet and intermingle with undeveloped wildland or vegetative fuels. Population growth within the WUI substantially increases the risk from wildfire.The Wildland Urban Interface (WUI) layer reflects housing density depicting where humans and their structures meet or intermix with wildland fuels. In the past, conventional wildland-urban interface data sets, such as USFS SILVIS, have been used to reflect these concerns. However, USFS SILVIS and other existing data sources did not provide the level of detail needed by the Colorado State Forest Service and local fire protection agencies, particularly reflecting encroachment into urban core areas.The new WUI data set is derived using advanced modeling techniques based on the Where People Live (housing density) data set and 2021 LandScan USA population count data available from the Department of Homeland Security, HSIP data. WUI is simply a subset of the Where People Live data set. The primary difference is populated areas surrounded by sufficient non-burnable areas (i.e. interior urban areas) are removed from the Where People Live data set, as these areas are not expected to be directly impacted by a wildfire. Fringe urban areas, i.e. those on the edge of urban areas directly adjacent to burnable fuels are included in the WUI. Advanced encroachment algorithms were used to define these fringe areas.Data is modeled at a 20-meter grid cell resolution, which is consistent with other CO-WRA layers. The WUI classes are based on the number of houses per acre. Class breaks are based on densities well understood and commonly used for fire protection planning.

fast-growing companies, including employee data, regionally organised by federal state, annual time series from 2008 onwards

BackgroundPublic health expenditure is one of the fastest-growing spending items in EU member states. As the population ages and wealth increases, governments allocate more resources to their health systems. In view of this, the aim of this study is to identify the key determinants of public health expenditure in the EU member states.MethodsThis study is based on macro-level EU panel data covering the period from 2000 to 2018. The association between explanatory variables and public health expenditure is analyzed by applying both static and dynamic econometric modeling.ResultsAlthough GDP and out-of-pocket health expenditure are identified as the key drivers of public health expenditure, there are other variables, such as health system characteristics, with a statistically significant association with expenditure. Other variables, such as election year and the level of public debt, result to exert only a modest influence on the level of public health expenditure. Results also indicate that the aging of the population, political ideologies of governments and citizens’ expectations, appear to be statistically insignificant.ConclusionSince increases in public health expenditure in EU member states are mainly triggered by GDP increases, it is expected that differences in PHE per capita across member states will persist and, consequently, making it more difficult to attain the health equity sustainable development goal. Thus, measures to reduce EU economic inequalities, will ultimately result in reducing disparities in public health expenditures across member states.

Cloud Computing Market Growth | Industry Analysis, Size & Forecast Report

Dataset updated: Jun 27, 2024

Dataset authored and provided by: Mordor Intelligence

License: https://www.mordorintelligence.com/privacy-policy

Time period covered: 2019 - 2029

Area covered: Global

Variables measured: CAGR, Market size, Market share analysis, Global trends, Industry forecast

Description: The Cloud Computing Market size is estimated at USD 0.68 trillion in 2024, and is expected to reach USD 1.44 trillion by 2029, growing at a CAGR of 16.40% during the forecast period (2024-2029).

Report Attribute

Quantitative Units: Revenue in USD Billion, Volumes in Units, Pricing in USD

Regions and Countries Covered:

Industry Segmentation Covered:

By Cloud Computing: IaaS, SaaS, PaaS

By End-User: IT and Telecom, BFSI, Retail and Consumer Goods, Manufacturing, Healthcare, Media and Entertainment

Market Players Covered: Amazon Web Services, Google LLC, Microsoft Corporation, Alibaba Cloud, and Salesforce

These data were created to describe the causes of land cover change that occurred in the Lower Rio Grande (LRG) Valley and Alluvial Floodplain ecoregions of Texas for the time intervals of 2001 to 2006 and 2006 to 2011. The study area covers approximately 600,000 hectares at the southernmost tip of Texas and is one of the fastest growing regions in the United States. Some of the largest cities in the area include Brownsville and Harlingen, Texas. Two raster maps showing the causes of land change were created at a 30-meter resolution using automated and manual photo interpretation techniques. There were 26 categories of land change causes (for example, urban expansion or surficial mining) identified across the LRG region. These categories can be used by researchers to summarize the historical patterns of land change for the region and to understand the impacts these land change causes may have on the region's ecology, hydrology, wildlife, and climate.