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.

About this dataset

Context

The dataset tabulates the Daviess County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Daviess County. The dataset can be utilized to understand the population distribution of Daviess County by age. For example, using this dataset, we can identify the largest age group in Daviess County.

Key observations

The largest age group in Daviess County, KY was for the group of age 10-14 years with a population of 7,255 (7.08%), according to the 2021 American Community Survey. At the same time, the smallest age group in Daviess County, KY was the 80-84 years with a population of 2,050 (2.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Daviess County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Daviess County total population. 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 Daviess County Population by Age. You can refer the same here

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

About this dataset

Context

The dataset tabulates the Elberta 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 Elberta 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 2022, the population of Elberta was 2,050, a 2.04% increase year-by-year from 2021. Previously, in 2021, Elberta population was 2,009, an increase of 1.26% compared to a population of 1,984 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Elberta increased by 444. In this period, the peak population was 2,050 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Elberta is shown in this column.
• Year on Year Change: This column displays the change in Elberta 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 Elberta Population by Year. You can refer the same here

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

The ECHAM climate model has been developed from the ECMWF atmospheric model (therefore the first part of its name: EC) and a comprehensive parameterisation package developed at Hamburg therefore the abbreviation HAM) which allows the model to be used for climate simulations. The model is a spectral transform model with 19 atmospheric layers and the results used here derive from experiments performed with spatial resolution T42 (which approximates to about 2.8 degrees longitude/latitude resolution). The model has also been used at resolutions in the range T21 to T106. ECHAM4 is the current generation in the line of ECHAM models (Roeckner, et al., 1992). A summary of developments regarding model physics in ECHAM4 and a description of the simulated climate obtained with the uncoupled ECHAM4 model is given in Roeckner et al. (1996). The initial sea surface temperature and sea-ice data is the COLA/CAC AMIP SST and sea-ice data set. The mean terrain heights are computed from high resolution US Navy data set. The fraction of grid area covered by vegetation based on the Wilson and Henderson-Sellers (1985) data set. The ocean albedo is a function of solar zenith angle and the land albedo from the satellite data of Geleyn and Preuss (1983). A diurnal cycle and gravity wave-drag is included. The time-step of the model is 24 minutes, except for radiation which uses two hours. The ocean model is an updated version of the isopycnal model (OPYC3) developed by Josef Oberhuber (Oberhuber, 1993) at the Max-Planck-Institute for Meteorology, Hamburg, Germany. The name OPYC is derived from Ocean and isoPYCnal co-ordinates. The concept to use isopycnals as the vertical co-ordinate system for an OGCM is based on the observation that the interior ocean behaves as a rather conservative fluid. Even over long distances the origin of water masses can be traced back by considering the distribution of active or passive tracers. Treating the ocean as a conservative fluid fails in areas of significant turbulence activity such as the surface boundary layer. A surface mixed-layer is therefore coupled to the interior ocean in order to represent near-surface vertical mixing and to improve the response time-scales to atmospheric forcing which is controlled by the mixed-layer thickness. Since the model is designed for studies on large scales, a sea ice model with rheology is included and serves the purpose of de-coupling the ocean from extreme high-latitude winter conditions and promotes a realistic treatment of the salinity forcing due to melting or freezing sea ice. The experiments from which results are used here are the 1000-year unforced control simulation using the coupled ECHAM4/OPYC3 model and then two climate change simulations. The greenhouse gas only forced experiment (referred to as GGa1) used historical greenhouse gas forcing from 1860 to 1990 followed by a 1 per cent annum increase in radiative forcing from 1990 to 2099. The greenhouse gas and sulphate aerosol forced experiment (referred to as GSa1) used the GGa1 forcing, plus the negative forcing due to sulphate aerosols. This was represented by means of an increase in clear-sky surface albedo proportional to the local sulphate loading. The indirect effects of aerosols were not simulated. For 1860 to 1990 the historic sulphate aerosol forcing estimate was used and for 1990 to 2049 the aerosol forcing estimated for the IS92a emissions scenario. The GSa1 experiment did not extend beyond 2049. Fuller details of the ECHAM4/OPYC3 coupled model can befound at the DDC Yellow Pages.Several papers describe results using this version of the model - see Bacher et al. (1998), Oberhuber et al. (1998), Zhang et al. (1998). The climate sensitivity of ECHAM4 is about 2.6 degrees C.The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attention on contributing to the local community. Elsewhere, the trend is towards ncreased investment in education and science and growth in economic productivity. Social and political structures diversify with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity.

About this dataset

Context

The dataset tabulates the Colfax 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 Colfax 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 Colfax was 2,050, a 0% decrease year-by-year from 2022. Previously, in 2022, Colfax population was 2,050, an increase of 1.08% compared to a population of 2,028 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Colfax increased by 547. In this period, the peak population was 2,050 in the year 2022. 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 Colfax is shown in this column.
• Year on Year Change: This column displays the change in Colfax 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 Colfax Population by Year. You can refer the same here

The ECHAM climate model has been developed from the ECMWF atmospheric model (therefore the first part of its name: EC) and a comprehensive parameterisation package developed at Hamburg therefore the abbreviation HAM) which allows the model to be used for climate simulations. The model is a spectral transform model with 19 atmospheric layers and the results used here derive from experiments performed with spatial resolution T42 (which approximates to about 2.8 degrees longitude/latitude resolution). The model has also been used at resolutions in the range T21 to T106. ECHAM4 is the current generation in the line of ECHAM models (Roeckner, et al., 1992). A summary of developments regarding model physics in ECHAM4 and a description of the simulated climate obtained with the uncoupled ECHAM4 model is given in Roeckner et al. (1996). The initial sea surface temperature and sea-ice data is the COLA/CAC AMIP SST and sea-ice data set. The mean terrain heights are computed from high resolution US Navy data set. The fraction of grid area covered by vegetation based on the Wilson and Henderson-Sellers (1985) data set. The ocean albedo is a function of solar zenith angle and the land albedo from the satellite data of Geleyn and Preuss (1983). A diurnal cycle and gravity wave-drag is included. The time-step of the model is 24 minutes, except for radiation which uses two hours. The ocean model is an updated version of the isopycnal model (OPYC3) developed by Josef Oberhuber (Oberhuber, 1993) at the Max-Planck-Institute for Meteorology, Hamburg, Germany. The name OPYC is derived from Ocean and isoPYCnal co-ordinates. The concept to use isopycnals as the vertical co-ordinate system for an OGCM is based on the observation that the interior ocean behaves as a rather conservative fluid. Even over long distances the origin of water masses can be traced back by considering the distribution of active or passive tracers. Treating the ocean as a conservative fluid fails in areas of significant turbulence activity such as the surface boundary layer. A surface mixed-layer is therefore coupled to the interior ocean in order to represent near-surface vertical mixing and to improve the response time-scales to atmospheric forcing which is controlled by the mixed-layer thickness. Since the model is designed for studies on large scales, a sea ice model with rheology is included and serves the purpose of de-coupling the ocean from extreme high-latitude winter conditions and promotes a realistic treatment of the salinity forcing due to melting or freezing sea ice. The experiments from which results are used here are the 1000-year unforced control simulation using the coupled ECHAM4/OPYC3 model and then two climate change simulations. The greenhouse gas only forced experiment (referred to as GGa1) used historical greenhouse gas forcing from 1860 to 1990 followed by a 1 per cent annum increase in radiative forcing from 1990 to 2099. The greenhouse gas and sulphate aerosol forced experiment (referred to as GSa1) used the GGa1 forcing, plus the negative forcing due to sulphate aerosols. This was represented by means of an increase in clear-sky surface albedo proportional to the local sulphate loading. The indirect effects of aerosols were not simulated. For 1860 to 1990 the historic sulphate aerosol forcing estimate was used and for 1990 to 2049 the aerosol forcing estimated for the IS92a emissions scenario. The GSa1 experiment did not extend beyond 2049. Fuller details of the ECHAM4/OPYC3 coupled model can be found at the DDC Yellow Pages. Several papers describe results using this version of the model - see Bacher et al. (1998), Oberhuber et al. (1998), Zhang et al. (1998). The climate sensitivity of ECHAM4 is about 2.6 degrees C.The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attentionon contributing to the local community. Elsewhere, the trend is towards ncreased investment in education and science and growth in economic productivity. Social and political structures diversify with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, and hence income per capita, are maintained or increased. With the emphasis on family and community life, fertility rates decline only slowly, although they vary among regions. Hence, this scenario family has high population growth (to 15 billion by 2100) with comparatively low incomes per capita relative to the A1 andB1 worlds, at US$7,200 in 2050 and US$16,000 in 2100.Technological change is rapid in some regions and slow in others as industry adjusts to local resource endowments, culture, and education levels. Regions with abundant energy and mineral resources evolve more resource intensive economies, while those poor in resources place very high priority on minimizing import dependence through technological innovation to improve resource efficiency and make use of substitute inputs. The fuel mix in different regions is determined primarily by resource availability. And divisions among regions persist in terms of their mix of technologies, with high-income but resource-poor regions shifting toward advanced post fossil technologies (renewables in regions of large land availability, nuclear in densely populated, resource poor regions) and low-income resource-rich regions generally relying on older fossil technologies.With substantial food requirements, agricultural productivity is one of the main focus areas for innovation and RD efforts in this future. Initially high levels of soil erosion and water pollution are eventually eased through the local development of more sustainable high-yield agriculture.Although attention is given to potential local and regional environmental damage, it is not uniform across regions. For example, sulfur and particulate emissions are reduced in Asia due to impacts on human health and agricultural production but increase in Africa as a result of the intensified exploitation of coal and other mineral resources. The A2 world sees high energy and carbon intensity, and correspondingly high GHG emissions. Its CO2 emissions are the highest of all four scenario families. Data are available for the following periods: 1961-1990, 2010-2039; 2040-2069; and 2090-2099, mean and monthly change fields.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model. From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not without its problems. In particular, many communities could face some of the problems of social exclusion encountered by the wealthiest countries in the 20th century and in many places income growth could come with increased pressure on the global commons. Energy and mineral resources are abundant in this scenario family because of rapid technical progress, which both reduce the resources need to produce a given level of output and increases the economically recoverable reserves. Final energy intensity (energy use per unit of GDP) decreases at a... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.296.1 for complete metadata about this dataset.

About this dataset

Context

The dataset tabulates the Oliver township 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 Oliver township 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 2022, the population of Oliver township was 2,059, a 0.44% increase year-by-year from 2021. Previously, in 2021, Oliver township population was 2,050, an increase of 0.20% compared to a population of 2,046 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Oliver township increased by 11. In this period, the peak population was 2,076 in the year 2009. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Oliver township is shown in this column.
• Year on Year Change: This column displays the change in Oliver township 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 Oliver township Population by Year. You can refer the same here

GCAM-USA Scenarios for GODEEEP

This dataset contains a set of twelve future (2020-2050) scenarios modeled by GCAM-USA for the GODEEEP project for the purpose of studying the effects of climate, socioeconomic change, technology change, current decarbonization incentives, and longer-term decarbonization policies on the U.S. energy-economy, the electricity grid, human well-being, and the environment.

GCAM-USA is a version of the Global Change Analysis Model (GCAM) with state-level detail in the United States. GCAM-USA simulates the supply/demand dynamics and interactions of four systems (energy, water, agriculture and land use, and the economy) in 32 geopolitical regions in the world, including the 50 states and the District of Columbia within the U.S. It can be configured to include climate impacts on energy demands, water availability, and crop yields. The GCAM-USA scenarios for GODEEEP include business-as-usual (BAU) as well as net-zero (NZ) greenhouse gas emissions by 2050 policy scenarios. All the NZ policy scenarios include a carbon-free electricity system by 2035, also referred to as a "clean grid." Net-zero greenhouse gas emissions by 2050 requires a combination of solutions including carbon sequestration, new fuels, long- and short-term energy storage, and new technologies such as direct air capture that have not previously been included in GCAM-USA. The GCAM-USA scenarios for GODEEEP represent alternative combinations of assumptions for climate impacts, decarbonization policies, decarbonization incentives, and carbon capture and sequestration technology availability.

GCAM-USA outputs are provided as XML databases, which can be read by the GCAM Model Interface or packages such as gcamreader for Python or gcamextractor for R.

Summaries of each scenario are provided below. For additional discourse on the scenarios, see Ou et al 2023 and other upcoming papers to be announced on the GODEEEP website.

Abbreviations used in scenario names and descriptions

• BAU: Business-As-Usual. These scenarios represent the continuation of policies from the recent past and include major state-level clean energy policies but do not include any federal policies or incentives for a clean grid or a net-zero economy.
• NZ: Net-Zero. These scenarios represent a U.S. decarbonization goal that requires a carbon-free electricity grid by 2035 and a net-zero greenhouse gas emissions economy by 2050.
• IRA: Inflation Reduction Act. These scenarios include the IRA incentives for energy efficiency as well as clean energy, transportation, and fuels between 2025 and 2035.
• CCS: Carbon Capture and Sequestration. These scenarios assume that electricity generators equipped with CCS technology are available, whereas the other scenarios assume CCS technology is unavailable.
• Climate. These scenarios include the dynamic effects of a climate pathway (RCP8.5) on heating and cooling degree days (HDD/CDD) in the period 2020-2050. Note that in scenarios without "climate" in their name, HDD/CDD are left static at their 2020 levels.
• SSP2: Shared Socioeconomic Pathway 2. A "middle of the road" socioeconomic pathway where population and economic growth trends follow historical patterns.

Scenario Descriptions

Acknowledgement

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Waverly by race. It includes the distribution of the Non-Hispanic population of Waverly across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Waverly across relevant racial categories.

Key observations

Of the Non-Hispanic population in Waverly, the largest racial group is Black or African American alone with a population of 2,050 (78.51% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Waverly
• Population: The population of the racial category (for Non-Hispanic) in the Waverly is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Waverly total Non-Hispanic population. 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 Waverly Population by Race & Ethnicity. You can refer the same here

Introduction

This dataset contains power plant infrastructure siting information modeled by the Capacity Expansion Regional Feasibility (CERF) model for the GODEEEP project for the purpose of studying power plant landscape evolution under alternative capacity expansion scenarios.

CERF is an open-source geospatial Python package for evaluating and analyzing future electricity technology capacity expansion feasibility.

Summaries of each of the two siting scenarios included are provided below. For additional information, see Ou et al. 2023and the GODEEEP website.

Scenario Descriptions

• business_as_usual_ira_ccs_climate :
  • This scenario does not include any long-term federal policies requiring decarbonization.
  • It does include the US Inflation Reduction Act (IRA) incentives.
  • It assumes that CCS technologies are available.
• net_zero_ira_ccs_climate:
  • This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.
  • It does include US IRA incentives.
  • It assumes that CCS technologies are available.

Climate and Socioeconomic Assumptions

• Climate: These scenarios include the dynamic effects of a climate pathway (RCP8.5) on heating and cooling degree days (HDD/CDD) in the period 2020-2050.
• Socioeconomic: Shared Socioeconomic Pathway 2. A "middle of the road" socioeconomic pathway where population and economic growth trends follow historical patterns.

Data Information

• Coordinates provided in each of the data files follows the NAD 1983 Albers contiguous USA Coordinate Reference System (ESRI:102003).
• New infrastructure sitings are provided for the 11 states in the Western Interconnection including: Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Oregon, Washington, and Wyoming.
• The file type represents a specific spatial resolution of infrastructure sitings:
  • 1 km level data:
    
    • Includes new power plant sitings by year at a 1 km-squared resolution.
    • For technologies where a representative power plant facility can be sited within the area of a single square km (e.g., natural gas, biomass), coordinates provided at this resolution are representative of the entire plant location where the facility is sited.
    • For Solar PV, Solar CSP, Onshore Wind, and Offshore Wind technologies, representative power plants (i.e., farms) are typically larger than a single square km. Therefore, coordinates provided at the 1 km-squared resolution in this file represent the total capacity (MW) of these technologies that was sited within a 1 km x 1km grid cell and does not represent a standalone wind or solar farm facility or an individual wind turbine.
  • Plant-level data
    • This file type includes all power plant locations at the plant level including both new additions and pre-existing facilities.
    • Coordinates in this file represent the center of a power plant facility.
    • For CERF-sited technologies where a representative power plant facility can be sited within the area of a single square km (e.g., natural gas, biomass), coordinates will match those provided in the CERF siting output file.
    • For coodinates associated with CERF-sited Solar PV, Solar CSP, Onshore Wind, and Offshore Wind farms, the center coordinates of the farm are provided. Farms in this file are spatially-clustered aggregates of individual 1 km-squared solar and wind sitings from the CERF-siting output.

File Descriptions

Data Descriptions

Acknowledgement

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

This raster dataset represents forecast development (2050) in the CHD REA.

The methodology used to produce ICLUS v2.1 population projections differs from ICLUS v2.0. The demographic components of change (i.e., rates of fertility and mortality) for ICLUS v2.1 were taken directly from the Wittgenstein Centre Data Explorer (http:witt.null2.netshinywic). These projections were produced much more recently than the Census projections used in ICLUS v2.0, and incorporate more recent observations of population change.

The SSP5 narrative describes a rapidly growing and flourishing global economy that remains heavily dependent on fossil fuels, and a U.S. population that exceeds 730 million by 2100. As such, ICLUS v2.1 land use projections under SSP5 result in a considerably larger expansion of developed lands relative to SSP2. Migration flows under SSP5 were concentrated more heavily into Micropolitan areas, drawing more migrants from both Metropolitan and rural areas, and output from the HadGEM2-ES climate model was used to dynamically update climate amenity values for all possible migration destinations. With respect to temperature, HadGEM2-ES is a more sensitive climate model in that it projects relatively large temperature increases over much of the conterminous United States. Climate model output used to inform domestic migrations was acquired from the Bias-Correction Spatial Disaggregation CMIP5 archive available at: http:gdo-dcp.ucllnl.org. The RCP8.5 emissions trajectory assumes steadily increasing greenhouse gas emissions through the year 2100.

About this dataset

Context

The dataset tabulates the Conemaugh township 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 Conemaugh township 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 2022, the population of Conemaugh township was 2,044, a 0.29% decrease year-by-year from 2021. Previously, in 2021, Conemaugh township population was 2,050, a decline of 0.29% compared to a population of 2,056 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Conemaugh township decreased by 358. In this period, the peak population was 2,402 in the year 2000. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Conemaugh township is shown in this column.
• Year on Year Change: This column displays the change in Conemaugh township 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 Conemaugh township Population by Year. You can refer the same here

The ECHAM climate model has been developed from the ECMWF atmospheric model (therefore the first part of its name: EC) and a comprehensive parameterisation package developed at Hamburg therefore the abbreviation HAM) which allows the model to be used for climate simulations. The model is a spectral transform model with 19 atmospheric layers and the results used here derive from experiments performed with spatial resolution T42 (which approximates to about 2.8 degrees longitude/latitude resolution). The model has also been used at resolutions in the range T21 to T106. ECHAM4 is the current generation in the line of ECHAM models (Roeckner, et al., 1992). A summary of developments regarding model physics in ECHAM4 and a description of the simulated climate obtained with the uncoupled ECHAM4 model is given in Roeckner et al. (1996). The initial sea surface temperature and sea-ice data is the COLA/CAC AMIP SST and sea-ice data set. The mean terrain heights are computed from high resolution US Navy data set. The fraction of grid area covered by vegetation based on the Wilson and Henderson-Sellers (1985) data set. The ocean albedo is a function of solar zenith angle and the land albedo from the satellite data of Geleyn and Preuss (1983). A diurnal cycle and gravity wave-drag is included. The time-step of the model is 24 minutes, except for radiation which uses two hours. The ocean model is an updated version of the isopycnal model (OPYC3) developed by Josef Oberhuber (Oberhuber, 1993) at the Max-Planck-Institute for Meteorology, Hamburg, Germany. The name OPYC is derived from Ocean and isoPYCnal co-ordinates. The concept to use isopycnals as the vertical co-ordinate system for an OGCM is based on the observation that the interior ocean behaves as a rather conservative fluid. Even over long distances the origin of water masses can be traced back by considering the distribution of active or passive tracers. Treating the ocean as a conservative fluid fails in areas of significant turbulence activity such as the surface boundary layer. A surface mixed-layer is therefore coupled to the interior ocean in order to represent near-surface vertical mixing and to improve the response time-scales to atmospheric forcing which is controlled by the mixed-layer thickness. Since the model is designed for studies on large scales, a sea ice model with rheology is included and serves the purpose of de-coupling the ocean from extreme high-latitude winter conditions and promotes a realistic treatment of the salinity forcing due to melting or freezing sea ice. The experiments from which results are used here are the 1000-year unforced control simulation using the coupled ECHAM4/OPYC3 model and then two climate change simulations. The greenhouse gas only forced experiment (referred to as GGa1) used historical greenhouse gas forcing from 1860 to 1990 followed by a 1 per cent annum increase in radiative forcing from 1990 to 2099. The greenhouse gas and sulphate aerosol forced experiment (referred to as GSa1) used the GGa1 forcing, plus the negative forcing due to sulphate aerosols. This was represented by means of an increase in clear-sky surface albedo proportional to the local sulphate loading. The indirect effects of aerosols were not simulated. For 1860 to 1990 the historic sulphate aerosol forcing estimate was used and for 1990 to 2049 the aerosol forcing estimated for the IS92a emissions scenario. The GSa1 experiment did not extend beyond 2049. Fuller details of the ECHAM4/OPYC3 coupled model can be found at the DDC Yellow Pages. Several papers describe results using this version of the model - see Bacher et al. (1998), Oberhuber et al. (1998), Zhang et al. (1998). The climate sensitivity of ECHAM4 is about 2.6 degrees C.The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attentionon contributing to the local community. Elsewhere, the trend is towards ncreased investment in education and science and growth in economic productivity. Social and political structures diversify with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially.

Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks.

Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits.

The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries.

Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities.

Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity.

The CSIRO Atmospheric Research Mark 2b climate model (Hirst et al., 1996, 1999) has recently been used for a number of more sophisticated climate change simulations. These start from 1880 to avoid the "cold start problem". This version of the CSIRO model includes the Gent-McWilliams mixing scheme in the ocean and shows greatly reduced climate drift relative to earlier versions (e.g. Dix and Hunt, 1998). The drift in global mean surface temperature in the new control run is about -0.02 degrees C/century. Note that the model uses flux correction. The model atmosphere has 9 levels in the vertical and horizontal resolution of spectral R21 (approximately 5.6 by 3.2 degrees). The ocean model has the same horizontal resolution with 21 levels. The equilibrium sensitivity to doubled CO2 of a mixed layer ocean version of the model is 4.3 degrees. This is at the high end of the range of model sensitivities (e.g. IPCC 1995, Table 6.3). In the basic greenhouse gas experiment the model combines the effect of all radiatively active trace gases into an "equivalent" CO2 concentration. Observed concentrations are used from 1880 to 1990 and the IS92a projections into the future. This gives close to a 1%/year compounding increase of equivalent CO2. Another model experiment includes the negative radiative forcing from atmospheric sulphate aerosol. The direct aerosol forcing is included via a perturbation of the surface albedo, similarly to the Hadley Centre experiments described by Mitchell et al (1995) and Mitchell and Johns (1997) . The sulphate concentrations are the same as used in the Hadley Centre experiments. However the chosen aerosol optical properties are different, giving a present day forcing due to anthropogenic sulphate of about -0.4 W/m^2. This can be compared to the 1880-1990 greenhouse gas forcing of about 2 W/m^2. The magnitude of the 20th century warming in the model including aerosol matches the observed reasonably well. However there are a number of forcings missing from the model, including solar variability, sulphate indirect effect and the effect of soot. The climate sensitivity of CSIRO-Mk2 is about 4.3 degrees C (Watterson et al.,1997). From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not without its problems. In particular, many communities could face some of the problems of social exclusion encountered by the wealthiest countries in the 20th century and in many places income growth could come with increased pressure on the global commons. Energy and mineral resources are abundant in this scenario family because of rapid technical progress, which both reduce the resources need to produce a given level of output and increases the economically recoverable reserves. Final energy intensity (energy use per unit of GDP) decreases at an average annual rate of 1.3 percent. With the rapid increase in income, dietary patterns shift initially significantly towards increased consumption of meat and dairy products, but may decrease subsequently with increasing emphasis on health of an aging society. High incomes also translate into high car ownership, sprawling suburbanization and dense transport networks, nationally and internationally. Land prices increase faster than income per ... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.74.5 for complete metadata about this dataset.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Granite Falls by race. It includes the distribution of the Non-Hispanic population of Granite Falls across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Granite Falls across relevant racial categories.

Key observations

Of the Non-Hispanic population in Granite Falls, the largest racial group is White alone with a population of 2,050 (87.57% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Granite Falls
• Population: The population of the racial category (for Non-Hispanic) in the Granite Falls is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Granite Falls total Non-Hispanic population. 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 Granite Falls Population by Race & Ethnicity. You can refer the same here

The experiments with the GFDL model used here were performed using the coupled ocean-atmosphere model described in Manabe et al. (1991) and Stouffer et al., (1994) and references therein. The model has interactive clouds and seasonally varying solar insolation. The atmospheric component has nine finite difference (sigma) levels in the vertical. This version of the model was run at a rhomboidal resolution of 15 waves (R15) yielding an equivalent resolution of about 4.5 degrees latitude by 7.5 degrees longitude. The model has global geography consistent with its computational resolution and seasonal (but not diurnal) variation of insolation. The ocean model is based on that of Byan and Lewis (1979) with a spacing between gridpoints of 4.5 degrees latitude and 3.7 degrees longitude. It has 12 unevenly spaced levels in the vertical dimension. To reduce model drift, the fluxes of heat and water are adjusted by amounts which vary seasonally and geographically, but do not change from one year to another. The model also includes a dynamic sea-ice model (Bryan, 1969) which allows the system additional degrees of freedom. The 1000-year unforced simulation used here is described in Manabe and Stouffer (1996). The drift in global-mean temperature during this unforced simulation is very small at about -0.023 degrees C per century. The two GFDL-R15 climate change experiments used here use the IS92a scenario of estimated past and future greenhouse gas (GGa1) and combined greenhouse gas and sulphate aerosol (GSa1) forcing for the period 1765-2065 (Haywood et al., 1997). For the GGa1 experiment only the 100-year segment from 1958-2057 are available through the IPCC DDC. The radiative effects of all greenhouse gases is represented in terms of an equivalent CO2 concentration, and the direct radiative sulphate aerosol forcing is parameterised in terms of specified spatially dependent surface albedo changes (following Mitchell et al., 1995). Results from these climate change experiments are discussed in Haywood et al. (1997). The model's climate sensitivity is about 3.7 degrees C. Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environme... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.181.1 for complete metadata about this dataset.