• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

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

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

About this dataset

Context

The dataset tabulates the Lookout Mountain 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 Lookout Mountain 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 Lookout Mountain was 2,050, a 0.49% decrease year-by-year from 2021. Previously, in 2021, Lookout Mountain population was 2,060, an increase of 0.05% compared to a population of 2,059 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Lookout Mountain increased by 76. In this period, the peak population was 2,060 in the year 2021. 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 Lookout Mountain is shown in this column.
• Year on Year Change: This column displays the change in Lookout Mountain 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 Lookout Mountain Population by Year. You can refer the same here

By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. JP - 2050 RCP 4.5 – Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b) JP - 2× 2050 RCP 4.5 - Juvenile period as 2× years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2× legend). Please see full metadata in 'Resources' section below.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

About this dataset

Context

The dataset tabulates the Deep River 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 Deep River 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 Deep River township was 2,070, a 0.98% increase year-by-year from 2021. Previously, in 2021, Deep River township population was 2,050, a decline of 0.00% compared to a population of 2,050 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Deep River township decreased by 154. In this period, the peak population was 2,244 in the year 2003. 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 Deep River township is shown in this column.
• Year on Year Change: This column displays the change in Deep River 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 Deep River township Population by Year. You can refer the same here

It has been established that the detailed figures of the PBL/CBS Regional Population and Household Forecasts published in 2019 and 2022 are insufficiently reliable. The forecasts for the number of inhabitants per municipality, province and COROP area do fall within the calculated reliability margins. The detailed information (for example, by age group or households) is not. PBL and CBS therefore advise against using the details from these regional population forecasts. This information has therefore been removed from StatLine. The data available in the table can be used. For more information, see the message At the moment no full Regional population and household forecast. For the figures that are still available, see Regional forecast 2023-2050; population, intervals, regional division 2021 Regional forecast 2023-2050; population, regional breakdown 2021 This table contains forecast figures on the composition of the population by gender, age and region on 1 January. In this new table, the previous forecast has been adjusted based on the most recent insights. The period for which the prognosis has been determined now runs from 2022 to 2050. The figures are based on the regional breakdown of 2021. In addition to provinces and COROP areas, this concerns municipalities with a population of 50,000 or more on 1 January 2021. Data available from: (forecast period) 2023 Status of the figures: The figures in this table are calculated forecast figures. Changes as of July 12, 2023: The details by age and gender have been removed from this table. It has been established that the detailed figures of the PBL/CBS Regional Population and Household Forecasts published in 2019 and 2022 are insufficiently reliable. That is why PBL and CBS advise not to use the details from these regional population forecasts. The forecast figures for the total population per municipality, province and COROP area do fall within the calculated reliability margins. When will new numbers come out? PBL and CBS cannot revise the relevant forecasts in the foreseeable future.

About this dataset

Context

The dataset tabulates the New Eagle 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 New Eagle 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 New Eagle was 2,027, a 0.69% decrease year-by-year from 2021. Previously, in 2021, New Eagle population was 2,041, a decline of 0.44% compared to a population of 2,050 in 2020. Over the last 20 plus years, between 2000 and 2022, population of New Eagle decreased by 202. In this period, the peak population was 2,280 in the year 2002. 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 New Eagle is shown in this column.
• Year on Year Change: This column displays the change in New Eagle 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 New Eagle Population by Year. You can refer the same here

PLANNING AREAS All Connections 2050 Long-Range Plan elements are available online at www.dvrpc.org/plan. The Plan has two primary documents: (1) The Connections 2050 Policy Manual (www.dvrpc.org/Products/21027) identifies the vision, goals, strategies, and a summary of the financial plan. (2) The Connections 2050 Process and Analysis Manual (www.dvrpc.org/Products/21028) provides a more detailed look at the Plan’s outreach, background information, analysis, and financial plan. Greater Philadelphia is a complex mosaic of 352 diverse cities, boroughs, and townships. The Connections 2045 Long-Range Plan characterizes each of the region’s municipalities as either a Core City, Developed Community, Growing Suburb, or Rural Area, as a means of categorizing the types of communities and defining the corresponding long-range planning policies most appropriate for each type. This categorization is shown on the Planning Areas and Centers dataset. Many municipalities have areas within their boundaries that fit the characteristics of more than one of these Planning Area types. Gloucester Township (in Camden County, New Jersey), for example, has neighborhoods that are fully developed, but it also has a significant number of undeveloped acres and forecasted population and employment growth more characteristic of a Growing Suburb. The intent of the Plan is to assign to each municipality the planning area type associated with the long-range planning policies that will be most beneficial to the entire community. While the Planning Areas and Centers map is a guide for policy direction at the regional scale, actual approaches should always be guided by local conditions. The region’s four Core Cities are Philadelphia, Trenton, Camden, and Chester. Targeted infrastructure investment, maintenance and rehabilitation, comprehensive neighborhood revitalization, and efforts focused on reinforcing a network of social and educational programs will help to rebuild and revitalize the region’s cities. Developed Communities are places that have already experienced most of their population and employment growth, and include inner ring communities adjacent to the Core Cities; railroad boroughs and trolley car communities; and mature suburban townships. Many of these communities are stable and thriving, offering affordable housing opportunities; access to transit; safe pedestrian and bicycling environments; and a strong community identity. Others, however, are experiencing population and employment losses; have deteriorating infrastructure systems; have aging resident populations living on limited incomes; and have stagnant or declining tax bases that cannot keep pace with rising service demands. Rehabilitation and maintenance of infrastructure systems and the housing stock, and local economic and community development can help to reinforce location advantages, while stabilizing neighborhoods and stemming decline. Growing Suburbs are communities that have a significant number of developable acres remaining and are experiencing or are forecast to experience significant population and/or employment growth. Key planning policies in these communities focus on the need to improve the form of development, reduce congestion, and mitigate the negative consequences of unmanaged growth, and include growth management and enhanced community design. Smart growth techniques that support a more concentrated development pattern (such as clustering, mixed uses, transit-oriented development, and transfer of development rights) can provide the critical mass necessary to support new transit services and other alternatives to the automobile. The quality of design and architectural character of the built environment, open space preservation, and the creation of an integrated system of open space and recreation are all priorities in these communities. Rural Areas include the region’s agricultural communities and communities with large remaining natural areas. Key policy approaches for these communities focus on preservation and limiting development, and include limited expansion of infrastructure systems, preservation of a rural lifestyle and village character, support for continued farming, and enhanced natural resource protection. Livable communities in these areas include rural centers that have an identi fi able main street, a mix of uses, higher densities than their surrounding uses, and a true sense of place.

Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of Calidris canutus roselaari (Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for C. c. roselaari. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of C. c. roselaari are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of C. c. roselaari (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR. Methods Study area and model species C. c. roselaari are long-distance migratory shorebirds that breed in Alaska (USA) and on Wrangel Island (Russia) during the boreal summer and migrate along the Pacific coast to spend their nonbreeding season in NW Mexico (Carmona et al., 2013). Their nonbreeding habitats are restricted to beaches, coastal lagoons, and deltas spanning 32 - 21° N, although incidental records have been recorded further south. The subspecies has an estimated population size of just 21,700 individuals (Lyons et al., 2016) and is thought to be declining, leading them to be listed as threatened and endangered in Canada and Mexico, respectively (COSEWIC, 2007; SEMARNAT, 2010). Due to their small population size, restricted range, high site fidelity, and specialist habitat requirements, C. c. roselaari are expected to be highly vulnerable to SLR and can serve as an umbrella species for other shorebirds that also spend the nonbreeding season in NW Mexico (Muñoz-Salas et al., 2023). Our study area extended from 32 - 21° N and included the states of Sonora, Sinaloa, and Nayarit, which we have termed the “mainland region,” as well as the Baja California Peninsula, which we have called the “peninsular region.” C. c. roselaari nonbreeding distribution is divided into three separate climatic zones: Mediterranean, in the northwestern tip of Baja California; arid, along the rest of Baja California and in western Sonora; and humid/dry tropical throughout the rest of the study area (Vidal-Zepeda, 2005). Except for Nayarit and parts of Sinaloa, coastal wetlands have a discontinuous distribution along the coastline, with discrete, well-defined wetlands separated by large expanses of arid-xeric ecosystems. Species Data Collection We assembled a dataset to model the current potential distribution of C. c. roselaari using ‘presence’ locations collected from 2000 – 2020 in NW Mexico from three different sources. (1) We gathered C. c. roselaari sightings from within this time period using the eBird Basic Dataset (eBird, 2020). (2) We tracked the annual movements of 58 C. c. roselaari using 3-g GPS satellite transmitters (PinPoint 75 Argos; Lotek Inc.) attached to the backs of adults with cyanoacrolyte glue at Grays Harbor, WA, during April–May 2017 and 2018. Tags recorded locations accurate to ±10 m every two days during the post-breeding period until early to mid-October when they fell off of molting birds or their batteries were depleted. (3) We carried out on-the-ground surveys from 7-15 December 2019 and 2-12 January 2020 along the coastlines of Sonora, Sinaloa, and Nayarit. We chose 110 survey locations following a stratified random sampling design that included sites within 4 km of the shoreline and the nearest road access. Sites were chosen using the land use and vegetation dataset for Mexico (INEGI, 2019), and consisted of habitats types that are known to be used by shorebirds. We conducted 5-minute point counts within 3 hours of high tide during which we counted every shorebird within a 400-m radius of a random site. Field surveys ensured that the C. c. roselaari nonbreeding distribution was well represented, helped reduce biases associated with eBird observations concentrated in popular birding areas, and added areas that could have been visited by tagged C. c. roselaari but not recorded after October — the point at which C. c. roselaari shed their satellite transmitters. Presence data preparation Records from eBird were manually inspected for positional uncertainty and only those that presented enough detail to ensure an accurate location were kept (n = 1,100). We filtered the transmitter locations by location class and kept only those in classes 3D and A3 (10- and <250-m accuracy, respectively; n = 980). Field surveys revealed 10 sites in NW Mexico where C. c. roselaari presence could be detected; each site was treated as an individual presence record. To account for the autocorrelation typical of transmitter datasets, and to reduce records close to each other, we pooled together the transmitter, eBird, and survey locations and, then, rarified the dataset by eliminating points closer together than 2 km. This procedure reduced our final dataset to a sample size of 112 presences. Environmental data preparation We selected 10 environmental predictor variables related to climatic conditions and the static physical environment that are thought to define the ecological niche of C. c. roselaari (Table 1). Climatic variables vary seasonally; we therefore selected mean values (e.g., mean temperature) from Dec and Jan, the months during which C. c. roselaari populations are thought to be most stable in their distribution across their non-breeding range (Carmona et al., 2013). In contrast, we included variables related to the static physical environment (e.g., elevation and distance to the nearest wetland) collected at any time of year within the past 10 years. All environmental predictors were transformed, cropped, and resampled to match their extents, projections, and resolutions (~0.8 x 0.8 km), respectively. To reduce multicollinearity, layers with variance inflation factors (USDM package; Naimi et al., 2013) >12 were excluded (Table 1). For instance, we included three metrics of the distance to the nearest wetland (Euclidean distance to intertidal habitat, Euclidean distance to the coastline, and Euclidean distance to the polygon of each coastal wetland) but only distance to the polygon of each coastal wetland was retained as all metrics were highly correlated and the latter provided the best results (Table 1). Table 1. Environmental predictor variables used to develop species distribution models for C. c. roselaari in NW Mexico

Environmental predictor

Description

Data Source

Elevation

Global elevation in meters

WorldClim (Fick and Hijmans, 2017)

Temperature

Mean December and January temperature

WorldClim (Fick and Hijmans, 2017)

Distance to wetland

Euclidean distance to the nearest polygon of coastal wetlands

Own data

Distance to urban areas

Euclidean distance to the nearest polygon of urban areas

Own data based on Inegi (2019)

Land cover and vegetation

MCD12Q1 MODIS/Terra+Aqua Land Cover Global 500m

Friedl & Sulla-Menashe (2019) https://lpdaac.usgs.gov/products/mcd12q1v006/

Nightlights

Proxy for disturbance and development intensity

NOAA (2020); https://ngdc.noaa.gov/eog/download.html

Silt

Global soil silt content 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Clay

Global soil clay content 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Soil Ph

Global soil pH x 10 in H20 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Soil Coarseness

Global coarse fragments 0-5 cm depth

https://soilgrids.org/; Poggio et al. 2021)

Sea level rise scenario for 2050 To assess the inundation risk in NW Mexico in 2050, we used a static ‘bathtub’ model obtained from Climate Central (2020). The model is based on global-scale datasets for elevation, tide, and coastal flooding likelihoods for the year 2050. The model parameters included the CoastalDEM elevation dataset (v. 1.1; Kulp & Strauss, 2018), an RCP scenario of 4.5, and medium ‘luck’ based on mid-range results from the sea-level projection range (50th percentile). The output of this product is a spatial layer with areas identified as vulnerable to permanent SLR alone and to minor floods that may rise and fall slowly. While static models can overestimate the extent of inundation and ignore bio-geomorphological feedbacks known to buffer the effects of SLR (e.g., marsh migration; Klingbeil et al., 2021), they are better suited for large-scale inundation assessments where a lack of high-resolution data — such as in the Global South — and high computational costs limit the use of dynamic models. As such, the static models used here should be considered a “worst-case”

About this dataset

Context

The dataset tabulates the Galesburg 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 Galesburg 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 Galesburg was 2,049, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Galesburg population was 2,049, a decline of 0.05% compared to a population of 2,050 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Galesburg increased by 79. In this period, the peak population was 2,092 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 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 Galesburg is shown in this column.
• Year on Year Change: This column displays the change in Galesburg 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 Galesburg Population by Year. You can refer the same here

Climate and land use/land cover change are expected to influence the stationary nonbreeding distributions of 4 Nearctic–Neotropical migrant bird species experiencing population declines: Cardellina canadensis (Canada Warbler), Setophaga cerulea (Cerulean Warbler), Vermivora chrysoptera (Golden-winged Warbler), and Hylocichla mustelina (Wood Thrush). Understanding how and where these species’ distributions shift in response to environmental drivers is critical to inform conservation planning in the Neotropics. For each species, we quantified current (2012 to 2021) and projected future (2050) suitable climatic and land use/land cover conditions as components of stationary nonbreeding distributions. Multi-source occurrence data were used in an ensemble modeling approach with covariates from 3 global coupled climate models (CMCC-ESM2, FIO-ESM-2-0, MIROC-ES2L) and 2 shared socioeconomic pathways (SSP2-RCP4.5, SSP5-RCP8.5) to predict distributions in response to varying climatic and land use/land cover conditions. Our findings suggest that distribution contraction, upslope elevational shifts in suitable conditions, and limited shifts in latitude and longitude will occur in 3 of 4 species. Cardellina canadensis and S. cerulea are expected to experience a moderate distribution contraction (7% to 29% and 19% to 43%, respectively), primarily in response to expected temperature changes. The V. chrysoptera distribution was modeled by sex, and females and males were projected to experience a major distribution contraction (56% to 79% loss in suitable conditions for females, 46% to 65% for males), accompanied by shifts in peak densities to higher elevations with minimal changes in the upper elevation limit. Expected changes in precipitation had the greatest effect on V. chrysoptera. Hylocichla mustelina experienced the smallest distribution change, consistent with the species’ flexibility in habitat selection and broader elevational range. We recommend defining priority areas for conservation as those where suitable conditions are expected to remain or arise in the next 25 years. For V. chrysoptera in particular, it is urgent to ensure that mid-elevation forests in Costa Rica and Honduras are adequately managed and protected. Methods Bird Occurrence Data We obtained current (2012 to 2021) bird occurrence data containing only Neotropical presence records from eBird (accessed in January 2023; Sullivan et al. 2009) and supplemented with species-specific georeferenced occurrence datasets to bolster presence record sample sizes and the spatial representation of records. 2012 to 2021 was identified as the “current” timeframe to capitalize on increased user engagement with eBird and align with prior research (Hightower et al. 2023). Date ranges for the stationary nonbreeding period were defined using expert input (N. Bayly, E. Cohen, I. Davidson, A. González, J. Hightower, J. L. Larkin, E. Montenegro, D. Raybuck, A. Roth, C. Rushing, C. Stanley, R. L. M. Stewart, and S. Wilson personal communication) to assess frequency distributions of daily presence records in the current timeframe. Experts emphasized date selection 2 weeks before or after most birds initiated or completed migration through the Neotropical flyway to minimize the signal from areas used during migration (C. canadensis: November 16 to March 17, S. cerulea: October 25 to March 10, V. chrysoptera: October 28 to March 31, H. mustelina: November 5 to March 28). eBird occurrence data were filtered in R with the auk package (Strimas-Mackey et al. 2018, R Core Team 2022) to select presence records collected using “traveling,” “stationary,” and “incidental” protocols with observer effort distances ≤2 km (Medina et al. 2023). Duplicate records, as well as outlier records from areas outside of known stationary nonbreeding locations (Fink et al. 2022), were removed. We added the species-specific datasets to filtered eBird datasets and resampled all presence records to a 1-km2 resolution (Fick and Hijmans 2017). The final dataset included 5,765 unique presence records for the current timeframe (C. canadensis: n = 1,586, S. cerulea: n = 546, V. chrysoptera ♀: n = 192, V. chrysoptera ♂: n = 283, H. mustelina: n = 3,158). We partitioned V. chrysoptera records by sex as it is a sexually dimorphic species allowing for possible identification by plumage. The sexes are known to segregate by habitat and elevation resulting in conservation planning bias in favor of higher elevations for males (Bennett et al. 2019). Thus, we removed records that did not specify sex (n = 1,860). Climate and Land Use/Land Cover Data We downloaded historical (1970 to 2000 averages) monthly climatic and bioclimatic raster datasets at a 30-arc second (~1-km2) spatial resolution from the WorldClim data repository (Fick and Hijmans 2017). Historical climate data aided predictions of current climatic and LULC conditions with documented ecological patterns (Acevedo et al. 2012). Bioclimatic covariates were selected based on literature review, expert input, principal component analysis (PCA) correlation circles, and predictor contribution percentages. PCA correlation circles and predictor contribution percentages were used to identify multicollinearities among bioclimatic covariates (Fick and Hijmans 2017, Guisan et al. 2017). We selected bioclimatic covariates for ensemble modeling (Thuiller et al. 2009, Guisan et al. 2017) that were above the expected average contribution percentage, a product of the covariate eigenvalues (Dray et al. 2023). Further, elevation and slope were derived from a ~1-km2 digital elevation model (Fick and Hijmans 2017) in ArcGIS Pro 3.0.0 (Esri Inc. 2022). Global LULC projections based on simulations of 16 plant functional types (i.e., forest, grassland, and cropland) and urban expansion (see figure 2 in Chen et al. 2022) were included to simulate effects of LULC change. We used the resulting covariates in ensemble modeling to capture species responses from the current timeframe based on historical climate (C. canadensis: n = 23 covariates, S. cerulea: n = 24, ♀ V. chrysoptera: n = 23, ♂ V. chrysoptera: n = 23, H. mustelina: n = 26). For future (2050) climatic and LULC conditions, we obtained climatic datasets (2041 to 2060 averages) identical to the historical dataset from WorldClim for 3 individual GCCMs: the CMCC-ESM2 (Cherchi et al. 2019), the FIO-ESM-2-0 (Bao et al. 2020), and the MIROC-ES2L (Hajima et al. 2020). For each GCCM, we used 2 2041 to 2060 SSP-RCP scenarios which represent independent climatic futures: SSP2-RCP4.5 and SSP5-RCP8.5 (Fick and Hijmans 2017). SSP2-RCP4.5 (hereinafter, best-case) represents a future where climate-smart practices increase and nonrenewable resource use declines (Van Vuuren et al. 2011, Riahi et al. 2017). In contrast, SSP5-RCP8.5 (hereinafter, worst-case) represents a future where technological advances and increased fossil fuel extraction lead to maximum global emissions (Van Vuuren et al. 2011, Riahi et al. 2017). The spatial extent used to extract climate and LULC data was identical among scenarios to project current species responses onto future climates (Guisan et al. 2017, Hightower et al. 2023). To accommodate potential distribution shifts in latitude and longitude by 2050, we initially included areas in the periphery of current stationary nonbreeding locations (Fink et al. 2022) for the spatial extents of each focal species. Preliminary analyses resulted in extralimital projections of species occurrence when suitable climatic and LULC conditions occurred well outside the current distribution of each species. To limit these projections, we defined the northern and southern termini of each spatial extent with a combination of the unique presence records and known current stationary nonbreeding locations (Fink et al. 2022). We applied a spatial constraint that prevented extralimital projections of occurrence that exceeded 200 km from known occurrences, but we filled gaps in presence record coverage where species are known to occupy (Fink et al. 2022). The 200-km distance was selected to accommodate reasonable dispersal distances for each species in the current timeframe (Barbet-Massin et al. 2012, Freeman et al. 2018). Ensemble Modeling and Projected Distributions We used an ensemble modeling framework within the R package biomod2 (Thuiller et al. 2009, Guisan et al. 2017) to model current and future projections of suitable climatic and LULC conditions for the 4 focal bird species (V. chrysoptera ♀ and ♂ separately). To address multicollinearity and biases in ecological studies (Fotheringham and Oshan 2016), we incorporated 4 successful modeling algorithms (Qiao et al. 2015, Guisan et al. 2017): generalized linear model (GLM), generalized boosting model (GBM), generalized additive model (GAM), and random forest (RF). Default settings in biomod2 were kept for GBM and RF, while settings were modified for GLM and GAM: We set the relationship between presence records and covariates to a polynomial function for GLM (Hightower et al. 2023), while the GAM modeling function was set to GAM_mgcv (Wood 2017). Predictive performance of individual models. For each modeling algorithm plus 1 full model (models that are calibrated and validated over an entire pseudo-absence dataset), we used 5K-fold cross-validations with 70% and 30% of the occurrence records allocated for training and validations, respectively (Guisan et al. 2017). We evaluated modeling algorithm performances using TSS and receiver operating characteristic (ROC) metrics, where TSS values > 0.6 are good and ROC values > 0.9 are excellent (Thuiller et al. 2009, Guisan et al. 2017). We randomly generated pseudo-absence points in the modeling framework due to limited true-absence records in the Neotropics during the current timeframe. The number of pseudo-absences and presence records were roughly equal to aid in decision tree dynamics for GBM and RF

About this dataset

Context

The dataset tabulates the Quitman 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 Quitman 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 Quitman was 1,985, a 2.02% decrease year-by-year from 2021. Previously, in 2021, Quitman population was 2,026, a decline of 1.17% compared to a population of 2,050 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Quitman decreased by 457. In this period, the peak population was 2,442 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 Quitman is shown in this column.
• Year on Year Change: This column displays the change in Quitman 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 Quitman Population by Year. You can refer the same here

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 capita. These factors along with high wages result in a considerable intensification of agriculture. Three scenario groups are considered in A1 scenario family reflecting the uncertainty in development of energy sources and conversion technologies in this rapidly changing world. Near-term investment decisions may introduce long-term irreversibilities into the market, with lock-in to one technological configuration or another. The A1B scenario group is based on a balanced mix of energy sources and has an intermediate level of CO2 emissions, but de... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.76.4 for complete metadata about this dataset.

About this dataset

Context

The dataset tabulates the Barr 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 Barr 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 Barr township was 2,035, a 0.54% decrease year-by-year from 2021. Previously, in 2021, Barr township population was 2,046, a decline of 0.20% compared to a population of 2,050 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Barr township decreased by 135. In this period, the peak population was 2,170 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 Barr township is shown in this column.
• Year on Year Change: This column displays the change in Barr 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 Barr township Population by Year. You can refer the same here

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). The central elements of the B1 future are a high level of environmental and social consciousness combined with a globally coherent approach to sustainable development. A strong welfare net prevents social exclusion on the basis of poverty. However, counter-currents may develop and in some places people may not conform to the main social and environmental intentions of the mainstream in this scenario family. Particular effort is devoted to increasing resource efficiency. Comprehensive incentive systems, combined with advances in international institutions, permit the rapid diffusion of cleaner technology. R and D to this end is also enhanced together with education and capacity building for clean and equitable development. Organizational measures are adopted to reduce material wastage, maximizing reuse and recycling. The combination of technical and organizational change yields high levels of material and energy saving as well as reductions in pollution. Labor productivity also improves as a byproduct of these efforts. Variants considered within the B1 family of scenarios include different rates of GDP growth and dematerialization (e.g., energy intensity declines). The demographic transition to low mortality and fertility occurs at the same rate as in A1 but for slightly different reasons, motivated partly by social and environmental concerns. Global population reaches nine billion by 2050 and declines to about seven billion by 2100. This is a world with high levels of economic activity and significant and deliberate progress toward international and national income equality. Global income per capita in 2050 averages US$13,000; somewhat lower than in A1. A higher proportion of this income is spent on services rather than on material goods, and on quality rather than quantity, because of less emphasis on material goods and also higher resource prices. The B1 storyline sees a relatively smooth transition to alternative energy systems as conventional oil resources decline. There is extensive use of conventional and unconventional gas as the cleanest fossil resource during the transition, but the major push is... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.108.4 for complete metadata about this dataset.

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

About this dataset

Context

The dataset tabulates the Granby 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 Granby 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 Granby was 2,076, a 0.29% increase year-by-year from 2021. Previously, in 2021, Granby population was 2,070, an increase of 0.98% compared to a population of 2,050 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Granby decreased by 47. In this period, the peak population was 2,244 in the year 2007. 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 Granby is shown in this column.
• Year on Year Change: This column displays the change in Granby 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 Granby Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Jefferson town 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 Jefferson town 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 Jefferson town was 2,047, a 0.15% decrease year-by-year from 2022. Previously, in 2022, Jefferson town population was 2,050, a decline of 0.49% compared to a population of 2,060 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Jefferson town decreased by 224. In this period, the peak population was 2,271 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 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 Jefferson town is shown in this column.
• Year on Year Change: This column displays the change in Jefferson town 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 Jefferson town Population by Year. You can refer the same here