The statistic shows the total population of India from 2020 to 2030. In 2024, the estimated total population in India amounted to approximately 1.44 billion people. Total population in India India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population. With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year. As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

The share of projected population increase in Uttar Pradesh, India from 2011 until 2036 is expected to grow by nearly ** percent. By contrast, the estimated population increase in Uttarakhand is expected to be less than *** percent during the same time period.

Why project population?
Population projections for a country are becoming increasingly important now than ever before. They are used primarily by government policy makers and planners to better understand and gauge future demand for basic services that predominantly include water, food and energy. In addition, they also support in indicating major movements that may affect economic development and in turn, employment and labour productivity. Consequently, this leads to amending policies in order to better adapt to the needs of society and to various circumstances.

Demographic projections and health interventions Demographic figures serve the foremost purpose of improving health and health related services among the population. Some of the government interventions include antenatal and neonatal care with the aim of reducing maternal and neonatal mortality and morbidity rates. In addition, it also focuses on improving immunization coverage across the country. Further, demographic estimates help in better preempting the needs of growing populations, such as the geriatric population within a country.

Excel sheet of the dataset

In 2023, the annual population growth in India was 0.88 percent. Between 1961 and 2023, the figure dropped by 1.52 percentage points, though the decline followed an uneven course rather than a steady trajectory.

This study was initiated by the administrator of a county jail in the Northern Plains of the United States who was concerned about the incidence of suicide behaviors in that facility, particularly among the American Indian population. It was a two-year project designed to evaluate the existing admissions suicide screening tool and to improve the instrument's cultural relevance for the American Indian population. The existing screening instrument used in the county jail to interview inmates at their intake was developed in New York. The main objective of the first year of the project was to determine if that instrument was culturally appropriate for the jailed American Indian population. The principal objective of the second year of the project was to determine whether the employment of different suicide screening protocols would make a difference in the responses of new detainees with regard to the likelihood of securing their honest reports of experiencing suicide ideation and its associated risk factors. For the duration of the project, all male and female inmates aged 18 and older who were booked into the jail went through the customary booking procedure that included the administration of the New York Suicide Prevention Screening Guidelines. In the first year of the project, researchers also administered a short self-report survey consisting of measures commonly associated with suicidal ideation. The self-report survey measured stress, anxiety, suicide ideation, hopelessness, and suicidal behavior history. The protocols in the second year of the project reflected efforts to test different screening conditions for four experimental groups and one control group of new detainees. The outcome variables of the short self-report survey consisted of measures of demographics, comfort experience during booking and the screening process, self-efficacy and management of depression, knowledge of mental health support available within the jail, and general well-being. In addition to the quantitative data collection, qualitative data were also collected to develop a straightforward assessment of suicide ideation criteria in this specific jail setting using semi-structured focus group interviews.

Striped snakehead (Channa striata) is a freshwater species of early Miocene period belonging to family Channidae. The genetic variability of the snakehead populations in India was not well known. Present study was undertaken using 149 sequences of control region of mitochondrial DNA from seven geographically distinct populations of Indian water, which resulted in 46 haplotypes with 137 variable nucleotide sites (60 singletons and 77 parsimony informative) and the nucleotide frequencies was: A = 33.0, T = 28.1, G = 15.4, and C = 23.5%. The presence of low-frequency of younger haplotypes with a large number of singletons indicates the absence of dominant haplotype. Hierarchical AMOVA showed highly significant genetic differentiation (FST = 0.56, p 

This dataset contains information on manatee sightings recorded in the north central Gulf of Mexico and compiled by DISL's Manatee Sighting Network. Records include historical (prior to 2007) manatee sighting data compiled from previously published sources and newly generated sightings (2007-2023) compiled from research activities and public reporting to DISL's Manatee Sighting Network. All sightings include at minimum the date, location of sighting, number of manatees per sighting, and animal condition. Purpose Increased awareness of the importance of West Indian manatee habitats at the edges of their range, such as the north central Gulf of Mexico, has prompted demand for studies to guide development of management programs outside areas covered under the Florida Manatee Recovery Program. The objective of this project is to determine when and how manatees use habitat in Alabama and nearby waters by: -Defining habitat use in terms of distribution and abundance of manatees within the waters, -Measuring the frequency of habitat use at discrete locations, -Determining relationships to other manatee populations, -Defining and distinguishing available and utilized food resources, and -Recording and sharing data with other researchers, managers, and the public. This dataset is part of a larger study of manatee habitat and resource use in the region which also includes aerial and ground surveys of manatees and use of satellite telemetry tags. DOI: Suggested Citation

Supporting tables. Table S1, Univariate analysis of co-occurring dermatological conditions for American Indian molluscum contagiosum (MC) cases and control patients <5 years of age at facility B. Table S2, Univariate analysis of previous and current dermatological conditions for American Indian molluscum contagiosum (MC) cases and control patients <5 years of age at facility B. (DOCX)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Methodological changes to citizenship edits may have affected citizenship data for those born in American Samoa. Users should be aware of these changes when using 2018 data or multi-year data containing data from 2018. For more information, see: American Samoa Citizenship User Note..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Background and ObjectivesPotassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) gene have a key role in insulin secretion and is of substantial interest as a candidate gene for type 2 diabetes (T2D). The current work was performed to delineate the genetic influence of KCNJ11 polymorphisms on risk of T2D in South Indian population through case-control association study along with systematic review and meta-analysis.MethodsA case-control study of 400 T2D cases and controls of South Indian origin were performed to analyze the association of KCNJ11 polymorphisms (rs5219, rs5215, rs41282930, rs1800467) and copy number variations (CNV) on the risk of T2D. In addition a systematic review and meta-analysis for KCNJ11 rs5219 was conducted in 3,831 cases and 3,543 controls from 5 published reports from South-Asian population by searching various databases. Odds ratio with 95% confidence interval (CI) was used to assess the association strength. Cochran's Q, I2 statistics were used to study heterogeneity between the eligible studies.ResultsKCNJ11 rs5215, C-G-C-C haplotype and two loci analysis (rs5219 vs rs1800467) showed a significant association with T2D but CNV analysis did not show significant variation between T2D cases and control subjects. Lower age of disease onset (P = 0.04) and higher body mass index (BMI) (P = 0.04) were associated with rs5219 TT genotype in T2D patients. The meta-analysis of KCNJ11 rs5219 on South Asian population showed no association on susceptibility to T2D with an overall pooled OR = 0.98, 95% CI = 0.83–1.16. Stratification analysis showed East Asian population and global population were associated with T2D when compared to South Asians.ConclusionKCNJ11 rs5219 is not independently associated with T2D in South-Indian population and our meta-analysis suggests that KCNJ11 polymorphism (rs5219) is associated with risk of T2D in East Asian population and global population but this outcome could not be replicated in South Asian sub groups.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Life in contemporary cities is often dangerous for stray cats, with strikingly low survival rates. In several countries, trap-neuter-return (TNR) programs have been employed to control urban stray cat populations. Management of stray cats in urban environments is not just about applying scientific solutions, but also identifying approaches that align with local cultural and ethical values. India has an estimated 9.1 million stray cats. TNR presents as a potential method for stray cat management in India, while also improving their welfare. Yet, to date, there has been no academic exploration on Indian residents’ attitudes towards stray cats. We conducted a survey in 13 cities in India reaching 763 residents, examining interactions with stray cats, negative and positive attitudes towards them, attitudes towards managing their population, and awareness of TNR. Results show a high rate of stray cat sightings and interactions. While most respondents believed that stray cats had a right to welfare, the majority held negative attitudes towards and had negative interactions with them. There was widespread lack of awareness about TNR, but, when described, there was a high degree of support. Gathering insights into opinions about stray cats, and the sociodemographic factors that impact these opinions, is an important first step to developing policies and initiatives to manage stray cat populations.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Methodological changes to citizenship edits may have affected citizenship data for those born in American Samoa. Users should be aware of these changes when using 2018 data or multi-year data containing data from 2018. For more information, see: American Samoa Citizenship User Note..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Malaria is a serious health threat to the Indian population. The World Bank, through the National Vector Borne Disease Control Program, is assisting the government of India to develop a new national response strategy. This impact evaluation study was undertaken to test the effectiveness of the new strategies of malaria control in India. These strategies included community-based management of fever and malaria with rapid diagnostic tests and artemisinin-combination therapy, and introduction of long lasting insecticidal treated bed nets. The impact evaluation was conducted in 120 villages in two high endemic districts in Orissa state. It was a three-arm randomized design with one intervention arm receiving supportive supervision of community health workers along with community mobilization, the second intervention arm with only community mobilization, and a third control arm without any intervention. The baseline data collection was carried out in Dec. 2008 Jan. 2009, and the endline data collection in Nov. 2010 Feb. 2011. Data from endline household questionnaires, the malaria service providers questionnaire and the community questionnaire is documented here.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

About the Dataset: Waste Management and Recycling in India

Overview:

This dataset provides comprehensive information on waste management and recycling practices in various cities across India. It includes key data related to waste generation, recycling rates, population density, municipal efficiency, landfill details, and more. The data spans multiple years (2019–2023) and covers a range of waste types, including plastic, organic waste, electronic waste (e-waste), construction waste, and hazardous waste.

Purpose:

The dataset aims to: - Promote efficient waste management practices across Indian cities. - Analyze trends in recycling and waste disposal methods. - Provide insights for improving municipal management systems. - Support research and development in sustainability, environmental science, and urban planning.

Columns:

1. City/District: The name of the Indian city or district.
2. Waste Type: Type of waste generated, e.g., Plastic, Organic, E-Waste, Construction, Hazardous.
3. Waste Generated (Tons/Day): Amount of waste generated in tons per day.
4. Recycling Rate (%): The percentage of waste that is recycled.
5. Population Density (People/km²): The number of people per square kilometer in the city.
6. Municipal Efficiency Score (1-10): A score indicating how effectively the municipality manages waste (e.g., waste segregation, collection, disposal).
7. Disposal Method: The method used for waste disposal (e.g., Landfill, Recycling, Incineration, Composting).
8. Cost of Waste Management (₹/Ton): The cost of managing one ton of waste in Indian Rupees.
9. Awareness Campaigns Count: The number of awareness campaigns organized by the municipality in that year related to waste management.
10. Landfill Name: The name of the landfill site used by the city.
11. Landfill Location (Lat, Long): The geographical location (latitude and longitude) of the landfill.
12. Landfill Capacity (Tons): The total waste capacity (in tons) that the landfill can hold.
13. Year: The year of the data entry, ranging from 2019 to 2023.

Applications:

• Urban Planning: The dataset can be used to analyze and optimize waste management infrastructure in urban areas.
• Sustainability Research: It can help in studying the progress of recycling and waste reduction strategies.
• Policy Making: Government bodies can use this data to craft policies aimed at improving waste management and recycling rates.
• Machine Learning/AI: The dataset can be used to build models for predicting waste generation trends, recycling outcomes, and municipal efficiency.

Sources:

• The data is simulated for this dataset based on average waste management practices observed in Indian cities.
• Real-world data could come from municipal corporations, environmental agencies, and government reports on waste management.

Column: Hap-Score shows haplotype score statistic; Base, part of the baseline; Frequencies and disease association of haplotype of SNP alleles was tested using haplo.cc extended application of Haplo.stasts software (v1.4.4) which combines the results of haplo.score, haplo.group and haplo.glm. Haplotype frequency was computed by maximum likelihood estimates of haplotype probabilities with progressive insertion algorithm and haplo.cc computed score statistic to test association between haplotype and traits with adjustment for non-genetic covariates (sex).pa Indicates the haplotype comparison statistics for patients vs controls.