About this dataset

Context

The dataset tabulates the Bombay 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 Bombay 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 Bombay town was 1,252, a 0.24% decrease year-by-year from 2022. Previously, in 2022, Bombay town population was 1,255, a decline of 0.55% compared to a population of 1,262 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Bombay town increased by 53. In this period, the peak population was 1,356 in the year 2010. 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 Bombay town is shown in this column.
• Year on Year Change: This column displays the change in Bombay 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 Bombay town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Bombay town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Bombay town. The dataset can be utilized to understand the population distribution of Bombay town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Bombay town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Bombay town.

Key observations

Largest age group (population): Male # 30-34 years (119) | Female # 30-34 years (126). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Bombay town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bombay town is shown in the following column.
• Population (Female): The female population in the Bombay town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Bombay town for each age group.

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 Bombay town Population by Gender. You can refer the same here

ObjectiveA focus on bacterial contamination has limited many studies of water service delivery in slums, with diarrheal illness being the presumed outcome of interest. We conducted a mixed methods study in a slum of 12,000 people in Mumbai, India to measure deficiencies in a broader array of water service delivery indicators and their adverse life impacts on the slum’s residents.MethodsSix focus group discussions and 40 individual qualitative interviews were conducted using purposeful sampling. Quantitative data on water indicators—quantity, access, price, reliability, and equity—were collected via a structured survey of 521 households selected using population-based random sampling.ResultsIn addition to negatively affecting health, the qualitative findings reveal that water service delivery failures have a constellation of other adverse life impacts—on household economy, employment, education, quality of life, social cohesion, and people’s sense of political inclusion. In a multivariate logistic regression analysis, price of water is the factor most strongly associated with use of inadequate water quantity (≤20 liters per capita per day). Water service delivery failures and their adverse impacts vary based on whether households fetch water or have informal water vendors deliver it to their homes.ConclusionsDeficiencies in water service delivery are associated with many non-health-related adverse impacts on slum households. Failure to evaluate non-health outcomes may underestimate the deprivation resulting from inadequate water service delivery. Based on these findings, we outline a multidimensional definition of household “water poverty” that encourages policymakers and researchers to look beyond evaluation of water quality and health. Use of multidimensional water metrics by governments, slum communities, and researchers may help to ensure that water supplies are designed to advance a broad array of health, economic, and social outcomes for the urban poor.

Historical dataset of population level and growth rate for the Mumbai, India metro area from 1950 to 2025.

About this dataset

Context

The dataset tabulates the Bombay town population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Bombay town. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 807 (62.51% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Bombay town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Bombay town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Bombay town is shown in the following column.

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 Bombay town Population by Age. You can refer the same here

Census: Population: City: Mumbai data was reported at 12,442.373 Person th in 03-01-2011. This records a decrease from the previous number of 16,368.000 Person th for 03-01-2001. Census: Population: City: Mumbai data is updated decadal, averaging 12,596.000 Person th from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 16,368.000 Person th in 03-01-2001 and a record low of 12,442.373 Person th in 03-01-2011. Census: Population: City: Mumbai data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB004: Census: Population: by Selected Cities.

Census: Population: Maharashtra: Greater Mumbai data was reported at 18,414,288.000 Person in 03-01-2011. This records an increase from the previous number of 16,434,386.000 Person for 03-01-2001. Census: Population: Maharashtra: Greater Mumbai data is updated decadal, averaging 3,866,199.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 18,414,288.000 Person in 03-01-2011 and a record low of 839,672.000 Person in 03-01-1901. Census: Population: Maharashtra: Greater Mumbai data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC021: Census: Population: By Towns and Urban Agglomerations: Maharashtra.

Census: Population: Maharashtra: Navi Mumbai data was reported at 1,120,547.000 Person in 03-01-2011. This records an increase from the previous number of 81,855.000 Person for 03-01-2001. Census: Population: Maharashtra: Navi Mumbai data is updated decadal, averaging 81,855.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 1,120,547.000 Person in 03-01-2011 and a record low of 42,732.000 Person in 03-01-1991. Census: Population: Maharashtra: Navi Mumbai data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC021: Census: Population: By Towns and Urban Agglomerations: Maharashtra.

As of year 2024, the population of Mumbai, India was over **** million inhabitants. This was a **** percent growth from last year. The historical trends indicate that the population of Mumbai has been steadily increasing since 1960. The UN estimates that the population is expected to reach over ** million by the year 2030.

Census: Population: Maharashtra: Navi Mumbai: Female data was reported at 510,487.000 Person in 03-01-2011. This records an increase from the previous number of 35,636.000 Person for 03-01-2001. Census: Population: Maharashtra: Navi Mumbai: Female data is updated decadal, averaging 35,636.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 510,487.000 Person in 03-01-2011 and a record low of 18,978.000 Person in 03-01-1991. Census: Population: Maharashtra: Navi Mumbai: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC021: Census: Population: By Towns and Urban Agglomerations: Maharashtra.

Census: Population: Maharashtra: Navi Mumbai: Male data was reported at 610,060.000 Person in 03-01-2011. This records an increase from the previous number of 46,219.000 Person for 03-01-2001. Census: Population: Maharashtra: Navi Mumbai: Male data is updated decadal, averaging 46,219.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 610,060.000 Person in 03-01-2011 and a record low of 23,754.000 Person in 03-01-1991. Census: Population: Maharashtra: Navi Mumbai: Male data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC021: Census: Population: By Towns and Urban Agglomerations: Maharashtra.

As per the Census data dated 2011, the slum dwellers population in Mumbai was the highest among all other major metropolitan cities of India, at around ************. Hyderabad and Delhi followed it. A total of about ** million people were estimated to be living in slums across the country.

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children. A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples. NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files. The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

The 2015-16 National Family Health Survey (NFHS-4), the fourth in the NFHS series, provides information on population, health, and nutrition for India and each state and union territory. For the first time, NFHS-4 provides district-level estimates for many important indicators. All four NFHS surveys have been conducted under the stewardship of the Ministry of Health and Family Welfare (MoHFW), Government of India. MoHFW designated the International Institute for Population Sciences (IIPS), Mumbai, as the nodal agency for the surveys. Funding for NFHS-4 was provided by the United States Agency for International Development (USAID), the United Kingdom Department for International Development (DFID), the Bill and Melinda Gates Foundation (BMGF), UNICEF, UNFPA, the MacArthur Foundation, and the Government of India. Technical assistance for NFHS-4 was provided by ICF, Maryland, USA. Assistance for the HIV component of the survey was provided by the National AIDS Control Organization (NACO) and the National AIDS Research Institute (NARI), Pune.

Abstract

The Enterprise Surveys of Micro firms (ESM) conducted by the World Bank Group's (WBG) Enterprise Analysis Unit (DECEA) in India. The survey covers nine cities: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

The primary objectives of the ESM are to: i) understand demographics of the micro enterprises in the covered cities, ii) describe the environment within which these enterprises operate, and iii) enable data analysis based on the samples that are representative at each city level.

Geographic coverage

Nine cities in India: Hyderabad, Telangana; Jaipur, Rajasthan; Kochi, Kerala; Ludhiana, Punjab; Mumbai, Maharashtra; Sehore, Madhya Pradesh; Surat, Gujarat; Tezpur, Assam; and Varanasi, Uttar Pradesh.

Analysis unit

• Firms

Universe

The universe of ESM includes formally registered businesses in the sectors covered by the ES and with less than five employees. The definition of formal registration can vary by country. The universe table for each of the nine cities covered by ESM in India was obtained from the 6th Economic Census (EC) of India (conducted between January 2013 and April 2014), which has its own well-defined definition of registration. Generally, this entails registration with any central/government agency, under Shops & Establishment Act, Factories Act etc.

In terms of sectors, the survey covers all non-agricultural and non-extractive sectors. In particular, according to the group classification of ISIC Revision 4.0, it includes: all manufacturing sectors (group D), construction (group F), wholesale and retail trade (group G), transportation and storage (group H), accommodation and food service activities (group I), a subset of information and communications (group J), some administrative and support service activities (codes 79) and other service activities (codes 95). Notably, the ESM universe excludes the following sectors: financial and insurance activities (group K), real estate activities (group L), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Enterprise Survey of Micro firms in India 2022 was selected using stratified random sampling, following the methodology explained in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling was preferred over simple random sampling for several reasons, including: a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision, along with the unbiased estimates for the whole population. b. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. c. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) d. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous. e. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

Two levels of stratification were used in this survey: industry and region. For stratification by industry, two groups were used: Manufacturing (combining all the relevant activities in ISIC Rev. 4.0 codes 10-33) and Services (remainder of the universe, as outlined above). Regional stratification was done across nine cities included in the study, namely: Hyderabad, Jaipur, Kochi, Ludhiana, Mumbai, Sehore, Surat, Tezpur and Varanasi.

Mode of data collection

Face-to-face [f2f]

## TNA_NETS

Terrorist Attack Network Datasets

This collection consists of annotated networks developed from documents related to various terrorist attacks in India. Each dataset is named after a specific case and visualizes relationships and interactions among individual entities involved in these incidents. These networks are instrumental for analyzing key actors, hierarchies, and patterns within terrorist organizations. Below is an overview of each file:

• TerroristData1 : Captures the network associated with the 2001 Parliament attack in India. This dataset outlines connections between key individuals, detailing both direct and inferred associations critical for understanding the network structure behind this event.
• TerroristData2: Represents the network surrounding the 1996 Dausa blast, in which a bomb exploded in a Rajasthan State Transport Corporation (RSTC) bus traveling from Agra to Bikaner. The explosion occurred at Samleti village in Dausa district, Rajasthan, killing 14 people and injuring 37. The incident took place a day after a similar blast in Delhi’s Lajpat Nagar. This dataset captures the interactions among involved individuals and entities, shedding light on the logistical and operational aspects of this tragic event.
• TerroristData3 : Documents the network based on the 2008 Mumbai 26/11 attacks. It highlights the coordination among individuals and entities involved in the planning and execution, giving insights into the operational dynamics of the group.
• TerroristData4: Visualizes the network behind the assassination of former Prime Minister of India, Mr. Rajiv Gandhi. The dataset details the connections and hierarchy within the network involved in the plot, showcasing the organizational structure and chain of command.
• TerroristData5: Maps the network involved in the 1993 Bombay blasts, displaying connections between perpetrators, facilitators, and key locations involved in the coordinated attacks across Mumbai.

These datasets are a resource for studying the structure and influence of clandestine networks in terrorist operations. They can support research on network centrality, influence analysis, and resilience, offering insights into the organizational dynamics of terror groups

Diabetes onset precedes diabetic retinopathy (DR) by 5–10 years, but many people with diabetes remain free of this microvascular complication. Our aim was to identify risk factors for DR progression in a unique and diverse population, the slums of Mumbai. We performed a nested case-control study of 1163 diabetics over 40 years of age from slums in 18 wards of Mumbai. Data was collected on 33 variables and assessed for association with DR using both univariate and multivariate analyses. Stratified analyses were also performed on males and females, separately. Among hypertensive individuals we also assessed whether duration of hypertension associated with DR. Of 31 non-correlated variables analysed as risk factors for DR, 15 showed evidence of significant association. The most prominent included sex, where being a female associated with decreased odds of DR, while longer duration of diabetes and poor glycaemic control associated with increased odds. The duration of diabetes effect was partially, but significantly, mediated by age of diabetes diagnoses (8.6% of variance explained, p = 0.012). Obesity as measured by several measures, including body mass index (BMI) and measures of central obesity had a negative association with DR; increased measures of obesity consistently reduced odds of DR. As in most earlier studies, DR was associated with the duration of diabetes and glycaemic control. However, other factors, especially obesity related measures were associated with DR, in ways that contrast with most prior studies. These results indicated that the overall pattern of association in the Mumbai slums was novel. Thus, in previously uncharacterized populations, such as the slums that we examined, it is important to evaluate all risk factors de novo to appropriately assess patterns of association as the patterns of association with DR can be complex and population specific.

Diabetes onset precedes diabetic retinopathy (DR) by 5–10 years, but many people with diabetes remain free of this microvascular complication. Our aim was to identify risk factors for DR progression in a unique and diverse population, the slums of Mumbai. We performed a nested case-control study of 1163 diabetics over 40 years of age from slums in 18 wards of Mumbai. Data was collected on 33 variables and assessed for association with DR using both univariate and multivariate analyses. Stratified analyses were also performed on males and females, separately. Among hypertensive individuals we also assessed whether duration of hypertension associated with DR. Of 31 non-correlated variables analysed as risk factors for DR, 15 showed evidence of significant association. The most prominent included sex, where being a female associated with decreased odds of DR, while longer duration of diabetes and poor glycaemic control associated with increased odds. The duration of diabetes effect was partially, but significantly, mediated by age of diabetes diagnoses (8.6% of variance explained, p = 0.012). Obesity as measured by several measures, including body mass index (BMI) and measures of central obesity had a negative association with DR; increased measures of obesity consistently reduced odds of DR. As in most earlier studies, DR was associated with the duration of diabetes and glycaemic control. However, other factors, especially obesity related measures were associated with DR, in ways that contrast with most prior studies. These results indicated that the overall pattern of association in the Mumbai slums was novel. Thus, in previously uncharacterized populations, such as the slums that we examined, it is important to evaluate all risk factors de novo to appropriately assess patterns of association as the patterns of association with DR can be complex and population specific.

Number of cases , age standardised (per 100 000) cancer incidence rates and number of person-years of observation for White & Indian children in Leicester, and for children in Mumbai & Ahmedabad, India. (All rates are standardised to the age distribution of the Segi standard population).

Inflation Rate in India increased to 2.07 percent in August from 1.61 percent in July of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.