About this dataset

Context

The dataset tabulates the population of Bombay town by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Bombay town across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.67% of total population being male. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Bombay town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Bombay town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bombay town Population by Race & Ethnicity. You can refer the same here

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 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

🏠 Real Estate Market Dataset

A comprehensive real estate dataset containing basic property information (location, size, price, age) and synthetic market analytics (ROI, demand, volatility, liquidity scores).

Essential Details:

• Dataset size (76,038 records)
• Column count (13 total)
• Data completeness (100%)

Column Explanations:

Property Info: bhk → Bedrooms + halls + kitchens count type → Property category (apartment, villa, etc.) locality → Neighborhood/area name area → Size in square feet price → Property price price_unit → Currency type region → Geographic location status → Construction stage age → How old the property is

Synthetic Market Data: expected_roi(%) → Investment return percentage demand_indicator → Market demand (1-10 scale) market_volatility_score → Risk level (1-10, lower=safer) property_liquidity_index → Ease of selling (1-10, higher=easier)

Note: Market analytics columns are synthetically generated for analysis purposes.

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.

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.

Embark on a comprehensive exploration of Mumbai's vibrant real estate market with our meticulously curated dataset, comprising over 12,685 entries. This extensive collection encapsulates a diverse array of properties, ranging from residential to commercial, providing invaluable insights into the dynamic landscape of Mumbai's real estate sector. Whether you are a property enthusiast, data analyst, or investor, this dataset offers a rich tapestry of information, empowering you to make informed decisions in this bustling metropolis.

Dataset Highlights: The dataset encompasses an extensive array of columns, each revealing intricate details about the properties. From essential information like possession status, floor details, and pricing to more nuanced aspects such as developer details, amenities, and property uniqueness, every facet of a property transaction is meticulously documented. The dataset also features data related to maintenance, booking amounts, covered and carpet areas, and specific features like electricity and water status.

Granular Property Information: Explore nuances such as the type of property, ownership details, and the number of bedrooms and bathrooms. Uncover insights into the furnishing status, parking facilities, and the direction a property faces. The dataset delves into the transaction type, offering a glimpse into the variety of property dealings within the city. From luxury flats to standard apartments, the dataset captures the essence of Mumbai's diverse real estate offerings.

Geographic Insights: For those interested in the geographical distribution of properties, the dataset includes information on landmarks, area names, and the city itself. This geographical granularity allows users to analyze property trends across different regions of Mumbai.

Amenities and Beyond: In addition to property-specific details, the dataset includes an exhaustive list of amenities. Whether you're interested in proximity to schools, shopping centers, or specific luxury features like a swimming pool or a private terrace, this dataset provides a holistic view of the lifestyle offerings associated with each property.

Data Integrity: Carefully curated and verified, this dataset ensures data integrity, offering a reliable foundation for in-depth analyses and modeling. With information sourced meticulously, users can trust the accuracy of each entry.

Empower Your Real Estate Insights: Whether you're a real estate professional, a prospective homebuyer, or an investor seeking opportunities in Mumbai, this dataset serves as an invaluable resource. Gain a holistic understanding of the city's real estate dynamics, identify emerging trends, and make well-informed decisions with the Mumbai Real Estate Properties Dataset.

Important Points: - Data was being collected for over 10 months, since this is real estate-based data, prices can fluctuate a little bit based upon various worldwide scenarios occurred. - This dataset contains real projects ongoing and developed across Mumbai. Don't depend on prices given by us while buying any property mentioned in dataset since these are collected from various sources, prices can fluctuate a bit in real-life buying scenarios. Although there won't be big differences in price. We tried our best while dealing with collection of data in order to ensure credibility. - For amenities, we have used 0 and 1 where 0 stands for false and 1 for true. This indicates whether that property has that particular amenity or not. - NA means Not Available, the particular data was not available during collection.

Thank You

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: 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.

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.

🇮🇳 Indian Housing Datasets — Ready-to-use for ML & Data Analysis

This dataset provides clean, ready-to-use Indian housing data for: - 🏙️ Ahmedabad - 🏙️ Gurugram - 🏙️ Mumbai

Each dataset includes features like: - Property size (sqft) - Location & locality - Price - Number of bedrooms - Furnishing details - Property type (apartment, villa, etc.) - Age of property

All datasets are formatted in CSV for quick loading and analysis in Python, Pandas, or any ML pipeline.

📦 Python Package (for easy access)

You can directly load these datasets using my PyPI library:

pip install india-housing-datasets

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.

This dataset presents the estimated population for 736 districts and 543 parliamentary constituencies (PCs) in India in 2020. Population estimates were calculated by summing the population count across 100m × 100m pixels over the shapefile boundaries using the WorldPop raster data (https://www.worldpop.org/geodata/summary?id=6527). Both PC and District shapefiles were downloaded from the Community Created Maps of India (CCMA) project published by Data {Meet}. The district shapefile was edited in alignment with the latest district boundary of 2020. Districts of Mumbai and Mumbai Suburban were both reported under Mumbai

Abstract

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.

Geographic coverage

• National (29 states )
• Regional (for HIV Prevalence : Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu)
• Local (population and health indicators for slum and non-slum populations for eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur)

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2005 DHS is defined as the universe of all ever-married women age 15-49, NFHS-3 included never married women age 15-49 and both ever-married and never married men age 15-54 as eligible respondents.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE

Since a large number of the key indicators to be estimated from NFHS-3 refer to ever-married women in the reproductive ages of 15-49, the target sample size for each state in NFHS-3 was estimated in terms of the number of ever-married women in the reproductive ages to be interviewed.

The initial target sample size was 4,000 completed interviews with ever-married women in states with a 2001 population of more than 30 million, 3,000 completed interviews with ever-married women in states with a 2001 population between 5 and 30 million, and 1,500 completed interviews with ever-married women in states with a population of less than 5 million. In addition, because of sample-size adjustments required to meet the need for HIV prevalence estimates for the high HIV prevalence states and Uttar Pradesh and for slum and non-slum estimates in eight selected cities, the sample size in some states was higher than that fixed by the above criteria. The target sample was increased for Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland, Tamil Nadu, and Uttar Pradesh to permit the calculation of reliable HIV prevalence estimates for each of these states. The sample size in Andhra Pradesh, Delhi, Maharashtra, Tamil Nadu, Madhya Pradesh, and West Bengal was increased to allow separate estimates for slum and non-slum populations in the cities of Chennai, Delhi, Hyderabad, Indore, Kolkata, Mumbai, Meerut, and Nagpur.

The target sample size for HIV tests was estimated on the basis of the assumed HIV prevalence rate, the design effect of the sample, and the acceptable level of precision. With an assumed level of HIV prevalence of 1.25 percent and a 15 percent relative standard error, the estimated sample size was 6,400 HIV tests each for men and women in each of the high HIV prevalence states. At the national level, the assumed level of HIV prevalence of less than 1 percent (0.92 percent) and less than a 5 percent relative standard error yielded a target of 125,000 HIV tests at the national level.

Blood was collected for HIV testing from all consenting ever-married and never married women age 15-49 and men age 15-54 in all sample households in Andhra Pradesh, Karnataka, Maharashtra, Manipur, Tamil Nadu, and Uttar Pradesh. All women age 15-49 and men age 15-54 in the sample households were eligible for interviewing in all of these states plus Nagaland. In the remaining 22 states, all ever-married and never married women age 15-49 in sample households were eligible to be interviewed. In those 22 states, men age 15-54 were eligible to be interviewed in only a subsample of households. HIV tests for women and men were carried out in only a subsample of the households that were selected for men's interviews in those 22 states. The reason for this sample design is that the required number of HIV tests is determined by the need to calculate HIV prevalence at the national level and for some states, whereas the number of individual interviews is determined by the need to provide state level estimates for attitudinal and behavioural indicators in every state. For statistical reasons, it is not possible to estimate HIV prevalence in every state from NFHS-3 as the number of tests required for estimating HIV prevalence reliably in low HIV prevalence states would have been very large.

SAMPLE DESIGN

The urban and rural samples within each state were drawn separately and, to the extent possible, unless oversampling was required to permit separate estimates for urban slum and non-slum areas, the sample within each state was allocated proportionally to the size of the state's urban and rural populations. A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage. In urban areas, a three-stage procedure was followed. In the first stage, wards were selected with PPS sampling. In the next stage, one census enumeration block (CEB) was randomly selected from each sample ward. In the final stage, households were randomly selected within each selected CEB.

SAMPLE SELECTION IN RURAL AREAS

In rural areas, the 2001 Census list of villages served as the sampling frame. The list was stratified by a number of variables. The first level of stratification was geographic, with districts being subdivided into contiguous regions. Within each of these regions, villages were further stratified using selected variables from the following list: village size, percentage of males working in the nonagricultural sector, percentage of the population belonging to scheduled castes or scheduled tribes, and female literacy. In addition to these variables, an external estimate of HIV prevalence, i.e., 'High', 'Medium' or 'Low', as estimated for all the districts in high HIV prevalence states, was used for stratification in high HIV prevalence states. Female literacy was used for implicit stratification (i.e., villages were

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.

Data includes House data of Mumbai, maharashtra area, you can find out different analysis with the help of above data

Data includes these are the columns : SR. AREA_TYPE
LOCATION
SIZE
TOTAL_SQFT BATH
BALCONY PRICE IN LAKHS

Abstract

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.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Kind of data

Sample survey data [ssd]

Sampling procedure

The NFHS-4 sample was designed to provide estimates of all key indicators at the national and state levels, as well as estimates for most key indicators at the district level (for all 640 districts in India, as of the 2011 Census). The total sample size of approximately 572,000 households for India was based on the size needed to produce reliable indicator estimates for each district and for urban and rural areas in districts in which the urban population accounted for 30-70 percent of the total district population. The rural sample was selected through a two-stage sample design with villages as the Primary Sampling Units (PSUs) at the first stage (selected with probability proportional to size), followed by a random selection of 22 households in each PSU at the second stage. In urban areas, there was also a two-stage sample design with Census Enumeration Blocks (CEB) selected at the first stage and a random selection of 22 households in each CEB at the second stage. At the second stage in both urban and rural areas, households were selected after conducting a complete mapping and household listing operation in the selected first-stage units.

The figures of NFHS-4 and that of earlier rounds may not be strictly comparable due to differences in sample size and NFHS-4 will be a benchmark for future surveys. NFHS-4 fieldwork for Bihar was conducted in all 38 districts of the state from 16 March to 8 August 2015 by the Academic Management Studies (AMS) and collected information from 36,772 households, 45,812 women age 15-49 (including 7,464 women interviewed in PSUs in the state module), and 5,872 men age 15-54.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires - household, woman's, man's, and biomarker, were used to collect information in 19 languages using Computer Assisted Personal Interviewing (CAPI).

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.

## 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

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).