The population of the southern city of Chennai in India amounted to about ten million inhabitants. This was an increase of approximately two million inhabitants compared to the year 2000. Chennai, formerly known as Madras is the capital city of the state of Tamil Nadu.

Original provider: Supraja Dharini

Dataset credits: Data provider Tree Foundation Originating data center Satellite Tracking and Analysis Tool (STAT) Project partner TREE Foundation http://www.treefoundationindia.org
Wildlife Wing of the Forest Department
Department of Fisheries, Tamil Nadu
Mote Marine Laboratory, Sarasota Florida, USA http://www.mote.org
Marinelife Alliance,Bangladesh
Project sponsor or sponsor description Whitley Fund for Nature, UK http://www.whitleyaward.org/
TREE Foundation www.treefoundationindia.org
Roots and Shoots, India http://www.rootsandshoots.org/

Abstract: The Olive Ridley turtle has a unique gene pool different from those sea turtles which nest along the Orissa coast, but little is known about this population. Thus we lack the ability to mitigate the effects of fisheries and other potential threats to this globally threatened species. Despite this, the political will and necessary infrastructure to protect Olive Ridleys is just picking up in India, and local attitudes towards sea turtles is just beginning to change. Unfortunately, the resources available to protect Olive Ridleys are extremely limited. The TREE Foundation's Olive Ridley Satellite Tagging study provides practical information to guide sea turtle management and serves as a model for other coastal states for leveraging the results of small-scale tracking projects into substantive management results. The first ever satellite telemetry study on this population of Olive Ridleys will deploy 2 satellite telemetry tags on olive ridleys along the Chennai coast, to determine their movements and turtle hotspots in the off shore waters.
The Wildlife Wing of the Forest department and Department of Fisheries will then use the results of this study to inform the mechanized and trawl fishermen of the areas which are feeding grounds and congregations areas of the turtles to in order to request them to keep away from those areas during the turtle nesting season.
Such enforcement can be implemented only through repeated awareness programs for the fishing community and general public. Only then will the adult nesting Olive Ridley as well as the juvenile Green turtle and Hawksbill population feeding in the off shore areas survive.
TREE Foundation was founded on the principles of community involvement and ownership. The success of the programs is largely due to the day-to-day participation of the major stakeholders. Fishers who adapt their practices to sustain marine life are providing livelihoods for the next generation. Students who learn about the importance of marine ecosystems and conservation will grow to teach their children and grandchildren. STPF members are given incentives to become community leaders by training the next group of the Sea Turtle Protection Force. In these ways, we plan to ensure the programs of the TREE Foundation grow along with the communities they serve.
To help ensure the sustainability of the conservation program, we are spearheading the efforts to unify conservation groups around the Bay of Bengal. Over the next five years, sea turtle, marine mammal and ecosystem conservation groups from India, Sri Lanka and Bangladesh will come together under the umbrella group BEACON (Bay Of Bengal Ecologists and Conservationists Network) to provide support, standardize all database formats, share best practices and bring science to the common man, resulting in better law enforcement, protection for sustainable coexistence of man and megafauna and the health of the Bay as a whole.
TREE Foundation was founded on the principles of community involvement and ownership. This is the main basis for sustainability of the program.
STPF members are given incentives to become responsible future community leaders by training the next group of the Sea Turtle Protection Force. In these ways, we plan to ensure the programs of the TREE Foundation grow along with the communities they serve. One of the specific goals of the community based conservation program is to empower the present generation with the will and tools to ensure that sea turtles continue to survive.
Specifically recruiting young fisher children, students from high schools and colleges is to get them involved in a life-long commitment to saving our ecosystem.
For further information about this satellite tracking project http://www.treefoundationindia.org/satellite.htm

Supplemental information: Visit STAT's project page for additional information.

This dataset is a summarized representation of the telemetry locations aggregated per species per 1-degree cell.

Delhi was the largest city in terms of number of inhabitants in India in 2023.The capital city was estimated to house nearly 33 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

This statistic represents the results of a survey regarding the share of affluent population living in urban areas across India in FY 2016, by region. During the measured time period, the share of affluent population across the country in the Chennai region was approximately 61.8 percent.

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

Registered Motor Vehicles: City: Chennai data was reported at 6,351.729 Unit th in 2020. This records an increase from the previous number of 5,996.624 Unit th for 2019. Registered Motor Vehicles: City: Chennai data is updated yearly, averaging 3,455.789 Unit th from Mar 2002 (Median) to 2020, with 19 observations. The data reached an all-time high of 6,351.729 Unit th in 2020 and a record low of 1,355.550 Unit th in 2002. Registered Motor Vehicles: City: Chennai data remains active status in CEIC and is reported by Ministry of Road Transport and Highways. The data is categorized under India Premium Database’s Automobile Sector – Table IN.RAE001: Number of Registered Motor Vehicles: by Cities.

Comprehensive population and demographic data for Perambur Tehsil

Comprehensive population and demographic data for Sholinganallur Tehsil

The estimated per capita income across the southern state of Tamil Nadu in India stood at around 315 thousand Indian rupees in the financial year 2024. There was a consistent increase in the income per capita in the state since the financial year 2012. Sikkim recorded the highest per capita income in the country.

As of 2024, Mumbai had a gross domestic product of 368 billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around 167 billion U.S. dollars. India’s megacities also boast the highest GDP among other cities in the country. What drives the GDP of India’s megacities? Mumbai is the financial capital of the country, and its GDP growth is primarily fueled by the financial services sector, port-based trade, and the Hindi film industry or Bollywood. Delhi in addition to being the political hub hosts a significant services sector. The satellite cities of Noida and Gurugram amplify the city's economic status. The southern cities of Bengaluru and Chennai have emerged as IT and manufacturing hubs respectively. Hyderabad is a significant player in the pharma and IT industries. Lastly, the western city of Ahmedabad, in addition to its strategic location and ports, is powered by the textile, chemicals, and machinery sectors. Does GDP equal to quality of life? Cities propelling economic growth and generating a major share of GDP is a global phenomenon, as in the case of Tokyo, Shanghai, New York, and others. However, the GDP, which measures the market value of all final goods and services produced in a region, does not always translate to a rise in quality of life. Five of India’s megacities featured in the Global Livability Index, with low ranks among global peers. The Index was based on indicators such as healthcare, political stability, environment and culture, infrastructure, and others.

In a survey conducted in 2022 among respondents from megacities of India, Surat emerged on the top in terms of clean mobility with a score of 0.61, among all megacities of India. It was closely followed by Chennai and Pune-Pimpri-Chinwad. The parameter of clean mobility includes impact of air pollution, clean mobility focused policies, willingness to adopt electric mobility, among others. Megacities are defined as the cities with a population of over four million as per the survey. The Ease of Moving Index is a composite index comprising nine parameters across 41 indicators. The parameters include seamless, inclusive, clean, efficient and shared mobility and investment in the city among others.