In 2023, approximately a third of the total population in India lived in cities. The trend shows an increase of urbanization by more than 4 percent in the last decade, meaning people have moved away from rural areas to find work and make a living in the cities. Leaving the fieldOver the last decade, urbanization in India has increased by almost 4 percent, as more and more people leave the agricultural sector to find work in services. Agriculture plays a significant role in the Indian economy and it employs almost half of India’s workforce today, however, its contribution to India’s GDP has been decreasing while the services sector gained in importance. No rural exodus in sightWhile urbanization is increasing as more jobs in telecommunications and IT are created and the private sector gains in importance, India is not facing a shortage of agricultural workers or a mass exodus to the cities yet. India is a very densely populated country with vast areas of arable land – over 155 million hectares of land was cultivated land in India as of 2015, for example, and textiles, especially cotton, are still one of the major exports. So while a shift of the workforce focus is obviously taking place, India is not struggling to fulfill trade demands yet.

According to the Population Census of India 2011 and the National Sample Survey, almost three percent of the elderly population living in urban areas above the age of 80, were wheelchair bound in 2017. By contrast, over 72 percent of the share of elderly in urban India was physically mobile during the same time period.

Figure S1 Kernel destiny distribution of people with waist circumference score stratified by Migration status among the middle-aged and older adults (aged 45+) in India, LASI wave 1 (2017−18). Figure S2 Sex stratified weighted prevalence of people with abdominal obesity by residence status (non-migrants) and duration of residence in urban areas among the middle-aged and older adults (aged 45+) migrants in India, LASI Wave 1 (2017−18). Figure S3 Sex stratified weighted prevalence of people with underweight by migration status among the middle-aged and older adults (aged 45+) in India, LASI Wave 1 (2017−18). Figure S4 Sex stratified weighted prevalence of people with high waist-to-hip ratio by migration status among the middle aged and older adults (aged 45+) in India, LASI Wave 1 (2017−18). Figure S5 Selection criteria of the study sample. (ZIP)

Passenger Trips to Major Cities (India Air Travel)

Investigating Passenger Numbers to and from Indian Cities

By Telangana Open Data [source]

About this dataset

This dataset provides comprehensive insights into the air traveling activity in the year 2017 for Hyderabad, India. It displays a list of domestic air travelers to and from this city to all other cities in India. You can access valuable specifics like the number of passengers recorded on each journey until October 2017. This useful collection of data from data.telangana.gov.in provides an essential glimpse into trends and patterns amongst Hyderabad's domestic air traffic, helping city planners and business make more informed decisions!

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How to use the dataset

How to Use 2017 Hyderabad Domestic Air Traffic Data

This dataset provides information about the number of air travelers that arrived in or left from Hyderabad, India in 2017. The data covers all major cities in India until October, giving users a chance to analyze and compare domestic air traffic between cities. This guide will provide an overview on how to use this data set effectively.

Exploring the Dataset

The dataset contains two columns: ‘level_0’ which is the index of the dataframe and ‘M passengers’ which is the number of passengers listed for each airport. It is important to remember that the numbers correspond to they year 2017 only and not current passenger rates. Exploring this data will allow users understand trends in travel patterns across different cities throughout India over a period of time.

Analyzing Trends with Maps

Using mapping technologies such as CartoDB will allow users build dynamic visualizations and gain a better understanding on temporal changes that occur within Indian domestic air travel since start of 2017 up until October 2017. Comparing these maps with socio-economic metrics will also allow deeper analysis on population demographics across India’s top flight routes; useful information when creating marketing plans or proposals related aviation expansion projects etc...

### Additional Analysis Tools Besides mapping tools such as CartoDB; other tools like R can be used to run various statistical models related estimating future traffic volumes based on present passenger patterns, creating correlation networks between selected cities compared side by side against socio-economic trends etc.. Finally SPSS can be used run qualitative analysis those interested in analyzing more subjective avaiation industry related studies such as airliners customer services ratings by destinations city or feedback surveys pre post domestic flights taken throughout certain regions within India etc.

Research Ideas

• Constructing a detailed visualization of the air transportation patterns from Hyderabad to all other cities in India, offering an increased understanding of both high traffic and low traffic destinations.
• Understanding passenger demand for different travel providers such as AirAsia, Indigo etc in the city and predicting possible growth trends for them.
• Refining marketing strategies for flight-based travel services by establishing their target market within the Hyerabad area and subsequently utilizing data-driven tactics to increase sales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2017 Hyderabad Domestic Air Traffic.csv | Column name | Description | |:--------------|:------------------------------------------| | level_0 | Unique identifier for each row. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Telangana Open Data.

Cardiovascular diseases were the leading causes of death across urban areas in India between 2017 and 2019. Road accident deaths amounted to about *** percent of the total share, although the leading cause of death during that time period, cardiovascular diseases amounted to over ** percent.

Sociodemographic characteristics of Rural non-migrant, Urban non-migrant, and rural-to-urban migrant population aged 45+ in India, LASI Wave 1 (2017−18).

This data collection is comprised of interviews with Smart City stakeholders and actors across four Smart Cities in India as well as a set of interviews with national-level actors in Delhi. These interviews took place between September 2018 and October 2019 and are a reflection of the nationally-led Smart City Mission from 2015-2020. The cities represented include Jaipur, Bengaluru, Kochi, Indore, and Delhi.

This research has two primary aims. The first is to develop cutting edge, theoretically informed, insights into the nature of mobility governance reform and the potential to generate more sustainable urban mobility in India. The combined pressures of a growing urban population, increasing urban sprawl, and rapidly rising income, coupled with inadequate public transport, lack of coordinated infrastructure, and increased motorisation have placed huge and unequal burdens on India's urban areas. This has resulted in highly congested roads, poor air quality, high pedestrian casualty rates and poor accessibility and quality of life particularly for the urban poor. In this context, redesigning urban mobility governance has been identified as a critical element of progress in delivering more inclusive and economically, environmentally and socially sustainable cities in India (MoUD, 2006, MoUD, 2015 and NITI Aayog, 2017). Efforts to reform urban transport governance, primarily through the bolstering of local-level capacity, have been underway in India since 2006 but with limited affect due to lack of meaningful delegation of authority and financial power. However, in 2015 the Indian national government launched the Smart Cities Mission, aimed at going beyond what has been achieved before at the local level. The focus of the initiative is to promote 'cities that provide core infrastructure and give a decent quality of life to its citizens' through the application of 'Smart' Solutions (MoUD, 2015, p5). Within this context then, this research uses the Smart Cities Mission as a major opportunity to understand the aims and processes of transport governance reform and the extent to which these reforms are capable of achieving a significant improvement in the mobility system. To this end, the research will undertake a qualitative comparative analysis of previous and planned reforms in four of India's designated smart cities; Jaipur, Kochi, Indore and Bangalore. The research will characterise governance arrangements and governance reforms across each of the four cities, and in using the multi-level governance framework to guide empirical analysis, will be innovative in developing this framework within a non-Western context. The research will also trace the impacts of governance reforms through to impacts on the economic prosperity and quality of life of citizens through analysing changing processes and outcomes. This is essential if we are to move beyond identifying problems to understanding how to overcome them. The second aim of the research is to bring together, develop and inspire a community of researchers and practitioners to advance the study and understanding of mobility governance across India and between the UK and India. The research will be bottom-up in its approach; working with WRI India, the project will engage practitioners in the four cities from the outset to ensure the findings are as meaningful as possible. The interview protocol will be co-created with stakeholders and the data collection informed by the key challenges of urban mobility governance identified by stakeholders through exploratory workshops at the start of the project. A study visit to three UK cities that have experienced different levels of transport governance reform will be held for stakeholders from each of the four 'smart cities' to learn lessons from the UK experience and draw on practitioner expertise. A special session of the World Conference on Transport Research in Mumbai will also be convened to bring practitioners into dialogue with scholars at the forefront of research on transport governance in India and beyond. The project will also convene a 'summer school' in India for researchers to develop their research methods, theoretical perspectives and networks in relation to transport governance and reform. These activities will build both professional and research capacity to address future transport governance challenges.

Abstract

The objective of PLFS is primarily on two aspects. The first is to measure the dynamics in labour force participation and employment status in the short time interval of three months for the urban areas only in the Current Weekly Status (CWS). Thus, in every quarter, PLFS will bring out the level and change estimates of the key labour force indicators in CWS viz. Worker Population Ratio (WPR), Labour Force Participation Rate (LFPR), Unemployment Rate (UR). Secondly, for both rural and urban areas, level estimates of all important parameters in both usual status and CWS will be brought out annually.

Geographic coverage

12800 FSUs (7024 villages and 5776 UFS blocks) will be covered annually at all-India level.

Sampling procedure

1. The first stage unit (FSU) is the 2011 census village in the rural sector. In the case of Kerala, Panchayat Wards will be FSUs in rural sector. FSU in the urban sector is the Urban Frame Survey (UFS) block. Latest list of UFS blocks, as available, will be used for selection of urban samples. The investigator, on arrival at a sample FSU, will ascertain the exact boundaries of it. This may be done with the help of the village officials like patwari, panchayat authorities etc. for rural areas and with the help of UFS maps/ ward maps/ town maps in the urban areas. With a view to control the workload mainly at the stage of listing of households, hamlet-group formation will be resorted to in the large villages. A large village will be divided into a certain number (D) of sub divisions called hamlet-groups (hgs). The number of hgs to be formed (i.e. the value of D) will depend on the approximate present population of the sample village. The criterion for deciding the number of hgs tobe formed in a large village has been discussed in details in Chapter one. For large sample village, two hgs will be selected. Out of all hgs formed in the village, the one with the highest percentage share of population will be selected with probability 1. In case there is more than one hg with same highest percentage share of population, the one among them which is listed first in Block 4.2will be selected with probability 1. The hg selected with certainty will be designated as hg '1'. Another hg will be selected randomly (Simple Random Sampling) from the rest of hg's of the village and designated as hg '2'. Listing and selection of households will be done separately for each selected hamlet-group. For the sample village without hg formation, entire village will be treated as hamlet-group.

2. In a large village, there exist usually a few localities or pockets where the houses of the village tend to cluster together. These are called 'hamlets'. In case there are no such recognised hamlets in the village, the census sub-divisions of the village (e.g. enumeration blocks or groups of census house numbers or geographically distinct blocks of houses) may be treated as 'hamlets'. Large hamlets may be divided artificially to achieve more or less equal population content for the purpose of hamlet-group formation. The procedure for formation of hamlet-groups is best described, perhaps, by listing sequentially the steps involved:

(i) Identify the hamlets as described above. (ii) Ascertain approximate present population of each hamlet. (iii) Draw a notional map in block 3 showing the location of the hamlets and number them in a serpentine order starting from the northwest corner and proceeding southwards. While drawing this map, uninhabited area (non-abadi area) of the village will be included as part of nearby hamlet, so that no area of the village is left out. The boundaries of the hamlets may be defined with the help of some landmarks like canals, footpaths, railway lines, roads, cadastral survey plot numbers etc., so that it would be possible to identify and locate the geographical boundaries of the hamlet-groups to be formed in the village. (iv) List the hamlets in Block 4.1 in the order of their numbering. Indicate the present population content in terms of percentages. (v) Group the hamlets into D hamlet-groups. The criteria to be adopted for hamlet-group formation are equality of population content and geographical contiguity (numbering of hamlets is not to be adopted as a guideline for grouping). In case there is a conflict between the two aspects, geographical contiguity is to be given priority. However, there should not be substantial difference between the population of the smallest and the largest hamlet-group formed. Indicate the grouping in the map. (vi) Number the hamlet-groups serially in column (1) of Block 4.2. The hamlet-group containing hamlet number 1 will be numbered as 1, the hamlet-group with next higher hamlet number not included in hg 1 will be numbered as 2 and so on. Indicate the numbers also in the notional map. It is quite possible that a hamlet-group may not be constituted of hamlets with consecutive serial numbers.

Mode of data collection

FACE TO FACE

Vital Statistics: Birth Rate: per 1000 Population: Tripura: Urban data was reported at 10.700 NA in 2020. This records a decrease from the previous number of 11.000 NA for 2019. Vital Statistics: Birth Rate: per 1000 Population: Tripura: Urban data is updated yearly, averaging 12.000 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 14.800 NA in 1998 and a record low of 10.300 NA in 2017. Vital Statistics: Birth Rate: per 1000 Population: Tripura: Urban 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.GAH002: Vital Statistics: Birth Rate: by States.

Fine particulate matter (PM***) concentrations in rural and urban India averaged **** and **** micrograms per cubic meter of air (μg/m³) in 2022, representing reductions of around ** percent relative to 2017. The lowest average annual PM*** levels in rural and urban India were recorded in 2020, which can be attributed to COVID-19-related lockdowns. The following year saw levels rise in both areas, owing to the lifting of these restrictions. Although PM*** levels in urban and rural areas have declined in recent years, they still remain well above World Health Organization air quality guidelines.

Estimates of Logistic regression for the people with risk of obesity (BMI ≥ 25 kg/m2) and people with morbid obesity (BMI ≥ 40 kg/m2) by duration of residence in urban areas among the migrants aged 45+ in India, LASI wave 1 (2017−18).

India Round 2 (Rajasthan) Household and Female (HQFQ) survey used a two-stage cluster design with urban and rural and regional strata. A sample of 147 enumeration areas (EAs) was drawn by the International Institute of Population Sciences (IIPS) from the census. Each EA was listed and mapped; 35 households were randomly selected. Occupants in selected households were enumerated and eligible females of reproductive age (15-49) were contacted and consented for interview. Data collection was conducted between February and April, 2017. The final completed sample included 4,986 households and 6,090 females. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/b88s-zx32

As a country with ***** million population, in 2020, India has around *** million active internet users in urban area. It was estimated that in 2025, number of active internet users would surpass *** million in the country. Although there was a gap between the percentage of urban and rural active internet users, it has been narrowing down through rural development.

Estimates of Logistic and Quantile regression for the people with risk of abdominal obesity by duration of residence in urban areas among the middle-aged and older adults (aged 45+) migrants in India, LASI wave 1 (2017−18).

Vital Statistics: Birth Rate: per 1000 Population: Tripura: Urban在2020达10.700NA，相较于2019的11.000NA有所下降。Vital Statistics: Birth Rate: per 1000 Population: Tripura: Urban数据按每年更新，1997至2020期间平均值为12.000NA，共23份观测结果。该数据的历史最高值出现于1998，达14.800NA，而历史最低值则出现于2017，为10.300NA。CEIC提供的Vital Statistics: Birth Rate: per 1000 Population: Tripura: Urban数据处于定期更新的状态，数据来源于Office of the Registrar General & Census Commissioner, India，数据归类于India Premium Database的Demographic – Table IN.GAH002: Vital Statistics: Birth Rate: by States。

Socio-demographic and economic profile of older adults in India, 2017–18.

Description of the explanatory factors included in the study, Longitudinal Aging Study (LASI) Wave 1, India 2017–18.

In 2017, migrants formed over ** percent of the entire male workforce in urban India. Sectors such as public and modern services held the maximum share of migrant workers in the category. On the other hand, the primary sector possessed fewer migrants.

Prevalence of any chronic disease per 100 elderly population in India, 2017–18.

This statistic represents the share of villages with electricity in urban areas across India from 2001 to 2017 with an estimate for 2019. In 2019, ******************* of urban villages in India were estimated to be electrified.