This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...

High Frequency Indicator: The dataset contains year-, quarter- and state-wise compiled data from the year 2014 to till date on the total number of broadband and narrowband internet subscribers in the rural and urban areas of India

Data from www.census.gov.in was downloaded and processed available in this link,

Used the table code :PC11_B07 which contains data regarding working population classified by industrial category, educational level and gender. From these 35 excel tables, data was extracted and transformed into required format using Excel and Power Query detailed here. Transformed it into two datasets of Urban and Rural.

There are two .csv files used here. One is Rural education data district wise and second one is Urban education data district wise.

The Socioeconomic High-resolution Rural-Urban Geographic Dataset on India (SHRUG) is a new administrative data source describing socioeconomic development in India. The first version of the SHRUG describes demographic, socioeconomic, firm and political outcomes at a high geographic resolution for the universe of Indian households and non-farm productive establishments, in both rural and urban India, from 1990-2013. The SHRUG is a platform for future collaboration and data sharing between researchers working with administrative data in India. We have created a set of consistent location identifiers for all geographic locations in India from 1990-2013, and establish a methodology to extend this classification to units from future data sources. Researchers working with geographic variation in India can thus benefit from linking to the SHRUG, and can benefit other researchers by making their data available to others through the SHRUG platform. In the accompanying paper, we describe the construction of the data and the strengths and weaknesses of administrative data like these for research on economic development. We then perform several validation exercises to show that the SHRUG is consistent with other data sources. Finally, we present an illustrative data exercise on recent trends in rural and urban development. This is an archive of a prior version of SHRUG (v1.3, or Harvard Dataverse version number 3.x). Harvard Dataverse v2.0 corresponds to SHRUG v1.2. For the most recent version, please go to devdatalab.org/shrug

This dataset is about books. It has 10 rows and is filtered where the book subjects is Rural-urban migration-India. It features 9 columns including author, publication date, language, and book publisher.

Internet in India began in 1986 and was available only to the educational and research community. General public access to the internet began on 15 August 1995, and as of 2020 there are 718.74 million active internet users that comprise 54.29% of the population.[1]

As of May 2014, the Internet is delivered to India mainly by 9 different undersea fibres, including SEA-ME-WE 3, Bay of Bengal Gateway and Europe India Gateway, arriving at 5 different landing points.[2] India also has one overland internet connection, at the city of Agartala near the border with Bangladesh.[3]

The Indian Government has embarked on projects such as BharatNet, Digital India, Brand India and Startup India to further expedite the growth of internet-based ecosystems.

The dataset contains year-, state- and month-wise compiled data from the year 2011 to till date on the consumer price index (CPI) in Rural and Urban areas of India for various items such as Pan, Supari, Tobacco, Beverages, Fuel, Light, Housing, Clothing, Bedding, Footwear, etc.

High Frequency Indicator: The dataset contains year- and month-wise All India compiled data from the year 2011 to till date on the teledensity in rural and urban areas of India by percentage of wireline and wireless telecom subscriptions

Teledensity refers to proportion of people per every 100 people using telephone services

The dataset contains Year- and region-wise All India compiled data on Total Number of Households who are using Principal Sources of Drinking Water such as bottled water, piped water into dwelling, yard or plot, public tap, stand pipe, tube well, bore hole, protected and unprotected wells, springs, rain water, tank, pond and other sources. Along with this, the dataset also contains All India independent data on Total Number of Households who have No or Any or All Types of Supplementary Sources of Drinking Water. Both the types of data are provided for the period of 1998 to 2018. The dataset has been compiled from Table nos. 8, 13 and 14 of NSS 54th, 69 and 76th round reports, respectively.

Context

This dataset contains state-wise Fertility Rate of India.

Content

It has total 4 columns:- State Total Urban Rural Population 'States': These contain names of all the States and Union Territories of India. 'Total': Fertility rate of each state. It is an estimate of the average number of children that a woman would have over her childbearing years (i.e. age 15-49), based on current birth trends. It is calculated using below formulae. 'Urban': Total Fertility rate of each state for urban dwellers. 'Rural': Total Fertility rate of each state for rural dwellers. 'Population': Total number of people in that state.

Acknowledgements

I am thankful to Indian government for maintaining these valuable data which can be used to understand demography of India in more clear way.

Inspiration

I am truly inspired by everyone on the Kaggle, with the level of their dedication and hard work.

Replication data and code for "The Geography of Citizenship Practice: How the Poor Engage the State in Rural and Urban India."

The dataset contains Year- and region-wise All India compiled data on distribution of households (per thousand) by Types of Access and Usages of Bathrooms such as exclusive, common, community, public, etc., during the period of 1998 to 2018. The dataset has been compiled from Table No. 17 and Statement No. 10 of 54th and 76th round reports of NSS.

Dataset

This dataset was created by Aravind M

Released under CC0: Public Domain

Contents

Urbanisation is a form of social transformation from traditional rural societies to modern, industrial and urban communities. It is long term continuous process. It is progressive concentration of population in urban unit. Kingsley Davies has explained urbanisation as process of switch from spread out pattern of human settlements to one of concentration in urban centers. Migration is the key process underlying growth of urbanization.

Challenges in urban development--->;

Institutional challenges

Urban Governance 74th amendment act has been implemented half-heartedly by the states, which has not fully empowered the Urban local bodies (ULBs). ULBs comprise of municipal corporations, municipalities and nagar panchayats, which are to be supported by state governments to manage the urban development. For this , ULBs need clear delegation of functions, financial resources and autonomy. At present urban governance needs improvement for urban development, which can be done by enhancing technology, administrative and managerial capacity of ULBs.

Planning Planning is mainly centralized and till now the state planning boards and commissions have not come out with any specific planning strategies an depend on Planning commission for it. This is expected to change in present government, as planning commission has been abolished and now focus is on empowering the states and strengthening the federal structure.

In fact for big cities the plans have become outdated and do not reflect the concern of urban local dwellers, this needs to be take care by Metropolitan planning committee as per provisions of 74th amendment act. Now the planning needs to be decentralized and participatory to accommodate the needs of the urban dwellers.

Also there is lack of human resource for undertaking planning on full scale. State planning departments and national planning institutions lack qualified planning professional. Need is to expand the scope of planners from physical to integrated planning- Land use, infrastructure, environmental sustainability, social inclusion, risk reduction, economic productivity and financial diversity.

Finances Major challenge is of revenue generation with the ULBs. This problem can be analyzed form two perspectives. First, the states have not given enough autonomy to ULBs to generate revenues and Second in some case the ULBs have failed to utilize even those tax and fee powers that they have been vested with.

There are two sources of municipal revenue i.e. municipal own revenue and assigned revenue. Municipal own revenue are generated by municipal own revenue through taxes and fee levied by them. Assigned revenues are those which are assigned to local governments by higher tier of government.

There is growing trend of declining ratio of own revenue. There is poor collection property taxes. Use of geographical information system to map all the properties in a city can have a huge impact on the assessment rate of properties that are not in tax net.

There is need to broaden the user charge fee for water supply, sewerage and garbage disposal. Since these are the goods which have a private characteristics and no public spill over, so charging user fee will be feasible and will improve the revenue of ULBs , along with periodic revision. Once the own revenue generating capacity of the cities will improve, they can easily get loans from the banks. At present due to lack of revenue generation capabilities, banks don’t give loan to ULBs for further development. For financing urban projects, Municipal bonds are also famous, which work on the concept of pooled financing.

Regulator

There is exponential increase in the real estate, encroaching the agricultural lands. Also the rates are very high, which are not affordable and other irregularities are also in practice. For this, we need regulator, which can make level playing field and will be instrumental for affordable housing and checking corrupt practices in Real estate sector.

Infrastructural challenges

Housing Housing provision for the growing urban population will be the biggest challenge before the government. The growing cost of houses comparison to the income of the urban middle class, has made it impossible for majority of lower income groups and are residing in congested accommodation and many of those are devoid of proper ventilation, lighting, water supply, sewage system, etc. For instance in Delhi, the current estimate is of a shortage of 5,00,000 dwelling units the coming decades. The United Nations Centre for Human Settlements (UNCHS) introduced the concept of “Housing Poverty” which includes “Individuals and households who lack safe, secure and healthy shelter, with basic infrastructure such as piped water and adequate provision for sanitation, drainage and the removal of hou...

Introduction

The Consumer Price Index (CPI) is a measure that examines the average change in prices paid by urban consumers for a basket of goods and services over time. It serves as an indicator of inflation or deflation within an economy. The CPI takes into account a wide range of products and services commonly purchased by households, such as food, housing, transportation, healthcare, and entertainment. By tracking changes in the prices of these items, the CPI provides valuable insights into the overall cost of living and helps in understanding how the purchasing power of consumers is affected by fluctuations in prices.

Navigating the dataset

• Sector: Rural, Urban, or Rural + Urban
• Year, Month: Year and month of the CPI data
• { ...products }: Normalized cost of different products
• General index: Average normalized index for the month.

Explore single year age data for India, including information on all ages, total persons, females, males, urban population, and more.

All ages, Total Persons, Females, Males, Urban, Age not stated, Rural Persons, Urban

India

Follow data.kapsarc.org for timely data to advance energy economics research.

Characteristics of sample households in rural India for the study (unweighted), NSSO 64th round (2007–2008) (in %).

Odds ratios and associated significance levels from the binary logistic regression models assessing the association between explanatory variables and temporary migration across several economic groups in rural India, NSSO 64th round (2007–2008).

The India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001 is a compilation of the finest level of administrative boundaries in India (village/town-level) and over 200 socio-economic variables collected during the Indian Census in 1991 and 2001. This data set was developed by digitizing village/town level boundaries from the official analog maps published by the Survey of India for 2001. This data set also utilized tabular data for 1991 and 2001 from the Primary Census Abstract (PCA) and Village Directory (VD) data series of the Indian census. The data are in UTM 44N projection and are distributed primarily as shapefiles. Separate files are provided for each of the 28 states (number of states during 1991 and 2001 census) and combined Union Territories for 1991 and 2001. To provide data that can be used in independent spatial statistical analyses, construction of development-related indices, or in combination with remote sensing data in order to identify spatio-temporal patterns and/or changes in different demographic categories, such as male, female, urban, rural, level of education, etc.

Consumer Price Indices (CPI) measure changes over time in general level of prices of goods and services that households acquire for the purpose of consumption. CPI numbers are widely used as a macroeconomic indicator of inflation, as a tool by governments and central banks for inflation targeting and for monitoring price stability, and as deflators in the national accounts. CPI is also used for indexing dearness allowance to employees for increase in prices. CPI is therefore considered as one of the most important economic indicators. For construction of CPI numbers, two requisite components are weighting diagrams (consumption patterns) and price data collected at regular intervals. The data refers to group wise all India Consumer Price Index for Rural & Urban with base year 2010. The dataset is published by Central Statistical Office and released on 12th of every month.