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.

Dataset Card for Dataset Name

Includes Images for different Indian Cities.

  Dataset Details

Each city has 2500 images

  Dataset Description

This dataset contains 2500 images per Cities of popular indian Cities, City included are Ahmendabad, Mumbai, Delhi, Koklakta and A state Kerala.

Curated by: Divax Shah and Team

  Dataset Sources

Google

Demo: here

arXiv : https://arxiv.org/abs/2403.10912

In India, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was ** percent. Hyderabad topped the list with the highest share of middle-class and above category of consumers. Cities from south India topped the list with the first four ranks, followed by the national capital, Delhi.

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

As of September 2025, Hyderabad was the leading Indian city in local purchasing power among other Indian cities, with an index score of over *****. It was followed by Bengaluru and Pune. The local purchasing power index depicts the relative purchasing power of goods and services in a city for the average net salary in that city.

Population in largest city in India was reported at 33807403 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

As of 2024, Mumbai had a gross domestic product of *** billion U.S. dollars, the highest among other major cities in India. It was followed by Delhi with a GDP of around *** 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.

The dataset contains air quality information for various cities across India. It includes parameters such as Air Quality Index (AQI), concentrations of particulate matter (PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), as well as geographical coordinates and time stamps. This dataset enables analysis and comparison of air quality levels among different cities, aiding in understanding environmental health impacts and informing policy decisions.

Abstract

Genesis
Reliable and timely data base is the basic infrastructure needed for any sound and systematic planning. Efficient sectoral planning depends to a large extent on the availability of detailed information, preferably at micro level. Though a fairly adequate system of agricultural statistics has already been developed in the country, such an information system has not yet been built up for the non-agricultural sector. While statistics in respect of organised segments of the non-agricultural economy are being collected more or less regularly, it is not so in regard to its unorganised segments even though unorganised sector assumes greater importance due to its significant contribution towards gross domestic product as also in generation of employment in developing economy. Earlier attempts

1.2 attempts were made in the past to bridge these data gaps by both Central agencies and the States. The National Sample Survey Organisation (NSSO) had conducted some surveys on household nonagricultural enterprises in the past. The first round of NSS (1950-51) covered non-agricultural enterprises as one of its subjects. Such enterprises were covered regularly up to the tenth round (1955-56). Subsequently, selected activities were taken up for survey intermittently in different rounds (14th, 23rd & 29th rounds). Establishment schedules were canvassed in 1971 population census. The census of unorganized industrial units was carried out during 1971-73. Census of the units falling within the purview of Development Commissioner, Small scale industries was carried out during 1973-74 and a survey on distributive trade was conducted by some of the States during the fourth five-year plan period (1969-74). All such efforts made prior to 1976 to collect data on unorganized nonagricultural enterprises have been partial and sporadic.

Economic Census 1.3 The first coordinated approach to fill these vital data gaps was made by the Central Statistical Organisation (CSO), Government of India by launching a plan scheme 'Economic census and Survey' in 1976. The scheme envisaged organising countrywide census of all economic activities (excluding those engaged in crop production and plantation) followed by detailed sample survey of unorganized segments of different sector on non-agricultural economy in a phased manner during the intervening period of two successive economic censuses. The basic purpose of conducting the economic census was to prepare a frame while follow up surveys collect more detailed sector specific information between two economic censuses. In view of the rapid changes that occur in the unorganised sectors of non-agricultural economy due to high mobility or morbidity of smaller units and also on account of births of new units, the scheme envisaged conducting the economic census periodically in order to update the frame from time to time.

First Economic Census (EC-1977) and Follow up Surveys 1.4 The First Economic Census was conducted through-out the country, except Lakshadweep, during 1977 in collaboration with the Directorate of Economics & Statistics (DES) in the States/Union Territories (UT). The coverage was restricted to only nonagricultural establishments employing at least one hired worker on a fairly regular basis. Data on items such as description of activity, number of persons usually working, type of ownership, etc. were collected.

1.5 Reports based on the data of EC-1977 at State/UT level and at all India level were published. Tables giving the activity group-wise distribution of establishments with selected characteristics and with rural and urban break up were generated. State-wise details for major activities and size-class of employment, inter-alia, were also presented in tables.

1.6 Based on the frame provided by the First Economic Census, detailed sample surveys were carried out during 1978-79 and 1979-80 covering the establishments engaged in manufacturing, trade, hotels & restaurants, transport, storage & warehousing and services. While the smaller establishments (employing less than six workers) and own account establishments were covered by NSSO as part of its 33rd and 34th rounds, the larger establishments were covered through separate surveys. Detailed information on employment, emoluments, capital structure, quantity & value of input, output, etc. were collected and reports giving all important characteristics on each of the concerned subjects were published.

Second Economic Census (EC-1980) and Follow up surveys

1.7 The second economic census was conducted in 1980 along with the house-listing operations of 1981 Population Census. This was done with a view to economizing resources, manpower, time and money. The scope and coverage were enlarged. This time all establishments engaged in economic activities - both agricultural and non-agricultural whether employing any hired worker or not - were covered, except those engaged in crop production and plantation. All States/UTs were covered with the sole exception of Assam, where population census, 1981 was not conducted.

1.8 The information on location of enterprise, description of economic activity carried on, nature of operation, type of ownership, social group of owner, use of power/fuel, total number of workers usually engaged with its hired component and break-up of male and female workers were collected. The items, on which information was collected in second economic census, were more or less the same as hose collected in the First Economic Census. However, based on experience gained in the First Economic Census certain items viz. years of activity, value of annual output/turnover/receipt, mixed activity or not, registered/ licensed/recognized and act or authority, if registered were dropped.

1.9 The field work was done by the field staff consisting of enumerators and supervisors employed in the Directorate of Census Operations of each State/UT. The State Directorates of Economics & Statistics (DES) were also associated in the supervision of fieldwork. Data processing and preparation of State level reports of economic census and their publication were carried out by the DES.

1.10 EC 1980 data were released in two series of tables ('A' series and 'B' series) with different set of groupings for minor and major activities as also for agricultural and non-agricultural sectors. 'A' series give the number of own-account enterprises and establishments with relevant characteristics classified according to nature of economic activity. 'B' series gives the principal characteristics of own-account enterprises and establishments classified by size class of total employment for each economic activity. Summary statements, which basically provide the sampling frame and planning material for follow-up enterprise survey, were generate for rural and urban sectors of each State/District separately. The reports were published both at State/UT level as well as All-India level.

1.11 Based on the frame thrown up by EC-1980, three follow-up surveys were carried out, one in 1983-84 on hotels & restaurants, transport, storage & warehousing and services, second in 1984-85 on unorganized manufacturing and third in 1985-86 on wholesale and retail trade.

1.12 The third economic census scheduled for 1986 could not be carried out due to resource constraints. The EC 1980 frame was updated during 1987-88 in 64 cities (12 cities having more than 10 lakh population and 52 class-I cities) which had problems of identification of enumeration blocks and changes due to rapid urbanization. On the basis of the updated frame, four follow-up surveys were conducted during 1988-89, 1989-90, 1990-91 and 1991-92 covering the subjects of hotels & restaurants and transport, unorganized manufacturing, wholesale & retail trade and medical, educational, cultural & other services respectively.

Third Economic Census (EC-1990) and follow up surveys 1.13 The Third Economic Census was synchronized with the house listing operations of the Population Census 1991 on the same pattern of EC 1980. The coverage was similar to that of EC1980. All States/UTs except Jammu & Kashmir, where population census 1991 was not undertaken, were covered.

1.14 The tabulation plan consisted of generation of tables giving the results of EC 1990 under for categories: (a) Agricultural own account enterprises, (b) agricultural establishments, (c) non-agricultural own account enterprises and (d) non-agricultural establishments. For each of these categories, details of number of enterprises, employment with rural - urban break up for each district were presented by size class of employment, major activity, etc. All these tables were grouped broadly in to three categories viz. (i) summary statements (ii) main tables and (iii) derived tables.

1.15 Based on the frame thrown up by EC 1990 four follow up surveys were carried out: (i) Enterprise Survey covering sectors of mining & quarrying, storage & warehousing in 1992-93; (ii) Enterprise Survey covering sectors of hotels & restaurants and transport in 1993-94; (iii) NSS 51st round covering directory, non-directory and own account enterprise in unregistered manufacturing sector in 1994-95 and (iv) Directory Trade Establishments Survey in 1996-97. NSS 53rd round covered the residual part of the unorganized trade sector in 1997.

Fourth Economic Census 1.16 With a view to meeting the demand of various user departments for the data on unorganized sectors of the economy and considering the nature of large number of small units which are subjected to high rates of mobility and mortality, it was felt that the economic census must be brought back to quinquennial nature so that an up-to-date frame can be made available once in five years for conducting the follow up surveys. It was also felt necessary to assess the impact of economic liberalization process on

As per the Global Liveability Index of 2024, five Indian cities figured on the list comprising 173 across the world. Indian megacities Delhi and Mumbai tied for 141st place with a score of **** out of 100. They were followed by Chennai (****), Ahmedabad (****), and Bengaluru (****). What are indicators for livability The list was topped by Vienna for yet another year. The index measures cities on five broad indicators such as stability, healthcare, culture and environment, education, and infrastructure. As per the Economic Intelligence Unit’s suggestions, if a city’s livability score is between ** to ** then “livability is substantially constrained”. Less than ** means most aspects of living are severely restricted. Least Liveable cities on the index The least liveable cities were in Sub-Saharan Africa and the Middle East and North Africa regions. Damascus and Tripoli ranked the lowest. Tel Aviv also witnessed significant drop due to war with Hamas.

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

Census: Population: Uttar Pradesh: Pratapgarh City data was reported at 15,071.000 Person in 03-01-2011. This records an increase from the previous number of 12,411.000 Person for 03-01-2001. Census: Population: Uttar Pradesh: Pratapgarh City data is updated decadal, averaging 4,862.000 Person from Mar 1901 (Median) to 03-01-2011, with 10 observations. The data reached an all-time high of 15,071.000 Person in 03-01-2011 and a record low of 3,121.000 Person in 03-01-1921. Census: Population: Uttar Pradesh: Pratapgarh City 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.GAC035: Census: Population: By Towns and Urban Agglomerations: Uttar Pradesh.

The COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.

Goal 11: Make cities and human settlements inclusive, safe, resilient, and sustainableHalf of humanity – 3.5 billion people – lives in cities today. By 2030, almost 60% of the world’s population will live in urban areas.828 million people live in slums today and the number keeps rising.The world’s cities occupy just 2% of the Earth’s land, but account for 60 – 80% of energy consumption and 75% of carbon emissions. Rapid urbanization is exerting pressure on fresh water supplies, sewage, the living environment, and public health. But the high density of cities can bring efficiency gains and technological innovation while reducing resource and energy consumption.Cities have the potential to either dissipate the distribution of energy or optimise their efficiency by reducing energy consumption and adopting green – energy systems. For instance, Rizhao, China has turned itself into a solar – powered city; in its central districts, 99% of households already use solar water heaters.68% of India’s total population lives in rural areas (2013-14).By 2030, India is expected to be home to 6 mega-cities with populations above 10 million. Currently 17% of India’s urban population lives in slums.This map layer is offered by Esri India, for ArcGIS Online subscribers, If you have any questions or comments, please let us know via content@esri.in.

Abstract

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

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

Geographic coverage

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

Analysis unit

• Firms

Universe

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

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

Mode of data collection

Face-to-face [f2f]

Abstract

The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 .

The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them at the national and state levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (schedule 10).

Geographic coverage

The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.) URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

Table (1.2) : Composition of urban strata

Stratum population class of town

number

(1) (2)

1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area) 7 " (other area) 8 another city with population 1 million and above

9 " (other area)

Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be collected, on the basis of which its means of livelihood will be identified as one of the following : "self-employed in non-agriculture " "rural labour" and "others" (see section Two for definition of these terms) . Also the area of land possessed as on date of survey will be ascertained from all households while listing. Now the households of sub-stratum 2 will be arranged in the order : (1)self-employed in non-agriculture, (2) rural labour, other households, with land possessed (acres) : (3) less than 1.00 (4) 1.00-2.49,(5)2.50-4.99, (6)

In 2021, the south Indian city Chennai with average internet speed of ***** Mbps ranked the first among cities in India. It was followed by Bengaluru and Hyderabad, both with internet speed around ** Mbps. Internet access speed has a crucial influence on the colocation of data center in the country.

Retail Price: DOAC: Crocin: 10 Tablets: National Capital: Delhi data was reported at 15.750 INR/Unit in Mar 2010. This stayed constant from the previous number of 15.750 INR/Unit for Feb 2010. Retail Price: DOAC: Crocin: 10 Tablets: National Capital: Delhi data is updated monthly, averaging 15.000 INR/Unit from Jan 2005 (Median) to Mar 2010, with 52 observations. The data reached an all-time high of 16.500 INR/Unit in Jan 2009 and a record low of 10.000 INR/Unit in Sep 2006. Retail Price: DOAC: Crocin: 10 Tablets: National Capital: Delhi data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Price – Table IN.PB140: Retail Price: Department of Agriculture and Cooperation: Non Food: by Cities: Crocin (Discontinued).

In the first half of 2024, the existing stock for warehousing in the Mumbai in India accounted for around **** million square meters. It ranks the top among major Indian cities. The Indian warehousing stock was at **** million square meters during the same period.