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

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fcc0eb2fff946bf6e89ce8a5a1fcf2b57%2Fnewdelhi2.png?generation=1730130146299892&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of Top 50 Most populous Indian cities. Weather data from https://open-meteo.com/ from January 01, 2020 to October 27, 2024.

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=2020-01-01&end_date=2024-10-27
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

India's Largest Cities Weather Data Weather Datasets

• 🇮🇳 India's Largest Cities Weather Data 1990-1999
• 🇮🇳 India's Largest Cities Weather Data 2000-2009
• 🇮🇳 India's Largest Cities Weather Data 2010-2019
• 🇮🇳 India's Largest Cities Weather Data 2020-YTD

Note

Image generated with Bing Image Generator

Japan’s largest city, greater Tokyo, had a staggering ***** million inhabitants in 2023, making it the most populous city across the Asia-Pacific region. India had the second largest city after Japan with a population consisting of approximately ** million inhabitants. Contrastingly, approximately *** thousand inhabitants populated Papua New Guinea's largest city in 2023. A megacity regionNot only did Japan and India have the largest cities throughout the Asia-Pacific region but they were among the three most populated cities worldwide in 2023. Interestingly, over half on the world’s megacities were situated in the Asia-Pacific region. However, being home to more than half of the world’s population, it does not seem surprising that by 2025 it is expected that more than two thirds of the megacities across the globe will be located in the Asia Pacific region. Other megacities are also expected to emerge within the Asia-Pacific region throughout the next decade. There have even been suggestions that Indonesia’s Jakarta and its conurbation will overtake Greater Tokyo in terms of population size by 2030. Increasing populationsIncreased populations in megacities can be down to increased economic activity. As more countries across the Asia-Pacific region have made the transition from agriculture to industry, the population has adjusted accordingly. Thus, more regions have experienced higher shares of urban populations. However, as many cities such as Beijing, Shanghai, and Seoul have an aging population, this may have an impact on their future population sizes, with these Asian regions estimated to have significant shares of the population being over 65 years old by 2035.

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.

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!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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.

About this dataset

Context

This list ranks the 473 cities in the California by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the California by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total California Indian Population: This tells us how much of the entire California Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

About this dataset

Context

This list ranks the 333 cities in the Massachusetts by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the Massachusetts by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Massachusetts Indian Population: This tells us how much of the entire Massachusetts Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Image Cover

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Ff66603745ee2d4e6aa1508bff8732f5c%2Findia2a.jpg?generation=1730196301446161&alt=media" alt="">

Context

Hourly and Daily Weather Dataset of Top 50 Most populous Indian cities. Weather data from https://open-meteo.com/ from January 01, 2000 to Dec 31, 2009

Field documentation go here

• https://open-meteo.com/en/docs/historical-weather-api#start_date=2000-01-01&end_date=2009-12-31
• Datetime is in GMT

Visually verify coordinates

• Replace the placeholders with the latitude and longitude values of the cities https://www.latlong.net/c/?lat=(latitude)&long=(longitude)
• Another way to visualize the coordinates https://www.ncei.noaa.gov/maps/radar/

Citations and Acknowledgements

• Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649
• Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47
• Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC
• Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

India's Largest Cities Weather Data Weather Datasets

• 🇮🇳 India's Largest Cities Weather Data 1990-1999
• 🇮🇳 India's Largest Cities Weather Data 2000-2009
• 🇮🇳 India's Largest Cities Weather Data 2010-2019
• 🇮🇳 India's Largest Cities Weather Data 2020-YTD

Note

Image generated with Bing Image Generator

About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

This statistic illustrates the consumption expenditure per capita across the largest cities in India in 2015. The nation capital region, Delhi, had a per capita consumer expenditure of approximately ******* Indian rupees. Bangalore had the highest per capita consumption expenditure during the measured time period.

The global per capita expenditure on apparel in 2015 and 2025, broken down by region, can be found here.

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.

📋 Dataset Overview

A comprehensive hourly dataset tracking atmospheric conditions and air quality across 29 major Indian cities (28 states + Delhi) from 2022 to 2025. This dataset provides detailed insights into India's environmental patterns, pollution trends, and meteorological conditions.

🗺️ Geographic Coverage

• Cities Covered: 29 major cities representing all 28 Indian states + Delhi
• Time Period: 2022 to 2025
• Records: 842,160 hourly observations
• Features: 63 parameters covering weather, pollution, and temporal data

📊 Dataset Specifications

• Total Records: 842,160
• Features: 63 columns
• Temporal Resolution: Hourly data
• Period: 2022-2025 (4 years)
• Memory Usage: ~405 MB

🏙️ Indian Cities Included

(Representing all 28 states + Union Territory of Delhi) - Delhi - Mumbai (Maharashtra) - Chennai (Tamil Nadu) - Kolkata (West Bengal) - Bengaluru (Karnataka) - Hyderabad (Telangana) - Ahmedabad (Gujarat) - Pune (Maharashtra) - Jaipur (Rajasthan) - Lucknow (Uttar Pradesh) - And other major state capitals...

🗂️ Data Columns

📍 Geographic & Temporal Context

🌡️ Meteorological Parameters

Temperature & Humidity - Temp_2m_C - Ambient temperature (°C) - Humidity_Percent - Relative humidity - Dew_Point_C - Dew point temperature - Humidity_Category - Comfort levels

Wind Patterns - Wind_Speed_10m_kmh - Surface wind speed - Wind_Dir_10m - Wind direction (critical for pollution dispersion) - Wind_Gusts_kmh - Wind gusts - Wind_Stagnation - Air stagnation events

Precipitation & Pressure - Precipitation_mm, Rain_mm - Monsoon rainfall tracking - Is_Raining, Heavy_Rain - Rain events - Pressure_MSL_hPa - Monsoon pressure systems

☀️ Solar & Cloud Data

• Solar_Radiation_Wm2 - Total solar radiation
• Direct/Diffuse_Radiation_Wm2 - Radiation components
• Cloud_Cover_Percent - Total cloud cover
• Cloud_Low/Mid/High_Percent - Cloud altitude distribution
• Sunshine_Seconds - Bright sunshine duration
• Is_Daytime - Day/night indicator

🏭 Air Quality Metrics (Critical for India)

Particulate Matter - PM2_5_ugm3 - Fine particulate matter (primary concern) - PM10_ugm3 - Coarse particulate matter - PM_Ratio - PM2.5/PM10 ratio (source identification) - Dust_ugm3 - Dust concentrations - AOD - Aerosol Optical Depth

Gaseous Pollutants - CO_ugm3 - Carbon monoxide (vehicular/industrial) - NO2_ugm3 - Nitrogen dioxide (traffic, industries) - SO2_ugm3 - Sulfur dioxide (industrial, power plants) - O3_ugm3 - Ozone (secondary pollutant)

📊 Air Quality Indices

US AQI System - US_AQI - Overall US AQI - US_AQI_PM25, US_AQI_PM10 - PM-specific indices - US_AQI_NO2, US_AQI_O3, US_AQI_CO - Gas-specific indices

EU AQI System - EU_AQI - European Air Quality Index - EU_AQI_PM25, EU_AQI_PM10 - European standards

India-Specific Categories - AQI_Category - Overall air quality category - PM25_Category_India - India-specific PM2.5 categorization

🌪️ Special Features

• Temp_Inversion - Temperature inversion events (critical for winter pollution in North India)

⚠️ Data Quality Notes

• High Completeness: Most columns have 842,160 non-null values
• Minor Missing Values:
  • US/EU AQI columns: ~145 missing records
  • AQI_Category: 2,516 missing
  • US_AQI_NO2: 2 missing
  • US_AQI_O3: 73 missing

🎯 India-Specific Analysis Opportunities

Seasonal Patterns

• Winter (Dec-Feb): Severe PM2.5 pollution in Indo-Gangetic plain
• Summer (Mar-May): Dust storms, high O3 levels
• Monsoon (Jun-Sep): Natural cleansing, improved AQI
• Post-Monsoon (Oct-Nov): Crop burning impacts in North India

Geographic Hotspots

• Northern Plains: Delhi, Lucknow - Winter smog, crop burning
• Coastal Cities: Mumbai, Chennai - Marine influence, humidity
• Southern Plateau: Bengaluru, Hyderabad - Moderate pollution
• Eastern India: Kolkata - Industrial and vehicular pollution

Event-Based Analysis

• Festival Impacts: Diwali fireworks, Holi bonfires
• Agricultural Cycles: Stubble burning seasons
• Meteorological Events: Western disturbances, monsoon progress

🛠️ Suggested Research Topics

Public Health

• C...

Context

The dataset was created by keeping in mind the necessity of such historical weather data in the community. The datasets for top 8 Indian cities as per the population.

Content

The dataset was used with the help of the worldweatheronline.com API and the wwo_hist package. The datasets contain hourly weather data from 01-01-2009 to 01-01-2020. The data of each city is for more than 10 years. This data can be used to visualize the change in data due to global warming or can be used to predict the weather for upcoming days, weeks, months, seasons, etc. Note : The data was extracted with the help of worldweatheronline.com API and I can't guarantee about the accuracy of the data.

Acknowledgements

The data is owned by worldweatheronline.com and is extracted with the help of their API.

Inspiration

The main target of this dataset can be used to predict weather for the next day or week with huge amounts of data provided in the dataset. Furthermore, this data can also be used to make visualization which would help to understand the impact of global warming over the various aspects of the weather like precipitation, humidity, temperature, etc.

In 2022, the union territory of Delhi had the highest urban population density of over ** thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

Indian Tourist Destinations Dataset Dataset Overview This dataset provides a comprehensive list of popular tourist destinations across India, categorized by city, with additional information on ratings, descriptions, and the best time to visit. The data is compiled from various travel survey platforms such as MakeMyTrip, Holidify, and other reliable travel resources.

Columns Description ID: A unique identifier for each city in the dataset. City: The name of the tourist destination city or region in India. Rating: An aggregate rating for the city, derived from surveys conducted on various travel platforms. The rating reflects the overall popularity, quality of tourist experience, and visitor satisfaction. About the City: A brief description of the city, highlighting its cultural, historical, or natural significance. This includes information on key attractions, local culture, and why it's a must-visit destination. Best Time to Visit: The recommended period or season to visit the city for the best tourist experience. This could be based on weather conditions, local festivals, or other seasonal factors that enhance the travel experience. Source of Data The ratings are based on aggregated data from well-known travel platforms such as:

MakeMyTrip Holidify TripAdvisor Other travel blogs and survey websites Potential Use Cases Travel Recommendations: Use the dataset to build travel recommendation systems or itinerary planning tools for tourists. Tourism Analysis: Analyze tourism trends, popular destinations, and visitor preferences across different regions of India. Sentiment Analysis: Combine this dataset with reviews and feedback from tourists to perform sentiment analysis and gain deeper insights into visitor experiences. Seasonal Trends: Study the impact of seasonal variations on tourism by analyzing the 'Best Time to Visit' column. Data Visualization: Create visual dashboards showcasing top-rated destinations, best times to visit, and key attractions for each city. Additional Information Data Format: CSV Total Records: 100 rows (one for each city/region) Data Refresh: This dataset can be periodically updated with more recent ratings and information as new data becomes available from travel platforms. Acknowledgments Special thanks to the platforms MakeMyTrip, Holidify, and other travel resources for providing the ratings and information used to compile this dataset. This dataset aims to promote travel and tourism in India by providing valuable insights into popular tourist destinations.

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.

BackgroundIndia has the highest burden of tuberculosis (TB). Although most patients with TB in India seek care from the private sector, there is limited evidence on quality of TB care or its correlates. Following our validation study on the standardized patient (SP) method for TB, we utilized SPs to examine quality of adult TB care among health providers with different qualifications in 2 Indian cities.Methods and findingsDuring 2014–2017, pilot programs engaged the private health sector to improve TB management in Mumbai and Patna. Nested within these projects, to obtain representative, baseline measures of quality of TB care at the city level, we recruited 24 adults to be SPs. They were trained to portray 4 TB “case scenarios” representing various stages of disease and diagnostic progression. Between November 2014 and August 2015, the SPs visited representatively sampled private providers stratified by qualification: (1) allopathic providers with Bachelor of Medicine, Bachelor of Surgery (MBBS) degrees or higher and (2) non-MBBS providers with alternative medicine, minimal, or no qualifications.Our main outcome was case-specific correct management benchmarked against the Standards for TB Care in India (STCI). Using ANOVA, we assessed variation in correct management and quality outcomes across (a) cities, (b) qualifications, and (c) case scenarios. Additionally, 2 micro-experiments identified sources of variation: first, quality in the presence of diagnostic test results certainty and second, provider consistency for different patients presenting the same case.A total of 2,652 SP–provider interactions across 1,203 health facilities were analyzed. Based on our sampling strategy and after removing 50 micro-experiment interactions, 2,602 interactions were weighted for city-representative interpretation. After weighting, the 473 Patna providers receiving SPs represent 3,179 eligible providers in Patna; in Mumbai, the 730 providers represent 7,115 eligible providers. Correct management was observed in 959 out of 2,602 interactions (37%; 35% weighted; 95% CI 32%–37%), primarily from referrals and ordering chest X-rays (CXRs). Unnecessary medicines were given to nearly all SPs, and antibiotic use was common. Anti-TB drugs were prescribed in 118 interactions (4.5%; 5% weighted), of which 45 were given in the case in which such treatment is considered correct management.MBBS and more qualified providers had higher odds of correctly managing cases than non-MBBS providers (odds ratio [OR] 2.80; 95% CI 2.05–3.82; p < 0.0001). Mumbai non-MBBS providers had higher odds of correct management than non-MBBS in Patna (OR 1.79; 95% CI 1.06–3.03), and MBBS providers’ quality of care did not vary between cities (OR 1.15; 95% CI 0.79–1.68; p = 0.4642). In the micro-experiments, improving diagnostic certainty had a positive effect on correct management but not across all quality dimensions. Also, providers delivered idiosyncratically consistent care, repeating all observed actions, including mistakes, approximately 75% of the time. The SP method has limitations: it cannot account for patient mix or care-management practices reflecting more than one patient–provider interaction.ConclusionsQuality of TB care is suboptimal and variable in urban India’s private health sector. Addressing this is critical for India’s plans to end TB by 2025. For the first time, we have rich measures on representative levels of care quality from 2 cities, which can inform private-sector TB interventions and quality-improvement efforts.