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.

The spreadsheet consists of multiple attributes of case study cities.

The population density in India was 479.43 people in 2022. In a steady upward trend, the population density rose by 329.23 people from 1961.

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.

About the Dataset: Waste Management and Recycling in India

Overview:

This dataset provides comprehensive information on waste management and recycling practices in various cities across India. It includes key data related to waste generation, recycling rates, population density, municipal efficiency, landfill details, and more. The data spans multiple years (2019–2023) and covers a range of waste types, including plastic, organic waste, electronic waste (e-waste), construction waste, and hazardous waste.

Purpose:

The dataset aims to: - Promote efficient waste management practices across Indian cities. - Analyze trends in recycling and waste disposal methods. - Provide insights for improving municipal management systems. - Support research and development in sustainability, environmental science, and urban planning.

Columns:

1. City/District: The name of the Indian city or district.
2. Waste Type: Type of waste generated, e.g., Plastic, Organic, E-Waste, Construction, Hazardous.
3. Waste Generated (Tons/Day): Amount of waste generated in tons per day.
4. Recycling Rate (%): The percentage of waste that is recycled.
5. Population Density (People/km²): The number of people per square kilometer in the city.
6. Municipal Efficiency Score (1-10): A score indicating how effectively the municipality manages waste (e.g., waste segregation, collection, disposal).
7. Disposal Method: The method used for waste disposal (e.g., Landfill, Recycling, Incineration, Composting).
8. Cost of Waste Management (₹/Ton): The cost of managing one ton of waste in Indian Rupees.
9. Awareness Campaigns Count: The number of awareness campaigns organized by the municipality in that year related to waste management.
10. Landfill Name: The name of the landfill site used by the city.
11. Landfill Location (Lat, Long): The geographical location (latitude and longitude) of the landfill.
12. Landfill Capacity (Tons): The total waste capacity (in tons) that the landfill can hold.
13. Year: The year of the data entry, ranging from 2019 to 2023.

Applications:

• Urban Planning: The dataset can be used to analyze and optimize waste management infrastructure in urban areas.
• Sustainability Research: It can help in studying the progress of recycling and waste reduction strategies.
• Policy Making: Government bodies can use this data to craft policies aimed at improving waste management and recycling rates.
• Machine Learning/AI: The dataset can be used to build models for predicting waste generation trends, recycling outcomes, and municipal efficiency.

Sources:

• The data is simulated for this dataset based on average waste management practices observed in Indian cities.
• Real-world data could come from municipal corporations, environmental agencies, and government reports on waste management.

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

Comprehensive socio-economic dataset for British Indian Ocean Territory including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

According to the 2011 census, the population density in the Indian state of Maharashtra was *** individuals per square kilometer. Located on the Deccan Plateau, it is the second-most populous state in the country. A steady increase in the population of the state can be attributed to growing urban districts such as Mumbai and Pune, with diverse employment opportunities in several sectors.

India's economic powerhouse

With a contribution of over ** trillion Indian rupees in the financial year 2017, the state of Maharashtra had the highest gross state domestic product in the country. A per capita income of over *** thousand Indian rupees was estimated across the state for the preceding year. Based on its economic model, the state was a highly preferred destination for domestic and foreign investments.

The most populous Indian state

Mumbai, the capital city of Maharashtra, was the most populous city after Delhi. As the country's economic core, it serves as the financial and commercial capital while providing numerous job opportunities. Many are attracted to this dream city in search of a lucrative career and to make it big in the world-famous Bollywood film industry.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

The growth in India's overall population is driven by its young population. Nearly ** percent of the country's population was between the ages of 15 and 64 years old in 2020. With over *** million people between 18 and 35 years old, India had the largest number of millennials and Gen Zs globally.

The density-dependent prophylaxis hypothesis predicts that risk of pathogen transmission increases with increase in population density, and in response to this, organisms mount a prophylactic immune response when exposed to high density. This prophylactic response is expected to help organisms improve their chances of survival when exposed to pathogens. Alternatively, organisms living at high densities can exhibit compromised defense against pathogens due to lack of resources and density associated physiological stress; the crowding stress hypothesis. We housed adult Drosophila melanogaster flies at different densities and measured the effect this has on their post-infection survival and resistance to starvation. We find that flies housed at higher densities show greater mortality after being infected with bacterial pathogens, while also exhibiting increased resistance to starvation. Our results are more in line with the density-stress hypothesis that postulates a compromised immune system when hosts are subjected to high densities. Methods This file ("Adult_density_experiment.xlsx") was generated in 2019-20 by Paresh Nath Das and others at the Evolutionary Biology Lab, IISER Mohali. GENERAL INFORMATION 1. Title of Dataset: "Increasing adult density compromises anti-bacterial defense in Drosophila melanogaster" 2. Author Information A. Principal Investigator Contact Information Name: Prof. N. G. Prasad Institution: Indian Institute of Science Education and Research, Mohali Address: IISER Mohali, Sector 81, Knowledge City, SAS Nagar, Punjab - 140306, India. Email: prasad@iisermohali.ac.in B. Associate or Co-investigator Contact Information Name: Paresh Nath Das Institution: Indian Institute of Science Education and Research, Mohali Address: IISER Mohali, Sector 81, Knowledge City, SAS Nagar, Punjab - 140306, India. Email: pareshnathd@gmail.com C. Associate or Co-investigator Contact Information Name: Aabeer Kumar Basu Institution: Indian Institute of Science Education and Research, Mohali Address: IISER Mohali, Sector 81, Knowledge City, SAS Nagar, Punjab - 140306, India. Email: aabeerkbasu@gmail.com 3. Duration of data collection: September 2019 - March 2020 4. Geographic location of data collection: Mohali, Punjab, India 5. Information about funding sources that supported the collection of the data: IISER Mohali, MHRD, Govt. of India. SHARING/ACCESS INFORMATION Links to publications that cite or use the data: bioRxiv: https://doi.org/10.1101/2022.01.02.474745 Journal of Insect Physiology (in press version): https://doi.org/10.1016/j.jinsphys.2022.104415 METHODOLOGICAL INFORMATION A. Details of fly populations Blue Ridge Baseline (BRB) population: BRB2 is a lab-adapted, large, outbred, wild-type population of Drosophila melanogaster, maintained on a 14-day discrete generation cycle, on standard banana-jaggery-yeast medium. The BRB population was originally derived by hybridising 19 iso-female lines caught from the wild population at Blue Ridge Mountains, USA. The experiments reported were conducted after 200 generations of lab-adaptation. B. Effect of density, 8 adults vs. 32 adults, on immune function and starvation resistance.

Experimental treatments: 2-3 day old adult flies were randomly distributed into two density treatments.

a. 8 adults per vial (1:1 sex ratio) b. 32 adults per vial (1:1 sex ratio) Vilas of both treatments had equal amout of standard fly food (1.5-2 ml). Flies were housed like this for 48 hours, and thereafter assayed for immune function and starvation resistance.

Immune function assay: Flies of both treatments (described above) were assayed separately for resistance against two entomopathogenic bacteria, Enterococcus faecalis and Erwinia c. carotovora, with two independent replicates per pathogen.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to infection, and 40 males and 40 females were subjected to sham-infections. Post-infection mortality was recorded for 120 hours; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial).

Starvation resistance assay: Flies of both treatments (described above) were assayed for starvation resistance; experiment independently replicated twice.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to starvation in vials with non-nutritive agar gel only. Post-starvation mortality was recorded till the last fly died; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial). C. Effect of density, 50 adults vs. 200 adults, on immune function and starvation resistance.

Experimental treatments: 2-3 day old adult flies were randomly distributed into two density treatments.

a. 50 adults per vial (1:1 sex ratio) b. 200 adults per vial (1:1 sex ratio) Vilas of both treatments had equal amout of standard fly food (1.5-2 ml). Flies were housed like this for 48 hours, and thereafter assayed for immune function and starvation resistance.

Immune function assay: Flies of both treatments (described above) were assayed separately for resistance against two entomopathogenic bacteria, Enterococcus faecalis and Erwinia c. carotovora, with two independent replicates per pathogen.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to infection, and 40 males and 40 females were subjected to sham-infections. Post-infection mortality was recorded for 120 hours; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial).

Starvation resistance assay: Flies of both treatments (described above) were assayed for starvation resistance; experiment independently replicated twice.

Within each replicate experiment, 80 males and 80 females from each treatment (described above) were subjected to starvation in vials with non-nutritive agar gel only. Post-starvation mortality was recorded till the last fly died; during this period, flies of both treatments were housed at equal density (4 males and 4 females per vial).

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

Abstract

A nationwide survey on "Particulars of Slums" was carried-out by the National Sample Survey Organisation (NSSO) during the period January-June, 1993 in its 49th round to ascertain the extent of civic facilities available in the slums. The 49th round survey among other objectives also collected data on the condition of slum dwellings as well as on some general particulars of slum areas. Apart from formulating the sampling design with an emphasis to obtain an adequate number of slum households for the survey on housing condition and migration, surveyed the slum areas and collected information on slums. The schedule 0.21 was canvassed in both the rural and urban areas. All the slums, both the declared ones as well as the others (undeclared), found in the selected first stage units were surveyed even if hamlet-group/sub-block selection was resorted to in some of then. To ascertain the extent of civic facilities available in the slums as well as the information regarding the improvement of slum condition during a period of last five years was also collected. Information was collected by contacting one or more knowledgeable persons in the FSU on the basis of predominant criterion in both declared and undeclared slums, and not through household approach.

Geographic coverage

The geographical coverage of the survey was the whole of the Indian Union except Ladakh & Kargil districts of Jammu & Kashmir, 768 interior villages of Nagaland and 172 villages in Andaman & Nicobar islands which remain inaccessible throughout the year. However, certain districts of Jammu & Kashmir viz. Doda, Anantanag, Pulwama, Srinagar, Badgam, Barmula & Kupwara, as well as Amritsar district in Punjab, had to be excluded from the survey coverage due to unfavourable field conditions.

Sampling procedure

Sample Design : The first stage units in the rural sector and urban sector were census villages and urban frame survey (UFS) blocks respectively. However for newly declared towns of the 1991 census,for which UFS frames were not available, census EBs were used as first stage units.

Sampling frame for fsu's : In the rural sector, the sampling frame in most of the districts was the 1981 census list of villages. However, in Assam and in 8 districts of Madhya Pradesh, 1971 Census lists of villages were used. For Nagaland, the villages situated within 5 kms of a bus route constituted the sampling frame. For the Andaman & Nicobar islands the list of accessible villages was used as sampling frame. In the urban sector, the lists of NSS urban frame survey (UFS) blocks were the sampling frames used in most cases. However, 1991 Census house - listing enumeration blocks were considered as the sampling units for some of the newly declared towns of the 1991 population census, for which UFS frames were not available.

Stratification : Each state/u.t. was divided into one or more agro-economic regions by grouping contiguous districts which are similar with respect to population density and crop pattern. In Gujarat, however, some districts were subdivided for the purpose of region formation on the basis of location of dry areas and the distribution of tribal population in the state. The total number of regions formed in the whole of India was 78.

In the rural sector, within each region, each district with a rural population of less than 1.8 million according to the 1981 Census formed a single basic stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils, similar as far as possible in respect of rural population density & crop pattern. In Gujarat, however, in the case of districts extending over more than one region, the portion of a district falling in each region constituted a separate stratum even if the rural population of the district as a whole was less than 1.8 million. Further, in Assam, the strata formed for the earlier NSS round on the basis of 1971 Census rural population exactly in the above manner, but with a cutoff point of 1.5 million population, were retained as the strata for rural sampling.

In the urban sector, strata were formed, within NSS regions, on the basis of 1981 (1991 in some of the new towns) Census population. Each city with a population of 10 lakhs or more formed a separate stratum itself. The remaining towns of each region were grouped to form three different strata on the basis of 1981 (1991 in a few cases) census population.

Sub stratification of urban strata : In order to be able to allocate a large proportion of the first stage sample to slum-dominated areas than would otherwise be possible, each stratum in the urban sector was divided into two "sub-strata" a s follows. Sub-stratum 1 was constituted of the UFS blocks in the stratum with a "slum area" indicated in the frame. Substratum 2 was constituted of the remaining blocks of the stratum.

Allocation of sample : A total all-India sample of 8000 first stage units (5072 villages and 2928 urban blocks) determined on the basis of investigator strength in different state/u.t's and the expected workload per investigator was first allocated to the states/u.t's in proportion to Central Staff available. The sample thus obtained for each state/u.t. was then allocated to its rural & urban sectors considering the relative sizes of the rural & urban population with double weightage for the urban sector. Within each sector of a state/u.t., the allotted sample size was reallocated to the different strata in proportion to stratum population. Stratum-level allocations were adjusted so that the sample size for a stratum (rural or urban) was at least a multiple of 4. This was done in order to have equal sized samples in each sub-sample and sub-round.

In the urban sector, stratum-level allocations were further allocated to the two sub-strata in proportion to the number of UFS blocks in the sub-strata, with double weightage to sub-stratum 1, with a minimum sample size of 4 blocks to sub-stratum 1 (2 if stratum allocation was only 4). Sub-stratum level allocations were made even in number.

Selection of fsu's : Sample villages except in Arunachal Pradesh were selected by pps systematic sampling with population as the size variable and sample blocks by simple random sampling without replacement. In both sectors the sample of fsu's was drawn in the form of two independent sub-samples. (In Arunachal Pradesh the sample of villages was drawn by a cluster sampling procedure. The field staff were supplied with a list of sample "nucleus" villages and were advised to select cluster of villages building up each cluster around a nucleus village according to prescribed guidelines. The nucleus villages were selected circular-systematically with equal probability in the form of two ) independent sub-samples.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consisted of 6 blocks (including 0) as given below : Block - 0 : descriptive identification of sample village/block having slum Block - 1 : identification of sample village/block having slum. Block - 3 : Remarks by investigator. Block - 4 : Comments by Supervisory Officer(s). Block - 5 : Particulars about slum.

Response rate

1572 slums spread over 5072 villages and 2928 urban blocks in the sample have been surveyed.

As of 2025, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37 million people living there. Delhi ranked second with more than 34 million, with Shanghai in third with more than 30 million inhabitants.

The internet penetration rate in India rose over ** percent in 2025, from about ** percent in 2015. Although these figures seem relatively low, it meant that more than half of the population of **** billion people had internet access that year. This also ranked the country second in the world in terms of active internet users. Internet availability and accessibility By 2021, the number of internet connections across the country tripled, with urban areas accounting for a higher density of connections than rural regions. Despite incredibly low internet prices, internet usage in India has yet to reach its full potential. Lack of awareness and a tangible gender gap lie at the heart of the matter, with affordable mobile handsets and mobile internet connections presenting only a partial solution. Reliance Jio was the popular choice among Indian internet subscribers, offering them wider coverage at cheap rates. Digital living Home to one of the largest bases of netizens in the world, India is abuzz with internet activities being carried out every moment of every day. From information and research to shopping and entertainment to living in smart homes, Indians have welcomed digital living with open arms. Among these, social media usage was one of the most common reasons for accessing the internet.