In 2022, the union territory of Delhi had the highest urban population density of over 18 thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

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.

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.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

In 2022, the population density in India remained nearly unchanged at around 479.43 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Sri Lanka and Pakistan.

According to the 2011 census, the population density in the Indian state of Maharashtra was 365 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 22 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 175 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.

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.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

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 Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three 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. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

BackgroundThe world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria.Methodology/principal findingsStatistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors.Conclusion/significanceClimate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission ‘hotspots’. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a “radical cure”.

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).

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)

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.

Members of the scientific community and the general public are raising concerns about the potential health and environmental effects of radio-frequency electromagnetic fields (RF-EMF) for those living nearby mobile phone base stations (MPBS). This study examined the impact of RF-EMF (900–1900 MHz) on symptoms spanning four health categories: mood-energy, cognitive-sensory, inflammatory, and anatomical issues. A questionnaire identifying health symptoms within these categories, was given to 183 highly exposed and 126 reference residents, matched on demographics. While years of residing near the MPBS influenced the prevalence of some symptoms, proximity to the base station and higher levels of exposure (measured using power density) influenced the prevalence of many of the symptoms. A higher proportion of symptoms was found in residents who were either living within 50 meters of a MPBS or who were exposed to power densities of 5–8 mW/m2, for all four health categories. This relationship between exposure level and symptom prevalence was further influenced by age, daily mobile phone use (over 5 h per day), and lifestyle factors, for certain symptoms. Hierarchical regression analysis revealed that level of exposure (power density) was the only factor contributing to the number of symptoms experienced by residents, for all four health categories. An unexpected finding was that among the more highly exposed residents, the younger individuals (under 40 years) reported more inflammation related issues than older individuals. These results underscore the need to inform policymakers regarding the benefits of adopting a precautionary approach to potential risks associated with RF-EMF exposures from MPBS. Investigating the health effects of man-made electromagnetic fields (RF-EMF) created by telecommunications signals from mobile phone base stations is relevant to people living in cities across the world today. The study was conducted in a hilly, highly populated city in Mizoram, India, where many people live close to and in line of sight of the masts on telecommunications towers. A survey was given to residents in their homes, asking about what health symptoms they were experiencing across a range of health categories (mood-energy, cognitive-sensory, inflammatory, and anatomical). At the same time, the level of RF-EMF in their lounge room was measured. The symptoms reported by people living closer to mobile phone base stations (less than 300 m) were compared with those from people living further away (more than 400 m). More people who lived closer to base stations reported health symptoms in all of the health categories investigated. Relatively fewer people who lived further away reported symptoms. Other factors such as age, high mobile phone use (more than 5 h/day) and smoking and drinking also influenced this outcome, for some of the symptoms. The most significant contributor to the number of symptoms reported by residents was the strength of RF-EMF to which they were exposed in their home. A surprising result was that younger people up to 40 years old showed more inflammatory conditions that were related to higher exposures than older people (such as headache, allergy and chest pain). These health effects of RF-EMF should be heeded by those responsible for the installation of mobile phone base stations in cities.

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.Data source: https://niti.gov.in/sites/default/files/SDG-India-Index-2.0_27-Dec.pdfPlease find detailed metadata here.This web 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

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.

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