Historical dataset of population level and growth rate for the Delhi, India metro area from 1950 to 2025.

Census: Population: Delhi: Delhi data was reported at 16,368,899.000 Person in 03-01-2011. This records an increase from the previous number of 12,877,470.000 Person for 03-01-2001. Census: Population: Delhi: Delhi data is updated decadal, averaging 1,898,271.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 16,368,899.000 Person in 03-01-2011 and a record low of 214,115.000 Person in 03-01-1901. Census: Population: Delhi: Delhi data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC026: Census: Population: By Towns and Urban Agglomerations: NCT of Delhi.

This dataset provides ward-level geographic boundary data and population statistics for Delhi, India, in KML format. It contains the outlines of municipal wards along with attributes such as:

• Ward number and name
• Assembly constituency (AC) number and name
• Total population per ward
• Scheduled Caste (SC) population
• 2022 naming conventions

The dataset is useful for geospatial analysis, urban planning, civic data visualization, and demographic research. It can be imported into GIS tools like QGIS or web-mapping libraries such as Leaflet or Google Maps.

Original Source: Open City - Delhi Wards Information
License: Open Database License (ODbL) v1.0
Format: XML-based KML
Projection: WGS 84 (EPSG:4326)

This version is suitable for direct use in Python (with fastkml, geopandas, or shapely), and in web GIS applications. The data has not been altered.

BackgroundIn India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.MethodsData on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.ConclusionTo decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.

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

Challenges in urban development--->;

Institutional challenges

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

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

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

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

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

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

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

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

Regulator

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

Infrastructural challenges

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

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.

Dataset summarizing study sample size and statistical findings related to pregnancy, abortion patterns, and women's reproductive health in a rural Delhi population.

This dataset contains de-identified patient-level data used in the study titled “Cachexia in Gynecologic Cancers: The Role of Biomarkers and Cachexia Index.” he study was conducted to evaluate the prevalence of cachexia and the utility of the Cachexia Index (CXI) in women with gynecologic malignancies, particularly in the Indian population.Variables include demographic information, cancer type and stage, treatment details, biochemical markers, anthropometric measurements, and cachexia index components. The data were collected prospectively the All India Institute of Medical Sciences (AIIMS), New Delhi, India, from July 2022 to June 2024.The dataset is intended to support transparency and reproducibility of the findings and is shared in accordance with ethical and privacy guidelines. All patient identifiers have been removed.

Hypothyroidism is the commonest endocrine disorder of pregnancy, with known adverse feto-maternal outcomes. There is limited data on population-based prevalence, risk factors and outcomes associated with treatment of hypothyroidism in early pregnancy. We conducted analysis on data from an urban and peri-urban low to mid socioeconomic population-based cohort of pregnant women in North Delhi, India to ascertain the burden, risk factors and impact of treatment, on adverse pregnancy outcomes- low birth weight, prematurity, small for gestational age and stillbirth. This is an observational study embedded within the intervention group of the Women and Infants Integrated Interventions for Growth Study, an individually randomized factorial design trial. Thyroid stimulating hormone was tested in 2317 women in early (9–13 weeks) pregnancy, and thyroxin replacement started hypothyroid (TSH ≥2.5mIU/mL). Univariable and multivariable generalized linear model with binomial family and log link were performed to ascertain risk factors associated with hypothyroidism and association between hypothyroidism and adverse pregnancy outcomes. Of 2317 women, 29.2% (95% CI: 27.4 to 31.1) had hypothyroidism and were started on thyroxin replacement with close monitoring. Overweight or obesity was associated with increased risk (adjusted RR 1.29, 95% CI 1.10 to 1.51), while higher hemoglobin concentration was associated with decreased risk (adjusted RR 0.93, 95% CI 0.88 to 0.98 for each g/dL) for hypothyroidism. Hypothyroid women received appropriate treatment with no increase in adverse pregnancy outcomes. Almost a third of women from low to mid socio-economic population had hypothyroidism in early pregnancy, more so if anemic and overweight or obese. With early screening and adequate replacement, adverse pregnancy outcomes may be avoided. These findings highlight the need in early pregnancy for universal TSH screening and adequate treatment of hypothyroidism; as well as for attempts to reduce pre and peri-conception overweight, obesity and anemia.Clinical trial registration: Clinical trial registration of Women and Infants Integrated Interventions for Growth Study Clinical Trial Registry–India, #CTRI/2017/06/008908; Registered on: 23/06/2017, (http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=19339&EncHid=&userName=society for applied studies).

*Denominator for the percentage is # influenza positive for that year.€p<0.01 (highly significant for Influenza A (H3N2) in 2009, OR = 1.8, CI – 1.2–2.7)for peri-urban area.Ψp<0.001 (highly significant for pandemic Influenza A(H1N1)pdm09 in 2009, OR = 7.7, CI – 4.2–14) and 2010 (OR = 3.0, CI – 1.6–5.6) for urban areas.

Mean Age = 13.4 years, s.d = 1.1Distribution of study population based on age, sex, gender and religion (n = 1386).

Vital Statistics: Death Rate: per 1000 Population: Delhi: Rural data was reported at 4.100 NA in 2020. This records an increase from the previous number of 3.800 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Delhi: Rural data is updated yearly, averaging 4.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 5.500 NA in 2005 and a record low of 3.700 NA in 2018. Vital Statistics: Death Rate: per 1000 Population: Delhi: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.