Census Mapping in India and Role of GIS: A Look Ahead at Census 2021

The 2021 Census of India for over 1.3 billion population deploying 3 million enumerators, has significant evidence value for 71 countries where census is scheduled during 2021. Census mapping plays a major role in accurate, complete and timely census. It delineates the exact and correct boundaries of all the administrative units. The Indian census has been using Geographic Information System (GIS) technologies over the last three censuses. In this study, we focus on the applications and methodologies being adopted for the census mapping in Census 2021 in India which is going to be the first digital Census of India. Five mobile apps have been developed for data collection and for map-related work. The 2021 Indian census utilises the latest census mapping techniques, namely standardisation of GIS spatial database design, geo-referencing of administrative units and latest mobile mapping application (Arc GIS Quick Capture) for field operations and built-up area digitisation work. We also discuss the various challenges and their solutions for census mapping in India, most prominently a high quality, updated, comprehensive and geo-referenced address registry for accurate data collection and mapping, and the use of geo-referenced high-resolution satellite images at village level for covering the gaps in rural boundary maps.

OPEN DEFECATION FREE (ODF) RURAL AND URBAN AREAS: ACHIEVEMENT OF SWACHH BHARAT MISSION

Sanitation is recognized as a basic human right. UN General Assemblyin July 2010 had adopted a resolution officially recognising Sanitationaccess to, and use of, excreta and wastewater facilities and servicesasa human right. For most of human history, people defecatedin theopen. But in the last century, a lot has changed with toilets becoming anintegral part of homes in most parts of the world. More than half of allpeopleintheworldwhodefecateintheopenliveinIndia.Accordingto 2011 Indian Census, 53.00 per cent of households do not use anykind of toilet or latrine. This essentially matches the 55.00 per centfound by the National Family Health Survey in 2005. In this paper Iattempt to study the role of government and schemes and peoples participation.

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India

There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.

Mapping India’s Language and Mother Tongue Diversity and its Exclusion in the Indian Census

In this article, we critique the process of linguistic data enumeration and classification by the Census of India. We map out inclusion and exclusion under Scheduled and non-Scheduled languages and their mother tongues and their representation in state bureaucracies, the judiciary, and education. We highlight that Census classification leads to the delegitimization of ‘mother tongues’ that deserve the status of the language and official recognition by the state. We argue that the blanket exclusion of languages and mother tongues based on numerical thresholds disregards the languages of about 18.7 million speakers in India. We compute and map the Linguistic Diversity Index of India at the national and state levels and show that the exclusion of mother tongues undermines the linguistic diversity of states. We show that the Hindi belt shows the maximum divergence in Language and Mother Tongue Diversity. We stress the need for India to officially acknowledge the linguistic diversity of states and make the Census classification and enumeration to reflect the true Linguistic diversity.

Birth of Census Towns in India: An Economic Analysis

In Indian census, the reclassification of villages as small towns (called census towns) has been startling during the decade 2001–2011 and accounted for almost 30 per cent of urbanization, which is significantly larger than their growth rate in previous decades. Though reclassified as towns, they are governed as rural settlements. This article applies urban economic theory along with rural–urban labour market dynamics to identify the factors behind the birth of census towns. It also attempts to empirically check the validity of some of the hypotheses of the theoretical model it develops by using data from the state of West Bengal during 2001–2011 where the growth rate of census towns had been one of the highest in India. It turns out that the higher formal sector income in the nearby urban centres with lower extent of urban sprawl is the major factor explaining the birth of census towns. JEL Classification: R11, R12, R23

Modelling COVID-19 With More Disaggregation and Less Nomothetic Parameterisation: UK and India Examples

Modelling of pandemic vulnerability in a development context can be improved through combining disciplines, combining data, and recognising the many nested levels of the epidemic. Models of transmission have been constructed at national level or for multiple nations. We instead construct a model allowing for social-group differentials in risk, along with conditioning regional factors and lifestyle factors. Severe COVID-19 disease is our innovative key outcome. We use three data sources at once: National Family and Health Survey for India, Indian Census 2011, and COVID-19 deaths. We provide results for 11 states of India, enabling best-yet targeting of policy actions. The future uses of such models are many. COVID-19 deaths in north and central India were higher in areas with older populations and overweight populations, and was more common among those with pre-existing health conditions, or who smoke or live in urban areas. Policy experts may both want to ‘follow World Health Organisation advice’ and yet also use disaggregated and spatially-specific data to improve wellbeing outcomes during the pandemic.

A Bayesian Estimation of Child Labour in India

Child labour in India involves the largest number of children in any single country in the world. In 2011, 11.8 million children between the ages of 5 and 17 were main workers (those working more than 6 mo) according to the Indian Census. Our estimate of child labour using a combined-data approach is slightly higher than that: 13.2 million (11.4–15.2 million) for ages 5 to 17. There are various opinions on how best to measure the prevalence of child labour. In this study, we use the International Labour Organization (ILO)‘s methodology to define hazardousness and combine it with the most recent United Nations Children’s Fund (UNICEF)‘s time thresholds for economic work and household chores. The specific aims of this study are to estimate the prevalence of child labour in the age group 5 to 17 and to suggest a combined-data approach using Bayesian inference to improve the accuracy of the child labour estimation. This study combines the National Sample Survey on Employment and Unemployment 2011/12 and the India Human Development Survey 2011/12 and compares the result with the reported figures for the incidence of child labour from the Indian Census. Our unique combined-data approach provides a way to improve accuracy, smooth the variations between ages and provide reliable estimates of the scale of child labour in India.

‘Other’ gender in India: An Analysis of 2011 Census Data

In spite of a lot of human rights protection given to the ‘other-gender’ population worldwide, they still have been a deserted community who faces a significant occupational challenge around the world. In India, the other-gender community encompasses persons with a variety of gender identities, forming a culturally unique gender group. Although they have always remained an integral part of the society from the very ancient time, unfortunately, their existence is grappling with abject poverty, illiteracy, hatred, and mockery. Such stigmatisation and segregation from society have left them to compromise with the employment opportunities available. It is important to identify the gap between the other-gender population and the general population in the field of literacy and workforce participation. This paper uses the data on other-gender published by the Census of India for the first time. According to the Indian Census 2011, there is around 4.9 lakh other-gender population in the country. The data reveal that other-gender have lower levels of literacy and labour force participation compared with the general population. Our attempt is here to conceptualise the findings along with some discussion on the data limitations.