Diccionari de dades

Informació addicional

Camp	Valor
Última actualització de les dades	4 de juny del 2024
Última actualització de les metadades	19 de desembre del 2025
Creat	16 d’octubre del 2023
Format	CSV
Llicència	No s'ha indicat la llicència
Additional info	nan
Data extraction page	B-01: Main workers, marginal workers, non-workers and those marginal workers, non-workers seeking/available for work classified by age and sex
Data insights	Data Insights that can be drawn: Demographic Distribution: Analysis can be done on the age and gender distribution across various states, districts, and sub-districts. This will reveal the age-wise and gender-wise distribution of the working population. Urban vs Rural Dynamics: By analyzing the 'rural_urban' column, insights can be drawn regarding the workforce distribution in urban versus rural areas. Socio-Economic Insights: The literacy rate and affiliation to various social groups can provide insights into the socio-economic conditions of the working population in various regions. Occupation Distribution: The 'occupation' column can help identify the primary sources of employment in different regions. Worker Classification: By studying the 'worker_type' and 'working_status' columns, insights can be obtained about the percentage of main workers, marginal workers, and non-workers across regions.
Data last updated	2.011
Data retreival date	6/23/2022
Datastore active	True
District no	729
Frequency	Decadal
Geo columns	['state_code', 'district_code', 'subdistrict_code']
Granularity	Sub District
Has views	True
Id	cd430261-1492-4a58-9e65-d6021f8aafdd
Idp ready	True
Lgd mapping	yes
Methodology	The data has been collected by the official Census body of India, adhering to standardized census-taking methods and practices. Given the scope of the dataset (spanning across states, districts, and sub-districts), there's a multi-tiered approach in data collection, which is then categorized by various parameters such as rural or urban setting, age, gender, etc. The methodology ensures a thorough representation of the diverse Indian population and its workforce.
Mimetype	text/csv
No indicators	1
Package id	2a41af7b-7922-4d8f-83c9-d5ebcb8a5f54
Position	3
Size	168,3 MiB
Sku	moha-census_pca_demography-sd-dc-abc
State	active
States uts no	36
Tags	['Census', 'Demography', 'Workforce Analysis', 'Literacy', 'Social Groups', 'Rural-Urban Divide', 'Population Distribution', 'Geographic Distribution', 'Age Distribution']
Url type	upload
Years covered	2.011
Methodology	The data has been collected by the official Census body of India, adhering to standardized census-taking methods and practices. Given the scope of the dataset (spanning across states, districts, and sub-districts), there's a multi-tiered approach in data collection, which is then categorized by various parameters such as rural or urban setting, age, gender, etc. The methodology ensures a thorough representation of the diverse Indian population and its workforce.
Indicators
Similar Resources
Granularity Level	Sub District
Data Extraction Page	B-01: Main workers, marginal workers, non-workers and those marginal workers, non-workers seeking/available for work classified by age and sex
Data Retreival Date	6/23/2022
Data Last Updated	2011
Sku	moha-census_pca_demography-sd-dc-abc
Dataset Frequency	Decadal
Years Covered	2011.0
No of States/UT(s)	36
No of Districts	729
No of Tehsils/blocks
No of Gram Panchayats
Informació addicional	nan
Number of Indicators	1
Insights from the dataset	Data Insights that can be drawn: Demographic Distribution: Analysis can be done on the age and gender distribution across various states, districts, and sub-districts. This will reveal the age-wise and gender-wise distribution of the working population. Urban vs Rural Dynamics: By analyzing the 'rural_urban' column, insights can be drawn regarding the workforce distribution in urban versus rural areas. Socio-Economic Insights: The literacy rate and affiliation to various social groups can provide insights into the socio-economic conditions of the working population in various regions. Occupation Distribution: The 'occupation' column can help identify the primary sources of employment in different regions. Worker Classification: By studying the 'worker_type' and 'working_status' columns, insights can be obtained about the percentage of main workers, marginal workers, and non-workers across regions.
IDP Ready	Yes
LGD Mapping	Yes
Mapping Status %
Geo Columns	['state_code', 'district_code', 'subdistrict_code']