Dicionario de datos

ID
id
Tipo
int4
Etiqueta
index

ID
year
Tipo
text
Etiqueta
Year

ID
state_name
Tipo
text
Etiqueta
State Name

ID
state_code
Tipo
text
Etiqueta
State Code

ID
district_name
Tipo
text
Etiqueta
District Name

ID
district_code
Tipo
text
Etiqueta
District Code

ID
subdistrict_name
Tipo
text
Etiqueta
Subdistrict Name

ID
subdistrict_code
Tipo
text
Etiqueta
Subdistrict Code

ID
rural_urban
Tipo
text
Etiqueta
Type of Area

ID
level
Tipo
text
Etiqueta
Level

ID
gender
Tipo
text
Etiqueta
Gender

ID
age
Tipo
text
Etiqueta
Age

ID
social_group
Tipo
text
Etiqueta
Social Group

ID
literacy
Tipo
text
Etiqueta
Literacy

ID
working_status
Tipo
text
Etiqueta
Working Status

ID
worker_type
Tipo
text
Etiqueta
Worker Type

ID
occupation
Tipo
text
Etiqueta
Occupation

ID
population
Tipo
numeric
Etiqueta
Population

Información adicional

Campo Valor
Última actualización de datos 4 de xuño de 2024
Última actualización de metadatos 22 de agosto de 2024
Creado 16 de outubro de 2023
Formato CSV
Licenza Non se fornece ningunha licenza
Additional infonan
Data extraction pageB-01: Main workers, marginal workers, non-workers and those marginal workers, non-workers seeking/available for work classified by age and sex
Data insightsData 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 updated2.011
Data retreival date6/23/2022
Datastore activeTrue
District no729
FrequencyDecadal
GranularitySub District
Has viewsTrue
Idcd430261-1492-4a58-9e65-d6021f8aafdd
Idp readyTrue
Lgd mappingyes
MethodologyThe 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.
Mimetypetext/csv
No indicators1
Package id2a41af7b-7922-4d8f-83c9-d5ebcb8a5f54
Position3
Size168,3 MiB
Skumoha-census_pca_demography-sd-dc-abc
Stateactive
States uts no36
Tags['Census', 'Demography', 'Workforce Analysis', 'Literacy', 'Social Groups', 'Rural-Urban Divide', 'Population Distribution', 'Geographic Distribution', 'Age Distribution']
Url typeupload
Years covered2.011
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
Información adicional 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 %