Census PCA Demography - Subdistricts
The "Census PCA Demography - Subdistricts" dataset provides a comprehensive overview of the working population demographics across different subdistricts in India. This dataset is sourced from the official Census India website, focusing on main workers, marginal workers, and non-workers. It also offers insights into those seeking or available for work, categorized by age and sex. The dataset further dives deep into the socio-economic aspects of the population, considering literacy levels, social group affiliation, and type of occupation, to provide a holistic perspective.
Data Dictionary
- ID
- id
- Type
- int4
- Label
- index
- ID
- year
- Type
- text
- Label
- Year
- ID
- state_name
- Type
- text
- Label
- State Name
- ID
- state_code
- Type
- text
- Label
- State Code
- ID
- district_name
- Type
- text
- Label
- District Name
- ID
- district_code
- Type
- text
- Label
- District Code
- ID
- subdistrict_name
- Type
- text
- Label
- Subdistrict Name
- ID
- subdistrict_code
- Type
- text
- Label
- Subdistrict Code
- ID
- rural_urban
- Type
- text
- Label
- Type of Area
- ID
- level
- Type
- text
- Label
- Level
- ID
- gender
- Type
- text
- Label
- Gender
- ID
- age
- Type
- text
- Label
- Age
- ID
- social_group
- Type
- text
- Label
- Social Group
- ID
- literacy
- Type
- text
- Label
- Literacy
- ID
- working_status
- Type
- text
- Label
- Working Status
- ID
- worker_type
- Type
- text
- Label
- Worker Type
- ID
- occupation
- Type
- text
- Label
- Occupation
- ID
- population
- Type
- numeric
- Label
- Population
Additional Information
| Field | Value |
|---|---|
| Data last updated | June 4, 2024 |
| Metadata last updated | August 22, 2024 |
| Created | October 16, 2023 |
| Format | CSV |
| License | No License Provided |
| 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 |
| 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 | |
| Additional Information | 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 |