Census PCA Demography - Subdistricts
The Census PCA Demography - Subdistricts dataset for Meghalaya provides a comprehensive view of the working population demographics in different subdistricts. It includes information on main workers, marginal workers, and non-workers, along with age and gender breakdowns. The dataset also offers insights into socio-economic factors such as literacy levels, social group affiliation, and types of occupations, providing a holistic understanding of the population dynamics.
Additional Information
Field | Value |
---|---|
Data last updated | June 5, 2024 |
Metadata last updated | December 3, 2024 |
Created | unknown |
Format | CSV |
License | No License Provided |
Created | 8 months ago |
Frequency | Decadal |
Media type | text/csv |
Size | 991,433 |
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 | The Census PCA Demography - Subdistricts dataset for Meghalaya offers valuable data insights including: 1. Analysis of age and gender distribution across states, districts, and sub-districts, revealing the demographic composition of the working population. 2. Differentiating between urban and rural areas based on the 'rural_urban' column, providing insights into workforce dynamics in these regions. 3. Socio-economic understanding through analysis of literacy rates and affiliation to social groups, shedding light on the working population's socio-economic conditions in different regions. 4. Identification of primary sources of employment in various regions by studying the 'occupation' column. 5. Classification of workers into main workers, marginal workers, and non-workers across regions based on the 'worker_type' and 'working_status' columns, offering insights into workforce characteristics. |
Data last updated | 2,011 |
Data retreival date | 6/23/2022 |
Datastore active | False |
District no | 11 |
Granularity | Village |
Has views | True |
Id | 53bfc992-1239-4120-b16f-4719b70aab93 |
Idp ready | False |
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. |
No indicators | 1 |
Package id | ef726171-b797-44a5-a2d5-3a1eef1a044f |
Position | 2 |
Sku | moha-census_pca_demography-sd-dc-meg |
State | active |
States uts no | 1 |
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. |
Similar Resources | |
Granularity Level | Village |
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-meg |
Dataset Frequency | |
Years Covered | 2011.0 |
No of States/UT(s) | 1 |
No of Districts | 11 |
No of Tehsils/blocks | |
No of Gram Panchayats | |
Additional Information | nan |
Number of Indicators | 1 |
Insights from the dataset | The Census PCA Demography - Subdistricts dataset for Meghalaya offers valuable data insights including: 1. Analysis of age and gender distribution across states, districts, and sub-districts, revealing the demographic composition of the working population. 2. Differentiating between urban and rural areas based on the 'rural_urban' column, providing insights into workforce dynamics in these regions. 3. Socio-economic understanding through analysis of literacy rates and affiliation to social groups, shedding light on the working population's socio-economic conditions in different regions. 4. Identification of primary sources of employment in various regions by studying the 'occupation' column. 5. Classification of workers into main workers, marginal workers, and non-workers across regions based on the 'worker_type' and 'working_status' columns, offering insights into workforce characteristics. |
IDP Ready | No |