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.
추가 정보
필드 | 값 |
---|---|
마지막으로 업데이트된 데이터 | 2024년 6월 5일 |
마지막으로 업데이트된 메타데이터 | 2024년 12월 3일 |
생성됨 | 알 수 없는 |
포맷 | CSV |
라이센스 | 라이센스를 제공하지 않음 |
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 |
생성됨 | 1년 전 |
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 | |
추가 정보 | 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 |