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
registration_circles
Type
text
Label
registration_circles

ID
male_below_5_years
Type
text
Label
male_below_5_years

ID
male_5_to_14_years
Type
numeric
Label
male_5_to_14_years

ID
male_14_to_18_years
Type
numeric
Label
male_14_to_18_years

ID
male_18_to_30_years
Type
numeric
Label
male_18_to_30_years

ID
male_30_to_45_years
Type
numeric
Label
male_30_to_45_years

ID
male_45_to_60_years
Type
numeric
Label
male_45_to_60_years

ID
male_60_years_and_above
Type
numeric
Label
male_60_years_and_above

ID
female_below_5_years
Type
numeric
Label
female_below_5_years

ID
female_5_to_14_years
Type
numeric
Label
female_5_to_14_years

ID
female_14_to_18_years
Type
numeric
Label
female_14_to_18_years

ID
female_18_to_30_years
Type
numeric
Label
female_18_to_30_years

ID
female_30_to_45_years
Type
numeric
Label
female_30_to_45_years

ID
female_45_to_60_years
Type
numeric
Label
female_45_to_60_years

ID
female_60_years_and_above
Type
numeric
Label
female_60_years_and_above

ID
trangender_below_5_years
Type
numeric
Label
trangender_below_5_years

ID
trangender_5_to_14_years
Type
numeric
Label
trangender_5_to_14_years

ID
trangender_14_to_18_years
Type
numeric
Label
trangender_14_to_18_years

ID
trangender_18_to_30_years
Type
numeric
Label
trangender_18_to_30_years

ID
trangender_30_to_45_years
Type
numeric
Label
trangender_30_to_45_years

ID
trangender_45_to_60_years
Type
numeric
Label
trangender_45_to_60_years

ID
transgender_60_years_and_above
Type
numeric
Label
transgender_60_years_and_above

Additional Information

Field Value
Data last updated September 4, 2024
Metadata last updated September 5, 2024
Created September 4, 2024
Format CSV
License No License Provided
Additional infonan
Data extraction pagehttps://ncrb.gov.in/crime-in-india.html
Data insightsData Insights that can be drawn:Distribution of missing persons based on gender across different states and districts.Age-wise breakdown of missing persons which can shed light on the age groups that are most vulnerable.Comparative analysis between states and districts to identify regions with high numbers of missing persons.Gender-wise distribution in specific age groups to understand if there's a significant disparity.Analysis of total missing children vs. adults can help in strategizing preventive measures for the vulnerable age groups.
Data last updated2024-08-01 00:00:00
Data retreival date2024-07-01 00:00:00
Datastore activeTrue
District no731
FrequencyYearly
GranularityDistrict
Has viewsTrue
Id90b83f06-7f08-4bc2-8ffb-4859129476bb
Idp readyTrue
Lgd mappingyes
MethodologyThe dataset is sourced from the official website of the National Crime Records Bureau (NCRB) of India. NCRB collates this data from various state and district police records. It is important to note that the data might be subject to changes based on further verifications and updates from respective state and district authorities.
No indicators32
Package ide311a510-ce48-4f4c-baf6-0ec5f9278285
Position9
Size508.5 KiB
Skuncrb-cii_missing_persons-dt-yr-prv
Stateactive
States uts no36
Url typeupload
Years covered2017-2020
Methodology The dataset is sourced from the official website of the National Crime Records Bureau (NCRB) of India. NCRB collates this data from various state and district police records. It is important to note that the data might be subject to changes based on further verifications and updates from respective state and district authorities.
Indicators
Similar Resources
Granularity Level District
Data Extraction Page https://ncrb.gov.in/crime-in-india.html
Data Retreival Date 2024-07-01 00:00:00
Data Last Updated 2024-08-01 00:00:00
Sku ncrb-cii_missing_persons-dt-yr-prv
Dataset Frequency Yearly
Years Covered 2017-2020
No of States/UT(s) 36
No of Districts 731
No of Tehsils/blocks
No of Gram Panchayats
Additional Information nan
Number of Indicators 32
Insights from the dataset Data Insights that can be drawn:Distribution of missing persons based on gender across different states and districts.Age-wise breakdown of missing persons which can shed light on the age groups that are most vulnerable.Comparative analysis between states and districts to identify regions with high numbers of missing persons.Gender-wise distribution in specific age groups to understand if there's a significant disparity.Analysis of total missing children vs. adults can help in strategizing preventive measures for the vulnerable age groups.
IDP Ready Yes
LGD Mapping Yes