Data Dictionary

Column	Type	Label	Description
id	int4	index
year	date	Year
state_name	text	State name
state_code	text	State code
district_name	text	District name
district_code	text	District code
registeration_circles	text	Case Registered in Circle
total_male	numeric	Total Male
male_below_5_years	numeric	Below 5 years (male)
male_5years_and_above_below_14years	numeric	5 years & Above–Below 14 years (male)
male_14years_and_above_below_18years	numeric	14 years & Above - Below 18 years (male)
male_18years_and_above_below_30years	numeric	18 years & Above – Below 30 years (male)
male_30years_and_above_below_45years	numeric	30 years & Above – Below 45 years (male)
male_45years_and_above_below_60years	numeric	45 years & Above – Below 60 years (male)
male_60years_and_above	numeric	60 years & Above (male)
total_female	numeric	Total Female
female_below_5_years	numeric	Below 5 years (female)
female_5years_and_above_below_14years	numeric	5 years & Above–Below 14 years (female)
female_14years_and_above_below_18years	numeric	14 years & Above - Below 18 years (female)
female_18years_and_above_below_30years	numeric	18 years & Above – Below 30 years (female)
female_30yearsand_above_below_45years	numeric	30 years & Above – Below 45 years (female)
female_45years_and_above_below_60years	numeric	45 years & Above – Below 60 years (female)
female_60years_and_above	numeric	60 years & Above (female)
total_transgender	numeric	Total Transgender
trans_below_5_years	numeric	Below 5 years (trans)
trans_5years_and_above_below_14years	numeric	5 years & Above–Below 14 years (trans)
trans_14years_and_above_below_18years	numeric	14 years & Above - Below 18 years (trans)
trans_18years_and_above_below_30years	numeric	18 years & Above – Below 30 years (trans)
trans_30yearsand_above_below_45years	numeric	30 years & Above – Below 45 years (trans)
trans_45years_and_above_below_60years	numeric	45 years & Above – Below 60 years (trans)
trans_60years_and_above	numeric	60 years & Above (trans)
grand_total	numeric	Grand Total (Male+Female+Transgender)
total_below_5_years	numeric	Below 5 years
total_5years_and_above_below_14years	numeric	5 years & Above–Below 14 years
total_14years_and_above_below_18years	numeric	14 years & Above - Below 18 years
total_18years_and_above_below_30years	numeric	18 years & Above – Below 30 years
total_30yearsand_above_below_45years	numeric	30 years & Above – Below 45 years
total_45years_and_above_below_60years	numeric	45 years & Above – Below 60 years
total_60years_and_above	numeric	60 years & Above

Additional Information

Field	Value
Data last updated	June 5, 2024
Metadata last updated	June 5, 2024
Created	unknown
Format	CSV
License	Open Data Commons Attribution License
Created	5 months ago
Frequency	Yearly
Size	10,924
Additional info	nan
Data extraction page	https://ncrb.gov.in/crime-in-india.html
Data insights	Insights that can be drawn from the Districtwise Missing Persons dataset for Meghalaya (2017-2020) include analyzing the distribution of missing persons across states and districts, gender-wise patterns, identifying vulnerable age groups through age bracket analysis, tracking trends over the years, conducting comparative analysis between states and districts based on missing person counts, and examining the distribution of missing individuals who identify as transgender across states, districts, and age brackets. These insights facilitate targeted interventions, resource allocation, and policy measures to address the issue of missing persons in Meghalaya effectively.
Data last updated	2,021
Data retreival date	2023-10-01 00:00:00
Datastore active	True
District no	11
Granularity	District
Has views	True
Id	107e4abe-01e1-4b6e-b62b-73a9b3a86809
Idp ready	True
Methodology	Data is presumably aggregated from various state and district police records and other relevant governmental agencies. Information is segmented by year, state, district, and further broken down by age groups and gender (male, female, transgender).
No indicators	32
Package id	c0c57ded-d115-40af-85a4-b5c22709b86c
Position	10
Sku	ncrb-cii_missing_persons-dt-yr-meg
State	active
States uts no	1
Url type	upload
Years covered	2017-2020
Methodology	Data is presumably aggregated from various state and district police records and other relevant governmental agencies. Information is segmented by year, state, district, and further broken down by age groups and gender (male, female, transgender).
Similar Resources
Granularity Level	District
Data Extraction Page	https://ncrb.gov.in/crime-in-india.html
Data Retreival Date	2023-10-01 00:00:00
Data Last Updated	2021
Sku	ncrb-cii_missing_persons-dt-yr-meg
Dataset Frequency
Years Covered	2017-2020
No of States/UT(s)	1
No of Districts	11
No of Tehsils/blocks
No of Gram Panchayats
Additional Information	nan
Number of Indicators	32
Insights from the dataset	Insights that can be drawn from the Districtwise Missing Persons dataset for Meghalaya (2017-2020) include analyzing the distribution of missing persons across states and districts, gender-wise patterns, identifying vulnerable age groups through age bracket analysis, tracking trends over the years, conducting comparative analysis between states and districts based on missing person counts, and examining the distribution of missing individuals who identify as transgender across states, districts, and age brackets. These insights facilitate targeted interventions, resource allocation, and policy measures to address the issue of missing persons in Meghalaya effectively.
IDP Ready	Yes