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
murder
Type
numeric
Label
Murder

ID
attempt_to_commit_murder
Type
numeric
Label
Attempt To Commit Murder

ID
culpable_homicide_not_amounting_to_murder
Type
numeric
Label
Culpable Homicide Not Amounting To Murder

ID
attempt_to_commit_culpable_homicide
Type
numeric
Label
Attempt To Commit Culpable Homicide

ID
rape
Type
numeric
Label
Rape

ID
attempt_to_commit_rape
Type
numeric
Label
Attempt To Commit Rape

ID
kidnapping_and_abduction
Type
numeric
Label
Kidnapping And Abduction

ID
dacoity
Type
numeric
Label
Dacoity

ID
mkng_preparation_and_assembly_for_comm_dacoity
Type
numeric
Label
Making Preparation And Assembly For Committing Dacoity

ID
robbery
Type
numeric
Label
Robbery

ID
criminal_trespass_or_burglary
Type
numeric
Label
Criminal Trespass Or Burglary

ID
theft
Type
numeric
Label
Theft

ID
unlawful_assembly
Type
numeric
Label
Unlawful Assembly

ID
riots
Type
numeric
Label
Riots

ID
criminal_breach_of_trust
Type
numeric
Label
Criminal Breach Of Trust

ID
cheating
Type
numeric
Label
Cheating

ID
forgery
Type
numeric
Label
Forgery

ID
counterfeiting
Type
numeric
Label
Counterfeiting

ID
arson
Type
numeric
Label
Arson

ID
grievous_hurt
Type
numeric
Label
Grievous Hurt

ID
dowry_deaths
Type
numeric
Label
Dowry Deaths

ID
assault_on_women
Type
numeric
Label
Assault On Women With Intent To Outrage Her Modesty

ID
insult_to_the_modesty_of_women
Type
numeric
Label
Insult To The Modesty Of Women

ID
cruelty_by_husband_or_his_relatives
Type
numeric
Label
Cruelty By Husband Or His Relatives

ID
importation_of_girls_from_foreign_country
Type
numeric
Label
Importation Of Girls From Foreign Country

ID
causing_death_by_negligence
Type
numeric
Label
Causing Death By Negligence

ID
offences_against_state
Type
numeric
Label
Offences Against State

ID
offences_promoting_enmity_between_diff_grp
Type
numeric
Label
Offences Promoting Enmity Between Different Groups

ID
extortion
Type
numeric
Label
Extortion

ID
disclosure_of_identity_of_victims
Type
numeric
Label
Disclosure Of Identity Of Victims

ID
incidence_of_rash_driving
Type
numeric
Label
Incidence Of Rash Driving

ID
human_trafficking
Type
numeric
Label
Human Trafficking

ID
unnatural_offence
Type
numeric
Label
Unnatural Offence

ID
other_ipc_crimes
Type
numeric
Label
Other Ipc Crimes

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 insightsAnalyzing the count of IPC (Indian Penal Code) crimes in each district provides valuable insights into the law and order situation. Identifying districts with the highest crime rates allows law enforcement agencies to allocate resources effectively to address security challenges in those areas. Examining the data over time can reveal whether crime rates are increasing, decreasing, or remaining stable in specific districts. This trend analysis helps in understanding the effectiveness of crime prevention efforts. So, the count of IPC crimes in each district provides a wealth of insights that inform law enforcement, policymaking, and community engagement efforts. It plays a crucial role in maintaining public safety and ensuring effective law and order across regions.
Data last updated2024-08-01 00:00:00
Data retreival date2024-07-01 00:00:00
Datastore activeTrue
District no684
FrequencyYearly
GranularityDistrict
Has viewsTrue
Id7d5e2cc6-a704-4248-aa44-13d7186f847c
Idp readyTrue
Lgd mappingyes
MethodologyThe methodology for collecting data on IPC (Indian Penal Code) crimes in districts is meticulous and systematic. It begins with reports from various sources, which law enforcement agencies receive and use to register FIRs (First Information Reports). Crimes are then categorized by relevant IPC sections and carefully documented. Trained personnel input this information into a central database, subjecting it to thorough quality control and privacy safeguards. The data is shared with government agencies and the public for research purposes, aiding law enforcement, resource allocation, and crime prevention. Regular reviews and technology integration enhance the effectiveness of this methodology, ensuring accurate and privacy-compliant crime data collection and analysis.
No indicators34
Package ide311a510-ce48-4f4c-baf6-0ec5f9278285
Position8
Size173.1 KiB
Skuncrb-cii_ipc_crimes-dt-yr-prv
Stateactive
States uts no35
Url typeupload
Years covered2,016
Methodology The methodology for collecting data on IPC (Indian Penal Code) crimes in districts is meticulous and systematic. It begins with reports from various sources, which law enforcement agencies receive and use to register FIRs (First Information Reports). Crimes are then categorized by relevant IPC sections and carefully documented. Trained personnel input this information into a central database, subjecting it to thorough quality control and privacy safeguards. The data is shared with government agencies and the public for research purposes, aiding law enforcement, resource allocation, and crime prevention. Regular reviews and technology integration enhance the effectiveness of this methodology, ensuring accurate and privacy-compliant crime data collection and analysis.
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_ipc_crimes-dt-yr-prv
Dataset Frequency Yearly
Years Covered 2016
No of States/UT(s) 35
No of Districts 684
No of Tehsils/blocks
No of Gram Panchayats
Additional Information nan
Number of Indicators 34
Insights from the dataset Analyzing the count of IPC (Indian Penal Code) crimes in each district provides valuable insights into the law and order situation. Identifying districts with the highest crime rates allows law enforcement agencies to allocate resources effectively to address security challenges in those areas. Examining the data over time can reveal whether crime rates are increasing, decreasing, or remaining stable in specific districts. This trend analysis helps in understanding the effectiveness of crime prevention efforts. So, the count of IPC crimes in each district provides a wealth of insights that inform law enforcement, policymaking, and community engagement efforts. It plays a crucial role in maintaining public safety and ensuring effective law and order across regions.
IDP Ready Yes
LGD Mapping Yes