Data Dictionary

Column Type Label Description
id int4 index
year text Year
state_name text State Name
state_code text State Code
district_name text District Name
district_code text District Code
registration_circles text Registration Circles
tampering_computer_source_documents numeric Tampering Computer Source Documents
ransom_ware numeric Ransom Ware
offences_other_than_ransom_ware numeric Offences Other Than Ransom Ware
dishonestly_recv_stolen_cmp_resrc_or_comm_device numeric Dishonestly Receiving Stolen Computer Resource Or Communication Device
identity_theft numeric Identity Theft
cheating_by_personation_by_using_computer_resource numeric Cheating By Personation By Using Computer Resource
violation_of_privacy numeric Violation Of Privacy
cyber_terrorism numeric Cyber Terrorism
pub_or_trans_obscene_material_in_electronic_form numeric Publishing Or Transmitting Obscene Material In Electronic Form
pub_or_trans_mtrl_cont_sxly_explct_act_elect_frm numeric Publishing Or Transmitting Of Material Containing Sexually Explicit Act In Electronic Form
pub_or_trans_mtrl_dpct_chl_sxly_explct_elect_frm numeric Publishing Or Transmitting Of Material Depicting Children In Sexually Explicit Act In Electronic Form
presrv_and_retention_of_info_by_intermediaries numeric Preservation And Retention Of Information By Intermediaries
other_sections_it_act numeric Other Sections It Act
interception_or_monitoring_or_decryption_of_info numeric Interception Or Monitoring Or Decryption Of Information
un_athryz_access_atmpt_access_prct_comp_sys numeric Un Authorized Access Attempt To Access To Protected Computer System
abetment_to_commit_offences numeric Abetment To Commit Offences
attempt_to_commit_offences numeric Attempt To Commit Offences
other_sections_of_it_act numeric Other Sections Of It Act
abetment_of_suicide_online numeric Abetment Of Suicide Online
cyber_stalking_bullying_of_women_children numeric Cyber Stalking Bullying Of Women Children
data_theft numeric Data Theft
credit_card_debit_card_fraud numeric Credit Card Debit Card Fraud
atms_fraud numeric Atms Fraud
online_banking_fraud numeric Online Banking Fraud
otp_frauds numeric Otp Frauds
other_frauds numeric Other Frauds
cheating numeric Cheating
forgery numeric Forgery
defamation_morphing numeric Defamation Morphing
fake_profile numeric Fake Profile
currency_counterfeiting numeric Currency Counterfeiting
stamps_counterfeiting numeric Stamps Counterfeiting
cyber_blackmailing_threatening numeric Cyber Blackmailing Threatening
fake_news_on_social_media numeric Fake News On Social Media
other_offences numeric Other Offences
total_offences_under_ip numeric Total Offences Under Ip
gambling_act numeric Gambling Act
lotteries_act numeric Lotteries Act
copy_right_act numeric Copy Right Act
trade_marks_act numeric Trade Marks Act
other_sll_crimes numeric Other Sll Crimes

Additional Information

Field Value
Data last updated December 2, 2024
Metadata last updated December 3, 2024
Created December 2, 2024
Format CSV
License Open Data Commons Attribution License
Additional infonan
Data extraction pagehttps://ncrb.gov.in/crime-in-india.html
Data insightsThe insights gleaned from cybercrime data are highly valuable. They help identify patterns and trends in cybercrimes, enabling law enforcement agencies to focus their efforts on areas with higher incidence rates. Moreover, this data aids in understanding the types of cybercrimes that are most prevalent, such as phishing, identity theft, or online fraud. Such insights are crucial for developing effective preventive measures, enhancing cybersecurity awareness, and strengthening legal frameworks. They also highlight the need for educational initiatives and resources to empower individuals and organizations to protect themselves from cyber threats, ultimately contributing to a safer digital environment.
Data last updated01-08-2024
Data retreival date2024-07-01 00:00:00
Datastore activeTrue
District no12
FrequencyYearly
GranularityDistrict
Has viewsTrue
Id172ccf93-709b-429c-b09f-bf0b02075888
Idp readyTrue
MethodologyThe process of generating data for cybercrimes in each district is intricate. It starts with incident reporting by local authorities and individuals, followed by data collection and maintenance by law enforcement agencies. Legal involvement, including interviews and digital forensics, supplements the data. Each district maintains its records, and this information is periodically reported to a central agency. Data undergoes classification, analysis, and anonymization, shedding light on trends and areas with high cybercrime rates. Insights guide prevention strategies, awareness campaigns, and legislation. The public's role in reporting and practicing safe online behavior is emphasized, while resource allocation benefits from understanding local cybercrime trends.
No indicators41
Package iddb72aca9-7907-456c-a7f5-407ecd3d0fc6
Position7
Size22.1 KiB
Skuncrb-cii_cyber_crimes_rct-dt-yr-him
Stateactive
States uts no1
Url typeupload
Years covered2017-2022
Methodology The process of generating data for cybercrimes in each district is intricate. It starts with incident reporting by local authorities and individuals, followed by data collection and maintenance by law enforcement agencies. Legal involvement, including interviews and digital forensics, supplements the data. Each district maintains its records, and this information is periodically reported to a central agency. Data undergoes classification, analysis, and anonymization, shedding light on trends and areas with high cybercrime rates. Insights guide prevention strategies, awareness campaigns, and legislation. The public's role in reporting and practicing safe online behavior is emphasized, while resource allocation benefits from understanding local cybercrime trends.
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 01-08-2024
Sku ncrb-cii_cyber_crimes_rct-dt-yr-him
Dataset Frequency Yearly
Years Covered 2017-2022
No of States/UT(s) 1
No of Districts 12
No of Tehsils/blocks
No of Gram Panchayats
Additional Information nan
Number of Indicators 41
Insights from the dataset The insights gleaned from cybercrime data are highly valuable. They help identify patterns and trends in cybercrimes, enabling law enforcement agencies to focus their efforts on areas with higher incidence rates. Moreover, this data aids in understanding the types of cybercrimes that are most prevalent, such as phishing, identity theft, or online fraud. Such insights are crucial for developing effective preventive measures, enhancing cybersecurity awareness, and strengthening legal frameworks. They also highlight the need for educational initiatives and resources to empower individuals and organizations to protect themselves from cyber threats, ultimately contributing to a safer digital environment.
IDP Ready Yes