Districtwise Cyber Crimes 2017 onwards
The data for the count of cybercrimes in each district is collected from multiple sources, including law enforcement agencies, cybercrime units, and online reporting platforms. These sources document various cybercrimes, such as hacking, online fraud, identity theft, and cyberbullying. The data includes details about the nature of the crime, the location of the incident, the parties involved, and the outcomes of investigations. It is essential for law enforcement, policymakers, and cybersecurity experts to monitor and analyze this data to develop strategies for preventing and addressing cybercrimes effectively. Additionally, public awareness campaigns and educational programs are often based on these statistics to promote online safety.
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
- tampering_computer_source_documents
- Type
- numeric
- Label
- Tampering Computer Source Documents
- ID
- ransom_ware
- Type
- numeric
- Label
- Ransom Ware
- ID
- offences_other_than_ransom_ware
- Type
- numeric
- Label
- Offences Other Than Ransom Ware
- ID
- dishonestly_recv_stolen_cmp_resrc_or_comm_device
- Type
- numeric
- Label
- Dishonestly Receiving Stolen Computer Resource Or Communication Device
- ID
- identity_theft
- Type
- numeric
- Label
- Identity Theft
- ID
- cheating_by_personation_by_using_computer_resource
- Type
- numeric
- Label
- Cheating By Personation By Using Computer Resource
- ID
- violation_of_privacy
- Type
- numeric
- Label
- Violation Of Privacy
- ID
- cyber_terrorism
- Type
- numeric
- Label
- Cyber Terrorism
- ID
- pub_or_trans_obscene_material_in_electronic_form
- Type
- numeric
- Label
- Publishing Or Transmitting Obscene Material In Electronic Form
- ID
- pub_or_trans_mtrl_cont_sxly_explct_act_elect_frm
- Type
- numeric
- Label
- Publishing Or Transmitting Of Material Containing Sexually Explicit Act In Electronic Form
- ID
- pub_or_trans_mtrl_dpct_chl_sxly_explct_elect_frm
- Type
- numeric
- Label
- Publishing Or Transmitting Of Material Depicting Children In Sexually Explicit Act In Electronic Form
- ID
- presrv_and_retention_of_info_by_intermediaries
- Type
- numeric
- Label
- Preservation And Retention Of Information By Intermediaries
- ID
- other_sections_it_act
- Type
- numeric
- Label
- Other Sections It Act
- ID
- interception_or_monitoring_or_decryption_of_info
- Type
- numeric
- Label
- Interception Or Monitoring Or Decryption Of Information
- ID
- un_athryz_access_atmpt_access_prct_comp_sys
- Type
- numeric
- Label
- Un Authorized Access Attempt To Access To Protected Computer System
- ID
- abetment_to_commit_offences
- Type
- numeric
- Label
- Abetment To Commit Offences
- ID
- attempt_to_commit_offences
- Type
- numeric
- Label
- Attempt To Commit Offences
- ID
- other_sections_of_it_act
- Type
- numeric
- Label
- Other Sections Of It Act
- ID
- abetment_of_suicide_online
- Type
- numeric
- Label
- Abetment Of Suicide Online
- ID
- cyber_stalking_bullying_of_women_children
- Type
- numeric
- Label
- Cyber Stalking Bullying Of Women Children
- ID
- data_theft
- Type
- numeric
- Label
- Data Theft
- ID
- credit_card_debit_card_fraud
- Type
- numeric
- Label
- Credit Card Debit Card Fraud
- ID
- atms_fraud
- Type
- numeric
- Label
- Atms Fraud
- ID
- online_banking_fraud
- Type
- numeric
- Label
- Online Banking Fraud
- ID
- otp_frauds
- Type
- numeric
- Label
- Otp Frauds
- ID
- other_frauds
- Type
- numeric
- Label
- Other Frauds
- ID
- cheating
- Type
- numeric
- Label
- Cheating
- ID
- forgery
- Type
- numeric
- Label
- Forgery
- ID
- defamation_morphing
- Type
- numeric
- Label
- Defamation Morphing
- ID
- fake_profile
- Type
- numeric
- Label
- Fake Profile
- ID
- currency_counterfeiting
- Type
- numeric
- Label
- Currency Counterfeiting
- ID
- stamps_counterfeiting
- Type
- numeric
- Label
- Stamps Counterfeiting
- ID
- cyber_blackmailing_threatening
- Type
- numeric
- Label
- Cyber Blackmailing Threatening
- ID
- fake_news_on_social_media
- Type
- numeric
- Label
- Fake News On Social Media
- ID
- other_offences
- Type
- numeric
- Label
- Other Offences
- ID
- total_offences_under_ip
- Type
- numeric
- Label
- Total Offences Under Ip
- ID
- gambling_act
- Type
- numeric
- Label
- Gambling Act
- ID
- lotteries_act
- Type
- numeric
- Label
- Lotteries Act
- ID
- copy_right_act
- Type
- numeric
- Label
- Copy Right Act
- ID
- trade_marks_act
- Type
- numeric
- Label
- Trade Marks Act
- ID
- other_sll_crimes
- Type
- numeric
- Label
- Other Sll Crimes
Additional Information
| Field | Value |
|---|---|
| Data last updated | November 20, 2024 |
| Metadata last updated | November 20, 2024 |
| Created | September 4, 2024 |
| Format | CSV |
| License | No License Provided |
| Additional info | nan |
| Data extraction page | https://ncrb.gov.in/crime-in-india.html |
| Data insights | 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. |
| Data last updated | 01-08-2024 |
| Data retreival date | 2024-07-01 00:00:00 |
| Datastore active | True |
| District no | 752 |
| Frequency | Yearly |
| Granularity | District |
| Has views | True |
| Id | 2353f85d-372a-4bb2-8617-32775891f39b |
| Idp ready | True |
| Lgd mapping | yes |
| 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. |
| Mimetype | text/csv |
| No indicators | 41 |
| Package id | e311a510-ce48-4f4c-baf6-0ec5f9278285 |
| Position | 7 |
| Size | 1.1 MiB |
| Sku | ncrb-cii_cyber_crimes-dt-yr-aaa |
| State | active |
| States uts no | 36 |
| Tags | ['Cyber crimes', 'identity theft', 'cyber terrorism', 'gambling', 'frogery', 'fraud'] |
| Url type | upload |
| Years covered | 2017-2022 |
| 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. |
| 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 | 01-08-2024 |
| Sku | ncrb-cii_cyber_crimes-dt-yr-aaa |
| Dataset Frequency | Yearly |
| Years Covered | 2017-2022 |
| No of States/UT(s) | 36 |
| No of Districts | 752 |
| 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 |
| LGD Mapping | Yes |