Districtwise Crime Against Women 2017 onwards
Data on crimes against women in each district holds immense significance for lawmaking and policy formulation. It provides a clear understanding of the prevalence, trends, and patterns of such crimes, enabling evidence-based decision-making. This data helps lawmakers identify areas with high incidence rates, facilitating the allocation of resources and intervention strategies where they are needed most. It also aids in evaluating the effectiveness of existing legal measures and the need for new legislation. Furthermore, this data raises awareness about the challenges women face, fostering a societal and legislative commitment to gender equality and women's safety. Ultimately, it plays a pivotal role in shaping laws and policies aimed at preventing and addressing crimes against women.
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_with_rape_gang_rape
- Type
- numeric
- Label
- Murder With Rape Gang Rape
- ID
- dowry_deaths
- Type
- numeric
- Label
- Dowry Deaths
- ID
- abetment_to_suicide_of_women
- Type
- numeric
- Label
- Abetment To Suicide Of Women
- ID
- miscarriage
- Type
- numeric
- Label
- Miscarriage
- ID
- acid_attack
- Type
- numeric
- Label
- Acid Attack
- ID
- attempt_to_acid_attack
- Type
- numeric
- Label
- Attempt To Acid Attack
- ID
- cruelty_by_husband_or_his_relatives
- Type
- numeric
- Label
- Cruelty By Husband Or His Relatives
- ID
- kidnapping_and_abduction
- Type
- numeric
- Label
- Kidnapping And Abduction
- ID
- kidnapping_abduction_in_order_to_murder
- Type
- numeric
- Label
- Kidnapping Abduction In Order To Murder
- ID
- kidnapping_for_ransom
- Type
- numeric
- Label
- Kidnapping For Ransom
- ID
- kidnp_and_abductn_of_women_above_18_for_marrg
- Type
- numeric
- Label
- Kidnapping And Abduction Of Women Above 18 For Marriage
- ID
- kidnp_and_abductn_of_girls_below_18_for_marrg
- Type
- numeric
- Label
- Kidnapping And Abduction Of Girls Below 18 For Marriage
- ID
- procuration_of_minor_girls
- Type
- numeric
- Label
- Procuration Of Minor Girls
- ID
- importation_of_girls_from_foreign
- Type
- numeric
- Label
- Importation Of Girls From Foreign
- ID
- kidnapping_and_abduction_of_women_others
- Type
- numeric
- Label
- Kidnapping And Abduction Of Women Others
- ID
- human_trafficking
- Type
- numeric
- Label
- Human Trafficking
- ID
- selling_of_minor_girls
- Type
- numeric
- Label
- Selling Of Minor Girls
- ID
- buying_of_minor_girls
- Type
- numeric
- Label
- Buying Of Minor Girls
- ID
- rape_women_above_18
- Type
- numeric
- Label
- Rape Women Above 18
- ID
- rape_girls_below_18
- Type
- numeric
- Label
- Rape Girls Below 18
- ID
- attempt_to_commit_rape_above_18
- Type
- numeric
- Label
- Attempt To Commit Rape Above 18
- ID
- attempt_to_commit_rape_girls_below_18
- Type
- numeric
- Label
- Attempt To Commit Rape Girls Below 18
- ID
- assault_on_womenabove_18
- Type
- numeric
- Label
- Assault On Women With Intent To Outrage Her Modesty Above 18
- ID
- assault_on_women_below_18
- Type
- numeric
- Label
- Assault On Women With Intent To Outrage Her Modesty Below 18
- ID
- insult_to_the_modesty_of_women_above_18
- Type
- numeric
- Label
- Insult To The Modesty Of Women Above 18
- ID
- insult_to_the_modesty_of_women_below_18
- Type
- numeric
- Label
- Insult To The Modesty Of Women Below 18
- ID
- dowry_prohibition
- Type
- numeric
- Label
- Dowry Prohibition
- ID
- procuring_inducing_children_for_the_sake_of_prostitution
- Type
- numeric
- Label
- Procuring Inducing Children For The Sake Of Prostitution
- ID
- detaining_a_prsn_in_premises_where_prost_is_carried
- Type
- numeric
- Label
- Detaining A Person In Premises Where Prostitution Is Carried On
- ID
- prostitution_in_or_in_the_vicinity_of_public_places
- Type
- numeric
- Label
- Prostitution In Or In The Vicinity Of Public Places
- ID
- seducing_or_soliciting_for_purpose_of_prostitution
- Type
- numeric
- Label
- Seducing Or Soliciting For Purpose Of Prostitution
- ID
- other_sections_under_itp_act
- Type
- numeric
- Label
- Other Sections Under Itp Act
- ID
- protection_of_women_from_domestic_violence
- Type
- numeric
- Label
- Protection Of Women From Domestic Violence
- ID
- publshng_or_transmitting_of_sexually_explicit_mtrl
- Type
- numeric
- Label
- Publishing Or Transmitting Of Sexually Explicit Material
- ID
- other_women_centric_cyber_crimes
- Type
- numeric
- Label
- Other Women Centric Cyber Crimes
- ID
- child_rape
- Type
- numeric
- Label
- Child Rape
- ID
- sexual_assault_of_children
- Type
- numeric
- Label
- Sexual Assault Of Children
- ID
- child_sexual_harassment
- Type
- numeric
- Label
- Child Sexual Harassment
- ID
- use_of_child_for_pornography
- Type
- numeric
- Label
- Use Of Child For Pornography Storing Child Pornography Material
- ID
- offences_of_pocso_act
- Type
- numeric
- Label
- Offences Of Pocso Act
- ID
- pocso_act_unnatural_offences
- Type
- numeric
- Label
- Pocso Act Unnatural Offences
- ID
- indecent_representation_of_women
- Type
- numeric
- Label
- Indecent Representation Of Women
Additional Information
| Field | Value |
|---|---|
| Data last updated | November 21, 2024 |
| Metadata last updated | November 21, 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 | These are a few insights that can be drawn from the Crimes against Women data: 1. Trends Over Years: Analyzing the data over different years can reveal trends in crime rates against women in various districts. 2. State-wise and District-wise Comparison: Comparing crime rates between states and districts can provide insights into regional variations in women's safety. 3. Identification of High-Risk Areas: Identifying districts or states with higher incidences of specific crimes can help prioritize law enforcement and support services. 4. Categorization of Crimes: The dataset covers a wide range of crimes, allowing for an in-depth analysis of different types of offenses against women. |
| Data last updated | 01-08-2022 |
| Data retreival date | 2022-07-01 00:00:00 |
| Datastore active | True |
| District no | 751 |
| Frequency | Yearly |
| Granularity | District |
| Has views | True |
| Id | c8d3ea7e-4855-45f3-9c26-557419e93b6a |
| Idp ready | True |
| Lgd mapping | yes |
| Methodology | The methodology for collecting data on crimes against women in districts is multifaceted. It begins with law enforcement, where police file reports based on victim statements. Medical examinations are conducted in cases of physical violence, providing crucial evidence. Specialized counselors and NGOs offer support and often report incidents. Legal processes track cases, and a dedicated database records all data, prioritizing victim privacy. Periodic audits maintain data accuracy. This comprehensive approach ensures the thorough documentation of crimes against women, enabling informed policymaking and targeted interventions for women's safety and rights. |
| Mimetype | text/csv |
| No indicators | 42 |
| Package id | e311a510-ce48-4f4c-baf6-0ec5f9278285 |
| Position | 6 |
| Size | 1.1 MiB |
| Sku | ncrb-cii_crime_against_women-dt-yr-aaa |
| State | active |
| States uts no | 36 |
| Tags | ['Crime Against Women', "Women's Safety", 'District-wise Crime Data', 'State-wise Crime Data', 'Gender-based Violence', 'Crime Trends'] |
| Url type | upload |
| Years covered | 2017-2022 |
| Methodology | The methodology for collecting data on crimes against women in districts is multifaceted. It begins with law enforcement, where police file reports based on victim statements. Medical examinations are conducted in cases of physical violence, providing crucial evidence. Specialized counselors and NGOs offer support and often report incidents. Legal processes track cases, and a dedicated database records all data, prioritizing victim privacy. Periodic audits maintain data accuracy. This comprehensive approach ensures the thorough documentation of crimes against women, enabling informed policymaking and targeted interventions for women's safety and rights. |
| Indicators | |
| Similar Resources | |
| Granularity Level | District |
| Data Extraction Page | https://ncrb.gov.in/crime-in-india.html |
| Data Retreival Date | 2022-07-01 00:00:00 |
| Data Last Updated | 01-08-2022 |
| Sku | ncrb-cii_crime_against_women-dt-yr-aaa |
| Dataset Frequency | Yearly |
| Years Covered | 2017-2022 |
| No of States/UT(s) | 36 |
| No of Districts | 751 |
| No of Tehsils/blocks | |
| No of Gram Panchayats | |
| Additional Information | nan |
| Number of Indicators | 42 |
| Insights from the dataset | These are a few insights that can be drawn from the Crimes against Women data: 1. Trends Over Years: Analyzing the data over different years can reveal trends in crime rates against women in various districts. 2. State-wise and District-wise Comparison: Comparing crime rates between states and districts can provide insights into regional variations in women's safety. 3. Identification of High-Risk Areas: Identifying districts or states with higher incidences of specific crimes can help prioritize law enforcement and support services. 4. Categorization of Crimes: The dataset covers a wide range of crimes, allowing for an in-depth analysis of different types of offenses against women. |
| IDP Ready | Yes |
| LGD Mapping | Yes |