Districtwise crime against women 2016
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
- Typ
- int4
- Etikett
- index
- ID
- year
- Typ
- text
- Etikett
- Year
- ID
- state_name
- Typ
- text
- Etikett
- State Name
- ID
- state_code
- Typ
- text
- Etikett
- State Code
- ID
- district_name
- Typ
- text
- Etikett
- District Name
- ID
- district_code
- Typ
- text
- Etikett
- District Code
- ID
- registration_circles
- Typ
- text
- Etikett
- Registration Circles
- ID
- rape
- Typ
- numeric
- Etikett
- Rape
- ID
- attempt_to_commit_rape
- Typ
- numeric
- Etikett
- Attempt To Commit Rape
- ID
- kidnapping_and_abduction
- Typ
- numeric
- Etikett
- Kidnapping And Abduction
- ID
- dowry_deaths
- Typ
- numeric
- Etikett
- Dowry Deaths
- ID
- assault_on_women
- Typ
- numeric
- Etikett
- Assault On Women
- ID
- insult_to_the_modesty_of_women
- Typ
- numeric
- Etikett
- Insult To The Modesty Of Women
- ID
- cruelty_by_husband_relatives
- Typ
- numeric
- Etikett
- Cruelty By Husband Relatives
- ID
- importation_of_girls_from_foreign
- Typ
- numeric
- Etikett
- Importation Of Girls From Foreign
- ID
- abetment_of_suicides_of_women
- Typ
- numeric
- Etikett
- Abetment Of Suicides Of Women
- ID
- dowry_prohibition
- Typ
- numeric
- Etikett
- Dowry Prohibition
- ID
- indecent_representation_of_women
- Typ
- numeric
- Etikett
- Indecent Representation Of Women
- ID
- protection_of_women_from_domestic_violence
- Typ
- numeric
- Etikett
- Protection Of Women From Domestic Violence
- ID
- immoral_traffic_prevention
- Typ
- numeric
- Etikett
- Immoral Traffic Prevention
- ID
- acid_attack
- Typ
- numeric
- Etikett
- Acid Attack
- ID
- attempt_to_acid_attack
- Typ
- numeric
- Etikett
- Attempt To Acid Attack
- ID
- deaths_caused_with_intent_to_cause_miscarriage
- Typ
- numeric
- Etikett
- Deaths Caused With Intent To Cause Miscarriage
- ID
- causing_miscarriage_without_consent_of_women
- Typ
- numeric
- Etikett
- Causing Miscarriage Without Consent Of Women
- ID
- human_trafficking
- Typ
- numeric
- Etikett
- Human Trafficking
- ID
- un_natural_offences
- Typ
- numeric
- Etikett
- Un Natural Offences
Mer information
| Fält | Värde |
|---|---|
| Data senast uppdaterad | 4 september 2024 |
| Metadata senast uppdaterad | 19 december 2025 |
| Skapad | 4 september 2024 |
| Format | CSV |
| Licens | Licens ej angiven |
| 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 | 2022-07-01 00:00:00 |
| Data retreival date | 2022-08-01 00:00:00 |
| Datastore active | True |
| District no | 684 |
| Frequency | Yearly |
| Geo columns | ['state_code', 'district_code'] |
| Granularity | District |
| Has views | True |
| Id | 0f12840c-47cf-407f-b132-6410a3c331ee |
| 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. |
| No indicators | 25 |
| Package id | e311a510-ce48-4f4c-baf6-0ec5f9278285 |
| Position | 5 |
| Size | 112,2 KiB |
| Sku | ncrb-cii_crime_against_women-dt-yr-prv |
| State | active |
| States uts no | 36 |
| Url type | upload |
| Years covered | 2 016 |
| 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 | 2022-08-01 00:00:00 |
| Data Last Updated | 2022-07-01 00:00:00 |
| Sku | ncrb-cii_crime_against_women-dt-yr-prv |
| Dataset Frequency | Yearly |
| Years Covered | 2016 |
| No of States/UT(s) | 36 |
| No of Districts | 684 |
| No of Tehsils/blocks | |
| No of Gram Panchayats | |
| Mer information | nan |
| Number of Indicators | 25 |
| 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 |
| Mapping Status % | |
| Geo Columns | ['state_code', 'district_code'] |