Delhi Man Turns Crime Data Into an AI-Powered Map of Women Safety Risks

A Delhi resident has drawn attention online after using artificial intelligence and publicly available crime data to create a map highlighting areas of the capital where women may face greater safety concerns, particularly after dark. The project, created by Raj Gomani, attempts to turn large amounts of crime information into a visual format that is easier for people to understand. 

Gomani used crime data from the National Crime Records Bureau (NCRB) along with Anthropic's AI assistant Claude to develop the map. Instead of presenting crime figures only through conventional tables or reports, the project uses geographic information to show where reported incidents involving women have been concentrated. 

The project was shared through a video on social media, where Gomani explained the idea behind the map and the information used to create it. He said the work also involved looking at offender profiles associated with particular areas. The video quickly attracted attention, with users discussing whether similar tools could be expanded and made available to a wider audience. 

The map is part of a broader conversation about how technology can make government and public datasets easier for ordinary people to interpret. Crime statistics can contain large amounts of information that are difficult to understand when presented only as spreadsheets or lengthy reports. By placing that information on a map, the project attempts to give users a more immediate picture of geographic patterns.

At the same time, a crime map should not automatically be treated as a definitive measure of whether a particular neighbourhood is safe or unsafe. Reported crime depends on several factors, including reporting practices, police records, population density and the type of offences included in the dataset. A location with more reported cases may also have a larger population or greater reporting activity, so the numbers require context before being interpreted.

The National Crime Records Bureau publishes crime statistics that are widely used for analysing patterns of crime in India. Government crime-data systems also recognise the value of geographic analysis, with the Ministry of Home Affairs' Digital Police platform noting that thematic reports can be used to examine patterns of crimes that are more prevalent in particular areas. 

The Delhi project comes at a time when women's safety in the capital is again receiving considerable public attention. On September 28, the Supreme Court took note of recent cases of rape and sexual assault in Delhi-NCR and ordered an audit of women's safety mechanisms in the city. The exercise includes issues such as vulnerable-area mapping, CCTV coverage, street lighting and technology-enabled safety systems. 

That timing gives data-driven safety tools an added relevance. Mapping crime patterns can potentially help identify locations where authorities may need to examine lighting, surveillance, police presence, public transport access or other safety infrastructure. However, such maps are most useful when combined with current ground-level information rather than being treated as a standalone answer.

Delhi has previously experimented with technology and geographic crime analysis. Police systems have used location-based data and statistical models to identify crime clusters and support deployment decisions. Earlier reporting on Delhi Police's crime-mapping initiatives described the use of spatial data to visualise areas where incidents or emergency calls were concentrated. 

The use of AI in Gomani's project adds another layer to that approach. AI can help organise, analyse and present large datasets quickly, potentially making complicated information more accessible. But the quality of the final map still depends on the quality, completeness and relevance of the underlying data.

The project has also prompted a wider discussion about responsibility for women's safety. Gomani argued that simply blaming governments or men would not address the entire problem and said that improving safety requires collective action and better allocation of resources to places where people may be particularly vulnerable. 

For residents, such a map could serve as an additional source of information when thinking about travel after dark, but it should not replace normal precautions or current local information. Conditions on a particular road can change depending on the time, lighting, traffic, public activity, transport availability and police presence.

The Delhi initiative ultimately demonstrates one way AI can be used to turn complex public information into something more visual and accessible. As cities collect increasing amounts of data, similar approaches could potentially be used not only to understand crime patterns but also to examine street lighting, public transport availability, emergency services and other factors that influence how people experience safety in urban spaces.