Our work got published in The Indian Express, you can read here.

Problem Statement:

TL;DR Built a real-time heat map, integrated with Google Maps, to provide Delhi travellers a way to navigate safely and be well-informed about the possible annoyances along their route (crowdsourced). Designed and led experiments for leveraging the human-centered design process to collect data and define clear user requirements for feature prototypes.

“Delhi, the capital city of India is called the ‘Crime Capital’ of the country.” (Vij, 2017)

“Delhi was the most unsafe among 19 major cities, accounting for nearly 40 per cent of rapes, 33 per cent of against women and the highest crime rate in 2016, according to the Crime Records Bureau data. A total of 236,476 cases were registered in 2018, with an increase in the number of murders (3.25 per cent) and thefts (7.7 per cent), particularly of vehicles (12.98 per cent).” (Standard, 2019)

Delhi has one of the highest shares of rapes (15.1% in 2004–06 and 10.8% in 2010–12) while kidnapping and abduction of girls constituted the highest share of well over one-third, followed by cruelty by parents and relatives in 2010–12.” (Satija & Datta, 2015)

“According to the Delhi Human Development Report 2006, public safety emerged as a serious concern among the city’s residents, with about 80% opining that the city was unsafe in general and half of these saying that the city was unsafe for women.

All these statistics only strengthen the need for a way to make Delhi a safer space to live. While a complete revolution or restructuring of the police departments seems a long way off, a possible interim solution is to avoid known crime hotspots or potentially unsafe areas.
Therefore, our research questions are:

To provide an application for navigation in Delhi based on safety.

Design Process:

We first conducted contextual inquiries to find out what needs people will have from an app like ours i.e what their specific security concerns are, what they would like to know before going to an area and how they choose routes for travel. Then we went through multiple iterations of making prototypes and conducting task analyses to make our app progressively easier and more intuitive to use.

Quick Data from CI

We collated the information obtained from contextual inquiries into an affinity diagram. This helped us broadly outline the expectations of users from our app. Some insights from the affinity diagram

1. Users wanted to know how crowded or deserted an area is
2. Users found deserted, low-light areas unsafe
3. Users wanted to know about the availability of public transport.
4. Users wanted to know about the lighting conditions in the area
5. Users wanted to know what precautionary measures they needed to take before visiting an area.
6. Users wanted information from trust-worthy sources about areas they needed to visit.
7. Users did not just want to know about safety concerns, they also wanted to learn about things that would cause an inconvenience in travelling.

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Our solution:

We decided to build an app wherein users can rate the areas on parameters like lighting, availability of public transport, crowd or lack thereof in an area and the general feeling of safety. Users can also add comments to mention other points like inconveniences and annoyances eg- construction sites. These ratings will be added up to make a safety score for a location which will be displayed on its drop pin. Other users can click on this pin to see what comments people have about this location to plan their travel accordingly. Based on the usefulness of their reviews, users will also get a rating so that other users are able to see how trustworthy their comments are.

Logo:

We did several iterations for the logo since we wanted a logo that aptly conveys what our app does and is catchy at the same time. We also wanted to have the full name of our app in the logo because it is the first thing that a user sees which not only makes the impression of what kind of an app it is but also lasts in their minds. The B in our logo is designed like an owl’s eye due to the fact that an owl has good vision and it can also see clearly in the night also which sort of can be attributed to the fact that people can first check out carefully the safety score of a place at any time and then venture out accordingly. The magnifying glass (which is used as an ‘a’) is self-explanatory. The glass has a location pin-like end which also relates to the fact that people can do safety audits of a place (i.e. drop pins) and find what parameters the place is to be rated upon.

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Iterations:

In each iteration, we followed the principle of empathise, define, ideate and prototype. We conducted about 20 contextual inquiries to understand the user perspective before jumping onto development. We used these to define the requirements. We made multiple prototypes. For each iteration, we made users test the interface through task analysis where they were asked to think out loud so that we could get ideas for improvement and build the next prototype after further interviews. We also updated the safety parameters in the iterations so that they become more suited to the users’ needs.

We implemented an Android app using the Android Studio platform. We used most of our icons from https://icons8.com/icons/set/android-apps and google images.