The best way to respond to a woman in crisis is either by including at least 1 woman in the response team or by Pink Police. The importance of Pink Police can't be stressed enough. From keeping monsters at bay to providing relief to victims, our country owes a lot to these officers.
Often it's not possible for Pink Police to be the first ones to respond to a crime. This could even lead to the monsters escaping, boasting their atrocities while putting us to shame.
We want to help in ensuring that Pink Police are the first and fastest ones to respond to a woman abuse crime.
In a Study In Pink we have used ML algorithms and Data Analytics to find the points that are at optimal distance from all the places that have a high crime probability. This not only reduces the response time but also reduces the use of resources.
Our compatibility makes us special. Instead of giving a concrete solution we have taken into account the varying demographics. Police officers will be able to allocate the points depending not only on the data but also on how many personnel they have at hand and the distance allocated to each officer or the optimal mileage of his/her car. Both these factors change over time but our algorithm caters to this problem.
The Study In Pink lets you chose a dataset, the officers at your disposal and the maximum distance each one has been allocated and this gives you an interactive map with all the points of concerns plotted. This map gets saved to a file which can later be used for various other purposes.
We hope to collaborate with the concerned authorities and optimize our results and check its success.
If it's a success then it could be extrapolated to the whole country. Support us and together we could save the lives of our sisters by preparing early on to face all the mosters out there...
Inaccurate or practically non-existent data was the main enemy. We took Bangalore as our primary area of study but we found a sheer lack of geospacial data in criminal cases. This was a disappointment but we got around it by generating some dummy data to fill in the gaps.
This required a bit of thinking as we wanted the Police Officers to be able to use the results outside our app as well.
Well that's every designer's nightmare... I'll say no more.
As you can see we've solved all the problems except the first one. We sincerely hope to collaborate with the authorities someday who would help us make our predictions more accurate as then we could add in a lot more factors to model the problem much more accurately.
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