IIIT-H | Big Data and Policing - Spring 2019 | Projects

Introduction Cyber bullying has become prevalent in today's social media driven world. Awareness about it however, is not very widespread. Given that there is usually no escape for cyber bullying victims from their bullies, it is even more devastating than traditional bullying. Sometimes it is also hard to distinguish between simple negative interactions and cyber-bullying. Keeping this in mind we wanted to create a program that would help detect cyber bullying on Instagram accounts given only a username. Relevance In India, nearly 40% of people have never heard of cyber-bullying. Furthermore a majority of people think that current cyber-bullying measures are insufficient. 45% of parents say that their children have been cyber-bullied. Out of all the various ways in which people can be bullied online social media is the most common and also the most personal.  Although the nature of the bullying changes from platform to platform the effect does not change. we picked

Introduction The problem is to handle the growing violence rate by estimating the probability of the upcoming violence, especially in metropolitan cities. Why is the problem important? This is important since if by doing so, we could somehow able to stop even 10-15% of upcoming threat then it can have a vast effect. Who will benefit : Police can analyze data in real time and may increase patrolling if required. Based on available data, police can effectively maintain law and order in  vulnerable areas. Our strategy For this we chose the social media platform twitter 1) First of all we collected tweets with geo tagged locations for the last 7 days for 4 citites hyderabad, mumbai, kolkata and delhi 2) But only 2% of total tweets have geo tagged locations. So what we have done is that, we made a dictionary of areas of these cities from maps of india and find   the location if it is mentioned in the tweet like My bag is stolen from CP Delhi.

Introduction Traffic prediction is crucial to many applications including traffic network planning, route guidance, and congestion avoidance. We have tried to minimize the time required for a vehicle to go from point A to point B, and maximize the efficiency of the flow of traffic, to help the traffic police in managing traffic. Several essential factors affect traffic prediction: Geographical factors such as topology, etc. Social factors such as holidays, concert, weekends, etc. Limited Dataset, i.e., either small or not a publicly available dataset. The primary aim of the project is to use historical and live traffic data to control the traffic lights for efficient traffic flow. Why is the problem statement important? The number of vehicles on the road in India have increased 2-fold in every 8 years since the year 2000. Apart from not having adequately constructed roads, there is no proper system for helping traffic police officers in controlling the flow of traffic

What are we trying to do? With vehicle thefts increasing to alarming levels, our platform allows in the identification of regions with high thefts and provides a medium to detect stolen vehicles using number plate scanning. The platform can be used to detect stolen vehicles in real-time and the project can be scaled to use live video stream instead of images. We scraped and worked on the vehicle theft data available for Delhi region as a part of this four-month project marathon. The project has been developed in a way that it can be later scaled to a larger domain. The Data The dataset was obtained from the Delhi police website (https://zipnet.delhipolice.gov.in/). The data was scraped from the Delhi region using Beautiful Soup. We scraped the data for a span of 8 months, but the method can be scaled to updates FIRs on a daily basis. Each FIR is manually curated by a police officer, so the data has a lot of noise and requires to be cleaned before any analysis o

Introduction With the advent of the smartphone era and the availability of 4G internet across the country, police forces have begun to use electronic receipts of the traditional traffic challans. E-Challans are electronically generated penalty receipt that takes the place of the physical paper receipts and helps in digitizing the whole process of collecting challans and penalizing violations. In this project, we analyze the set of all unpaid E-Challans collected in metropolitan cities over a large span of time to gain unique insights about the nature of traffic violations in such cities. The problem is very relevant for a course on Big Data & Policing as it tries to answer the following important questions: How are traffic violations distributed spatially and temporally across the city boundaries? Can the most common violation types be characterized and be used for providing intervention insights? How can police leverage social media for increasing awareness and for targe

Problem Statement The course Big Data and Policing  has given us a detailed account about the prominence of Data and how it can influence Policing and general safety.  We as students had the chance to attend talks from policemen to lawyers who discussed their role in collecting and analysing data of any form to conduct policing in a smarter way. Our focus was to try and develop something that can tackle the issue of safety and provide a service that helps in general policing. We decided to come up with an application that could aid the process. Preliminary Idea  We started off with the idea to develop a web and mobile application primarily intended for women safety. We wanted to collect data about narrow streets and roads and understand how unsafe it would be for women mainly as pedestrians. The application allows the users to mark a particular spot on the street which they deem as unsafe. It also allows them to enter a short description about the area and