Spatio temporal crime analysis and forecasting using social media data

Title

Spatio temporal crime analysis and forecasting using social media data

Creator

Prathap, Boppuru Rudra.

Contributor

Ramesha, K.

Description

Now a days, people communicate, share ideas, and interact through social media platforms. It has given us an ability to talk about career interests, post videos, and pictures for sharing with others. The data present in social media enables the analysis of various human aspects. The social media data and domain is used for crime analysis, customer behaviour analysis, and healthcare analysis provides much information useful to predict human behaviours. Crime is the most common social problem faced in a developing country. In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. An example of these initiatives includes an accurate and real-time prediction of crime occurrences. Crime analytics and prediction have lengthily studied among research analytics communities. In recent years, crime knowledge from one of a kind heterogeneous source (Twitter, News Feeds, Facebook, Instagram and so forth.) have given enormous opportunities to the research group to comfortably study crime pattern and prediction duties in specific real knowledge. Data mining and predictive analytics provide the best options for the same. Law enforcement organizations are increasingly looking to use data from social media such as Facebook, Newsfeeds, Twitter, etc. investing in research in this area. Using the intelligence gained through these data, the agencies can identify future incidents and plan for active patrolling.

Source

Author's Submission

Date

2020-01-01

Publisher

Christ(Deemed to be University)

Subject

Computer Science and Engineering

Rights

Open Access

Relation

No Thesis

Format

PDF

Language

English

Type

PhD

Identifier

http://hdl.handle.net/10603/426668