Improving Women’s Safety by accelerating Spatio Temporal Crime Prediction of Hotspots

© 2020 by IJCTT Journal
Volume-68 Issue-12
Year of Publication : 2020
Authors : Satvik Shukla, Hari Purnapatre, Gitalee Jadhav, Rasika Gohokar
DOI :  10.14445/22312803/IJCTT-V68I12P108

How to Cite?

Satvik Shukla, Hari Purnapatre, Gitalee Jadhav, Rasika Gohokar, "Improving Women’s Safety by accelerating Spatio Temporal Crime Prediction of Hotspots," International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 34-39, 2020. Crossref, 10.14445/22312803/IJCTT-V68I12P108

Spatio-temporal crime prediction, machine learning, the alert system.

Crimes against women are a common social problem affecting the quality of life of women. Crimes could occur everywhere. However, it is common that criminals work on crime opportunities they face in the most familiar areas for them. By providing a machine learning approach to determine the criminal hotspots and find the type, location, and time of committed crimes, we hope to make our community safer for the women living there and the ones who will travel there. With the increase of crimes, law enforcement agencies demand advanced geographic information systems and new machine learning approaches to improve crime analytics and prediction to protect their communities better. We aim at building an alert system for women`s safety, using machine learning prediction models. These models will help to achieve a deeper understanding of criminal hotspots. The alert system will function through an Android application that will deliver women alerts if they enter a neighborhood susceptible to danger. The alerts will be based on a static database obtained as an output of the machine learning prediction.

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