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Crime prediction is an effort to cut down the crime rate and criminal activities in the society. This paper concentrates on monitoring the crime trends in India using data from 2001 to 2019. The work analyzes the crime condition in India by following a top-to bottom approach and implementing visualization and machine learning algorithms. Heat map visualization of India is done to focus on specific crime types for all states. Furthermore, various demography of human population like age-groups, sex, caste, etc., are considered to have a larger perspective of the issue. The model also suggest proactive measures based on these findings. [ABSTRACT FROM AUTHOR]

Origin–destination (OD) visualizations can help to understand movement data. Unfortunately, they are often cluttered due to the quadratic growth of the data and complex depictions of the multiple dimensions in the data. Many domain experts have designed visualizations to reduce visual complexity and display multiple data variables. However, OD visualizations have not been well classified, which makes it hard to employ such methods for reducing the visual complexity systematically. In this article, we propose a novel classification scheme for static OD visualizations that considers five aspects: the granularity of flows, the dimensionality in and of the display space, the semantics of the display space, the representation of nodes and flows, and the ways of relating two visualizations. We evaluate the proposed classification scheme using published visualization examples and show that it is effective and expressive. [ABSTRACT FROM AUTHOR]

This article describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is useful for exploring the sensitivity of structure to particular variables. The user can also interactively shift the center of a slice, for example, to explore how structure changes in local subspaces. The Mathematica package as well as example notebooks are provided, which contain functions enabling the user to experiment with these new manual controls, with one specifically for exploring regions and boundaries produced by classification models. The advantage of Mathematica is its linear algebra capabilities and interactive cursor location controls. Some limited implementation has also been made available in the R package tourr. for this article are available online. [ABSTRACT FROM AUTHOR]