Road Accidents in the UK (Analysis and Visualization)

TL;DR

This paper employs Multiple Correspondence Analysis, hypothesis testing, and time series analysis to explore and visualize the complex factors involved in UK road accidents, aiming to identify key correlations and patterns.

Contribution

It introduces a combined approach using MCA, hypothesis testing, and time series analysis to effectively analyze multi-variable road accident data in the UK.

Findings

Identification of key variables influencing accidents

Effective visualization of multi-variable correlations

Insights into temporal patterns of accidents

Abstract

Analysis of road accidents is crucial to understand the factors involved and their impact. Accidents usually involve multiple variables like time, weather conditions, age of driver, etc. and hence it is challenging to analyze the data. To solve this problem, we use Multiple Correspondence Analysis (MCA) to first, filter out the most number of variables which can be visualized effectively in two dimensions and then study the correlations among these variables in a two dimensional scatter plot. Other variables, for which MCA cannot capture ample variance in the projected dimensions, we use hypothesis testing and time series analysis for the study.