The process of visual analytics consists of: information gathering, data preprocessing, data analysis, data visualization, interaction and decision making. The main objective of visual analytics is not to only allow users in detecting expected patterns, but to enable them in identifying unexpected patterns and relationships to observe the hidden insights and relationships. interaction, collaboration, cognition, perception, etc.) in visual analytics plays an important role in the decision making process . On the other hand, visual analytics is not only graphical representation of the data – it is an integrated approach that combines data analysis, data visualization, and human interventions. One the one hand, information visualization relies on visual computing in order to help humans acquire abstract information . Different types of data visualizations including Information Visualization and Visual Analytics are used to help people in understanding and exploring their data . 2.1 Data Visualizationĭata visualization refers to the process of graphically representing data in order to illustrate the relationships within data and to reveal hidden patterns and structures . Finally, we cover traffic data visualization in Sect. Then, we present the process of visual analytics in Sect. We start this section by discussing data visualization in Sect. Section 4 presents the visualizations of traffic accidents in the city of Riyadh as a case study. A description of our visualization platform is presented in Sect. Section 2 provides a brief background about data visualization in general, with an emphasis on traffic data visualization. The remainder of this paper is organized as follows. Due to the characteristics of traffic data, its multivariate nature, and the importance of its spatial and temporal properties, the visualization techniques we explore are: spatial visualizations, temporal visualizations, spatio-temporal visualizations, and multivariate visualizations . In particular, we aim to contribute to improving road safety in the Kingdom of Saudi Arabia by capturing insights from an Accidents Dataset collected from the General Directorate of Traffic (GDT) in Riyadh and providing those perceptions through an interactive visualization platform to the policy makers in the GDT. In this paper, we aim to aid people understand the mobility patterns of their cities by exploring different visualization techniques for traffic data and investigate what insights each visualization technique yields. Insights gained from visualizations of accidents’ data demonstrate how valuable visual analytics can be to authorities and policy makers to better understand traffic, enhance future planning, and determine corrective actions.Īlthough the prevalence and accessibility of traffic data are changing the way people view mobility in their cities and roads, the task of retrieving such insights from huge and heterogeneous traffic datasets and presenting them to people is very challenging. We believe that enhancing road safety is paramount to traffic regulators and understanding traffic accidents temporal and spatial patterns can help in achieving such a goal. Moreover, a significant number of these accidents are severe, which makes road traffic injuries the leading cause of death for young males between the age of 16 and 30 . It is well-recognized that road safety in the Kingdom of Saudi Arabia is in an extremely dire state, where more than half a million accidents occur each year . Given the critical importance of the problem, many efforts, in both industry and academia, have explored systematic approaches to addressing the challenges of data visualization, which refers to the process of graphically representing data in order to illustrate the relationships within data and to uncover hidden patterns . The volume, velocity, and variety of traffic data in urban areas are growing at an exponential rate and thus, understanding and capturing the semantics of traffic data is an increasingly complex challenge. In traffic modeling, researchers have been collecting, analyzing, and visualizing large datasets in the past two decades .
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