Visual Exploration of Large Transportation Asset Data using Ontology-Based Heat Tree
Tuyen Le, Chau Le, H. David Jeong, C T Jahren · International Journal of Transportation · 2018
The national priority to enhance the service life of aging transportation assets has triggered the collection of a huge amount of asset data across the United States.Data visualization is widely recognized as a pressing need to assist highway professionals in quickly capturing overall trends and understanding the meaning of big data sets with millions of records and hundreds of data attributes.A key requirement for big data visualization is the selection of what data to be visualized among a large number of attributes.In addition, since most of the asset data are categorical attributes that are associated with complex hierarchical classifications, the capacity of comparing statistics throughout various classification levels on the same graph is needed.Despite the wide availability of methods, data visualization is still a major challenge to the practitioners of highway agencies due to the lack of a domain-specific automated tool that can successfully address those visualization requirements.This study proposes an ontology based visualization technique for automatically visualizing hierarchical transportation data.A domain ontology of highway asset data is developed to support the selection of related data attributes to be visualized.The proposed method also includes a novel technique for creating a heat tree that indicates the distribution of asset information over another hierarchy of categories.The system is expected to effectively assist professionals in developing the visual representation of data, which will in turn, translate into better and faster visualization, time savings, and enhanced decision making.