Comparing Hierarchical Data Structures for Sparse Volume Rendering with Empty Space Skipping

<div> <div> <div> <p><i>Empty space skipping can be efficiently implemented with hierarchical data structures such as k-d trees and bounding volume hierarchies. This paper compares several recently published hierarchical data structures with regard to construction and rendering performance. The papers that form our prior work have primarily focused on interactively building the data structures and only showed that rendering performance is superior to using simple acceleration data structures such as uniform grids with macro cells. In the area of surface ray tracing, there exists a trade-off between construction and rendering performance of hierarchical data structures. In this paper we present performance comparisons for several empty space skipping data structures in order to determine if such a trade-off also exists for volume rendering with uniform data topologies. </i></p> </div> </div> </div>

Comparing Hierarchical Data Structures for Sparse Volume Rendering with Empty Space Skipping

<div> <div> <div> <p><i>Empty space skipping can be efficiently implemented with hierarchical data structures such as k-d trees and bounding volume hierarchies. This paper compares several recently published hierarchical data structures with regard to construction and rendering performance. The papers that form our prior work have primarily focused on interactively building the data structures and only showed that rendering performance is superior to using simple acceleration data structures such as uniform grids with macro cells. In the area of surface ray tracing, there exists a trade-off between construction and rendering performance of hierarchical data structures. In this paper we present performance comparisons for several empty space skipping data structures in order to determine if such a trade-off also exists for volume rendering with uniform data topologies. </i></p> </div> </div> </div>

Perceived Sustainable Urbanization Based on Geographically Hierarchical Data Structures in Nanjing, China

Concentrating on geographically hierarchical data structures and using large-scale satisfaction survey data in Nanjing, this study employs Bayesian spatial multilevel model (MLM) to evaluate Nanjing’s perceived sustainable urbanization. In this study, we consider the geographically hierarchical data structures and the city’s individual perceptions of sustainable urbanization to explore the effect of environment and self-rated health on perceived sustainable urbanization, controlling for individual sociodemographic attributes and household. Through clarifying the spatial dependence and heterogeneity, this paper provides a flexible framework for assessing sustainable urbanization and dealing with the geographical hierarchical data. In particular, by drawing on existing studies, our questionnaire is more representative of the overall characteristics of Nanjing’s population than census data, which can be helpful for understanding whether urbanization is sustainable from individual perspective and further for correcting practices. Based on a survey of 10,077 questionnaires, this paper finds the geographically hierarchical data structures have significantly influenced the evaluation of sustainable urbanization, and the Bayesian spatial MLM is an effective tool for evaluating China’s sustainable urbanization. In particular, this paper takes spatial effects into consideration and compares the geographically hierarchical data. Results show that spatial patterns significantly influence the assessment of sustainable urbanization, and perceived pollution, age, education level, and income are the four key factors influencing individual perceived sustainable urbanization.