Quantitatively describing effects caused by trees is a challenging issue for sky view factor (SVF) studies. The complex geometrical shapes of trees and the seasonally changing canopy volume caused by leaf growth and defoliation have forced SVF users to disregard trees in their analyses or to apply a simple geometric object, such as a rectangular pole or vertically elongated ellipsoid. The three-dimensional point cloud (3DPC) method is useful for quantitative analysis of urban settings by describing the structured spatial complex in detail, not only by shape itself but also with many meaningful indices such as SVF. We here proposed a new SVF analysis method based on 3DPC. Stereoscopic projection was applied to project 3DPC on the virtual hemisphere. From intensive analysis of 3DPC SVF in a normal urban complex area, we discerned the effects caused by trees. The results showed that the tree effect derived from 3DPC SVF in an urban complex is clearly described by a comparison between two cases (trees and no trees). Trees with topography play an important role and contribute to the heat balance in an urban complex.KEY WORDS sky view factor (SVF); three-dimensional point cloud (3DPC); virtual hemisphere; stereoscopic projection; tree effect; urban complex; hemispherical 3DPC SVF; continuous 3DPC SVF
In this paper, we proposed a scalable RDF triple store for massive-scale RDF data that processes the SPARQL query with many join operations in efficient manner. Graph characteristic of RDF data model hinders scalable and efficient indexing and querying over RDF triples. To address the problem, our query processing uses the pruning algorithm based on Bitstructure and summarized information to minimize data-reading. Our approach guarantees scalability and flexibility even for massive-scale RDF data by storing RDF triples in distributed fashion, providing the modifiable structure, and optimizing memory footprint of usage. The experiments shows that our system is better performing for queries with many join operations while uses less memory footprints.
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