An Accelerated Slicing Algorithm for Frep Models

Complex 3D objects with microstructures can be modelled using the function representation (FRep) approach and then manufactured. The task of modelling a geometric object with a sophisticated microstructure based on unit cell repetition is often too computationally expensive for CAD systems. FRep provides efficient tools to solve this problem. However, even for FRep the slicing step required for manufacturing can take a significant amount of time. An accelerated slicing algorithm for FRep 3D objects is proposed in this paper. This algorithm allows the preparation of FRep models for 3D printing without surface generation stage. The spatial index is employed to accelerate the slicing process. A novel compound adaptive criterion and a novel acceleration criterion are proposed to speed up the evaluation of the defining function of an FRep object. The use of these criteria is significantly reducing the computational time for contour construction during the slicing process. The k-d tree and R-tree data structures are used as spatial indexes. The performance of the accelerated slicing algorithm was tested. The contouring time was reduced 100-fold due to using the novel compound adaptive criterion with the novel acceleration criterion.

LEVANTAMENTO DA COBERTURA VERDE DAS PRAÇAS CENTRAIS DO MUNICÍPIO DE SANTA FÉ DO SUL – SP

O presente trabalho tem por objetivo realizar um diagnóstico ambiental do complexo arbóreo urbano, usando índices espaciais para avaliar, quantitativamente, a arborização das áreas verdes públicas de Santa fé do Sul, SP. Para o presente estudo, escolheram-se quatro praças do município, onde foram classificadas as espécies arbóreas presentes, e calculado o percentual de cobertura vegetal (PCV), assim como o índice de Sombreamento Arbóreo (ISA), Índice de Densidade Arbórea (IDA). O estudo foi desenvolvido no município de Santa Fé do Sul, no período de março a setembro de 2019. Foram amostrados 281 indivíduos distribuídos em 23 espécies, inclusos 10 em famílias botânicas sendo essas Apocynaceae, Araliaceae, Arecaceae, Bignoniaceae, Chrysobalanaceae, Fabaceae, Lecythidaceae, Meliaceae, Moracea e Myrtaceae. As praças avaliadas próximas às áreas comerciais apresentaram índices satisfatórios próximos aos recomendados, já a praça Adilson D. Poleto foi a praça que apresentou os menores índices, muito abaixo do recomendado para praças com predomínio de residências. Conclui-se que a Praça Salles Filho apresentou uma alta dominância de indivíduos, com presença de 48% de oitis, as praças Stélio M. Loureira e Praça Salles Filho apresentaram valores abaixo de ISA, porém próximos ao recomendado para áreas comerciais, em relação às praças de áreas residenciais, a Adilson D. Poleto apresentou um ISA muito abaixo do indicado pelos autores e, apesar do baixo índice, a praça apresenta uma grande área de gramado possibilitando uma permeabilidade a chuvas e algum conforto térmico no período noturno. GREEN AREA MAPPING AT PUBLIC SQUARES IN THE MUNICIPALITY OF SANTA FÉ DO SUL- SP* ABSTRACT The present study aims to carry out an environmental assessment of the urban forestry, using spatial indexes to evaluate, quantitatively, the public green areas in Santa Fé do Sul, SP. Four public squares were chosen in this study, where the existing tree species were classified, the percentage of plant covering (PPC), as well as Shading Arboreal indexes(SAI), Arboreal Density Indexes (ADI). The study was carried out in the municipality of Santa Fé do Sul, in the period from March to September 2019. It was sampled 281 individuals distributed in 23 species, including 10 botanical families, they are Apocynaceae, Araliaceae, Arecaceae, Bignoniaceae, Chrysobalanaceae, Fabaceae, Lecythidaceae, Meliaceae, Moracea e Myrtaceae. The squares which were close to commercial areas presented satisfactory indexes close to the recommended index. Conversely, Adilson D. Poleto presented the lowest index, far below the recommended in residential areas. It was concluded that Praça Salles Filho presented a high predominance of individuals, 48% were Oiti trees, the squares Stélio M. Loureira and Praça Salles Filho had low values for SAI, however, close to the recommended for commercial areas, regarding squares in residential areas, Adilson D. Poleto presented a very low SAI value according to the author’s recommendation, though it’s low value, the square presents a great lawn area enabling water permeability when it rains as well as some thermal comfort at night. Keywords: Afforestation. Green area. Urbanization.

Review article: The spatial dimension in the assessment of urban socio-economic vulnerability related to geohazards

Society and economy are only two of the dimensions of vulnerability. This paper aims to elucidate the state of the art of data sources, spatial variables, indicators, methods, indexes and tools for the spatial assessment of socio-economic vulnerability (SEV) related to geohazards. This review was first conducted in December 2018 and re-run in March 2020 for the period between 2010 and 2020. The gross number of articles reviewed was 27, from which we identified 18 relevant references using a revised search query and six relevant references identified using the initial query, giving a total sample of 24 references. The most common source of data remains population censuses. The most recurrent spatial variable used for the assessment of SEV is households without basic services, while critical facilities are the most frequent spatial category. Traditional methods have been combined with more innovative and complex methods to select and weight spatial indicators and develop indices. The Social Vulnerability Index (SoVI®) remains the benchmark for the assessment of SEV and a reference for its spatial assessment. Geographic information systems (GIS) is the most common tool for conducting a spatial assessment of SEV regarding geohazards. For future spatial assessments of SEV regarding geohazards, we recommend considering 3-D spatial indexes at the microscale at the urban level and involving the community in the assessments.

Enhancing Quad Tree for Spatial Index Using Space Filling Curves

Spatial indexes, such as those based on the Quad Tree, are important in spatial databases for the effective implementation of queries with spatial constraints, especially when queries involve spatial links. The quaternary trees are a very interesting subject, given the fact that they give the ability to solve problems in a way that focuses only on the important areas with the highest density of information. Nevertheless, it is not without the disadvantages because the search process in the quad tree suffers from the problem of repetition when reaching the terminal node and return to the behavior of another way in the search and lead to the absorption of large amounts of time and storage. In this paper, the quad tree was improved by combining it with one of the space filling curve types, resulting in reduced storage space requirements and improved implementation time

Multi-Dimensional Indexes in DBMSs

Multi-dimensional data is present across multimedia, data mining and other data-driven applications. The R-Tree is a popular index structure that DBMSs are implementing as core for efficient retrieval of such data. The gap between the best and worst-case performance is very wide in an R-tree. Thus, building quality R-trees quickly is desirable. Variations differ in how node overflow are approached during the building process. This article studies the R-Tree technique that the open-source PostgreSQL DBMS uses. Focus is on a specific parameter controlling node overflows as an optimisation target, and improved configurations are proposed. This parameter is hard-wired into the DBMS, and therefore, an implementation is presented to allow this parameter to become accessible through an SQL construct. The access method designer can resort to configuring this parameter when trying to meet specific storage or time-related performance targets. With this study, the reader can gain an insight into the effects of changing the parameter by considering the spatial indexes on well-known workloads.

HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

Keyword Search Mechanisms in Geo-Spatial Databases

Most web and mobile applications are based on searching the location-based objects called spatial objects. In spatial database systems, searching such objects is a challenging task since it deals with geo-spatial capabilities. Sometimes, the spatial queries are associated with text information in order to obtain the most relevant answers nearest to the given location. Such queries are called spatial textual query. Conventional spatial indexes and text indexes are not suitable for resolving such queries. Since these indexes use various approaches to perform searching, they can cause performance degradation. Effective processing of the query mainly depends on the index structure, searching algorithms, and location-based ranking. This chapter reviews the different hybrid index structures and search mechanisms to extract the spatial objects, the different ranking model it supports, and the performance characteristics.