Learned spatial indexes

2020 ◽  
Author(s):  
Panagiotis Simatis
Keyword(s):  
Spatial Indexes

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

2018 ◽  
Vol 7 (12) ◽  
pp. 467 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Wenze Luo ◽  
Luo Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Buffer Analysis ◽  
Data Volume ◽  
Time Buffer ◽  
Real World Datasets ◽  
Spatial Indexes

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.

scholarly journals Enhancing Quad Tree for Spatial Index Using Space Filling Curves

2020 ◽  
Vol 38 (1B) ◽  
pp. 15-25
Author(s):  
Ali A. Hussain ◽  
Rehab F. Hassan
Keyword(s):  
Spatial Databases ◽  
Space Filling ◽  
Space Filling Curve ◽  
Effective Implementation ◽  
Quad Tree ◽  
Implementation Time ◽  
Spatial Indexes ◽  
Space Requirements ◽  
Filling Curve ◽  
And Storage

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

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

2020 ◽  
Vol 20 (6) ◽  
pp. 1663-1687 ◽  
Author(s):  
Diana Contreras ◽  
Alondra Chamorro ◽  
Sean Wilkinson
Keyword(s):  
Total Sample ◽  
Vulnerability Index ◽  
Spatial Dimension ◽  
Common Source ◽  
Social Vulnerability Index ◽  
Spatial Assessment ◽  
Economic Vulnerability ◽  
Critical Facilities ◽  
Basic Services ◽  
Spatial Indexes

Abstract. 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.

scholarly journals A NOVEL APPROACH OF INDEXING AND RETRIEVING SPATIAL POLYGONS FOR EFFICIENT SPATIAL REGION QUERIES

2017 ◽  
Vol IV-4/W2 ◽  
pp. 131-138
Author(s):  
J. H. Zhao ◽  
X. Z. Wang ◽  
F. Y. Wang ◽  
Z. H. Shen ◽  
Y. C. Zhou ◽  
...  
Keyword(s):  
Geospatial Data ◽  
Spatial Index ◽  
Remote Sensing Images ◽  
Web Based ◽  
Spatial Region ◽  
Novel Approach ◽  
Data Volume ◽  
Efficient Query Processing ◽  
Query Algorithm ◽  
Spatial Indexes

Spatial region queries are more and more widely used in web-based applications. Mechanisms to provide efficient query processing over geospatial data are essential. However, due to the massive geospatial data volume, heavy geometric computation, and high access concurrency, it is difficult to get response in real time. Spatial indexes are usually used in this situation. In this paper, based on k-d tree, we introduce a distributed KD-Tree (DKD-Tree) suitbable for polygon data, and a two-step query algorithm. The spatial index construction is recursive and iterative, and the query is an in memory process. Both the index and query methods can be processed in parallel, and are implemented based on HDFS, Spark and Redis. Experiments on a large volume of Remote Sensing images metadata have been carried out, and the advantages of our method are investigated by comparing with spatial region queries executed on PostgreSQL and PostGIS. Results show that our approach not only greatly improves the efficiency of spatial region query, but also has good scalability, Moreover, the two-step spatial range query algorithm can also save cluster resources to support a large number of concurrent queries. Therefore, this method is very useful when building large geographic information systems.

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