AID*: A Spatial Index for Visual Exploration of Geo-Spatial Data

2020 ◽  
pp. 1-1
Author(s):  
Saheli Ghosh ◽  
Ahmed Eldway
Keyword(s):  
Spatial Data ◽  
Visual Exploration ◽  
Spatial Index

Visual exploration of high-dimensional spatial data: requirements and deficits

2000 ◽  
Vol 26 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Christoph Uhlenküken ◽  
Benno Schmidt ◽  
Ulrich Streit
Keyword(s):  
Spatial Data ◽  
Visual Exploration ◽  
High Dimensional ◽  
Data Requirements

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549 ◽  
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

A Mobile Storage System for Massive Spatial Data

2014 ◽  
Vol 962-965 ◽  
pp. 2730-2734
Author(s):  
Zhen Song ◽  
Jian Chen ◽  
Jiu Yan Ye
Keyword(s):  
Mobile Devices ◽  
Data Storage ◽  
Spatial Data ◽  
Storage System ◽  
Hierarchical Classification ◽  
Spatial Index ◽  
Index Structure ◽  
Real Time Visualization ◽  
Spatial Data Organization ◽  
Low Efficiency

This paper aims at the solution to problems of large amounts of data storage, low efficiency calculate and poor user experience which actually exists in GIS application on mobile devices. In order to solve these issues we use GIS technologies including spatial data organization, map browser, and spatial index. We focused on the research of how to effectively utilize system resources and rational and efficient out organize the spatial data. What’s more, we improved the R-tree indexing algorithm to establish a rapid spatial index structure and used hierarchical classification techniques to optimize the efficiency of the real-time visualization of spatial data for mobile devices.

scholarly journals Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data

2019 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Luo Chen ◽  
Jun Li ◽  
Ning Jing
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Large Scale ◽  
Optimization Method ◽  
Spatial Index ◽  
Overlay Analysis ◽  
Analysis Model ◽  
Time Buffer ◽  
Real World Datasets ◽  
Processing Architecture

Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn: 8080/hibo).

Method for managing and querying geo-spatial data using a grid-code-array spatial index

2018 ◽  
Vol 12 (2) ◽  
pp. 173-181 ◽  
Author(s):  
Shuang Li ◽  
Guoliang Pu ◽  
Chengqi Cheng ◽  
Bo Chen
Keyword(s):  
Spatial Data ◽  
Spatial Index

scholarly journals A QUADTREE SPATIAL INDEX METHOD WITH INCLUSION RELATIONS FOR THE INCREMENTAL UPDATING OF VECTOR LANDCOVER DATABASE

2018 ◽  
Vol XLII-4 ◽  
pp. 757-761
Author(s):  
X. G. Zhou ◽  
H. S. Wang
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Index Method ◽  
Incremental Updating ◽  
Indexing Methods ◽  
Data Indexing ◽  
Inclusion Relations ◽  
Complex Polygon

<p><strong>Abstract.</strong> In vector landcover database, there are a lot of complex polygons with many holes, even nesting holes. In the incremental updating (i.e., using the change-only information to update the land cover database), a new changed parcel usually has 2-dimensional intersections (e.g., overlap, cover, equal and inside, etc.) with several existing regions, automatic updating operations need to identify the affected objects for the new changes at first. If the existing parcels include complex polygons (i.e., the polygon with holes), it is still needed to determine if there are 2-dimensional intersections between the new changed polygon and each holes of the involved complex polygons. The relation between the complex polygon and its holes has not been presented in the current spatial data indexing methods, only the MBB (Minimum Bounding Box) of the exterior ring of the complex polygon has been stored, the non-involved holes can not be filtered at the first step of spatial access methods. As the refinement geometric operation is costly, therefore the updating process for the complex polygons is very complicated and low efficient using the current spatial data indexing methods. In order to solve this problem, an improved quadtree spatial index method is presented in this paper. In this method, the polygons is divided to two categories according to the relations with the quadrant axes, i.e., disjoint to the axes and intersect with the axes. The intersect polygons are still divided to 5 cases according to the intersection position among the polygons and the different level quadrant axes. The intersection polygons are stored in the different level root nodes in our index tree, and five buckets denoted as <i>XpB, XnB, YpB, YnB, XYB</i> are used to store the polygons intersecting the different level quadrant axes respectively. The polygons disjoint to all quadrant axes are stored in the leaf nodes in this method. The authors developed the spatial index structure with inclusion relations and the algorithms of the corresponding index operations (e.g., insert, delete and query) for the complex polygons. The effectiveness of the improved index is verified by an experiment of land cover data incremental updating. Experimental results show that the proposed index method is significantly more efficient than the traditional quadtree index in terms of spatial query efficiency, and the time efficiency of the incremental updating is increased about 3 times using the proposed index method than that using the traditional quadtree index.</p>

scholarly journals An Efficient Parallel Algorithm for Polygons Overlay Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4857 ◽  
Author(s):  
Yuke Zhou ◽  
Shaohua Wang ◽  
Yong Guan
Keyword(s):  
Parallel Algorithm ◽  
Spatial Data ◽  
Large Scale ◽  
High Efficiency ◽  
Spatial Index ◽  
Overlay Analysis ◽  
Data Decomposition ◽  
Space Filling Curve ◽  
Index Decomposition ◽  
Filling Curve

Map overlay analysis is essential for geospatial analytics. Large scale spatial data pressing poses challenges for geospatial map overlay analytics. In this study, we propose an efficient parallel algorithm for polygons overlay analysis, including active-slave spatial index decomposition for intersection, multi-strategy Hilbert ordering decomposition, and parallel spatial union algorithm. Multi-strategy based spatial data decomposition mechanism is implemented, including parallel spatial data index, the Hilbert space-filling curve sort, and decomposition. The results of the experiments showed that the parallel algorithm for polygons overlay analysis achieves high efficiency.

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