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.

Study on the Implementation of Quadtree Index on E-Chart System

2011 ◽  
Vol 63-64 ◽  
pp. 627-632
Author(s):  
Zhong Jie Zhang ◽  
De Peng Zhao ◽  
De Qiang Wang
Keyword(s):  
Experimental Data ◽  
Mobile Devices ◽  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Index Structures ◽  
Application Development ◽  
Electronic Chart ◽  
Comparison And Analysis

As embedded and mobile devices application development and many micro-computing devices need to run the e-chart system. The tree spatial index structures can efficiently organize and retrieve spatial data. This paper discusses the index to organize and retrieve e-chart data through the quadtree. We discussed the electronic chart in the two typical quadtree on the index structure, and give the comparison and analysis of experimental data.

Research on Improved Method of Storage and Query of Large-Scale Remote Sensing Images

2018 ◽  
Vol 29 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jing Weipeng ◽  
Tian Dongxue ◽  
Chen Guangsheng ◽  
Li Yiyuan
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Index Structure ◽  
Hilbert Curve ◽  
Remote Sensing Images ◽  
Storage Model ◽  
Sensing Data ◽  
Low Efficiency

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability.

Research on Cache Quadtree: A Cache Index for Spatial Data Based on Intelligent Material System

2011 ◽  
Vol 63-64 ◽  
pp. 795-799
Author(s):  
Zhong Jie Zhang ◽  
De Peng Zhao ◽  
De Qiang Wang
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Index Structure ◽  
Material System ◽  
Spatial Indexing ◽  
Data Query ◽  
Intelligent Material

This paper presents a new spatial index structure - cache quadtree. Cache quadtree combined with the advantages of spatial indexing and caching. According to the characteristics of spatial data, query uses the previous query results as much as possible, only the necessary queries are performed on the server. Meanwhile, full use of query results in the cache tree; reduce the server's query and its query range. Our paper gives the cache quadtree structure and the key algorithm in detail.

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>

LAZY R-tree: The R-tree with lazy splitting algorithm

2019 ◽  
Vol 46 (2) ◽  
pp. 243-257
Author(s):  
Yang Yang ◽  
Pengwei Bai ◽  
Ningling Ge ◽  
Zhipeng Gao ◽  
Xuesong Qiu
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Splitting Method ◽  
Hash Table ◽  
Spatial Index ◽  
Index Structure ◽  
Splitting Algorithm ◽  
Tree Construction ◽  
Tree Index

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

scholarly journals Literature Mapper: A QGIS Plugin for Georeferencing Citations in Zotero

2021 ◽  
Vol 14 ◽  
pp. 117862212110092
Author(s):  
Michele M Tobias ◽  
Alex I Mandel
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Future Research ◽  
Web Mapping ◽  
Web Map ◽  
Public Sharing ◽  
Academic Publications ◽  
Spatial Coordinates ◽  
Study Sites ◽  
Soil And Water

Many studies in air, soil, and water research involve observations and sampling of a specific location. Knowing where studies have been previously undertaken can be a valuable addition to future research, including understanding the geographical context of previously published literature and selecting future study sites. Here, we introduce Literature Mapper, a Python QGIS plugin that provides a method for creating a spatial bibliography manager as well as a specification for storing spatial data in a bibliography manager. Literature Mapper uses QGIS’ spatial capabilities to allow users to digitize and add location information to a Zotero library, a free and open-source bibliography manager on basemaps or other geographic data of the user’s choice. Literature Mapper enhances the citations in a user’s online Zotero database with geo-locations by storing spatial coordinates as part of traditional citation entries. Literature Mapper receives data from and sends data to the user’s online database via Zotero’s web API. Using Zotero as the backend data storage, Literature Mapper benefits from all of its features including shared citation Collections, public sharing, and an open web API usable by additional applications, such as web mapping libraries. To evaluate Literature Mapper’s ability to provide insights into the spatial distribution of published literature, we provide a case study using the tool to map the study sites described in academic publications related to the biogeomorphology of California’s coastal strand vegetation, a line of research in which air movement, soil, and water are all driving factors. The results of this exercise are presented in static and web map form. The source code for Literature Mapper is available in the corresponding author’s GitHub repository: https://github.com/MicheleTobias/LiteratureMapper

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