Spatial Data Organization and Encoding Based on SVG and Its Application to WebGIS

2012 ◽  
Author(s):  
Xinchuang Wang ◽  
Shidong Wang
Keyword(s):  
Spatial Data ◽  
Data Organization ◽  
Spatial Data Organization

Structure and Design of Intelligent System for Police GIS Spatial Database

2013 ◽  
Vol 473 ◽  
pp. 148-152
Author(s):  
Zhi Wei Cao ◽  
Wen Ke Ding ◽  
Cai Li Fang
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Intelligent System ◽  
Spatial Database ◽  
Data Organization ◽  
Effective Solution ◽  
Organization And Management ◽  
Efficient Management ◽  
Spatial Data Organization ◽  
Structure And Design

According to the application of intelligent system for police GIS ,it needs to query and locateassist police,command and dispatch,patrol management and so on, According to the characteristics of police spatial information, All the spatial data needs to be unified storage. This paper designs the spatial database based on Geodatabase, then the database management for police is constructed and to achieve a unified and efficient management, so it provide an effective solution for the spatial data organization and management.

A Delaunay Triangulation Algorithm Based on Dual-Spatial Data Organization

2019 ◽  
Vol 87 (1-2) ◽  
pp. 19-31
Author(s):  
Niantao Liu ◽  
Bingxian Lin ◽  
Guonian Lv ◽  
A-Xing Zhu ◽  
Liangchen Zhou
Keyword(s):  
Delaunay Triangulation ◽  
Spatial Data ◽  
Data Organization ◽  
Spatial Data Organization

scholarly journals Object-oriented spatial data organization and management in GeoStar

1999 ◽  
Vol 2 (1) ◽  
pp. 49-54
Author(s):  
Zhu Xinyan ◽  
Gong Jianya ◽  
Zhang Xiaodong ◽  
Xiong Hanjiang
Keyword(s):  
Spatial Data ◽  
Object Oriented ◽  
Data Organization ◽  
Organization And Management ◽  
Spatial Data Organization

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 C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

2020 ◽  
Vol 27 (3) ◽  
pp. 29-43
Author(s):  
Sihem Oujdi ◽  
Hafida Belbachir ◽  
Faouzi Boufares
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Spatial Data ◽  
Computation Time ◽  
Spatial Relations ◽  
Spatial Data Analysis ◽  
Data Organization ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.

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