Distributive Generation Algorithm of Long Range Contact for Remote Spatial-data Access on P2P Delaunay Network

2007 ◽  
Author(s):  
Masaaki Ohnishi ◽  
Shinji Tsuboi ◽  
Masao Hirayama ◽  
Takayuki Eguchi ◽  
Shinichi Ueshima
Keyword(s):  
Long Range ◽  
Spatial Data ◽  
Data Access ◽  
Generation Algorithm

scholarly journals DEVELOPMENT OF A HETEROGENIC DISTRIBUTED ENVIRONMENT FOR SPATIAL DATA PROCESSING USING CLOUD TECHNOLOGIES

2016 ◽  
Vol XLI-B4 ◽  
pp. 385-390
Author(s):  
A. S. Garov ◽  
I. P. Karachevtseva ◽  
E. V. Matveev ◽  
A. E. Zubarev ◽  
I. V. Florinsky
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
3D Visualization ◽  
New Technology ◽  
Main Idea ◽  
Remote Sensing Data ◽  
Data Access ◽  
Working Environment ◽  
Software Environment ◽  
Spatial Data Processing

We are developing a unified distributed communication environment for processing of spatial data which integrates web-, desktop- and mobile platforms and combines volunteer computing model and public cloud possibilities. The main idea is to create a flexible working environment for research groups, which may be scaled according to required data volume and computing power, while keeping infrastructure costs at minimum. It is based upon the "single window" principle, which combines data access via geoportal functionality, processing possibilities and communication between researchers. Using an innovative software environment the recently developed planetary information system (<a href="http://cartsrv.mexlab.ru/geoportal"target="_blank">http://cartsrv.mexlab.ru/geoportal</a>) will be updated. The new system will provide spatial data processing, analysis and 3D-visualization and will be tested based on freely available Earth remote sensing data as well as Solar system planetary images from various missions. Based on this approach it will be possible to organize the research and representation of results on a new technology level, which provides more possibilities for immediate and direct reuse of research materials, including data, algorithms, methodology, and components. The new software environment is targeted at remote scientific teams, and will provide access to existing spatial distributed information for which we suggest implementation of a user interface as an advanced front-end, e.g., for virtual globe system.

scholarly journals Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Majid Hojati ◽  
Colin Robertson
Keyword(s):  
Big Data ◽  
Spatial Analysis ◽  
Cellular Automata ◽  
Spatial Data ◽  
Data Model ◽  
Data Access ◽  
Environmental Modeling ◽  
Modeling Framework ◽  
Global Grid

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Towards a Rwandan NSDI

2013 ◽  
pp. 2040-2050
Author(s):  
Felicia O. Akinyemi
Keyword(s):  
Spatial Data ◽  
Information Technologies ◽  
Data Use ◽  
Data Access ◽  
Data Infrastructure ◽  
Geographic Information Technologies ◽  
Key Players ◽  
Data Policy ◽  
Governmental Institutions ◽  
Multi Stakeholder

Awareness of the importance of spatial data in achieving development strategies is high in Rwanda. Government and non-governmental institutions are aspiring to use Geographic Information Technologies (GITs) in their day-to-day activities. The non-existence of a National Spatial Data Infrastructure (NSDI) in Rwanda brings to light serious issues for consideration. Still lacking is a spatial data policy relating to spatial data use. A mechanism to ease spatial data access and sharing is imperative. This paper describes SDI related efforts in Rwanda in a bid to establish the NSDI. Employing a multi-stakeholder approach to drive the process is advocated. To support this, SDI models in some countries are presented that could be applicable to the Rwandan context. Key players with potential roles in the NSDI were identified.

DAPR-tree: a distributed spatial data indexing scheme with data access patterns to support Digital Earth initiatives

2020 ◽  
Vol 13 (12) ◽  
pp. 1656-1671 ◽  
Author(s):  
Jizhe Xia ◽  
Sicheng Huang ◽  
Shaobiao Zhang ◽  
Xiaoming Li ◽  
Jianrong Lyu ◽  
...  
Keyword(s):  
Spatial Data ◽  
Data Access ◽  
Indexing Scheme ◽  
Data Indexing ◽  
Digital Earth ◽  
Data Access Patterns ◽  
Access Patterns

Open-Pit Mine Fine DEM Model Establishment and Updating

2012 ◽  
Vol 524-527 ◽  
pp. 508-514
Author(s):  
Ai Gong Xu ◽  
Yang Cui ◽  
Xin Sui
Keyword(s):  
Spatial Data ◽  
Model Updating ◽  
Mining Area ◽  
Open Pit Mine ◽  
Open Pit ◽  
Improved Method ◽  
Generation Algorithm ◽  
Topological Relationships ◽  
Model Update ◽  
Dem Model

In view of open-pit mine complex special terrain, to improved method of constructing triangulated irregular net and combined with the extended boundary, that can remission the phenomenon that steps were flattened and the problem of inefficiency mining area model update. Using constrained triangulated irregular net once generation algorithm, through extraction of spatial data, establish topological relationships and spatial indexing, eventually build a constraints of triangulated irregular net within the boundary constraint line. Combining extended boundary with the triangulated irregular net generated algorithm, can realize the establishment partial model as well as partial and overall model splicing. Thus enhanced the efficiency of the mining area overall model updating.

Spatial data access control in grid environment

2009 ◽  
Author(s):  
Menglong Yan ◽  
Yong Gao ◽  
Lun Wu ◽  
Pengfei Wu ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Access Control ◽  
Spatial Data ◽  
Data Access ◽  
Grid Environment ◽  
Data Access Control

scholarly journals Evaluasi Kesiapan Implementasi Infrastruktur Data Spasial untuk Manajemen Penanggulangan Bencana

2018 ◽  
Vol 2017 (1) ◽  
Author(s):  
Herlina ◽  
Sumarno ◽  
Indrianawati
Keyword(s):  
Decision Making ◽  
Disaster Management ◽  
Spatial Data ◽  
Data Access ◽  
Spatial Data Infrastructure ◽  
Data Availability ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Ease Of Access ◽  
Interview Questionnaire

ABSTRAK Akses data spasial yang cepat dan akurat mempunyai peranan yang penting dalam pengambilan keputusan untuk manajemen penanggulangan bencana. Infrastruktur Data Spasial (IDS) merupakan suatu cara untuk memudahkan pengguna untuk mengakses data spasial secara konsisten, mudah, dan aman. Dengan kata lain, IDS dapat meningkatkan ketersediaan data, kemudahan dalam akses, dan implementasi data spasial dalam pengambilan keputusan. Dalam hal manajemen penanggulangan bencana, BPBD dan stakeholder kebencanaan Kabupaten Bandung belum mengimplementasikan IDS kebencanaan. Tujuan penelitian ini adalah menentukan model IDS kebencanaan dan mengevaluasi kesiapan implementasi dalam manajemen penanggulangan bencana di Kabupaten Bandung. Metode yang digunakan dalam penelitian adalah penentuan model IDS kebencanaan yang mengacu pada model IDS yang dirumuskan oleh Rajabifard kemudian didetailkan dengan indikator penilaian IDS yang dikeluarkan Badan Informasi Geospasial tahun 2016. Pengambilan data dilakukan pada 18 stakeholder kebencanaan Kabupaten Bandung dengan wawancara, kuesioner, dan penilaian melalui website. Hasil evaluasi dari kesiapan implementasi IDS kebencanaan Kabupaten Bandung adalah 45,8%. Kata kunci: Infrastruktur Data Spasial, Manajemen Penanggulangan Bencana, Kabupaten Bandung ABSTRACT Fast and accurate spatial data access has an important role in decision making for disaster management. Spatial Data Infrastructure (SDI) is a way to facilitate the users to access spatial data consistently, easily, and safety. In the case, SDI can improve data availability, ease of access and implementation of spatial data for decision making. In disaster management, BPBD and disaster stakeholders in Bandung District have not implemented SDI of disaster. The objective of this study is to determine the SDI model of disaster and evaluate the readiness of implementation in disaster management in Bandung District. The method used in this study is determining SDI model of disaster, referred to IDS model which is formulated by Rajabifard, and then the SDI model of disaster is detailed by SDI assessment indicator issued by Geospatial Information Agency (2016). The data collection has been taken on 18 disaster stakeholders in Bandung District with interview, questionnaire, and assessment through the website. The evaluation result of the readiness of implementation the SDI of disaster in Bandung District is 45.8%. Keywords: Spatial Data Infrastructure, Disaster Management, Bandung District

scholarly journals DATA ARCHIVING AND DISTRIBUTION OF LiDAR AND DERIVED DATASETS IN THE PHILIPPINES

2016 ◽  
Vol XLI-B4 ◽  
pp. 195-199
Author(s):  
M. E. A. Tupas ◽  
S. C. Lat ◽  
R. A. Magturo
Keyword(s):  
Data Acquisition ◽  
Spatial Data ◽  
Distribution System ◽  
Data Exchange ◽  
Flood Hazard ◽  
Data Access ◽  
The Philippines ◽  
Data Sets ◽  
Data Archiving ◽  
Main Site

LiDAR programs in the Philippines have been generating valuable resource and hazard information for most of the country at a substantial rate since 2012. Significant progress have been made due to the programs design of engaging 16 Universities and research institutions spatially distributed across the country. Because of this, data has been accumulating at a brisk rate which poses significant technical and logistic issues. While a central node, the University of the Philippines, Diliman, handles data acquisition, pre-processing, and quality checking, processing and ground validation are devolved to the various nodes. For this setup to be successful, an efficient data access and distribution system should be in place. <br><br> In this paper, we discuss the spatial data infrastructure and data access protocols implemented by the program. At the center of the data access and distribution operations is LiPAD or our LiDAR portal for archiving and distribution. LiPAD is built on open source technologies, established web standards, and protocols. At its back-end a central data archive has been established using state of the art Object Storage technology to store both raw, processed Lidar and derived data sets. Catalog of available data sets ranging from data acquisition foot prints, to DEM coverages, to derived products such as flood hazard, and crop suitability are viewable and accessible on the main site based on the popular GeoNode application. Data exchange is performed using varying protocols to address various logistical problems. Given the various challenges the program is successful in distributing data sets not just to partner processing nodes but to other stakeholders where main requesters include national agencies and general research and academic institutions.

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