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Big Data Cogn. Comput. 2018, 2(2), 9; https://doi.org/10.3390/bdcc2020009

Development of Framework for Aggregation and Visualization of Three-Dimensional (3D) Spatial Data

1
The Chang School of Continuing Education, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
2
School of Computing & Information Systems, Athabasca University, 10011, 109 Street, Edmonton, AB T5J 3S8, Canada
3
Faculty of Science and Technology, Athabasca University, 10011, 109 Street, Edmonton, AB T5J 3S8, Canada
*
Author to whom correspondence should be addressed.
Received: 17 February 2018 / Revised: 14 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system. View Full-Text
Keywords: spatial data fusion; environmental modelling; geospatial information; data mapping; software; big data spatial data fusion; environmental modelling; geospatial information; data mapping; software; big data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Miu, M.; Zhang, X.; Dewan, M.A.A.; Wang, J. Development of Framework for Aggregation and Visualization of Three-Dimensional (3D) Spatial Data. Big Data Cogn. Comput. 2018, 2, 9.

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Big Data Cogn. Comput. EISSN 2504-2289 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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