scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

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.

scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

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.

scholarly journals Developing UAV-Based Forest Spatial Information and Evaluation Technology for Efficient Forest Management

2020 ◽  
Vol 12 (23) ◽  
pp. 10150
Author(s):  
Yongyan Zhu ◽  
Seongwoo Jeon ◽  
Hyunchan Sung ◽  
Yoonji Kim ◽  
Chiyoung Park ◽  
...  
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Layer Structure ◽  
Vegetation Type ◽  
Control Point ◽  
Forest Policy ◽  
Three Dimensional ◽  
Geospatial Information ◽  
Kappa Value ◽  
National Forest Planning

Forest spatial information is regularly established and managed as basic data for national forest planning and forest policy establishment. Among them, the grade of vegetation conservation shall be investigated and evaluated according to the value of vegetation conservation. As the collection of field data over large or remote areas is difficult, unmanned aerial vehicles (UAVs) are increasingly being used for this purpose. Consequently, there is a need for research on UAV-monitoring and three-dimensional (3D) image generation techniques. In this study, a new method that can efficiently collect and analyze UAV spatial data to survey and assess forests was developed. Both UAV-based and LiDAR imaging methods were evaluated in conjunction with the ground control point measurement method for forest surveys. In addition, by fusing the field survey database of each target site and the UAV optical and LiDAR images, the Gongju, Samcheok, and Seogwipo regions were analyzed based on deep learning. The kappa value showed 0.59, 0.47, and 0.78 accuracy for each of the sites in terms of vegetation type (artificial or natural), and 0.68, 0.53, and 0.62 accuracy in terms of vegetation layer structure. The results of comparative analysis with ecological natural maps by establishing vegetation conservation levels show that about 83.9% of the areas are consistent. The findings verified the applicability of this UAV-based approach for the construction of geospatial information on forests. The proposed method can be useful for improving the efficiency of the Vegetation Conservation Classification system and for conducting high-resolution monitoring in forests worldwide.

Geospatial Interoperability

2011 ◽  
pp. 1652-1658
Author(s):  
Manoj Paul ◽  
S.K. Ghosh
Keyword(s):  
Information Sharing ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Representation ◽  
Geospatial Data ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Geography Markup Language ◽  
Standard Data ◽  
Data Formats

Spatial information is an essential component in almost all decision support system due to the capability it provides for analyzing anything that has reference to the location on earth. Spatial data generally provides thematic information of different aspects over a region. Geospatial information, a variant of spatial information, is generally collected on thematic basis, where individual organizations are involved on any particular theme. Geospatial thematic data is being collected from decades and huge amount of data is available in different organizations (Stoimenov, Dordevi´c, & Stojanovi´c 2000). Information communities find it difficult to locate and retrieve required geospatial information from other geospatial sources in reliable and acceptable form. The problem that has been incurred is the lack of standards in geospatial data formats and storage/access mechanism (Devogele, Parent, Spaccapietra, 1998). Heterogeneity in geospatial data formats and access methods poses a major challenge for geospatial information sharing among a larger user community. With the growing need of geospatial information and widespread use of Internet has fostered the requirement of geospatial information sharing over the Web. The Geo-Web (Lake, Burggraf, Trninic, & Rae, 2005) is being envisioned to be a distributed network of interconnected geographic information sources and processing services that are: • Globally accessible, that is, they live on the internet and are accessed through standard Open Geospatial Consortium (OGC) and W3C interfaces, • Globally integrated data sources that make use of standard data representation for sharing and transporting geospatial data. Unless a standard means for geospatial information sharing is developed, interoperability cannot be realized. Without successful interoperability approaches, the realization of Geo-Web is not possible. Geo-Web is being developed to address the need for access to current and accurate geospatial information from diverse geospatial sources around the world. The National Spatial Data Infrastructure (NSDI) initiative has been taken by many nations for providing integrated access of geospatial information (Budak, Sheth, & Ramakrishnan, 2004). Actual data will be kept under the jurisdiction of the organization producing that data. A user will be interested in availing geospatial services through well-defined interface. Without some internationally agreed upon standards for geospatial data and computational methodology, this cannot be made into existence. This chapter discusses several issues towards geospatial interoperability and adoption of geography markup language (GML) (Cox, Cuthbert, Lake, & Martell, 2001; Lake et al., 2005) as a common geospatial data format. The associated technologies that can be used for realizing geospatial interoperability have also been discussed.

Spatial Data Management in IoT Systems: Solutions and Evaluation

2021 ◽  
Vol 15 (01) ◽  
pp. 117-139
Author(s):  
Maria Krommyda ◽  
Verena Kantere
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Spatial Information ◽  
Efficient Solutions ◽  
Data Sources ◽  
The Internet ◽  
Multiple Sources ◽  
Spatial Data Management ◽  
The Internet Of Things ◽  
Integrate Data

As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the IoT systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multi-dimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real-world scenarios.

scholarly journals Towards an Underground Utilities 3D Data Model for Land Administration

2019 ◽  
Vol 11 (17) ◽  
pp. 1957 ◽  
Author(s):  
Jingya Yan ◽  
Siow Jaw ◽  
Kean Soon ◽  
Andreas Wieser ◽  
Gerhard Schrotter
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Urban Areas ◽  
Spatial Information ◽  
Three Dimensional ◽  
Data Governance ◽  
Land Administration ◽  
3D Data ◽  
Underground Utilities ◽  
2D Data

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three-dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How can we map reliable 3D underground utility networks and use them in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

Research on Construction of Visual Platform of Beijing Power Grid Based on Multi-Technology Fusion

2014 ◽  
Vol 608-609 ◽  
pp. 928-932
Author(s):  
Dong Ya Jin
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Power Distribution ◽  
Spatial Information ◽  
Power Transmission ◽  
Power Grid ◽  
Three Dimensional ◽  
Visual Display ◽  
Decision Makers ◽  
Control Measures

The platform uses three-dimensional data modeling, visual simulation and spatial data storage to make the business of regulation center, operation and maintenance center and marketing and management center of Beijing Power grid implement visualized operation, and load the spatial information data, equipment data and operation data of Beijing power grid into the system platform, and the data is displayed with the form of graphic or image, which not only realizes managing space resource data in real three-dimensional scene, but also make the system operator to know the operation state of the system directly, and makes the control measures more effective. And the paper uses virtual reality technology to establish visual scene of ground to realize integrated visual display of power transformation, power transmission and power distribution, which not only makes the producers, managers and decision makers directly master the situation of production line in power station, but also realizes that producers and managers affiliate decision makers to formulate production plan.

scholarly journals Integrating Transposable Elements in the 3D Genome

2019 ◽  
Author(s):  
Alexandros Bousios ◽  
Hans-Wilhelm Nuetzmann ◽  
Dorothy Buck ◽  
Davide Michieletto
Keyword(s):  
Transposable Elements ◽  
Cell Fate ◽  
Large Scale ◽  
Spatial Information ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Predictive Modelling ◽  
Polymer Physics ◽  
Modelling Framework ◽  
Polymer Folding

Chromosome organisation is increasingly recognised as an essential component of genome regulation, cell fate and cell health. Within the realm of transposable elements (TEs) however, the spatial information of how genomes are folded is still only rarely integrated in experimental studies or accounted for in modelling. Here, we propose a new predictive modelling framework for the study of the integration patterns of TEs based on extensions of widely employed polymer models for genome organisation. Whilst polymer physics is recognised as an important tool to understand the mechanisms of genome folding, we now show that it can also offer orthogonal and generic insights into the integration and distribution profiles (or “topography”) of TEs across organisms. Here, we present polymer physics arguments and molecular dynamics simulations on TEs inserting into heterogeneously flexible polymers and show with a simple model that polymer folding and local flexibility affects TE integration patterns. The preliminary discussion presented herein lay the foundations for a large-scale analysis of TE integration dynamics and topography as a function of the three-dimensional host genome.

scholarly journals K-NEAREST NEIGHBOUR QUERY PERFORMANCE ANALYSES ON A LARGE SCALE TAXI DATASET: POSTGRESQL VS. MONGODB

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1531-1538 ◽  
Author(s):  
İ. B. Coşkun ◽  
S. Sertok ◽  
B. Anbaroğlu
Keyword(s):  
New York ◽  
Spatial Data ◽  
Large Scale ◽  
Information Science ◽  
Spatial Information ◽  
Database Management System ◽  
Nearest Neighbour ◽  
Spatial Accuracy ◽  
K Value ◽  
Spatial Information Science

<p><strong>Abstract.</strong> The increasing volume of transport network data necessitates the use of a DataBase Management System (DBMS) to store, query and analyse data. There are two main types of DBMS: relational and non-relational. Many different DBMS are available on the market but only some of them could handle spatial data. Therefore, determining which DBMS to use for operational purposes is of interest to researchers and analysts working in spatial information science. One of the commonly used spatial queries in GIS is the k-Nearest Neighbour (kNN) of a given point. This paper analyses the performance of the kNN query in PostgreSQL and MongoDB, both being a representative of relational and NoSQL DBMS respectively. Two different metrics have been investigated to determine the performance: i) spatial accuracy and ii) run time. Haversine and Vincenty formulas are used to calculate the distance between the point and the determined neighbours, which are then used to determine the spatial accuracy of the DBMS. Sensitivity analysis have been carried out by varying the k value and the execution times are recorded. The experiments are carried out on New York City’s openly available taxi dataset consisting of millions of taxi pickup and dropoff points. The results indicate that MongoDB outperforms Postgres both in terms of execution time and spatial accuracy regardless the value of k. In order to facilitate reproducibility of the results, the developed software is shared on GitHub.</p>

scholarly journals DESIGN AND IMPLEMENTATION OF CROWDSOURCING BASED CHINA'S NATIONAL PUBLIC GEOSPATIAL INFORMATION COLLECTION SYSTEM

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1621-1624
Author(s):  
H. Zhang ◽  
J. Jiang ◽  
W. Huang ◽  
L. Yang
Keyword(s):  
Data Collection ◽  
Spatial Data ◽  
Information Service ◽  
Geospatial Data ◽  
Process Data ◽  
Geospatial Information ◽  
Service Platform ◽  
The Public ◽  
The Government ◽  
Data Update

<p><strong>Abstract.</strong> As the basis of public geospatial information service, geospatial information data with wide coverage, strong timeliness, rich content and high positioning accuracy is the key infrastructure of geospatial infrastructure for building smart cities and digital China. How to collect and update massive geospatial information resources fast and efficiently has always been the bottleneck for the rapid development of surveying and mapping geospatial information science and technology and industries, and the construction of China's national spatial data infrastructure. At present, the mainstream of geospatial data collecting and updating in China is that the government and enterprise employ professional engineers to for this work, but the problems of professional and complex data processing process, data management level by level, closed and off-line data update mode lead to a relatively slow update rate and poor data actuality, which seriously restricted the applications of geospatial information service in the fields of government decision-making, planning and construction, resource and environment monitoring, and emergency response. The contributions to the geospatial information data from the public community has also been ignored. This paper addresses the issues of low efficiency on data collection and update occurring in China's national geospatial information service platform, proposing a solution for public geospatial data collection and update based on crowdsourcing. The key technologies of data collection, storage, reviews and publication are studied, and the basic technical process of online data update for the government and the public users is designed. In order to verify the effectiveness and practicality of the mode discussed above, a prototype system is developed by expanding China's national geospatial information service platform, which fulfilled data online collection and reviewing, such as POIs, roads, and residential areas. The system can provide new methods and reference ideas for the collection and update of spatial data for the national geospatial information public service platform. It is a useful supplement to the current spatial data collection and update, and has important significances.</p>

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