Method for managing and querying geo-spatial data using a grid-code-array spatial index

Li, Shuang; Pu, Guoliang; Cheng, Chengqi; Chen, Bo

doi:10.1007/s12145-018-0362-6

Cited by 14 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GeoSOT (Geographical coordinate grid Subdivision by One-dimension-integer and Two to n th power) subdivision theory ( Figure 1) proposed by Cheng, Tong, Chen, and Zhai (2016) has offered an efficient and scientific way of dividing the whole Earth seamlessly and overlappingly with multiple scales. Recently, it has been widely used and verified in spatial data management (Lu, Cheng, Jin, & Ma, 2013), urban component management (Zhang, Cheng, & Miao, 2019), massive trajectory data (Qian et al, 2019), remote-sensing image data organization (Wang, Cheng, Wu, Wu, & Teng, 2015), and other GIS applicational scenarios (Li, Pu, Cheng, & Chen, 2019). According to the GeoSOT subdivision theory, subdivision grids cover the complete Earth space without any leaks or overlaps.…”

Section: Geosot Grid Code Methodsmentioning

confidence: 99%

STGI：a spatio-temporal grid index model for marine big data

Wang

Jian

et al. 2020

Big Earth Data

Self Cite

View full text Add to dashboard Cite

Marine big data are characterized by a large amount and complex structures, which bring great challenges to data management and retrieval. Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database, this paper proposes a spatiotemporal grid index model (STGI) for efficient optimized query of marine big data. A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data. Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models. Through theoretical analysis and experimental verification, the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency, such as marine big data.

show abstract

Section: Geosot Grid Code Methodsmentioning

confidence: 99%

STGI：a spatio-temporal grid index model for marine big data

Wang

Jian

et al. 2020

Big Earth Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because of the natural advantages of DGGS, it has been applied to management, analysis, and visualization of spatial data [68][69][70]. The indexing method based on grid encoding from DGGS is the most common application mode for big EO data [71,72]. Besides, simulation-visualization based on DGGS [70] was carried out for BEOD, and a multiresolution digital Earth model has been developed for managing and processing spatial datasets [73].…”

Section: Dggs For Beodmentioning

confidence: 99%

“…The goal of data organization and management is to be able to quickly retrieve and query any content in the dataset [14]. DGGS itself has natural advantages, such as the use of grid coding can not only quickly achieve spatial positioning, but also can easily find child nodes and parent nodes [71,80,81]. Besides, the multilevel of DGGS can be highly consistent with the multiscale of EO data, so that the data at different scales (medium, high, and low resolutions) can be uniformly organized and managed, which provide a good data foundation for multisource data fusion analysis in the later stage.…”

Section: Eo Data Organization and Managementmentioning

confidence: 99%

“…In terms of grid retrieval efficiency and performance, on the one hand, it needs to rely on the advantages of grid coding [90] and on the other hand, it needs to improve the query efficiency of data with the help of cloud computing [40]. Grid coding has been the hotspot of DGGS basic theory research [71,72,91]. According to the coding principle, coding schemes are roughly divided into three categories [20,92]: hierarchical coding operation, filling curve coding operation, and integer coordinate coding operation.…”

Section: Dggs Grid Coding In Cloud Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

show abstract

“…In this research, we referred to geographical coordinate subdividing grid with one-dimension integer coding on a 2 n tree in three dimensions (GeoSOT-3D) theory to construct the basic spatial reference framework. GeoSOT-3D has been widely studied in various fields, including spatial data reintegration [32,33], database indexing [34,35], risk analysis for natural gas pipelines [36], building information modeling (BIM) data visualization [37], remote sensing data management [38], and city component identification [39]. The applications of GeoSOT-3D theory have demonstrated its importance and suitability for highly efficient and accurate spatial data processing and management endeavors.…”

Section: Introductionmentioning

confidence: 99%

Collision Detection for UAVs Based on GeoSOT-3D Grids

Zhai

Tong

Miao

et al. 2019

IJGI

Self Cite

View full text Add to dashboard Cite

The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios.

show abstract

Method for managing and querying geo-spatial data using a grid-code-array spatial index

Cited by 14 publications

References 23 publications

STGI：a spatio-temporal grid index model for marine big data

STGI：a spatio-temporal grid index model for marine big data

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Collision Detection for UAVs Based on GeoSOT-3D Grids

Contact Info

Product

Resources

About