GIScience research challenges for realizing discrete global grid systems as a Digital Earth

Hojati, Majid; Robertson, Colin; Roberts, Steven A.; Chaudhuri, Chiranji

doi:10.1080/20964471.2021.2012912

Cited by 26 publications

(6 citation statements)

References 45 publications

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“…DGGS grid, similar to quad-tree, provides the same index value per each grid cell in the space. It also provides methods to aggregate data on multi-resolution levels [78][79][80] and also provides built-in data locality and directionality of space [81]. For instance, in sharing police department information over time, the reports can be censored using distributed k-anonymity methods on the P2P networks (e.g., [82]) if the DGGS system is used as feature data storage.…”

Section: Discussionmentioning

confidence: 99%

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks

Hojati,

Roberts,

Robertson

2024

MCA

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The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial architectures have changed from clusters to cloud architectures and more parallel and distributed processing platforms to be able to tackle these challenges. Peer-to-peer (P2P) systems as a backbone of distributed systems have been established in several application areas such as web3, blockchains, and crypto-currencies. Unlike centralized systems, data storage in P2P networks is distributed across network nodes, providing scalability and no single point of failure. However, managing and processing queries on these networks has always been challenging. In this work, we propose a spatio-temporal indexing data structure, DSTree. DSTree does not require additional Distributed Hash Trees (DHTs) to perform multi-dimensional range queries. Inserting a piece of new geographic information updates only a portion of the tree structure and does not impact the entire graph of the data. For example, for time-series data, such as storing sensor data, the DSTree performs around 40% faster in spatio-temporal queries for small and medium datasets. Despite the advantages of our proposed framework, challenges such as 20% slower insertion speed or semantic query capabilities remain. We conclude that more significant research effort from GIScience and related fields in developing decentralized applications is needed. The need for the standardization of different geographic information when sharing data on the IPFS network is one of the requirements.

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Section: Discussionmentioning

confidence: 99%

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks

Hojati,

Roberts,

Robertson

2024

MCA

Self Cite

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“…Due to the multiplicity of available spatial formats, files are often scattered on different servers, namely, file, web, email, and document management servers [9]. Spatial "data silos" housing file data across multiple formats and the associated physical and virtual mediums reduce their utility and availability for critical decision-making scenarios [10][11][12]. These data silos also increase access points to sensitive or proprietary information.…”

Section: Introductionmentioning

confidence: 99%

A Usable Encryption Solution for File-Based Geospatial Data within a Database File System

Sharma,

Govorov,

Martin

2024

JCP

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Developing a security solution for spatial files within today’s enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in “data silos” of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution’s usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS.

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“…A DGGS is a special case of a spatial reference system that uses tessellations rather than lattice points to encode location. After decades of development, discrete global grid systems have been applied to the organization, management, analysis, visualization, and other aspects of spatiotemporal data [3] as a digital Earth [4]. A specification for describing DGGSs was incorporated into OGC in 2014 and into ISO 19170-1:2021 in 2001.…”

Section: Introductionmentioning

confidence: 99%

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

Yao

et al. 2023

IJGI

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The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology.

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GIScience research challenges for realizing discrete global grid systems as a Digital Earth

Cited by 26 publications

References 45 publications

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks

DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks

A Usable Encryption Solution for File-Based Geospatial Data within a Database File System

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

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