Introduction to Big Data Computing for Geospatial Applications

Li, Zhenlong; Tang, Wenwu; Huang, Qunying; Shook, Eric; Guan, Qingfeng

doi:10.3390/ijgi9080487

Cited by 14 publications

(8 citation statements)

References 23 publications

(41 reference statements)

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“…For example, it can only display the analysis results in two-dimensional flattened graphics, and cannot express more content in combination with spatial information such as latitude and longitude coordinates. For a better overview, we have introduced spatial analysis methods [94]. We mapped the results of CiteSpace analysis to geographic space to discover the hidden relationship between the publication hotspot institutions/authors and geographical coordinates.…”

Section: Discussionmentioning

confidence: 99%

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Ding

Zheng

2021

Land

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Land use optimization (LUO) first considers which types of land use should exist in a certain area, and secondly, how to allocate these land use types to specific land grid units. As an intelligent global optimization search algorithm, the Genetic Algorithm (GA) has been widely used in this field. However, there are no comprehensive reviews concerning the development process for the application of the Genetic Algorithm in land use optimization (GA-LUO). This article used a bibliometric analysis method to explore current state and development trends for GA-LUO from 1154 relevant documents published over the past 25 years from Web of Science. We also displayed a visualization network from the aspects of core authors, research institutions, and highly cited literature. The results show the following: (1) The countries that published the most articles are the United States and China, and the Chinese Academy of Sciences is the research institution that publishes the most articles. (2) The top 10 cited articles focused on describing how to build GA models for multi-objective LUO. (3) According to the number of keywords that appear for the first time in each time period, we divided the process of GA-LUO into four stages: the presentation and improvement of methods stage (1995–2004), the optimization stage (2005–2008), the hybrid application of multiple models stage (2009–2016), and the introduction of the latest method stage (after 2017). Furthermore, future research trends are mainly manifested in integrating together algorithms with GA and deepening existing research results. This review could help researchers know this research domain well and provide effective solutions for land use problems to ensure the sustainable use of land resources.

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Section: Discussionmentioning

confidence: 99%

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Ding

Zheng

2021

Land

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“…The economic, scientific, technical, and political resources required to complete such a mission are immense. The topic of satellite hardware value chains has been studied elsewhere (The European Commission and PwC Advisory France, 2019), as have the technical aspects of services oriented architectures that enable geoscience analysis at scale (Yang et al, 2010;Vescoukis et al, 2012;Li et al, 2015;Li et al, 2020). In this paper we examine the LDE downstream of EROS by following the processing of raw reflectance data from the Landsat sensors into standardized Landsat data collections, Analysis Ready Data (ARD) products, and information products which are used in decision-making, with a focus on the organizational actors that participate in the addition of value to those products along the value chain, all from the actors' perspective.…”

Section: Mapping the Landsat Data Ecosystemmentioning

confidence: 99%

“…Entities in the LDE (referred to as "actors" in this analysis) store, process, analyze, and deliver data, platforms, software, services, and information products which travel through ecosystem nodes to downstream actors. In contrast with other analyses which focus on the technical elements that enable these activities (Yang et al, 2010;Vescoukis et al, 2012;Li et al, 2015;Li et al, 2020), the focus here is on the roles that the different actors play in the LDE. Discrete roles or categories of system actors are not clear-cut, leading to variation in conceptual classifications depending on the case study focus.…”

Section: Landsat Data Ecosystem Actor Rolesmentioning

confidence: 99%

“…This data provider and data user centered approach complements the more vertically integrated technical perspective that is embedded in services oriented architectural (SOA) models and processing workflows for geospatial data broadly, and EO data specifically such as those described by Yang et al (2010); Vescoukis et al (2012); Li et al (2015); Li et al (2020). These models emphasize the technical components (e.g., data capture, storage, processing, analysis, encoding, visualization) and the interaction between these component systems through both standards-based web services (e.g., the Open Geospatial Consortium's web map, web feature, and web coverage services (OGC Standards, 2021)) and custom service interfaces.…”

Section: Introductionmentioning

confidence: 99%

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Landsat Data Ecosystem Case Study: Actor Perceptions of the Use and Value of Landsat

Molder

Schenkein

McConnell

et al. 2022

Front. Environ. Sci.

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It is well-known that Earth observation (EO) data plays a critical role in scientific understanding about the global environment. There is also growing support for the use of EO data to provide context-specific insights, with significant implications for their use in decision support systems. Technological development over recent years, including cloud computing infrastructure, machine learning techniques, and rapid expansion of the velocity, volume, and variety of space-borne data sources, offer huge potential to provide solutions to the myriad environmental problems facing society and the planet. The USGS/NASA Landsat Program, the longest continuously gathered source of land surface data, has played a central role in our understanding of environmental change, particularly for its contribution of longitudinal products that offer greater context for present research and decision support activities. The challenge facing the Landsat and EO data community, however, now lies in moving beyond context-specific knowledge generation to translating such knowledge into tangible value for society. Drawing from an open data ecosystem framework and qualitative social science methods, we map the Landsat data ecosystem (LDE) and the relationships linking multiple actors responsible for processing, indexing, analyzing, synthesizing, and translating raw Landsat data into information that is useful, useable, and used by end users in particular social-environmental contexts. Both the role of Big Data and associated technologies are discussed as they relate to the ultimate use of Landsat-derived information products to guide decision-making, and key data ecosystem characteristics that shape the likelihood of these products’ use are highlighted.

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“…Thanks to the advancement of earth observation and model simulation systems, unprecedented amounts of spatio-temporal data with various resolutions have been accumulated [1,2]. On one hand, these massive amounts of data provide opportunities to investigate complex patterns and knowledge to help decision-making [3]; on the other hand, how to extract patterns from these data becomes a challenging issue [4]. As one of the most important data mining tasks, clustering methods group data elements into clusters by identifying similar ones and separating dissimilar ones, thus helping to extract underlying patterns in the data [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Co-Clusters with Coherent Trends in Geo-Referenced Time Series

2022

IJGI

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Several studies have worked on co-clustering analysis of spatio-temporal data. However, most of them search for co-clusters with similar values and are unable to identify co-clusters with coherent trends, defined as exhibiting similar tendencies in the attributes. In this study, we present the Bregman co-clustering algorithm with minimum sum-squared residue (BCC_MSSR), which uses the residue to quantify coherent trends and enables the identification of co-clusters with coherent trends in geo-referenced time series. Dutch monthly temperatures over 20 years at 28 stations were used as the case study dataset. Station-clusters, month-clusters, and co-clusters in the BCC_MSSR results were showed and compared with co-clusters of similar values. A total of 112 co-clusters with different temperature variations were identified in the Results, and 16 representative co-clusters were illustrated, and seven types of coherent temperature trends were summarized: (1) increasing; (2) decreasing; (3) first increasing and then decreasing; (4) first decreasing and then increasing; (5) first increasing, then decreasing, and finally increasing; (6) first decreasing, then increasing, and finally decreasing; and (7) first decreasing, then increasing, decreasing, and finally increasing. Comparisons with co-clusters of similar values show that BCC_MSSR explored coherent spatio-temporal patterns in regions and certain time periods. However, the selection of the suitable co-clustering methods depends on the objective of specific tasks.

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Introduction to Big Data Computing for Geospatial Applications

Cited by 14 publications

References 23 publications

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

Landsat Data Ecosystem Case Study: Actor Perceptions of the Use and Value of Landsat

Identification of Co-Clusters with Coherent Trends in Geo-Referenced Time Series

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