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Editor's note
Today, we present to you the abstract of the article in Volume 26, Issue 7, 2024. This issue contains 3 columns: "Theory and Methods of Geo-Information Science", "Comprehensive Application of Geospatial Analysis", and "Remote Sensing Science and Application Technology", with a total of 13 articles. Read on! The full text can be downloaded from CNKI or the official website of the journal.
The journal mainly publishes academic papers with geographic system information flow as the research object, geographic information cognitive theory, geographic spatio-temporal big data mining, geospatial intelligent analysis, earth information map, remote sensing information extraction, virtual geographic environment, geospatial comprehensive analysis, etc., as well as related comments and newsletters, focusing on the report of the innovative achievements of geoinformation science theory and method. Researchers are welcome to pay attention to and contribute to their contributions!
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Journal of Geo-information Science
Table of Contents for this issue
Theories and Methods of Geo-Information Science
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Construction of multi-granularity spatio-temporal data model in cyberspace and its adaptation to bivariate uncertainty expression symbols
Shuai Zhao, Zheng Zhang, Yixin Hua*, Wenshuang Zhao, Xinke Zhao, Minjie Chen, Xiaoyu Ji
Citation Format:
赵帅,张政,华一新,等.网络空间多粒度时空数据模型构建及其适配双变量不确定性表达符号研究[J].地球信息科学学报,2024,26(7):1577-1593. [ Zhao S, Zhang Z, Hua Y X, et al. Research on the construction of data model of multi-granularity spatio-temporal object and its adaptive double variables uncertainty representation symbols in cyberspace[J]. Journal of Geo-information Science, 2024,26(7):1577-1593. ] DOI:10.12082/dqxxkx.2024.230733
Abstract:Uncertainty visualization is the key and difficult point in the field of cyberspace map visualization, and reasonable uncertainty symbol design is very important for the rapid reading and mining of cyberspace map information and accurate analysis and decision-making. In this paper, we propose a bivariate uncertainty symbol expression method based on multi-granularity spatiotemporal object data model to solve the problem that the variable symbols in the cyberspace node link graph cannot reflect the inconsistent expression of node and edge uncertainty in a timely and efficient manner. Taking geographic social network as an example, firstly, the method of multi-granularity spatiotemporal data model modeling is used to analyze the content and process of cyberspace object modeling, and the cyberspace entity object class is designed and the corresponding spatiotemporal objects are created. On this basis, combined with the problems existing in the representation of traditional symbols in the link graph of cyberspace nodes, the principle and model of uncertainty expression in cyberspace are analyzed, and the corresponding visual entropy symbols are made. Finally, control experiments were carried out, and statistical methods were used to test the results of the symbolic experiments. The results show that the object-based modeling method is conducive to the multi-granular, full-type, and multi-dimensional dynamic expression of cyberspace, and can vividly and intuitively express the development and change of cyberspace in a comprehensive and multi-dimensional way through visualization and interaction technology. The newly designed visual entropy symbol has a good effect on the expression of bivariate uncertainty difference in cyberspace, which is helpful to obtain uncertainty information in cyberspace in a timely, efficient and accurate manner. This research can provide some reference for the development of the field of cyberspace map visualization.
Keywords: cyberspace; multi-granularity spatiotemporal data model; visual entropy symbols; bivariate; Uncertain; Visualization of cyberspace maps
A framework for the representation of cyberspace entity modeling
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Outlier distribution detection of points of interest based on the scale of local agglomeration features and its interpretability analysis
Peng Wu*, Hasbargen, Fuying Qin
Citation Format:
吴鹏,哈斯巴根,秦福莹. 基于局部集聚特征尺度判定的兴趣点离群分布探测及其可解释性分析[J].地球信息科学学报,2024,26(7):1594-1610. [ Wu P, Hasibagen, Qin F Y. POI outliers detection based on local aggregation characteristic scale determination and its interpretability analysis [J]. Journal of Geo-information Science, 2024,26(7):1594-1610. ] DOI:10.12082/dqxxkx.2024.240039
Abstract:Point of interest (POI) plays an increasingly prominent role in deepening the understanding of human activities and environmental characteristics in geographic space, and the detection of outliers that are significantly different from the surrounding environment from large-scale spatial data is an important research direction to enhance the cognition of human-environment system. The existing outlier mining methods have the shortcomings of insufficient expression and quantification of local spatial distribution features when applied to POI, and their effectiveness needs to be further discussed. In view of this, this paper proposes a method for detecting the outlier distribution of points of interest based on the local agglomeration scale. Firstly, the spatial adjacency relationship of POI was constructed with the help of Delaunay triangulation network, and the local agglomeration characteristic scale of the point group was judged based on the cross-K nearest neighbor distance and multi-scale characteristic parameters. Then, the points and their adjacent edge sets that meet the scale constraints are extracted. Finally, the edge length constraint index was used to eliminate the local long edges that did not meet the conditions and integrate the outliers to complete the POI outlier detection. According to the experimental results of the comparison of actual data, it can be concluded that the proposed method has good generalization ability, and can effectively and robustly detect outliers without destroying the inherent distribution characteristics of POI. In this paper, the interpretability analysis of the outlier detection results is further carried out, and the causes of the outlier distribution of points of interest are closely related to the factors such as type proportion, spatial layout, land area and public awareness level. This study provides new methods and research perspectives for comprehensively grasping urban development trends, optimizing resource allocation, improving urban sustainability and quality of human settlements.
Keywords: points of interest; spatial outliers; Delaunay triangulation; cross-K proximity distance; agglomeration characteristic scale; edge length constraint index; interpretability analysis; Public awareness
The basic framework of the methodology in this article
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Coupling mixed cell decomposition and land cover change deduction for mixed cell simulation
Cao Wei, Xiao Yao, Liang Xun*, Guan Qingfeng
Citation Format:
曹玮,肖瑶,梁迅,等.耦合混合像元分解和混合元胞模拟的土地覆盖变化推演[J].地球信息科学学报,2024,26(7):1611-1628. [ Cao W, Xiao Y, Liang X, et al. Coupling mixed pixel decomposition and mixed-cell simulation for land cover change deduction[J]. Journal of Geo-information Science, 2024,26(7):1611-1628. ] DOI:10.12082/dqxxkx.2024.230571
Abstract:Cellular automata (CA) is an important part of land use/land cover change simulation