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    Spatiotemporal Knowledge Graph: Advances and Perspectives
    LU Feng, ZHU Yunqiang, ZHANG Xueying
    Journal of Geo-information Science    2023, 25 (6): 1091-1105.   DOI: 10.12082/dqxxkx.2023.230154
    Abstract1892)   HTML151)    PDF (3611KB)(999)      

    The continuous generalization of geographic information poses a huge challenge to the classic geographic information analysis modes. Networked knowledge services will gradually become a new mode for geographic information applications, facilitating to transform the form of geographic computing into social computing. Geographic knowledge services need to connect people, institutions, natural environments, geographical entities, geographical units and social events, so as to promote knowledge assisted data intelligence and computational intelligence. Facing the urgent need for spatiotemporal knowledge acquisition, formal expression and analysis, this paper firstly introduces the concepts and characteristics of spatiotemporal knowledge graph. The spatiotemporal knowledge graph is a directed graph composed of geographic spatiotemporal distribution or geo-locational metaphors of knowledge that is a knowledge graph centered on spatiotemporal distribution characteristics. Secondly we proposes a research framework for spatiotemporal knowledge graph. The framework includes various levels from multimodal spatiotemporal big data to spatiotemporal knowledge services that contain ubiquitous spatiotemporal big data layer, spatiotemporal knowledge acquisition technique layer, spatiotemporal knowledge management layer, spatiotemporal knowledge graph layer, software/tools layer, and industrial application layer. Thirdly this paper introduces relevant research progress from text implied geographic information retrieval, heterogeneous geographic semantic web alignment, spatiotemporal knowledge formalization and representation learning. Combined with application practice, we then enumerate the construction and application approaches of domain oriented spatiotemporal knowledge graph. Finally, it discusses the key scientific issues and technical bottlenecks currently faced in the research of spatiotemporal knowledge graph. It is argued that in the era of large models, constructing explicit spatiotemporal knowledge graph and conducting knowledge reasoning to meet domain needs is still the only way for spatiotemporal knowledge services.

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    Research on Chinese Fine-grained Geographic Entity Recognition Model based on Joint Lexicon Enhancement
    LI Fadong, WANG Haiqi, KONG Haoran, LIU Feng, WANG Zhihai, WANG Qiong, XU Jianbo, SHAN Yufei, ZHOU Xiaoyu, YAN Feng
    Journal of Geo-information Science    2023, 25 (6): 1106-1120.   DOI: 10.12082/dqxxkx.2023.220464
    Abstract800)   HTML24)    PDF (6707KB)(141)      

    Named Entity Recognition (NER) is the basis of many researches in natural language processing. NER can be defined as a classification task. The aim of NER is to locate named entities from unstructured texts and classify them into different predefined categories. Compared with English, Chinese have the features of flexible formation and no exact boundaries. Because of the features of Chinese and the lack of high-quality Chinese named entity datasets, the recognition of Chinese named entities is more difficult than English named entities. Fine-grained entities are subdivisions of coarse-grained entities. The recognition of Chinese fine-grained named entities especially Chinese fine-grained geographic entities is even more difficult than that of Chinese named entities. It is a great hardship for Chinese geographic entity recognition to take both accuracy and recall rate into account. Therefore, improving the performance of Chinese fine-grained geographic entities recognition is quite necessary for us. In this paper we proposed two Chinese fine-grained geographic entity recognition models. These two models are based on joint lexical enhancement. Firstly, we injected the vocabulary into the experimental models. The vocabulary was considered as the 'knowledge' in the models. Then we explored the appropriate fine-grained named entity recognition method based on vocabulary enhancement. And we found two models, BERT-FLAT and LEBERT, that were suitable for fine-grained named entity recognition. Secondly, to further improve the performance of these two models in fine-grained geographical named entities recognition, we improved the above two models with lexical enhancement function in three aspects: pre-training model, adversarial training, and stochastic weight averaging. with these improvements, we developed two joint lexical enhancement models: RoBERTa-wwm-FLAT and LE-RoBERTta-wwm. Finally, we conducted an ablation experiment using these two joint lexical enhancement models. We explored the impacts of different improvement strategies on geographic entity recognition. The experiments based on the CLUENER dataset and one microblog dataset show that, firstly, compared with the models without lexical enhancement function, the models with lexical enhancement function have better performance on fine-grained named entities recognition, and the F1-score was improved by about 10%; Secondly, with the improvements of pre-training model, adversarial training, and stochastic weight averaging, the F1-score of the fine-grained geographic entity recognition task was improved by 0.36%~2.35%; Thirdly, compared with adversarial training and stochastic weight averaging, the pre-trained model had the greatest impact on the recognition accuracy of geographic entities.

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    Multi-level Knowledge Modeling Method of Battlefield Environment based on Temporal Knowledge Hypergraph Model
    JIANG Bingchuan, HUANG Zihang, REN Yan, SUN Yong, FAN Aimin
    Journal of Geo-information Science    2023, 25 (6): 1148-1163.   DOI: 10.12082/dqxxkx.2023.220967
    Abstract704)   HTML45)    PDF (23706KB)(277)      

    The new combat style places new requirements for battlefield environment service support. The intelligent service of battlefield environment urgently needs to improve knowledge based on the global multidimensional battlefield environment data. In view of the knowledge modeling problem of intelligent cognition of battlefield environment, this paper puts forward the classification method of battlefield environment knowledge and considers the battlefield environment knowledge graph as a new form of battlefield environment knowledge representation under the context of big data and artificial intelligence. To solve the fragmentation problem of triplet knowledge representation, a temporal hypergraph representation model of battlefield environment is constructed, a multi-level unified graph model combining entity knowledge, event knowledge, influence process knowledge, and service decision-making knowledge is realized, and all kinds of knowledge are represented as a unified knowledge hypergraph network with spatiotemporal and scene characteristics. Finally, the experimental verification is carried out based on the data of map, event, impact process, and combat impact effectiveness. The hypergraph network realizes the correlation of various battlefield environment knowledge from the semantic level, which can provide support for the further realization of intelligent reasoning and service decision-making based on hypergraph.

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    New Paradigm of Geographic Information Systems Research
    HUA Yixin, ZHAO Xinke, ZHANG Jiangshui
    Journal of Geo-information Science    2023, 25 (1): 15-24.   DOI: 10.12082/dqxxkx.2023.220300
    Abstract632)   HTML46)    PDF (2393KB)(197)      

    In the era of big data, the spatio-temporal category, information content, and application scenarios of Geographic Information Systems(GIS) have expanded unprecedentedly. GIS needs to transform from the passive adaptation mode of exception processing into an active kernel-supported mode, forming a new generation of spatio-temporal information system. Given that the essence of GIS is an information system with cartographic data model as the core, this paper summarizes the research paradigm of GIS from three aspects: research objects, basic principles, and technical methods, and analyzes the new requirements of spatio-temporal information expansion on the GIS research paradigm. Secondly, by analyzing the cognitive model of Pan-Spatial Information System (PSIS) and the multi-granularity spatio-temporal object data model, the theoretical and technical routes of the PSIS based on spatio-temporal entities are concluded, and its practice and application in many fields are summarized. Then, it systematically analyzes the specific extension mode of PSIS in GIS research object, basic principles, and technical methods, respectively, and proposes the PSIS research paradigm. Finally, this paper summarizes the basic content of the PSIS research paradigm, compares the core content with the GIS research paradigm, and looks forward to the impacts and changes that the advanced research paradigm of GIS would bring.

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    Formal Representation and Reasoning Mechanism for Vague Spatial Location Description based on Supervaluation
    ZHANG Xueying, YE Peng, ZHANG Huifeng
    Journal of Geo-information Science    2023, 25 (6): 1135-1147.   DOI: 10.12082/dqxxkx.2023.230025
    Abstract614)   HTML25)    PDF (13250KB)(147)      

    Location description is the natural language expression of human spatial cognition. Since natural language is the primary and basic means of information transmission in human society, location description is an important medium for transmitting spatial location information in human communication. Spatial positioning based on spatial location description is the key to intelligent transformation of location-based services in the era of big data. To solve the problem that the vagueness of location description in different contexts is significantly different and results in difficulty in positioning, this paper proposes a representation method and reasoning mechanism for vague location description. Firstly, by combing the law of human spatial cognition, the types of elements concerned in the description of natural language are clarified. Based on the analysis of the sources of vagueness, a formal representation of vague location description is constructed. Different from the traditional spatial information modeling which focuses on spatial relationship, the formal representation proposed in this paper establishes the vagueness relation and influence among different information factors by the strategy of multi-factors representation. The formal representation also enhances the semantic analysis ability for the vagueness of location description. Secondly, based on supervaluation theory, the reasoning mechanism of vague location description is proposed from three aspects: spatial object, distance relation, and direction relation. Considering the context semantics of spatial location description, the threshold of observation value is used to carry out spatial reasoning. By being super-valued to different contexts, the reasoning results in different situations are obtained. The aim of the reasoning mechanism is to establish the mapping relationship between vague location description and real spatial location. Thirdly, a Question-Answering (Q&A) system is designed to collect contexts of location description, and a case study on the method is conducted. In the case study, a group of users' viewpoints from Q&A on spatial cognition are transformed into the spatial scope in the real world. These spatial scopes can establish the relationship between qualitative spatial concepts and quantitative spatial data, so as to realize the representation of vague location description in GIS. The results show that the proposed method in this paper can adjust the granularity of formal representation of location description in time according to actual application scenarios, and the spatial reasoning results fit intuitive cognition. In the future, knowledge graphs will be introduced to further improve the semantic reasoning ability and positioning accuracy for vague location description.

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    Hyperparameter Selection for Urban Metro Travel Knowledge Graph Embedding
    LUO Qiuyu, YUE Yang, GU Yanyan
    Journal of Geo-information Science    2023, 25 (6): 1164-1175.   DOI: 10.12082/dqxxkx.2023.230054
    Abstract559)   HTML33)    PDF (15380KB)(153)      

    Knowledge graphs are an important data infrastructure in AI technologies and applications, and have become a hot research topic in geosciences. The size and topological features in geographic knowledge graphs are usually different from universal knowledge graphs, which are not typical small-world networks. However, existing studies often use the default network search depth when learning geographic knowledge graph representations, and its rationality needs further demonstration. For this purpose, this paper constructs a metro travel knowledge graph based on the topological structure features of metro line network, combined with passenger flow data, POI (Point of Interest) data and built environment data, etc.; then GraphSAGE model is used to learn node multidimensional feature embedding and combine POI data for semantic recognition of station classification results to verify the suitable network search depth for metro travel knowledge graph. The results showed that, compared to the default 2 layers search depth, the node embedding features of this metro travel knowledge graph work optimally when the search depth is 3 layers. This study shows that the hyperparameter selection of the geographic knowledge graph representation is supposed to take into account the geographic features, and it is important to avoid the use of results from fields such as computer science that have not been distinguished. When the search depth is 3 layers, the metro station classification results are also more reasonable and explanatory, which can provide a basis for station planning and passenger flow prediction using knowledge graph and AI methods.

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    Terrain Rebuilding Method based on Open Source Data and Conditional Generative Adversarial Networks
    CHEN Kai, LEI Shaohua, DAI Wen, WANG Chun, LIU Aili, LI Min
    Journal of Geo-information Science    2023, 25 (2): 252-264.   DOI: 10.12082/dqxxkx.2023.220701
    Abstract559)   HTML13)    PDF (23924KB)(91)      

    How to use a small number of topographic features to restore the topography has been a difficult problem in the field of geology. In this paper, we extract topographic features from open source datasets, and construct Conditional Generative Adversarial Networks (CGAN) for DEM generation using topographic features as constraints, a comparative experiment was designed based on the combination of open-source DEM, open-source DEM and remote sensing image, as well as the generation of DEM by extracting topographic features from the high-precision DEM with a resolution of 5 m, the results were compared and evaluated by visual effect, correlation analysis and topographic factors. The results show that: (1) in the visual effect, the DEM generated by three different methods are very close to the original DEM with a resolution of 5m, which is much better than the traditional interpolation method, (2) the correlation between DEM generated by three different methods and the original DEM with a resolution of 5m is more than 0.75, and the result of reconstruction based on dem with a resolution of 5 m extracted from open source and remote sensing image with a resolution of 1m is closest to that of the original DEM with a resolution of 5m, the correlation between DEM and original 5m DEM can reach more than 0.85. (3) in the aspect of terrain factor, based on dem with a resolution of 5 m and remote sensing image with a resolution of 1m, the distribution trend of slope and aspect of reconstructed DEM is most consistent with the original DEM with a resolution of 5 m. This paper provides a new idea for high-precision DEM modeling. In the areas where high-precision DEM is difficult to obtain, high-precision terrain modeling can be carried out by using open source data sets and Conditional Generative Adversarial Networks, so as to conduct geoscience analysis and geographical simulation.

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    A Quality Assessment Framework for Implicit Geographic Information from Web Texts
    HUANG Zongcai, LU Feng, QIU Peiyuan, PENG Peng
    Journal of Geo-information Science    2023, 25 (6): 1121-1134.   DOI: 10.12082/dqxxkx.2023.220617
    Abstract541)   HTML24)    PDF (6017KB)(133)      

    Web texts are an important data source for constructing and completing a large-scale knowledge graph that contains a great deal of ubiquitous geographic information. However, the extensive sources, casual expression, and dynamic nature of web texts, as well as the varied quality of implicit geo-information bring great challenges such as multi-level evaluation objects, unclear quality dimensions, diversified evaluation indicators, difficult access to deep-seated indicators, and diversified evaluation methods in the process of geographic information quality assessment. Therefore, a Quality Assessment Framework for implicit Geographic Information from Web Texts (QAF-GIWT) is proposed in this study. The QAF-GIWT is oriented to the process of acquiring geographic information from web texts and defines three levels of quality evaluation objects, i.e., data source level, data item level, and dataset level. The data source level contains websites and web pages, the data item level includes the triplet-formed information extracted from the webpage, and the dataset level is the information aggregated into a Geographic Knowledge Graph (GeoKG). The QAF-GIWT defines four quality dimensions including relevance, novelty, reliability, and integrity, and proposes the corresponding quantitative evaluation indicators for different level evaluation objects including Cell Geographic Semantic Ratio (CGSR), Geographic Semantic Ratio (GSR), Average Geographic Information Ratio (AGIR), Geographic Information Ratio(GIR), Event Time Length, Triplet Existence, Publish Time, Time Validation, Domain Name Time Length, Update Frequency, Average Freshness, Comprehensive Ranking, Category Ranking, Daily Page Visit, Daily User Visit, User Attention, Picture Number, Word Number, Geographic Entities Ratio (GER), Window's Geo-Information Ratio (GIWR), Triplet Missing Rate, Event Information Missing Rate, Relation Missing Rate, Attribute Missing Rate, Location Missing Rate, Relation Redundancy, Attribute Redundancy, etc. It systematically summarizes the characteristics and applicability of the indicator calculation, indicator synthesis, and quality prediction methods involved in the quality evaluation process. Among them, with the help of natural language processing technology and corresponding quality indicator calculation methods, quality indicators are newly constructed from the deep mining of the web texts including CGSR, GSR, AVGIR, GIR, GIWR, GER, etc. In our experiment, the QAF-GIWT framework was designed to adapt to the characteristics of various types of websites e.g., Mafengwo. Aiming at the comprehensive evaluation of multi-level quality indicators, the analytic hierarchy process was used for comprehensive reliability evaluation. Our experiment verified the effectiveness of the QAF-GIWT framework. The QAF-GIWT provides a systematic scheme including quality dimensions, quality indicators, and quality assessment methods for the quality evaluation of geographic information extracted from massive, heterogeneous, and dynamic web texts. The proposed QAF-GIWT can assist in the screening of data sources and filtering of acquired information, greatly reducing the complexity of information acquisition and the redundancy of data storage, and assisting the quality control process of the acquisition of geographic information from web texts.

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    Research and Practice on the Framework for the Construction, Sharing, and Application of Large-scale Geoscience Knowledge Graphs
    ZHU Yunqiang, SUN Kai, HU Xiumian, LV Hairong, WANG Xinbing, YANG Jie, WANG Shu, LI Weirong, SONG Jia, SU Na, MU Xinglin
    Journal of Geo-information Science    2023, 25 (6): 1215-1227.   DOI: 10.12082/dqxxkx.2023.210696
    Abstract471)   HTML45)    PDF (6887KB)(344)      

    Geoscience Knowledge Graph (GKG) has strong capabilities of knowledge representation and semantic reasoning, thereby becoming a required infrastructure for the development of geoscience big data and geoscience artificial intelligence. However, existing studies on GKG were mainly conducted under the experimental scenarios. Because of a lack of research on the general framework of construction methods, sharing, and application of large-scale GKG for practical applications, it has not been used in practical applications in the geoscience field. For this reason, towards the needs of research and applications of geoscience big data and artificial intelligence for GKG, this paper first studied the construction techniques of large-scale GKG. Then, a general framework for covering the lifecycle of GKG including its construction, sharing, and application was proposed. Taking the big science program “Deep-Time Digital Earth (DDE)” as an example, the practice of developing GKG platform towards the practical application of DDE was carried out. Using this platform, this paper realized the construction of DDE large-scale GKG, the open sharing and application of built GKG, proving that the proposed framework can effectively support the construction, sharing, and application of large-scale GKG. This paper plays an important role in promoting the realization of the practical application value of GKG.

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    Review and Prospect: Management, Multi-Scale Transformation and Representation of Geospatial Data
    WANG Di, QIAN Haizhong, ZHAO Yuzhe
    Journal of Geo-information Science    2022, 24 (12): 2265-2281.   DOI: 10.12082/dqxxkx.2022.220163
    Abstract452)   HTML31)    PDF (4146KB)(165)      

    Multi-scale representation is one of the important research contents of geospatial data. This paper summarizes the research status of multi-scale representation of geospatial data from three aspects: geospatial data management, geospatial data scale transformation, and multi-scale representation of the map, and makes a systematic analysis and prospect of current research results. The main conclusions are as follows: ① In terms of multi-scale database and multi-scale spatial index of geospatial data management, three kinds of multi-scale database can provide better data support for multi-scale representation methods, and the hierarchical multi-scale index is the mainstream construction structure for the multi-scale database. However, at present, multi-scale database and multi-scale spatial index still have limited integration and matching ability of data at different levels, and the real-time consistency adjustment ability of data at different scales is also insufficient; ② In terms of the multi-scale transformation of geospatial data, automatic map generalization can be well combined with artificial intelligence technology. But due to the limitation of knowledge acquisition, there is still a long way to achieve automatic map generalization. The relevant achievements of intelligent automatic generalization research are mainly used to assist decision-making now, and the autonomous learning of comprehensive knowledge needs further research. Currently, most of the research is based on a discrete scale transformation model, which is incapable of continuous scale transformation. And due to the lack of a strong quality control mechanism, the results of automatic scaling have great uncertainty; ③ In terms of multi-scale representation of the map, map data types are multi-source, diverse, and flexible to use, and the multi-scale display is highly complex. Currently, the phenomena of hidden geographic information in map visualization need to be further explored. Finally, the future prospect of research on geospatial data presentation is proposed from the aspects of intelligent automatic generalization method, continuous multi-scale representation model, deep learning and cartographic synthesis, and multi-scale representation in the "new" era.

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    Topographic Change Detection that Considers the Spatial Autocorrelation of DEM Errors
    DAI Wen, CHEN Kai, WANG Chun, LI Min, TAO Yu
    Journal of Geo-information Science    2022, 24 (12): 2297-2308.   DOI: 10.12082/dqxxkx.2022.220209
    Abstract409)   HTML9)    PDF (17640KB)(70)      

    Traditional topographic change detection methods often ignore the spatial autocorrelation of DEM errors. To solve this problem, a topographic change detection method that considers the spatial autocorrelation of DEM errors is proposed in this paper. Firstly, the DEM of Difference (DoD) is obtained from two original DEMs, and the spatial distribution of DEM errors is evaluated by the Monte Carlo method. Secondly, based on spatially distributed DEM errors, DoD errors are calculated by error propagation and their spatial autocorrelation degree is analyzed using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial autocorrelation analysis and significance detection. The results in four small catchments show that the elevation errors of UAV-photogrammetry DEM are spatially autocorrelated, which can be simulated by the Monte Carlo method. The use of spatially distributed error instead of RMSE for topographic change detection effectively reduces the sensitivity of the detection results to the significance threshold. When the significance threshold is increased from 68% to 95%, the loss of observations using the spatially distributed error is 5.39%~6.75% lower than that using the RMSE. The proposed method can be effectively used in the fields of surface deformation monitoring, erosion monitoring, sediment transport assessment, and so on.

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    M2T: A Framework of Spatial Scene Description Text Generation based on Multi-source Knowledge Graph Fusion
    CHEN Huixuan, GUO Danhuai, GE Shiyin, WANG Jing, WANG Yangang, CHEN Feng, YANG Weishi
    Journal of Geo-information Science    2023, 25 (6): 1176-1185.   DOI: 10.12082/dqxxkx.2023.230034
    Abstract404)   HTML27)    PDF (8037KB)(189)      

    Natural language is an effective tool for humans to describe things, with diversity and ease of dissemination, and can contain human spatial cognitive results. How to use natural language to describe geographic spatial scenes has always been an important research direction in spatial cognition and geographic information science, providing important application values in personalized unmanned tour guides, blind navigation, virtual space scene interpretation, and so on. The essence of natural language description of geographic spatial scenes is the process of transforming the two-dimensional vector of geographic space into a one-dimensional vector in word space. Traditional models perform well in handling spatial relationships, but are somewhat inadequate in natural language description: (1) spatial relationship description models are one-way descriptions of the environment by humans, without considering the impact of the environment on the description; (2) spatial scenes emphasize traversal-based descriptions of spatial relationships, where each set of spatial relationships is equally weighted, which is inconsistent with the varying attention paid by humans to geographic entities and spatial relationships in the environment; (3) the spatial relationship calculation of traditional models is a static description of a single scene, which is difficult to meet the requirement of dynamic description of continuous scenes in practical applications; (4) the natural language style of traditional models is mechanical, lacking necessary knowledge support. This article proposes a spatial scene natural language generation framework Map2Text (M2T) that integrates multiple knowledge graphs. The framework establishes knowledge graphs for spatial relationships, language generation style, and spatial attention, respectively, and realizes the fusion of multiple knowledge graphs and the generation of natural language descriptions of spatial scenes within a unified framework. The spatial scene description knowledge graph solves the pruning problem of traversing spatial relationships, and establishes the relationship between spatial scenes by building a spatial relationship graph, supporting continuous expression of spatial scenes; the natural language style knowledge graph establishes the relationship between spatial expression and language style, achieving diversified language styles that are appropriate for spatial natural language expression; the spatial attention knowledge graph captures the nuances of natural language spatial expression by establishing an attention matrix based on the interaction state between the subject and object of the spatial scene. An experimental prototype system designed based on the Beijing Forbidden City demonstrates that the system-generated results are close to human travel notes, with more complete content coverage and more diverse styles, verifying the effectiveness of the M2T framework and demonstrating the potential value of natural language description of spatial scenes.

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    Analyzing Spatial-Temporal Pattern and Climate Factors of Blue-Green Space in Urban Built-Up Areas in Prefecture-level Cities in China
    ZHANG Xinyue, GAO Xiaolu, CHAI Qi, SONG Dunjiang
    Journal of Geo-information Science    2023, 25 (1): 190-207.   DOI: 10.12082/dqxxkx.2023.220261
    Abstract380)   HTML20)    PDF (34716KB)(117)      

    Blue-green space plays a prominent role in urban ecological security. This study built a blue-green database of 272 prefecture-level urban built-up areas in China using NDVI and MNDWI in 2005, 2010, 2015, and 2020 based on Google Earth Engine (GEE). Combining with the coverage rate, 300-meter service coverage rate, the fractal index distribution, and the landscape division index, the spatiotemporal pattern of the blue-green space and its climate factors were examined. The results show that: (1) The blue-green space in urban built-up areas in prefecture-level cities presented an overall pattern of “higher coverage in south than that in north”. While the south showed a pattern of “higher in west than east”, and the north had a pattern of “higher in east than west”. Particularly, the Bohai Rim area was marked as a basin of low coverage. The temporal trend of overall blue-green space was increasing except for a few cities in Central China; (2) In terms of different zones, the highest coverage rate (> 65%) of blue-green space in urban built-up areas occurred in Southwest China where the landscape division index was the lowest (< 0.60), and the coverage rate of Northwest China varied greatly. The North China indicated the lowest coverage (10%~30%) of blue-green space and a highest landscape division index (~0.98); (3) Based on the Multi-scale Geographically Weighted Regression (MGWR), the R-square value and the adjusted R-square value were 0.85 and 0.83, respectively. The impact of precipitation on the blue-green space coverage in urban built-up areas was significant and positive, while the temperature had negative impact on blue-green space. The impacts of climate factors were mostly equivalent to human activities but were stronger in certain periods.

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    A Land Use Change Simulation Model: Coupling of Evolutionary Algorithm and FLUS Model
    YU Qinping, WU Zhenhua, WANG Yabei
    Journal of Geo-information Science    2023, 25 (3): 510-528.   DOI: 10.12082/dqxxkx.2023.220637
    Abstract375)   HTML12)    PDF (35210KB)(131)      

    It is of great significance to study how to set parameters of land use change simulation models more scientifically and objectively, in order to avoid the problem of poor simulation caused by improper parameters setting in a complex model. In this paper, the EA-FLUS model with parameter optimization function was constructed by coupling Evolutionary Algorithm (EA) and FLUS model. This model first optimized the parameters of the artificial neural network model in the FLUS model through evolutionary strategy to improve the prediction accuracy of the probability distribution of each land use type. On this basis, combined with geospatial partition, the parameters of the cellular automaton model in the FLUS model were adjusted by using the combination of elitist genetic algorithm and evolutionary strategy to improve the simulation accuracy. In the empirical study phase, taking Guilin as the study area, this paper analyzed the improvement of EA-FLUS model by partition simulation of land use change. In addition, the natural development scenario, cultivated land protection scenario, and ecological priority scenario were set up to simulate the land use change in Guilin from 2020 to 2030. The results show that: (1) Compared with the parameters setting based on experience and historical characteristics of land use change, the parameters optimization result using evolutionary algorithms was closer to the policy orientation in the study area, and better reflected the diversified development trends of various land use types in different geospatial partition; (2) Compared with the FLUS model, the EA-FLUS model had more advantages in land use change simulation with geospatial partition. The overall accuracy, Kappa coefficient, and FoM coefficient of the simulation result were increased by 0.56%, 0.011, and 0.009, respectively; (3) The construction land and cultivated land in Guilin showed a strong expansion trend, but the forested land showed a shrinking trend. Further strengthening the protection of ecological space would help to slow down the expansion of construction land and cultivated land. The research results not only enrich the existing land use change simulation techniques and methods, but also provide a certain theoretical basis and scientific basis for urban planning and sustainability research.

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    Spatial Expansion and Succession Characteristics of Urban Function in Kaifeng City based on POI Big Data
    ZHANG Huixin, ZHANG Lijun, QIN Yaochen, WANG Jingfan, DUAN Jieran, TIAN Mengnan
    Journal of Geo-information Science    2023, 25 (3): 560-672.   DOI: 10.12082/dqxxkx.2023.220434
    Abstract330)   HTML20)    PDF (18703KB)(202)      

    The social sensing data such as POI provides new insights into fine-scale urban spatial structure study. However, the relationship between POI data and traditional remote sensing data needs to be further explored. Moreover, due to limited long-term POI data, the study on spatial expansion and succession characteristics of urban functions is still relatively rare. Therefore, this article took the main urban area of Kaifeng City as an example and used POI data between 2005 and 2020 to analyze the succession characteristics of urban functions and composite function changes. An analytical framework on spatial agglomeration and diffusion of urban functions was constructed to investigate the spatial process of human activity at block scale. Spatial statistical indicators and location entropy indices were used to analyze the characteristics of urban function succession and compound function change. The results show that: (1) Urban socioeconomic activities in Kaifeng showed a gradual expansion trend, and the area of activity space expanded by 11.26 km2 from 2005 to 2020. The average annual expansion rate increased from 3.5% during 2005 to 2015 to 5.1% during 2015 to 2020. POI data are more sensitive than remote sensing data on succession characteristics of activity spaces. (2) The process of agglomeration and diffusion of various urban activities results in the succession and change of different functions. Especially, Kaifeng has a relatively high degree of agglomeration for commercial service functions. Among them, the degree of agglomeration of commercial service functions in Kaifeng is relatively high. Although affected by the diffusion of commercial centers in new urban areas, the single-center urban development mode still does not change. The public administration and public service functions also show a central peripheral structure clustered in the old town, and the functional supply of the new urban areas needs to be improved. The residential function gradually changes from a relatively balanced functional configuration to agglomeration distribution, and the separation of employment and housing become more and more obvious. The overall industrial function gradually shifts from dispersion to agglomeration, spreading and moving from the central urban areas to the peripheral areas as a result of the policy on eliminating secondary and advancing tertiary industries, and then rapidly agglomerating together; (3) The number of urban blocks with commercial services and business offices is increasing, but the specialization advantages of business office functions are more prominent. And the number of urban blocks with complex functions is also increasing, which provides useful support for the single-center urban structure. The above results demonstrate that urban blocks with strong complex functions are mainly distributed in areas with high vitality and more convenient living residents. This research can not only provide theoretical and technical support for the study of urban spatial planning, but also provide new ideas for urban renewal, sustainable development and territorial space planning.

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    Evaluation of Improvement of Public Transport Accessibility Considering Riding Instead of Walking
    GAO Shunxiang, CHEN Zhen, ZHANG Zhijian, CHEN Yue, XIAO Zhongsheng, DENG Jin, XU Qi
    Journal of Geo-information Science    2023, 25 (3): 439-449.   DOI: 10.12082/dqxxkx.2023.220495
    Abstract308)   HTML18)    PDF (15461KB)(94)      

    Improving the slow travel environment at public transport stations is a key issue to enhance the competitiveness of green transportation. It is an important measure to improve the service level of public transportation to further coordinate public transportation, especially the end connection between urban rail transit and slow traffic, and to open up the ' last mile '. Existing studies mostly analyze the improvement of travel efficiency at bus stations, and do not fully consider the interaction between urban public transport system and land use. Achieving good accessibility is the main goal of building a livable city, and the spatial analysis of public transport accessibility provides a core indicator to measure the integration of public transport and urban development. As a location-based accessibility evaluation method, the cumulative opportunity method has advantages in understanding the relationship between transportation and land use and is easy to use. To this end, we construct a “door-to-door” fine-scale public transport trip chain based on multi-source traffic big data and develop a two-step calculation method to compute travel time of public transport under two modes of walking and cycling. The two-step calculation of this method has the characteristics of less calculation and flexible data update mechanism, which is suitable for the study of public transport accessibility at large spatial scale. A case study based on Beijing in 2020 shows that the average travel time of public transport via cycling is reduced by 315 seconds and 12.8%. Improvement of travel efficiency at stations improves the public transport accessibility for urban activities such as employment, health care, catering, green space, shopping, and leisure. The improvement range is 90%, 74%, 94%, 33%, 107%, and 77%, respectively, and the improved areas are concentrated in the central urban area and the surrounding residential areas. In addition, the improvement effect of accessibility of public transport shows a spatial feature of a radial-decreasing circular structure. As the main network of public transport, urban rail transit presents an improvement in employment, medical treatment, catering, green space, shopping, and leisure activities by 1.43, 1.43, 1.70, 1.42, 1.70, and 1.71 times, respectively, compared to ground bus. The results show that compared with the ground bus system, cycling substitution will significantly improve the integration of rail transit and city. According to the co-opetition relationship between rail transit and ground bus, rational allocation of bicycle facilities and optimization of shared bicycles will further increase the competitiveness of green transportation.

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    Geographical Properties and Thinking of Cyberspace
    JIANG Dong, GAO Chundong, GUO Qiquan, CHEN Shuai, HAO Mengmeng
    Journal of Geo-information Science    2023, 25 (10): 1923-1932.   DOI: 10.12082/dqxxkx.2023.220169
    Abstract283)   HTML50)    PDF (3315KB)(241)      

    With the development of science and technology, cyberspace has been deeply integrated with people's daily lives and represents a new spatial form of human activities. The cyberspace correlates to the real world, but on the other hand it also differs from it. Cyberspace has distinct geographical characteristics, and the spatial-temporal relationship in geograph remains an indispensable element in cyberspace. Therefore, it is of great significance to apply geographical thinking to the cognition of cyberspace in order to describe the situation of cyberspace and maintain cybersecurity. In this paper, we review the emergence and development of cyberspace, analyze the basic structure and characteristics of cyberspace, and examine the geographical properties of cyberspace based on different views of cyberspace. From the perspective of the three laws of geography, this paper discusses how to use geographical thinking and Geographic Information Science (GIS) methods to describe cyberspace, and takes the visualization of cyberspace, the construction of geographic knowledge graph of cyberspace, and the intelligent analysis of cyberspace behavior as examples to illustrate how to apply geographical thinking to the analysis and research of cyberspace. Exploring the geographical properties of cyberspace and applying geographical techniques to cyberspace protection can provide new insights into the comprehensive governance of cybersecurity, thus improving the cognitive level and governance capabilities of cyberspace in the new era.

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    Code Operation Scheme for the Icosahedral Hexagonal Discrete Global Grid System
    ZHAO Long, LI Guoqing, YAO Xiaochuang, MA Yue
    Journal of Geo-information Science    2023, 25 (2): 239-251.   DOI: 10.12082/dqxxkx.2023.220725
    Abstract277)   HTML10)    PDF (11990KB)(69)      

    The discrete global grid system refers to the discrete partitioning of the earth's surface into grid cells with multi-resolution hierarchical structure according to certain rules, which is widely used in organization, management, and analysis of massive multi-source spatial data. The hexagonal global discrete grid has excellent geometric properties and is well suited for spatial data processing. However, how to further improve the efficiency of the hexagonal global discrete grid coding operation is still the focus of current research. In this paper, we adopt the model of icosahedral snyder equal-area projection aperture 4 hexagonal discrete global grid system and construct the base coding structure of aperture 4 hexagon based on the correspondence between the hexagonal triaxial coordinates and the coded binary numbers, consisting of 7 base digits in the first layer and 4 base digits int the other layer. We divide the icosahedron into 3 base hexagonal subdivision tiles according to the different subdivision structures and adopt the base coding structure for coding scheme in each hexagonal subdivision tile to establish the aperture 4 hexagonal discrete global grid coding scheme. Besides, this paper designs and implements a fast conversion between aperture 4 hexagonal code and hexagonal triaxial coordinates, based on which an efficient aperture 4 hexagonal discrete global grid encoding operation scheme is constructed, including arithmetic operation of encoding, spatial topology operation, and neighbourhood retrieval operation and cross-plane operation of encoding. Compared with the existing hexagonal discrete global grid coding scheme, the coding scheme proposed in this paper has fewer base code digits, is more concise, and facilitates faster conversion to the hexagonal triaxial coordinates of the grid. Compared with the existing coding operation scheme, the proposed scheme further improves the efficiency of coding arithmetic operation, spatial topology operation, and neighbourhood retrieval operation. The coding addition operation is 2~3 times more efficient than HLQT. The neighbourhood retrieval operation is 3~5 times and 2~3 times more efficient than HLQT and H3, respectively, and is less affected by the coding level of the grid coding. The proposed coding scheme in this paper has the same efficiency of additive operation and subtractive operation, and the efficiency of spatial topology operation is 2 times that of arithmetic operation. The coding cross-plane neighbourhood retrieval operation time is slightly longer than that of the in-plane operation, and the impact on the overall operation time is not significant. This study provides support for the research application of discrete global grid system.

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    Virtual Real Registration Assisted by Structural Semantic Constraint for Digital City Scene
    HU Xiaofei, ZHOU Yang, LAN Chaozhen, HUANG Gaoshuang, ZHAO Luying
    Journal of Geo-information Science    2023, 25 (5): 883-895.   DOI: 10.12082/dqxxkx.2023.220544
    Abstract271)   HTML77)    PDF (17216KB)(235)      

    Digital city is one of the main requirements of three-dimensional (3D) real scene and leads the direction of future smart city construction. Digital city usually uses the 3D model of the real scene as the spatial data volume and integrates the object-linked data of various sensors to achieve virtual-real fusion. The integration of spatial data volume and object-linked perception data is the key to digital city applications. Visual sensor is an important sensor type which is widely used in urban life, such as surveillance cameras, vehicles, and other devices. The key to digital city application is registering the visual sensors with virtual 3D model accurately. The purpose of spatial registration for visual sensor is to estimate or optimize the position and orientation of the visual sensor and to get the accurate spatial position of any object in the image. It is one of the key technologies for applications such as Augmented Reality and Video GIS. Currently, the spatial registration methods for visual sensors can be divided into hardware-based and vision-based methods. Due to the popularity of vision sensors, vision-based registration methods have been widely used. However, in digital city applications, seasons and weather always change, there are often large differences in appearance between the real image taken by visual sensor and the image of virtual scene. Therefore, the accuracy of outdoor 6 Degree of Freedom (DOF) position obtained by existing methods is usually insufficient, resulting in low registration accuracy of the visual sensor. In order to improve the accuracy of visual sensor spatial registration in digital city scene, this paper presents a method of virtual-real registration for digital city scene with structural semantic information in urban area. Firstly, the virtual perspective image of digital city scene is obtained, the plumb line which contains structural semantic information is extracted from the target image, and the properties of global constraints of the plumb line is used to restore the camera's position accurately and achieve the registration of monocular image in the virtual digital scene. Experiments show that this method achieves accurate registration of virtual and real images with large differences in appearance. Compared with the existing methods, the position and orientation errors are reduced by 35.9% and 39.3%. This method can effectively optimize the initial pose and improve the registration accuracy of visual sensors in digital city scene. A lightweight cloud-edge registration framework is designed and can be used in image geolocation tasks based on portable devices.

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    Complex Roof Structure Reconstruction by 3D Primitive Fitting from Point Clouds
    ZHANG Wenyuan, CHEN Jiangyuan, TAN Guoxin
    Journal of Geo-information Science    2023, 25 (8): 1531-1545.   DOI: 10.12082/dqxxkx.2023.220927
    Abstract267)   HTML44)    PDF (11001KB)(212)      

    Geometric and semantic integration of 3D building models are important infrastructure data for smart city, they are conducive for promoting the refined management and intelligent application of building facilities. However, most of the existing point cloud-based modeling methods focus on the reconstruction of geometric models with simple roof structure, and semantic and topological relations are ignored. Moreover, these methods are sensitive to noise, which are difficult to assure topological consistency and geometric accuracy. To solve these problems, this paper proposes a 3D primitive fitting algorithm for automatically reconstructing building models with complex roof structure from point clouds. Firstly, a 3D building primitive library is designed, including various 3D building primitives with simple and complex roof types. Secondly, an individual building point cloud input is segmented into multiple planes using RANSAC algorithm. The Roof Topology Graph (RTG) is then generated according to the relationship of roof planes, and the roof type of point cloud is subsequently recognized by comparison of RTG between point cloud and building primitives. Thirdly, the reconstruction is formulated as an optimization problem that minimizes the Point-to-Mesh Distance (PMD) between the point cloud and the candidate meshed building primitive. The sequential quadratic programming optimization algorithm with necessary constraints is adopted to perform holistically primitive fitting, so as to estimate the shape and position parameters of a 3D primitive. Finally, the parameterized model is automatically converted into City Geography Markup Language (CityGML) building model based on the prior 3D building primitive. The generated CityGML LoD2 (second level of detail) models are different from mesh models created by conventional building modeling methods, which are represented with geometric, semantic, and topological information. To evaluate the quality and performance of the proposed approach, airborne lidar and photogrammetric building point clouds with different roof structures are collected from public datasets for test. Several building models with complex roof types are successfully reconstructed by using this approach, and the average PMD of five models is 0.17 m. The proposed algorithm is also compared with three other methods. Experimental results indicate that the proposed method achieves the best geometric accuracy, because the average PMD of each model is less than that of other methods. Moreover, this automatic primitive fitting method is efficient, and it is also robust to noise and local data missing. This study demonstrates that the resulting building models can well fit the input point cloud with topologic integrity and rich semantic. This method provides great potential for accurate and rapid reconstruction of geometric-semantic coherent building models with complex roof condition.

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    The Angular Distribution Models of the Earth's Radiation Budget Measurement: from LEO and GEO Satellites to the Moon-based Platform
    LI Qingquan, LIU Huizeng, ZHU Ping, QIU Hong, SONG Mi, HUANG Shaopeng
    Journal of Geo-information Science    2023, 25 (1): 2-14.   DOI: 10.12082/dqxxkx.2023.220455
    Abstract265)   HTML14)    PDF (5048KB)(80)      

    Monitoring the spatiotemporal variations of the Earth Radiation Budget (ERB) could help to improve our understanding of global climate change. The Earth's reflected shortwave and emitted longwave radiation are important components of energy exchange between the Earth-Atmosphere system and outer space, and are main parameters to be measured by ERB sensors. The Earth radiation radiometer is intended for measuring the parameters of Earth radiation budget. The Angular Distribution Models (ADMs) refer to a series of factors for correcting the anisotropy of the Earth-Atmosphere radiation at the Top-of-Atmosphere (TOA), and it is an effective way to convert the broadband radiance measured by satellite-borne or Moon-based Earth radiation sensor to the Earth radiant flux. Therefore, the consistency between ADMs anisotropic factors and the anisotropy of TOA radiances would directly determine the accuracy of derived flux. This paper focused on the ADMs, reviewed the development progress of the ADMs over the past decades, introduced the current operational ADMs applied by the Clouds and the Earth's Radiant Energy System (CERES) onboard the Terra and Aqua satellites, and analyzed the advantages, problems, and potential of geostationary satellites and Moon-based Earth observations in the development of ADMs. Based on above reviews and analyses, the determinant factors for further improving the ADMs for satellite-borne and Moon-based Earth radiation measurements were discussed.

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    Research on the Digital Holographic Earth Data Cube Model
    LUO Bin, REN Liqiu, MAO Yue, SHI Ruipeng, ZHU Yunqiang, WU Chaowei
    Journal of Geo-information Science    2023, 25 (7): 1282-1296.   DOI: 10.12082/dqxxkx.2023.230105
    Abstract264)   HTML41)    PDF (13705KB)(191)      

    With the development of big data and artificial intelligence, the scope of digital earth modeling has extended to full-time holography beyond the earth surface. However, the current data model of digital earth still remains in the data modelling of earth tile or grid subdivision. This model severely limits the application of scenario-based and intelligent digital earth development. This paper proposes the concept of digital holographic earth and a corresponding data organization model of earth data cube. By using global multi-level grid reference system to describe and express multi-scale space and using two or three-dimensional grid cells to describe spatial positions, the traditional spatiotemporal description of "longitude, latitude, elevation, and time" is transformed to a new spatiotemporal description system of "time granularity, time coverage, grid position, and grid scale". The proposed model is characterized by the dimensions of "time-space-scale-attribute" based on spatiotemporal big data in the digital earth. The model encapsulates vectors, rasters, grids, time series arrays, and 3D models into an unified system. This unified system ensures that any data value of a specific earth data cube is aligned perfectly in time, space, and scale, which solves the problem of multi-dimensional or spatiotemporal dynamic fusion of big earth data. Finally, this paper develops a deep-time and spatiotemporal dynamic visualization simulation system to verify the data model based on the requirements of the Deep-time Digital Earth International Science Program.

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    Progress in Research and Practice of Spatial-temporal Crime Prediction over the Past Decade
    HE Rixing, LU Yumei, JIANG Chao, DENG Yue, LI Xinran, SHI Dong
    Journal of Geo-information Science    2023, 25 (4): 866-882.   DOI: 10.12082/dqxxkx.2023.220808
    Abstract259)   HTML28)    PDF (9459KB)(218)      

    As a forward-looking and proactive policing mode, predictive policing has been a major innovation of modern policing reforms across the USA and European countries since it was proposed in 2008. As it does not involve the use of personal privacy data and can be integrated with police patrolling and precise crime prevention strategies, place -based spatial -temporal crime prediction has been a hot research topic and main component of policing practices. This research presents a systematic review of the progress of spatial-temporal crime prediction across the world since 2013 when the RAND Corporation released its special report on predictive policing. It contributes to the literature with the following five aspects: (1) summarizing the new trends in the field of spatiotemporal crime prediction studies in terms of the number of papers, research topics, leading scholars, and academic journals. The studies on spatial-temporal crime prediction have received extensive attention from various countries in recent years, and the research themes have shown a diversified trend. The most productive scholars are mainly from China and the USA, with the main focus on spatial-temporal crime prediction model development; (2) describing the new dynamics and progress of six basic components involved in the spatial-temporal crime prediction research, which are the prediction target, temporal scale, spatial scale, prediction method, performance evaluation measure, and practical evaluation. The four most widely studied types of crimes are theft, robbery, burglary, and motor vehicle theft. For burglary crime, the typical temporal unit for spatial-temporal prediction is 1-month; For the other three types of crime, the typical temporal unit is 1-day. For these four types of crime, the typical spatial unit is 200-meter grid. The top three models with the best prediction performance are random forest model, spatial-temporal neural network model, and Hawkes process model; (3) introducing several main commercial softwares for spatial-temporal crime prediction and global predictive policing practices; (4) investigating the relevant ethical issues and potential challenges that are embedded in each stage of practical applications, including data & algorithm biases, lack of transparency and countability mechanism; (5) prospecting future research directions in spatial-temporal crime prediction areas. This research provides a brief and panoramic image of the field of spatial-temporal crime prediction and can act as a reference for researchers and practitioners in relevant fields including crime geography, smart policing, and Policing Geographic Information System (PGIS).

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    Reasoning of Spatial Distribution Pattern of Building Cluster based on Geographic Knowledge Graph
    TANG Zengyang, AI Tinghua, XU Haijiang
    Journal of Geo-information Science    2023, 25 (6): 1202-1214.   DOI: 10.12082/dqxxkx.2023.220761
    Abstract248)   HTML21)    PDF (22465KB)(175)      

    The graph structure-based knowledge graph plays important roles not only in the description and reasoning of semantic network, but also in the structured abstraction and spatial reasoning of spatial entities. The relational information of spatial entities is recorded in edges in the knowledge graph. Through the edge-based knowledge graph computational reasoning such as path detection, sub graph alignment, pattern discovery, etc., it can play an important role in spatial scene cognition. Geographic knowledge graph is a knowledge system that formally describes geographic concepts, entities, and their interrelationships. It has both the connotation and characteristics of general knowledge and the specific spatiotemporal characteristics of geographic knowledge. It can connect semantic models with spatiotemporal models to describe semantic relations, spatial relations, and temporal relations, and has great application potential in the expression, understanding, acquisition, and reasoning of geographic knowledge. The existing research work of geographic knowledge graph is mostly focused on semantics, and the extraction and expression of semantic relations are very rich and comprehensive, which can support further functions such as semantic search and association analysis of geographic knowledge. However, the knowledge expression of geographic knowledge graph in spatiotemporal model is relatively lacking, and the existing spatial relationship is limited between elements, rarely involving the further distribution situation and spatial pattern in spatial cognition. Thus, the geographic knowledge graph needs to be strengthened in terms of spatial semantic knowledge. Based on the principle of knowledge graph construction, this paper takes the construction of geographic knowledge graph of buildings as an example to realize the grid-pattern recognition of buildings. Firstly, the buildings are abstracted into entities and expressed as nodes of the graph, and the spatial neighborhood relations between buildings is extracted based on geometric proximity analysis, so as to build the geographic knowledge graph of the building group. On this basis, combined with the domain knowledge of building pattern recognition, it further infers and constructs other spatial semantic relations, and improves the geographic knowledge graph. Then the grid-pattern of the buildings complex scene is expressed as the rules of the knowledge graph, which is based on NoSQL language for reasoning. The results show that this method can effectively extract the linear pattern of buildings and further deduce the grid-pattern, which demonstrates the important role of geographic knowledge graph in spatial reasoning and its good adaptability in domain problem research, and provides ideas for the application of geographic knowledge graph in the field of spatial cognition.

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    "Production-Living-Ecological Spaces" Recognition Methods based on Street View Images
    WAN Jiangqin, FEI Teng
    Journal of Geo-information Science    2023, 25 (4): 838-851.   DOI: 10.12082/dqxxkx.2023.220534
    Abstract246)   HTML15)    PDF (15585KB)(231)      

    Understanding the urban spatial pattern from the perspective of the “Production-Living -Ecological” function not only paves the way to the optimization of land spatial structure, but also reflects the internal functional form and combination mode of urban land. However, in the past, the recognition of urban “Production-Living -Ecological Spaces” (PLES) mainly relied on remote sensing images, Point of Interest (POI), and land use data, and there was a lack of three-dimensional information within a city. Street View Images (SVI) can reflect the characteristics of the streets in the city and capture large-scale and high-resolution objective measurements of the physical environment within a street from a close-up view. Therefore, based on the semantic features of the scene extracted from the SVI, this paper proposes a method of identifying PLES in the central urban area and analyzing the importance of different features of the PLES. Taking the Fourth Ring Road of Chengdu as the study case, the classification system of PLES is constructed based on POI data, and the proportion of PLES is calculated at each SVI sampling point. The eXtreme Gradient Boosting (XGBoost) algorithm is used to identify the urban PLES, and a comparative test of model accuracy is also carried out. The spatial distribution of PLES in the study area is analyzed from three scales, i.e., road network, 500-m grid, and traffic analysis zone. The SHapley Additive exPlanation (SHAP) method is introduced to identify the important features that contribute to PLES. The results are as follows: (1) The proposed method of identifying PLES based on SVI in this paper has a high accuracy. The R2 of the model for identifying PLES reaches 0.6, indicating the feasibility of SVI for identifying PLES; (2) The spatial pattern of PLES reveals that the study area is dominated by production-living spaces, which are large in number and distributed in pieces in the study area. The number of units dominated by ecological space is small, and they are mainly distributed in large parks; (3) Among the semantic features of the seven types of scenes, street openness and motorization level have the greatest impact on the formation of the PLES. Based on PLES, this study uses SVI data to conduct a fine-scale analysis of land use in central urban areas and determine the types of urban land. This paper enriches the data and methods of PLES identification and provides a new tool for the optimization of urban spatial structure and development decision making.

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    Attention-based Multi-step Short-term Passenger Flow Spatial-temporal Integrated Prediction Model in URT Systems
    ZHANG Jinlei, CHEN Yijie, Panchamy Krishnakumari, JIN Guangyin, WANG Chengcheng, YANG Lixing
    Journal of Geo-information Science    2023, 25 (4): 698-713.   DOI: 10.12082/dqxxkx.2023.220817
    Abstract245)   HTML41)    PDF (9406KB)(220)      

    Accurate and reliable short-term passenger flow prediction can support operations and decision-making of the URT system from multiple perspectives. In this paper, we propose a URT multi-step short-term passenger flow prediction model at the network level based on a Transformer-based LSTM network, Depth-wise Attention Block, and CNN network, named as Spatial-Temporal Integrated Prediction Model (STIPM). The STIPM comprises three branches. The first branch takes time-series inflow data as input, and a Transformer-based LSTM network is selected to extract the temporal correlations. The second one takes timestep-based OD data as input, and many spatial and temporal features are captured using Depth-wise Attention Blocks. Meanwhile, timestep-based OD data can better include inter-station relations and global information. The third branch takes Point of Interest data (POI) as input and CNN network is utilized for spatiotemporal features extraction, which can also become the bridge between spatial and temporal features. Moreover, the “Multi-input-multi-output Strategy” for multi-step prediction is used to obtain a longer prediction period and more detailed information under a relatively high forecasting accuracy. The STIPM is applied to two large-scale real-world datasets from the URT system, and the obtained prediction results are compared with ten baselines and four variants from itself, in which STIPM model achieves highest prediction accuracy indicated by RMSE, MAE, and WMAPE evaluations, which demonstrates the superiority and robustness of the STIPM.

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    Evaluation Method of Morphological Efficiency for "Three Functional Spaces" based on Quadtree Algorithm
    XIA Junnan, WEI Wei, YIN Li, HONG Mengyao, BO Liming
    Journal of Geo-information Science    2023, 25 (3): 450-467.   DOI: 10.12082/dqxxkx.2023.220386
    Abstract241)   HTML23)    PDF (49445KB)(92)      

    In the context of the reform of China's spatial planning of land, the three functional spaces (i.e., urban, agricultural, and ecological spaces) are the key elements of the connection between macroscopic main functional zones and microscopic land use. In order to analyze the morphological efficiency of three functional spaces in multi-scale, all-elements, and long time series, and analyze the morphological efficiency evolution mechanism of the three functional spaces, this paper constructs a method of morphological efficiency identification based on "quadtree" algorithm, and uses "morphological efficiency value" as the core measure of spatial morphological efficiency to build a unified and comparable morphological efficiency measurement channel of three functional spaces. Based on the morphological efficiency values, this research applies the Dugam Gini coefficient method and spatial regression analysis method to verify the effectiveness of the algorithm based on "quadtree" in describing the spatial morphology of the three functional spaces. And we further use the above methods to explore the differences of morphological efficiency and the influence mechanism of socio-economic factors, to provide a basic support method with strong data adaptability and high accuracy of discrimination for the morphological monitoring of land space at macro, meso, and micro scales. Taking the Yangtze River Economic Belt as the research object, this paper analyzes the characteristics, differences in changes and evolutionary mechanisms of the spatial efficiency of the three functional spaces in the past 40 years. Our results show that: (1) In the past 40 years, the level of spatial integrity of urban space expansion has increased rapidly, with morphological efficiency values rising by 0.46, while the level of agricultural space spatial concentration and contiguity has fallen sharply by 0.17, and the ecological space has remained almost unchanged; (2) The risks of the development and protection of the three functional spaces in the Yangtze River Economic Belt include: the risk of disorderly development of urban space in the central and western regions where are less developed, the risk of fragmentation of agricultural space in the whole region, and the risk of massive damage to the integrity of the ecological space in the eastern region; (3) The key to optimize the morphological efficiency of the three functional spaces in the Yangtze River Economic Belt is to pay attention to the morphological efficiency changes of the three functional spaces in regions with fragile resource and environmental carrying capacity, regions with high socio-economic development, and regions with relatively poor location conditions, and formulate policies for the optimization of spatial structure and morphology.

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    NSGA Multi-objective Optimization Algorithms and Geographic Decision-making: Principles, State of the Art, and the Future
    GAO Peichao, WANG Haoyu, SONG Changqing, CHENG Changxiu, SHEN Shi
    Journal of Geo-information Science    2023, 25 (1): 25-39.   DOI: 10.12082/dqxxkx.2023.220214
    Abstract237)   HTML8)    PDF (3714KB)(92)      

    The focus of geography is shifting from qualitative descriptions and quantitative analysis to support decision-making. The process of geographic decision-making usually involves multiple factors to consider and balance to achieve an optimal solution. It is a typical process of multi-objective optimization. Thus, multi-objective optimization algorithms from the field of mathematics are fundamental and have great potential to be applied in geographic decision-making. New algorithms of multi-objective optimization serve as an important source of new methods and tools for geography. This paper reviews a series of Nondominated Sorting Genetic Algorithms (NSGA-I/II/III), which are among the cutting edge and most popular algorithms in the field of multi-objective optimization. This review summarizes the principles, applications, improvements, and problems of these NSGA algorithms. Our findings include: NSGA-II is the most popular algorithm among the series because of its low computational complexity and high usability; NSGA-III has few applications in geographic decision-making for its sophisticated principles; currently, water resource management is the most successful field in applying the NSGA algorithms, and the experiences from this field are of use to others; and land use planning is the most successful field in improving the NSGA algorithms, making the NSGA algorithms more applicable to geographic decision-making. In the future, it is necessary to reduce the difficulty of applying the NSGA algorithms by summarizing typical issues in geographic decision-making and by developing user-friendly software tools for geographers. The efficiency of the NSGA algorithms can be further improved by coupling local searching strategies. It is also recommended to deeply incorporate the NSGA algorithms into the processes of geographic simulations.

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    Knowledge Graph Construction Method of Gold Mine based on Ontology
    ZHANG Chunju, LIU Wencong, ZHANG Xueying, YE Peng, WANG Chen, ZHU Shaonan, ZHANG Dayu
    Journal of Geo-information Science    2023, 25 (7): 1269-1281.   DOI: 10.12082/dqxxkx.2023.210772
    Abstract237)   HTML39)    PDF (13043KB)(170)      

    Geological and mineral resource survey and scientific research in "geology, geophysics, geochemistry, and remote sensing " have established a large amount of geological and mineral survey data, which contain rich knowledge related to mineralization and distribution of gold mine, such as the metallogenic and tectonic setting, geological environment of occurrence, geological characteristics of mineral mine, genesis and metallogenic model of mine, and so on. The transformation from massive mineral related data to effective metallogenic knowledge has become one of the most important breakthroughs to improve the accuracy of geological prospecting. To solve this problem, through the in-depth analysis of knowledge representation, information extraction, and knowledge fusion in knowledge engineering, this paper explores the knowledge graph construction method of gold mine based on ontology. Firstly, referring to industry norms, gold mine knowledge base, and reference material of geological and mineral resource exploration, the metallogenic model of gold mine is sorted out, and the gold mine concept, gold mine entity, gold mine relationship, gold mine geological attribute, and gold mine metallogenic attribute are determined. In addition, the schema layer of gold mine knowledge graph is constructed by using the top-down ontology knowledge representation method, which represents the conceptual model and logical basis of gold mine knowledge graph. Secondly, based on structured, semi-structured, and unstructured multi-source heterogeneous geological data, the deep learning model is used to realize gold mine knowledge extraction, semantic analysis, and knowledge fusion, which enriches the data layer of gold mine knowledge graph and provides data support for gold mine knowledge graph. The gold mine knowledge graph is constructed in a bottom-up way, and the gold mine knowledge triplet is stored by Neo4j graph database, in which nodes represent gold mine concept, gold mine entity, and gold mine attribute value, while edges represent relation and attribute. Finally, the gold mine knowledge management system is developed based on the graph database. It can be applied to the management of gold mine data, acquisition of knowledge, visualization representation of gold mine knowledge graph, inquiry of knowledge, management and presentation of knowledge base, and other functions well, so as to lay a foundation for the intelligent analysis and mining of geological big data. This study develops a geological prospecting method driven by data and knowledge, and provides a reference for identifying, controlling, and managing mineral resources, which can improve the prospecting accuracy in geological exploration.

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    Map Retrieval Intention Formalization and Recognition by Considering Geographic Semantics
    GUI Zhipeng, HU Xiaohui, LIU Xinjie, LING Zhipeng, JIANG Yuhan, WU Huayi
    Journal of Geo-information Science    2023, 25 (6): 1186-1201.   DOI: 10.12082/dqxxkx.2023.230019
    Abstract235)   HTML20)    PDF (17684KB)(112)      

    Mainstream map retrieval methods for spatial data infrastructures are mainly based on metadata text matching or image similarity calculation, but such approaches lack active perception and understanding of user retrieval intention, and in turn fail to truly meet user requirements. While existing intention recognition methods are incapable to express and recognize map retrieval demands with joint constraints of complex geographic concepts. To address this issue, this paper proposes a map retrieval intention formalization and recognition method by considering geographic semantics, aiming to improve the accuracy of map retrieval in an intention-driven and explainable manner by using relevance feedback samples. More specifically, a formalization model constrained by geographic ontology in the form of "intention-sub-intention-dimension component" is designed for expressing user's map retrieval intention. With the support of the formalization model, a recognition algorithm based on Minimum Description Length (MDL) principle and Random Merging (RM) strategy, named MDL-RM, is proposed by treating intention recognition as a combinational optimization problem. MDL-RM takes the description length of the sample set from relevance feedback as the optimization goal, merges samples randomly with the assistance of geographic ontologies and semantic similarities among geographic terminologies to generate sub-intention candidates, and searches the optimal intention using a greedy search approach. In order to evaluate the accuracy of recognized intention, we proposed a semantic metric, named Best Map Average Semantic Similarity (BMASS), and calculated it along with Jaccard index in five typical map retrieval scenes. Meanwhile, we analyzed the time cost and the influence of parameter settings and validated the effectiveness of random merge and sample augmentation strategy. The experimental results on the synthetic data demonstrate that the proposed method has higher accuracy and sample noise tolerance in most retrieval scenes comparing with the method based on Gene Ontology (RuleGO) and the Decision Tree learning method with Hierarchical Features (DTHF). The random merge strategy can reduce average computing time effectively without declining accuracy, and the sample augmentation strategy facilitates retrieval intention recognition even when the sample size is as low as 20. The proposed method is expected to be adapted and applied into geoportals and catalogue services to improve the service quality and user experiences upon the sharing and discovery of geographic information resources.

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