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  • WANG Zhonghui, YANG Leiting
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    As important methods for geographic information retrievals, direction relation queries have been widely applied in many fields such as data mining, intelligent reasoning, map navigation, and multi-scale data processing. In direction relation queries, it is necessary to use the direction relation models to calculate the direction relations between spatial objects. Among the proposed direction relation models, the cone-based model and the matrix model are mainly used for direction relation queries due to their simplicity and strong query capabilities. However, these two models ignore the influences of the sizes and shapes of spatial objects and the distance between them on direction relations, potentially leading to unreasonable query results. To solve the problem, this paper proposes a direction relation model that combines the cone-based model, the matrix model, and the Voronoi-based model to determine direction relations. The idea is to divide direction relations into external direction relations and internal direction relations and integrate the advantages of different models. The cone-based model and the matrix model are combined to achieve the external direction relation queries, taking into account the influences of the sizes of spatial objects and the distance between them on direction relations. The Voronoi-based model is employed for the internal direction relation queries, considering the influences of the shapes of spatial objects on direction relations. The experimental results show that the combinational model has good applicability and feasibility in direction relation queries, maintaining high consistency with people's spatial cognitions. The main advantages of the combinational model are that: (1) it fully considers the influences of the sizes and shapes of spatial objects and the distance between them on direction relations, and overcomes the disadvantages of the cone-based model and the matrix model in direction relation queries; and (2) it integrates the strengths of the cone-based model, the matrix model, and the Voronoi-based model, enabling the unified querying of external direction relations and internal direction relations and resulting in improved accuracy of direction relation queries. Moreover, the combinational model will help improve the accuracy and reliability of spatial data processing such as intelligent querying and reasoning of spatial information and the calculation of multi-scale spatial relation similarity.

  • ZHANG Hao, WANG Jingxue, XIE Xiao
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    The dense point cloud of the urban scene reconstructed by Multi-View Stereo reconstruction technology (MVS) often contains noise, resulting in surface distortion of the generated model and loss of some edge features, which cannot well reflect the real information of the reconstructed target. To solve these problems, a variational method combining 3D edge constraints is proposed to optimize the mesh model. Based on the initial grid data obtained by MVS algorithm, the energy function is constructed by the variational principle, and the grid model optimization problem is transformed into an energy function minimization problem. Firstly, the initial grid model is reconstructed from the dense point cloud. Then, the energy function is constructed by using the luminosity consistency measure, using the vertex curvature as the smooth term, and using the three-dimensional edge point constraint as the additional constraint term. Finally, the gradient descent method is used to solve the minimum energy function iteratively, and the grid deformation is driven by discretizing the gradient change to the vertex of the triangle to optimize the model. In order to construct 3D edge constraints, 3D edges must be extracted first. In this paper, 2D edges are extracted from multi-view images first, and the 2D edges are represented as multi-segment lines according to the polar constraints. Then, the 2D multi-segment line nodes are restored as 3D edge points according to the polar constraints, and the 3D edge points of the recovery points are a series of 3D multi-segment lines representing the edge outline. Finally, the edge region of the mesh model is located by taking the vertex of the mesh model closest to the 3D edge point as the neighborhood point. In this way, 3D edge features are constructed. In order to verify the effectiveness of the proposed algorithm, two real outdoor scenes from the Strecha dataset and one real indoor scene from the ETH3D dataset are selected to evaluate the reconstruction results of the proposed algorithm. In addition, the efficiency of this algorithm is analyzed by comparisons with other algorithms. Experimental results show that the proposed algorithm can effectively improve the accuracy and integrity of the grid model and retain the edge features of the target better on the grid model.

  • HUANG Jing, CAI Siqin, PANG Tiantian, WANG Huimin
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    Disaster early warning plays an important role in disaster reduction management by proactively disseminating disaster information to guide residents in taking timely evacuation actions, thus effectively reducing disaster losses and casualties. The dynamic response process of residents to disaster early warning information and the assessment of the effectiveness of different flood disaster early warning strategies are pressing issues. This paper proposes a simulation method for urban rainstorm flood disaster early warning strategies based on Agent-Based Modeling (ABM). Firstly, three warning strategies are established: rainfall forecast-based, flood inundation-based, and population exposure-based. Secondly, individual risk perception is assessed by considering a variety of socio-demographic characteristics, and a probabilistic model of individual travel decision-making is constructed. Based on this, an agent-based model for urban flood disaster early warning strategies is developed. Finally, taking Futian District in Shenzhen, China as a case study, residents' travel behavior and flood risk are simulated and analyzed with different flood warning strategies under 20-year, 50-year, and 100-year return period rainstorm scenarios. The results show that: (1) The ABM simulation model, considering residents' perception of flood disaster risk and the probability of individual travel decision-making, accurately simulates residents' travel response behavior and changes in flood disaster risk under different warning strategies. It provides a scientific and comprehensive evaluation of the effectiveness of urban flood disaster early warning strategies; (2) Different warning strategies lead to significant differences in population travel response behavior, resulting in varying effectiveness in reducing urban rainstorm flood disaster risk. Faced with a 20-year rainfall scenario, flood inundation-based and population exposure-based early warning strategies help residents in the study area quickly identify high-risk areas, significantly reducing the risk to buildings and roads. Faced with a 20-year return period rainstorm scenario, the study area shows minimal changes in residents' travel behavior under rainfall forecast-based warnings. However, flood inundation-based, and population exposure-based warning strategies help residents rapidly identify high-risk areas, significantly reducing the number of people heading to red and orange warning zones. This results in a noticeable decrease in risks to buildings and roads; (3) Under different rainstorm scenarios, the effectiveness of various flood disaster early warning strategies varies. In the face of smaller rainstorm scenarios, refined flood disaster early warning strategies, such as flood inundation-based, and population exposure-based, demonstrate effectiveness in reducing urban flood disaster risk. However, when dealing with extreme rainstorm scenarios, adopting a unified flood disaster early warning strategy, such as rainfall forecast-based, is more effective than a refined warning strategy. Therefore, urban flood disaster early warning systems should be tailored to local conditions and varying circumstances, establishing a graded, zonal, and scenario-based warning system.

  • FU Xuan, YAN Haowen, WANG Xiaolong, YAN Xiaojing, WANG Zhuo, MA Wenjun
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    The escalating urbanization in China has exacerbated waterlogging disasters, posing substantial threats to both human lives and property. In response to the challenges of inadequate mapping and redundant map data in urban waterlogging contexts, this study introduces a comprehensive four-stage methodology for We-Map cartography. This cartography encompasses data acquisition, extraction of waterlogging points, route optimization, and scene application. The initial step involves the retrieval of social media text data through queries to the Weibo Application Programming Interface (API) within a defined timeframe. The retrieved data are subsequently subjected to thorough cleaning and preprocessing procedures. Following this, the BiLSTM-CRF model is harnessed to discern urban waterlogging locations from the social media content, thereby enhancing recognition accuracy through contextual insights. Then, users are provided with optimal route for bypassing perilous road segments, achieved via the shortest path algorithm. Leveraging the online map as the foundational framework, the We-Map is generated within the urban waterlogging setting by overlaying multiple layers. Notably, the proposed method attains an impressive overall accuracy rate of 92.7% in pinpointing urban waterlogging locations, thereby substantially enhancing mapping efficiency. A comparative analysis between map-derived waterlogging points and official records reveals a substantial overlap, thus offering valuable supplemental information to conventional monitoring techniques. Furthermore, a road network-level map of urban waterlogging points is also generated to avoid redundancies in vast geospatial information. The identified flood-prone road sections can serve as a reference, while real-time display of urban waterlogging points, coupled with the shortest path algorithm, aids in recommending optimal routes. By leveraging the inherent attributes of "we-content" within the We-Map, this method expedites rapid mapping and fulfills the exigencies of swift mapping during emergencies. To cater to diverse user needs, urban flooding scenarios map are categorized with different tags aligned with their intended applications, encompassing home-bound routes, rescue maps, driving maps, walking maps, storm assistance maps, nearest rescue supplies maps, and more. Each map is endowed with at least one tag, streamlining accurate searches and usage by other users, and concurrently providing a reference for urban rescue operations. The proposed method ensures the coherence of map content and user requisites, facilitating efficient map sharing among users. The real-time dissemination of urban waterlogging information empowers users to swiftly comprehend disaster scenes, engendering their active involvement in We-Map production, and combining optimal path recommendation to augment cartographic responsiveness in emergency disaster scenarios. This approach bears substantial practical significance and promising application potential, constituting a robust for urban waterlogging emergency responses.

  • LI Huarong, MAO Hongyu, ZHAO Yi, BI Ailin, CHEN Tuan, XIN Wei, ZHONG Tao
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    With the development of 3D sensors and 3D reconstruction techniques, the registration and fusion of cross-source point clouds have become a research hotspot. However, traditional registration methods use a single feature as the registration primitive, which leads to problems such as weak spatial geometric constraints. Combining multiple structural features with joint constraints can improve the registration accuracy to a certain extent. In order to fuse cross-source point cloud data with high accuracy and fully express the façade information in the scene, this paper proposes a cross-source point cloud registration method based on the constraints of line and surface features. Firstly, the homonymous line and plane features in the cross-source point cloud are extracted by RANSAC algorithm, which are mainly used to constrain the point cloud model in registration. Then the quaternion method is used to describe the spatial transformation parameters based on the line and surface features. The rotation and transformation in arbitrary 3D space can be realized at a faster calculation speed compared with other representations while also avoiding the gimbal lock phenomenon. The line features are used as the constraints of registration, the spatial transformation objective function is constructed, and the parameters related to the transformation are estimated to complete a coarse registration and solve the scale variability. Based on the coarse registration, the surface features are further used as the constraints to solve the rotation matrix and translation parameters to achieve a fine registration. The use of surface features instead of point features as the registration primitives can avoid the selection of common points from massive point cloud data, reducing the accidental errors selected by human selection, avoiding the accumulation of errors, and further improving the registration accuracy. Finally, experiments are conducted using the image-matched point clouds and LiDAR point cloud data for a small area and a large area to study the feasibility of this paper's method in different scales. Results show that the RMSE values for the small-area single building, multiple buildings, and large-area building clusters are 0.364 7, 0.032, and 0.614 6, respectively. The maximum angle between the homonymous surfaces does not exceed 1.5°, the minimum is less than 0.1°, and the mean value of the angle is within the range of 1°. The coarse registration based on line feature constraints can solve the scaling problem well in different scenarios, and the fine registration based on surface feature constraints can further improve the accuracy of the rotation matrix and translation parameters. These results indicate that the cross-source point cloud registration method based on line and surface feature constraints is feasible at different scales.

  • CHEN Jianhui, WANG Xiaoqin, KONG Lingfeng
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    A healthy ecological environment forms a crucial foundation for the sustainable development of both nations and humanity. In the domain of ecological environment assessment, the comprehensive indicator system model represents the mainstream evaluation approach, both domestically and internationally. The extensive application of big geodata within this context offers significant potential for addressing ecological problems characterized by vast scales, intricate processes, and a variety of influencing factors. However, as the acquisition of big geodata becomes increasingly accessible, the coverage of the index system has significantly expanded, raising the pivotal issue of objectively and scientifically selecting crucial indicators capable of representing the distinctive characteristics of the study area. This challenge is particularly critical in today’s ecological health assessment. The Pressure-State-Response (PSR) model offers a causal perspective, comprehensively considering the systemic relationships between the ecological environment and human socioeconomic activities. The Ecological Hierarchy Network (EHN) model is capable of reflecting the overlap and interconnections between upper and lower-layer indicators. In this study, by integrating the frameworks of PSR and EHN and taking into account the potential information overlap from multiple available parameters, we established a five-layer networked indicator system consisting of the Target Layer, Criteria Layer, Element Layer, Indicator Layer, and Homogeneous Indicator Layer. We also proposed a two-stage adaptive indicator reduction model that combines Homogeneous Indicator Layer reduction using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Indicator Layer reduction based on target optimization theory. Combining both approaches, we developed an adaptive indicator reduction model tailored for ecological environmental health assessment. Leveraging big geodata comprising remote sensing thematic products, topography, meteorology, soil, and population information, we applied the proposed model to assess the ecological health of seven ecologically diverse regions in China, including Yunnan, Fujian, Beijing-Tianjin-Hebei, Shaanxi, Hubei, Xinjiang, and Jilin during the period 2001—2021. The results show that: (1) The selected indicators obtained through the two-stage indicator adaptive reduction model effectively reflected the distinct characteristics of ecosystems in different regions. Furthermore, indicators with higher weights among the selected ones have been widely employed in constructing indicator systems across various regions. These findings highlighted the universality and rationality of both the constructed indicator system and the two-stage indicator adaptive reduction model, effectively mitigating the subjectivity associated with manual indicator system construction; (2) The spatial distribution and temporal trends of the ecological environment health of the seven regions aligned with real-world conditions and were corroborated by existing literature and data, which indicated the effectiveness of the model proposed in this study. The proposed models presented in this paper can serve as a reference for constructing indicator systems and selecting indicators in other domains and provide methodological support for ecological environment health assessment across diverse regions on a large scale.

  • JIANG Yongqing, ZHAO Xinzheng, LI Peiqing, XiANGLI Bochen, ZHANG Dekang
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    Conducting research on urban multi-center is of significant importance for optimizing urban spatial structure, rational allocation of spatial elements, and strengthening urban planning and management. Existing studies have conducted numerous empirical research on urban multi-center identification, but a unified set of evaluation criteria has yet to be established. Therefore, based on multi-source open data such as Points of Interest (POI), population distribution, and road networks, this paper constructs a set of quantitative identification methods for urban multi-center in terms of structural morphology, intensity level, and functional classification. For the structural morphology, based on the multi-attribute weighted analysis of POI data, we constructed a multi-center structural characteristic index "center clustering degree" and identified the scope and boundary of urban multi-center through the parameter-optimized local contour tree algorithm. For the intensity level, by constructing the Urban Multi-center Comprehensive Service Intensity Indicator (UCSI), and according to "Head-Tail Breaks" rule, we divided the intensity patches within the multi-center and calculated the Urban Multi-center Intensity Level (UCL). For functional classification, the functional combination relationship and classification of multi-center was identified by calculating the frequency share of POI data after incorporating weighted attributes. Finally, an empirical simulation was conducted using the city of Xi'an as a case study. Results show that Xi'an city's traditional single-core urban structure exhibited a distinct diffusion trend, having a total of 21 urban centers (three main urban centers, five secondary urban centers, and 13 regional centers) with their locations and boundaries specified. The intensity level of each urban center was characterized by a decreasing "core-periphery" circle. And four urban composite functional centers, 12 urban dual-functional centers, and five urban single-functional centers were identified. The research results show that the quantitative identification method established in this paper can accurately and systematically identify the characteristics of urban multi-center development. This paper also puts forward corresponding policy suggestions for the prominent problems in the process of multi-center development of large cities and provides reference for the preparation and implementation of urban planning.

  • LIU Chengcheng, ZHU Haihong, LI Lin
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    With the increasing awareness of property rights among residents, the number of legal disputes concerning the differentiation of ownership, easements, and right to light in urban buildings has been steadily rising. This trend has adverse effects on the establishment of harmonious urban communities and entails a substantial expenditure of social resources. However, current research predominantly focuses on legal descriptions of these rights, and falls within a limited scope that solely employs three-dimensional technology to construct ownership spatial models. There exists a dearth of in-depth exploration into the spatial structures of the complex ownerships and their associated rights (easements and right to light) pertaining to buildings. This study conducts an in-depth analysis of the data requirements for three-dimensional modeling of building ownership, easements, and right to light within the legal context. Through the extension of the Industry Foundation Classes (IFC), an integration of the Land Administration Domain Model (LADM) and the Building Information Modeling (BIM) is achieved, resulting in a three-dimensional data model. This model adheres to the extension logic within the IFC standard. It is based on the concepts of property sets and user-defined properties in the IFC standard, and maps the Party Package, Spatial Unit Package, Administrative Package, and Surveying and Representation Package from the LADM to the IFC standard. This model takes into account the intricate spatial structure of building ownership and its computational rules. For easement rights, special consideration is given to three-dimensional instantiation in both horizontal and vertical directions. As for light to right, calculations encompass adjacent daylighting rights, daylighting easements, and their respective parameters. The amalgamation of LADM and BIM empowers the proposed model to vividly present the spatial characteristics of physical objects and concurrently reflecting the legal information of Rights, Restrictions, and Responsibilities (RRR) of the buildings. To validate the feasibility and applicability of the proposed model, three actual legal dispute cases were selected for analytical experiments. These cases encompassed: the visualization and analysis of property ownership within the three-dimensional data model, quantification of the impact of easements on the spatial utilization of property ownership, and the evaluation of the effect of right to light on residential units within a building. The results affirm that the constructed model proficiently distinguishes the internal spatial aspects of ownership, encompassing complex structures, and effectively expresses easements and right to light in three dimensions. Moreover, it encompasses sufficient ownership information, providing a clearer understanding of the legal space and furnishing a reliable and robust technical solution for resolving relevant disputes.

  • HUANG Dapeng, WANG Yanjiao, XIAO Fengjin, CHEN Yanhong
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    Climate change research requires the support of long time series, stable, and uniform Land Surface Temperature (LST) climate datasets. Real-time monitoring of climate change has requirements on datasets, such as requiring LST climate datasets to be updated in real-time and accessible conveniently. However, there are still limited publicly available LST climate datasets with real-time updates internationally. Scholars have conducted extensive research on the development of long time series LST datasets and achieved a series of innovative results. However, most of the existing LST datasets utilize polar orbit satellite data or geostationary meteorological satellite data, and there are few LST datasets that integrate geostationary meteorological satellites and polar orbit satellites. This study evaluated the quality of the real-time FY-4A LST data available in China using the long term MODIS LST historical dataset as a reference. A random forest bias correction algorithm was developed, and the FY-4A LST data were corrected to the quality level of MODIS LST, so as to develop a real-time bias-corrected LST climate dataset integrating FY-4A geostationary meteorological satellite and polar orbit satellite AQUA/MODIS. The evaluation results of the bias-corrected FY-4A LST in January, April, July, and October 2022 showed that the mean bias between the FY-4A LST and MODIS LST decreased from -1.98 ℃ to -0.42 ℃, the mean absolute bias decreased from 4.02 ℃ to 2.84 ℃, the root mean square error decreased from 5.13 ℃ to 3.91 ℃, and the correlation coefficient increased from 0.86 to 0.90 after bias correction. This random forest bias correction algorithm can effectively reduce the bias between FY-4A LST and MODIS LST. The corrected FY-4A LST data exhibited good consistency with MODIS LST in different regions of China and over different time periods. The monitoring and evaluation results of high temperature process in China showed that the mean bias of corrected FY-4A LST and MODIS LST was relatively small in most regions of the country, and there was a significant bias only in mountainous areas with higher elevations and relatively complex terrain. After the bias correction, the daily mean bias between corrected FY-4A LST and MODIS LST decreased to around -1 ℃, and the mean bias decreased significantly compared to that before the correction. Therefore, the LST climate dataset developed in the study by integrating FY-4A LST and MODIS LST can effectively support China's climate change research and provide better data quality for climate service.

  • ZHANG Chunxiao, MA Tongbin, QI Yazhou, WANG Aijia, HAO Xiaoyang
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    As an important method for simulating and understanding geographical environments, model based geographic simulation is essential for extracting geographical knowledge and revealing geographical laws. The meteorological process, as a crucial component of geographic environment, plays a significant role in various aspects of geographic simulation, thereby influencing the comprehensive simulation and resolution of complex problems within the geographic environment. However, meteorological models usually require a high level of expertise, involving intricate simulation knowledge and encompassing various components such as input data, simulation scheme settings, and others. Therefore, efficiently managing and sharing meteorological simulation knowledge has become the key step to improve the scientific rigor and accuracy of simulation. In this study, case-based reasoning and knowledge graph are applied to unify heterogeneous knowledge of meteorological simulation, and a retrieval method of meteorological simulation knowledge considering semantic and graph structure similarity is proposed to enhance the sharing capability of meteorological simulation knowledge. Firstly, based on Chinese meteorological simulation literature data, this study extracts meteorological simulation knowledge by using Bidirectional Long Short-Term Memory with Conditional Random Fields model (BiLSTM-CRF) and constructs a meteorological simulation knowledge base in the knowledge graph with simulation cases as the carrier of knowledge. As an example, in this study, a total of 795 cases, expressed as nodes and edges, are constructed and stored in knowledge graph. Secondly, a similarity assessment model for meteorological simulation cases is constructed to achieve the meteorological simulation knowledge retrieval considering both semantic and graph structure aspects. In detail, the model utilizes the Bert semantic model trained by the meteorological simulation corpus to mine the semantic features of meteorological simulation knowledge cases. The structural features of cases stored in the graph are also extracted, and the weights are set using the Analytic Hierarchy Process (AHP) hierarchical analysis method, so as to accurately measure the similarity of meteorological simulation knowledge cases. Thirdly, a visual prototype system for sharing meteorological simulation knowledge is developed, which can directly recommend parameter schemes for meteorological simulation knowledge based on requirements from users and provide knowledge reference for geographic system simulation personnel involved in meteorological processes. To validate the stability of the retrieval system, we selected 300 input cases, and the retrieval results provide the top 5 similar cases for each case. In this context, the accuracy of the system reaches 91.3%, improving the efficiency of meteorological simulation knowledge sharing and reuse.

  • LÜ Zheng, SUN Qun, WEN Bowei, ZHANG Fubing, MA Jingzhen
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    Spatial conflict processing is a difficult problem in cartographic generalization. At present, the focus of spatial conflict processing is topological consistency, and less attention is paid to the shape similarity. Shape similarity is the specific embodiment of spatial correlation in local areas. Road and residential area are a pair of geographical features with strong correlation. After selection, merger, exaggeration, segmentation, and other operations, some residential areas are shown as block with large area. The distance between block residential areas and adjacent roads is small and the shape is similar. At this time, if the roads and residential areas are simplified separately, uncontrollable topology conflict and shape conflict are likely to occur. To maintain topology consistency and shape similarity, the paper proposes a collaborative simplification method for roads and residential areas. First, Delaunay triangulation is used to build the adjacent relationship between roads and residential areas and extract the paired adjacent pieces. Then, the dynamic time warping algorithm is used for rough matching of nodes, and further optimization is made according to the included angle of nodes, the angular bisector direction of nodes, and the direction of connecting lines. Finally, the roads are simplified by the algorithm based on node selection, and the result of node selection is synchronized to the contours of the residential areas. After simplification, abnormal line segments are detected and corrected. The experiment is carried out on the roads and residential areas in a 1:500 00 map of a region in Zhejiang Province, and the results show that the method can realize the synchronous simplification of roads and adjacent residential areas on both sides. After simplification, the shape and structure of roads and residential areas coincide well, effectively maintaining the shape similarity, topology consistency, and visual effect of roads and residential areas in adjacent areas.

  • QI Kai, ZHANG Heng, ZHOU Yang, LIU Yifan, LI Qingxiang
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    Visualization serves as an incredibly effective method for analyzing and understanding cyberspace resources. By leveraging elements such as points, lines, surfaces, symbols, and terrain, it offers valuable insights for the management, maintenance, and optimization of cyberspace by transforming abstract data into tangible and comprehensible forms. In this context, this paper recognizes the significance of spatial relationships and the importance of resource nodes in the network and adopts the concept of a metaphorical map as a means of visualization. It draws an analogy between the resource nodes in cyberspace and the metaphorical representation of peaks and contouring lines in traditional geographic space, facilitating visual expression and understanding. The methodology proposed in this paper involves constructing spatial weight matrices and transfer matrices based on the topological relationships among nodes in cyberspace. These matrices enable the computation of indices such as the Moreland index and PageRank value for each node, which are vital metrics for evaluating the local importance of nodes within the network. To comprehensively assess the spatial correlation and significance of nodes on both local and global scales, the local Moran index is standardized, and the PageRank values are transformed to a consistent range. These standardized indices are then combined to derive a comprehensive evaluation index called the PI value. Subsequently, the network space resource nodes are arranged around the most prominent node utilizing the FR algorithm. The PI value guides the assignment of height values and sizes to each node, facilitating their visual representation. Finally, the visualization is performed using a metaphorical map that allows for an intuitive depiction of the nodes' positions in the network space and their relationships with other nodes. Our experimental results demonstrate that this method effectively incorporates the Moreland index, commonly employed to measure spatial aggregation in geospatial data, into the network space analysis. As a result, the method effectively highlights nodes and their interconnected nodes with higher attribute values. Furthermore, the visual representation based on the metaphorical map provides a natural and intuitive understanding of the nodes' positions and their interconnections in network space. It offers a means of effectively communicating complex information about the network's structure and topology. Moreover, when rendering formatted data, the efficiency of the metaphorical map exceeds that of traditional topology maps, particularly as the scale of the network expands. The metaphorical map proves to be a powerful tool for visualizing and comprehending the intricate relationships within cyberspace resources.

  • JIANG Yu, LIU Mengmeng, LI Zhichao, XUE Qingwen, DAI Yaoyu
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    With the development of the low-altitude economy, many provinces have released general airport layout plans, and the construction of general airports has reached its climax. Reasonable layout and functional positioning of general airports are the foundation for the development of the general aviation industry. However, there is still a lack of methods for generating scientific and reasonable airport layout. Therefore, it is urgently necessary to develop a scientific method for layout planning and functional positioning of general airports. Moreover, the existing research has not fully considered the spatial diversity of multi-dimensional demand of general airports, resulting in a one-sided general airport layout plan which is decoupled from functional positioning. To address these problems, this paper aims to construct a research framework for multi-dimensional demand analysis, layout planning, and functional positioning, and generate layout planning of general airports based on multi-dimensional functional demand. First, the demand for transportation and non-transportation functions of general airports is a systematically analyzed. Combining with real data, the impact index system for transportation and non-transportation demand is constructed based on an econometric model and characteristics of non-transportation functions. Then, a combined weighting method is used to overlay the impact indicators to obtain the distribution of regional transportation and non-transportation demand. Second, the improved Polygon Intersection Point Set method is employed to discretize the continuous facility sitting problem based on multi-dimensional functional demand coverage assumptions. After that, the multi-dimensional functional demand-oriented general airport layout planning models are constructed. The NSGA-III algorithm is designed to solve the multi-objective model. Finally, the k-means clustering-based general airport functional positioning model is proposed. An example region is selected to carry out general airport layout planning. The results show that in the near-term planning, when the number of general aviation airports increases by 29, the regional transportation demand coverage reaches 58.78% and the non-transportation demand coverage reaches 66.17%. In the long-term planning, when the number of general aviation airports increases by 64, the transportation demand coverage reaches 89.20% and the non-transportation demand coverage reaches 97.57%, which basically achieves a complete coverage of the multi-dimensional functional demand in the region. The 1h commuting area share of general aviation in the region is increased from the current 28.73% to 60.63% and 78.35% in the near-term and long-term planning, respectively. Finally, the grade and function of general aviation airports are determined based on a long-term layout scheme, which ensures the compatibility between functional positioning and the multi-dimensional functional demand distribution, providing a theoretical basis for the planning and construction of general aviation airports.

  • CHENG Dongya, ZHANG Xiaolin, LI Hongbo, CHEN Xinwei
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    Analyzing the characteristics of land cover changes from a new perspective holds significant value for a better understanding of land cover and land use. In this study, we proposed a research framework of land cover and land use based on the relevant theories and methods from community ecology. Based on this framework, we analyzed the land cover changes in the rural area of Jurong City, Zhejiang, Jiangsu Province during the period of 2000-2020. The results showed that: (1) The entire land cover consists of specific land cover types, which can be recognized and characterized from three aspects: boundary, aggregation, and interaction. The interaction of different land cover types can be recognized and understood from aspects of niche, niche differentiation, and competition. Specifically, there were niches in various land cover types, niche differentiation was fundamental to land cover distribution, and competition drove the land cover evolution; (2) For the overall land cover change in the study area, rank-abundance showed a similar trend, with the Shannon-Wiener index of land cover increasing during 2000-2020. Through the spatial autocorrelation analysis of the 1 km×1 km land cover, the distribution of high-high type of richness in the study area showed a decreasing trend from 2000 to 2020, the distribution of low-low type of maximum dominance in the northern rural areas was relatively large, and the distribution of high-high type of Shannon-Wiener index in the northern rural areas was also relatively large. During 2000-2020, the overall land cover rank-abundance in the study area showed a relatively stable evolution, with an overall Shannon-Wiener index increase of 0.14. At the scale of 1 km×1 km, the richness, maximum dominance, and Shannon-Wiener index had their own characteristics, indicating that it is necessary to observe land cover change at small scales and small units; (3) By exploring the role of community ecology methods in future land cover and land use research, we found that rank-abundance can reflect the dominant type of land cover and land use, richness can highlight the spatial difference of land cover and land use type by using small unit observations, dominance can indicate the status and role of a certain land cover and land use type, and Shannon-Wiener index can quantify the comprehensive diversity and complexity of land cover and land use. For future studies of land cover and land use, this paper suggests the use of small-scale and small-unit grid for changes in land cover and land use analysis to find out the change characteristics distinct from the past. The results of this study can provide new research paradigms and ideas for land cover and land use studies, serving as a reference and inspiration for rural development in developed areas.

  • LIU Songyan, WEI Lingna, DONG Jianzhi, GE Hui, QI Dan
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    At present, the multi-source precipitation data fusion technology is mostly based on the correction and fusion of ground rainfall station observations. However, there are still uncertainties in ground observations, especially in areas with a scarcity of ground rainfall stations. The multi-source precipitation fusion technique based on the theory of mathematical uncertainty can determine the errors and inter-correlations of each precipitation dataset without relying on ground observation data and establish optimal clear-rain classification time series for each grid, effectively enhancing the reliability of the fusion products. This study aims to evaluate the performance of the new generation precipitation data optimization fusion product, Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER), developed based on this theory. The assessment is conducted in the upper Hanzhong basin and the middle Guotan basin of the Han River basin. Ground-based high-density rainfall station data along with ECMWF Reanalysis v5 (ERA5) reanalysis precipitation data and Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation products are used to evaluate the accuracy and performance of SUPER at various spatiotemporal scales. The results show that: (1) Compared with ERA5 and IMERG, SUPER product performs better at daily and monthly scales. SUPER has a higher consistency with ground measured precipitation, smaller errors, the lowest false alarm rate, and the highest detection success rate; (2) SUPER precipitation product performs better in regions with gentle terrain than in areas with complex topography. The accuracy of SUPER is higher in the Guotan Basin than in the Han River Basin. SUPER has a higher CSI index in the Guotan Basin. In areas with complex topography, the accuracy of precipitation products decreases with the increase of elevation, for example, in the Han River Basin, the accuracy of SUPER product decreases with increasing elevation from the south to the north; (3) SUPER's fusion algorithm and datasets can effectively reduce the random error of precipitation data and the clear-rain classification error. However, the treatment of systematic bias is relatively simple, leaving room for further improvement. Also note that, SUPER fusion includes SM2Rain precipitation data based on microwave soil moisture retrieval, and the accuracy of precipitation based on microwave soil moisture in areas with complex topography is relatively low. This study comprehensively analyzes the performance of SUPER in the study area, laying a solid research foundation for the practical application and future enhancements of subsequent products.