Transitions of spatial data infrastructures (SDIs) support applications from 2D landscapes to 3D scenes. The existing methods for describing, managing, and providing services for 3D spatial data often lack coordination and efficiency. Moreover, the added complexity of 3D data structures necessitates novel approaches for component-level management and streaming capabilities. In response, we developed a generic conceptual model suitable for component-level management of diverse 3D spatial data in SDIs and discussed the design rationales and key considerations underlying the model. We formalized the flexible data composition and fine-grained lifecycle management in this model and specified this model at the cloud-optimized encoding level to enable efficient CRUD operations and streaming delivery of massive 3D spatial data. Our approach enabled direct streaming of the managed 3D spatial data without the need for redundant replication. We implemented, evaluated, and discussed the proposed approach in terms of service, accessibility, visualization, analysis cases, and efficiency. The results show that the proposed method is efficient in managing 3D spatial data and enables users to conduct 3D geo-analysis on the basis of specific parts of the data as needed. This work provides a scientific exploration that integrates the management and services of 3D spatial data in SDIs.

Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least "good enough," and (ii) enable others to lead the change towards better model-sharing practices.

Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.

The historical atlases provide a wealth of information about the evolution of geography over time and space. The alignment of geographical entities across varying time periods is a crucial aspect of extracting meaningful insights into the spatio-temporal dynamics of geography. This paper proposes a geographic entity alignment approach coupling Natural Gradient Boosting (NGBoost) with SHapley Additive exPlanation (SHAP). Taking the historical atlas of China as a case study, a geographic entity alignment model based on NGBoost is constructed considering the different kinds of similarity features of geographic entities, including semantic, distance, shape, size and topology. The contribution of similarity features in the NGBoost model is analyzed using the SHAP framework so as to improve the explanatory capacity of the model. The spatio-temporal evolution relationships of geographic entities are generated by association rules depending on alignment types and represented as quadruples, for constructing geographic knowledge graphs. The proposed NGBoost method was found a superior accuracy by comparing with BP neural networks, random forests, and other alternative methods for aligning geographic entities. The constructed geographic spatio-temporal evolution knowledge graphs offer valuable support for the queries of evolutionary knowledge.

Polycentric urban structures determine the combination and correlation of urban resources. In the past, nighttime light data were often used to identify the center locations, but the borders of polycentric urban regions (PURs) could not be obtained. Using multisource remote sensing data and graph, this research proposes an effective method for polycentric structure identification. First, we regard nighttime light data as a continuous mathematical surface, which can be constructed as nighttime light intensity graphs (NLIGs). Then, the space-optimized Girvan–Newman (SGN) method is proposed to detect the communities, and the eigenvector centrality (EC) and gray value are used to discover the central node of each community. Finally, the geographical location mapping (GLM) between Landsat 8 data segmentation objects and nodes is established, and the PURs and centers can be mapped to the communities and central nodes. This study took Shenyang, Chengdu, and Xi’an as study areas and used monthly Visible Infrared Imaging Radiometer-National Polar-orbiting Partnership (NPP-VIIRS) data in April 2019 and Landsat 8 data in January and August 2019. The average accuracies of PURs and centers identified by the proposed method were 86.24% and 72.5%, respectively. The developed method can provide technical support and data support for urban planning.

Studies on the riding modes of shared bicycles have aimed to heighten the understanding of cycling characteristics. This paper analyzes the spatiotemporal riding modes of shared bicycles based on tensor decomposition in Beijing, China. Two third-order tensors are constructed for the origin and destination points of shared bicycles in the day, hour, and space dimensions. Three factor matrices explicitly reveal two modes, three modes, and six modes in the day dimension, hour dimension, and space dimension, respectively. The relationships among the different modes in the three dimensions are demonstrated in an interaction table. Further, the density for different types of points of interest (POIs) are calculated to further analyze the potential riding purpose for different riding modes. Notably, the main POI types for the areas of O2 and D2 modes are consistent with the areas of D3 and O3 modes, which reflects the tidal characteristics of the commuting activities of shared bicycles. The main functional areas are inferred according to the riding modes and POIs, which enables verification of the correctness of the obtained riding modes to some extent. By method comparison, tensor decomposition shows the advantage of being able to reveal the spatiotemporal modes among multiple dimensions.

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.

Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution.

Lunar impact craters are the most typical geomorphic feature on the moon and are of great importance in studies of lunar terrain features. This paper presents a crater detection algorithm (CDA) that is based on terrain analysis and mathematical morphology methods. The proposed CDA is applied to digital elevation models (DEMs) to identify the boundaries of impact craters. The topographic and morphological characteristics of impact craters are discussed, and detailed steps are presented to detect different types of craters, such as dispersal craters, connective craters, and con-craters. The DEM from the Lunar Reconnaissance Orbiter, which has a resolution of 100 m, is used to verify the proposed CDA. The results show that the boundaries of impact craters can be detected. The results enable increased understanding of surface processes through the characterization of crater morphometry and the use of crater size-frequency distributions to estimate the ages of planetary surfaces.

Maps are widely used to visualize geo-information so that map users can develop related understandings about the real world. Such a process for communicating information is largely dependent on the rendering of map elements using different symbols (points and linear and area symbols). To meet the demand of more dynamic and comprehensive visualization in map rendering, it is essential to improve the rendering efficiency. This paper focuses on these research topics, especially the difficulty in constructing and drawing linear map symbols. By employing shader language, a function-based linear symbol building and rendering method is presented in this paper. The basic idea of this function-based method is to build a map-rendering solution that employs graphic processing unit (GPU) acceleration technology to improve the rendering efficiency. A ‘function’ is used to represent the algorithm that draws certain simple or complex linear map symbols. This function reflects the structure of a linear map symbol (describing the symbol construction information) and also the rendering process of the symbolized linear map elements (handled on a per-pixel basis by the shader program). Based on the Open Geospatial Consortium (OGC), Styled Layer Descriptor (SLD) specifications, four basic line types (i.e., solid lines, dashed lines, gradient color lines, and transition lines) are implemented in the proposed method, and the implementation of line markers, line joins and line caps is also discussed. Three experiments are conducted to demonstrate improvements in map rendering. The results show that a variety of linear map symbols can be constructed in a uniform way, which suggests that the proposed method addresses the difficulty in drawing linear map symbols. With this method, the efficiency of rendering linear map elements is substantially improved compared to using the graphics device interface plus (GDI+) and anti-grain geometry (AGG) methods; it also provides an applicable approach for developing map rendering systems. Using this function-based concept, the complexity of building linear map symbols and drawing linear map elements can be decreased.

Spatial index is a key component of Geographic Information Systems (GISystems). To date, an increasing number of spatial indexes have been developed to enhance the efficiency of spatial analysis and spatial query. Approximate expressions are adopted in the foundation of spatial index construction, to assist data organisation, e.g., bounding box of vector elements can be employed to build R-tree index. However, R-tree index using such bounding box expresses elements approximately, which usually results in redundancy and excessiveness due to inherent roughness of this method. This study proposes a characteristic bitmap coding method, termed the QCODE method, which generates approximate expressions of vector elements based on self-adaptive gridding. Based on the sizes of vector elements, this method selects the grid at an appropriate level in a self-adaptive manner, discretises the vector elements into grids through a rasterisation operation, as well as compresses and encodes this information as the code of a characteristic bitmap, i.e., the QCODE. The ‘bit’ data type is used in the design of QCODE to restrict the approximate expression of vector elements into finite bytes, providing more precise filtering as compared to the case in which only bounding box is used. With its distinct characteristics, the QCODE is introduced for the improvement of R-tree index. The results of experiments show that, combined with R-tree index, this method can reduce the filtering amount of vector elements as required for spatial analysis, and accelerate the execution efficiency of the entire process of GIS spatial analysis.