In recent years, with the continuous development and rapid iteration of emerging technologies such as mobile communication, big data, the Internet of Things (IoT), Artificial Intelligence (AI), digital twins, and autonomous driving, new smart cities have become a significant frontier in the field of Geographic Information Systems (GIS) applications. Digital twin cities represent a complex integrated technological system that underpins the development of next-generation smart cities. Intelligent, holistic mapping for digital twin cities relies on comprehensive urban sensing, and the interactive control of urban sensing facilities plays a pivotal role in achieving the seamless integration of the physical and digital aspects of digital twin cities, fostering the convergence of entities within the urban environment. Describing spatiotemporal entities of the real world through a spatiotemporal data model, as well as modeling the behavioral capabilities of these entities using spatiotemporal object behavior, represents not only an innovative extension of GIS spatiotemporal data models but also addresses the practical requirements of triadic fusion and interactive analysis of human, machine, and object components with the development of digital twin city. As a crucial facet of urban infrastructure, urban sensing facilities epitomize distinctive spatiotemporal entities. Current research into the interactive control of these facilities is predominantly concentrated within the domains of the IoT, Virtual Reality/Augmented Reality (VR/AR), and GIS. However, these domains often lack research pertaining to interactive control of urban sensing facilities within the GIS-based digital realm. To tackle these issues, a viable approach involves mapping the direct physical control processes of humans over objects in the Internet of Things domain to the realm of GIS. Specifically, this involves using a GIS spatiotemporal data model to abstractly represent urban sensing facilities in the real world as spatiotemporal entities. These entities are then expressed as spatiotemporal objects within a spatial information system. Subsequently, the changes or actions of these facility spatiotemporal entities are uniformly abstracted as the behavioral capabilities of these spatiotemporal facility objects. Ultimately, the interaction control of these sensing facilities by humans is transformed into a process where humans invoke the behavioral capabilities of facility spatiotemporal objects, resulting in specific outcomes. Based on the aforementioned idea, this study employs a multi-granular spatiotemporal object data model to construct behavior capabilities for urban sensing facilities. Building upon this foundation, a spatiotemporal object behavior-driven approach for interactive control of urban sensing facilities with virtual-reality integration is introduced. By constructing a "quintuple" model for interactive control of facility objects, this approach facilitates users in engaging in interactive control through a reciprocal linkage between virtual scenarios and physical facilities. This mechanism effectively translates the process of urban sensing facility interaction control based on direct communication commands into the digital world, providing theoretical and technical support for the intelligent and interactive analytical applications of sensing facilities within digital twin cities. Experimental results substantiate the effectiveness and feasibility of the proposed method for interactive control of urban sensing facilities.

The new smart city is an inevitable requirement for the development of urban digitalization to intelligence and further to wisdom, and is an important part of achieving high-quality development. This paper first introduces the background and basic concept of smart city, and analyzes the relationship and difference between the three stages of digital city, smart city and new smart city. Digital cities use computer networks, spatial information and virtual reality to digitize urban information, and focus on building information infrastructure. Smart cities, on the other hand, use spatio-temporal big data, cloud computing, and the Internet of Things to integrate systems across urban life, emphasizing intelligent management through a unified digital platform. New smart cities combine technologies such as digital twins, blockchain, and the meta-universe for citywide integration, and employ AI-based intelligent lifeforms for decision-making, blending real and virtual elements for advanced city management. This paper then explores the construction of new smart cities, focusing on high-quality urban development driven by technology and societal needs. It highlights the transition from digital to smart cities, emphasizing the role of information infrastructure and intelligent technology in this evolution. The paper discusses key technologies such as 3D urban modeling, digital twins, and the metaverse, and details their impact on urban planning and governance. It also examines how smart cities contribute to economic growth, meet national needs, and ensure public health and safety. The integration of technologies such as AI, IoT, and blockchain is shown to be critical to creating connected, efficient, and sustainable urban environments. The paper concludes by assessing the role of smart cities in measuring economic development, demonstrating their potential as a benchmark for national progress. Finally, based on the latest advances in AI technology, this paper analyzes and systematically looks forward to the key role AI can play in building new smart cities. AI's ability to analyze massive amounts of data, improve decision-making, and integrate various urban systems all provide important support for realizing the vision of a truly smart city ecosystem. With the synergy of "AI + IoT", "AI + Big Data", "AI + Big Models", and "AI + High Computing Power", the new smart cities are expected to achieve an unparalleled level of urban intelligence and ultimately a high quality of sustainable, efficient, and people-centered urban development.

This study aims to explore the complex spatiotemporal patterns of bicycle-sharing trips, reveal the influence of urban factors on the OD of bicycle-sharing trips, and improve the accuracy of OD prediction. Combining the theory of urban computing, urban factors such as the epidemic, months, weather conditions (minimum temperature, maximum temperature, and wind speed), and whether it is a weekday along with the length information of non-motorized lanes are selected to construct a bicycle-sharing demand prediction model (USTARN) that integrates urban computing and spatiotemporal attention residual network. USTARN first captures the spatiotemporal dependence of sharing bicycle flow through spatial area division and time series slicing, then combines the attention mechanism for deep residual learning, and finally adjusts the deep residual prediction results according to the urban factor prediction results to improve the model performance. Using the big data from bicycle orders and urban factor datasets in Shenzhen obtained from the government data open platform, this study visualizes the spatiotemporal distribution patterns of bicycle-sharing trips and analyzes their influencing factors using the Python development environment. The OD data set is divided into training set, verification set, and test set in a 7: 1:2 ratio, and the model training, model parameter adaptive adjustment, and model result comparison are carried out, respectively. The results show that the average error of the USTARN model for OD prediction of bike-sharing trips is 7.68%, which is 5.93%, 7.55%, and 6.07% lower than that of the STARN model without urban computing and the traditional CNN model, which is good at data feature extraction, and the BiLSTM model, which is good at dealing with bi-directional time-series data, respectively. The USTARN model fully reflects the influence of time, space, epidemic, weather, and other factors on the OD of bike-sharing trips. Our results have theoretical guiding significance for the accurate prediction of bike-sharing trip OD, which can provide a scientific basis for urban non-motorized roadway planning and have practical application value for the promotion of bike-sharing travel mode and solving the 'last mile' problem of residents travel.

Under the background of high-density urban areas and aging population in China, it is not only necessary but also urgent to strengthen the research on the design and construction of urban pocket parks. This paper uses CiteSpace, literature review, technical analysis and some other methods to conduct cluster analysis and comprehensive literature analysis on the study of urban pocket parks in China from 2000 to 2022. The results indicate that the current research hotspots in this field are pocket parks, roadside green space, landscaping, vest-pocket park, public space, landscape architecture, micro green spaces, street green land, design strategy, planning and design, etc. The research progress of pocket parks is divided into three stages: basic research (2000—2006), steady progress (2007—2018), and rapid development (2019—2022). In the basic research stage, the paper mainly studies the basic theories of street green space and vest-pocket park, which are the predecessor of the concept of pocket park, such as the development status at home and abroad, humanized design, and behavioral psychology, which lays a good foundation for the research of pocket park in China. In the stage of steady progress, the concept of pocket park is clearly proposed, the connotation of pocket park is interpreted, and the basic strategy of pocket park planning and landscape design is summarized. In the stage of rapid development, the research perspective turns to more micro aspects such as urban renewal, spatial layout of pocket park in the context of park city, optimization strategy, accessibility, fairness, interactivity, and comprehensive evaluation, etc. The research focus includes basic research, planning and design research, and evaluation research. The basic research has systematically sorted out and summarized the concept and connotation, construction scale, construction types, and usage functions of pocket parks. The planning and design research has extracted design strategies related to pocket parks from aspects such as spatial layout, landscape design, and elderly-oriented design. The evaluation research has evaluated the current situation of pocket parks from three aspects: social benefits, landscape benefits, and spatial structure. The development directions of urban pocket park research in our country in the future include: research on collaborative group layout of multiple pocket parks and optimization of internal spatial layout of a single pocket park, optimization of landscape facility layout, and plant configuration and optimization; research on the adaptability of pocket parks to the elderly, children, accessibility, and humanization according to the behavioral characteristics and psychological needs of residents, based on the theoretical foundations of environmental behavior and environmental psychology; systematically study on the coupling relationship between pocket parks and the natural environmental effects in the area by comprehensively applying architectural environmental theory, Remote Sensing (RS) technology, and Geographic Information System(GIS) technology; normative research on design guidelines, construction, operation and maintenance standard paradigms of pocket parks; research on digitization of pocket parks design and intelligent operation and maintenance management, as well as evaluation system, evaluation method and statistical analysis of pocket parks on this basis.

Being able to penetrate clouds and fog, Synthetic Aperture Radar (SAR) imagery has been widely used in flood mapping and flood detection regardless of time and weather condition. Improving the accuracy of flood maps retrieved from SAR images is of both scientific and practical significance. However, errors in SAR-derived flood maps can come from SAR image measuring principles, image acquisition and pre-processing system, water detection algorithms, and the remarkable temporal dynamics of the flooding process. The aim of this paper is to provide an extensive literature review of flood detection using SAR images (about 108 peer reviewed journal papers), including SAR data sources, flood detection methods, application of auxiliary information, accuracy evaluation, and challenges and opportunities for future research. Based on the articles reporting flood detection methods, it is found that the threshold segmentation methods such as the OTSU and KI algorithms are computationally fast and have been most widely used. The classification methods (e.g., the support vector machine and K-means clustering algorithms) have the flexibility to account for both subjectivity and objectivity, and the change detection method using the difference and ratio algorithms can effectively suppress over-detection and image geometric errors. Additionally, combining SAR images with four major types of auxiliary data to increase flood detection accuracy has become a hot topic in the past decades. Specifically, terrain information such as Digital Elevation Model (DEM), Height Above Nearest Drainage (HAND), and topographic slope can effectively reduce the impacts of shadows and exclude non-flooded areas. SAR image textural and multispectral optical information (e.g., Landsat data and aerial photos) can enhance the recognition ability of water features. Land cover/use data facilitate removing non-water features that are similar to water features, and hydrological data can help excluding permanent water bodies from temporary flood areas. From the perspectives of SAR image types, image preprocessing, detection algorithms, and accuracy assessment, major challenges are further discussed including insufficient understanding of the complexity of SAR backscattering information, limited progress in improving the signal-to-noise ratio during image pre-processing, lack of versatile flood detection algorithms, and low availability of high-quality verification data. While opportunities for future SAR-based flood detection research include combination of auxiliary information in detection algorithms, use of multiple rather than single threshold for water detection, and transition from deterministic toward probabilistic flood mapping.

The assurance of a consistent supply of daily necessities in megacities is pivotal in fortifying community supply resilience. It is axiomatic that a community system is not an insular entity; rather, it intricately intertwines with various elements of urban systems. As a foundational unit of urban governance, the urban community is instrumental in facilitating a congruent nexus between supply and demand, thereby augmenting urban resilience. This study proposes an exploratory evaluation method for the urban community supply support and resilience based on complex network theory, attempting to achieve a breakthrough in the underlying theoretical framework of resilience assessment from "single system assessment" to "multi-system correlation assessment". Taking the six districts in the central city of Guangzhou as an example, we build a supply-demand network based on citizens' spatio-temporal behaviors using multi-source data such as mobile phone signaling data and other data. The attacking strategies of network are based on five community resilience indicators. Besides, the cascade failure mechanism is introduced to evaluate the network resilience, and the entropy-weighted method is employed to obtain resilience evaluation results. The influence mechanism of community resilience on the supply system is further analyzed by studying the factors affecting community node failure at different stages of supply network. The findings are as follows: (1) The proposed evaluation model of the community supply support and resilience can effectively simulate urban community supply-demand networks and evaluate the resilience of communities. Low-resilience communities are mainly categorized into three spatial types: old blocks, urban villages, and suburban blocks; (2) Through the analysis of network resilience under five different attack strategies, it is found that the dominant influencing factors are different, with the population density being the primary factor; (3) There exists a complex bidirectional relationship between community resilience and supply security, including the obvious vulnerability of low-resilience communities. And the community self-organization ability, the supply facility layout, and the linkage scheduling between supply points all affect the overall community resilience.

With the expansion of urban areas, a mix of transportation modes has become prevalent during the daily commutes of city dwellers. That is, commuters often need to transfer between various modes to reach their destinations. Accurate identification and analysis of these transfer behaviors are crucial for advancing urban transportation research. Current research tends to focus on distance or time thresholds, typically derived from walking speeds or anecdotal experience. However, these approaches often overlook the distinct station densities within cities. Other studies, while utilizing GPS, GTFS, and similar datasets, construct intricate transfer identification methods that lack generalizability. Against this backdrop, we introduce a time-distance dual-constraint transfer recognition algorithm. Firstly, leveraging extensive traffic IC card data, based on the statistical characteristics of the proximity distance sequences between bus or subway stations and their M neighboring stations, distance thresholds for bus-bus, bus-subway, and subway-bus transfer are detected individually. Subsequently, a filtering algorithm based on these distance thresholds is applied to daily data to produce a candidate transfer data set. Based on this, four time thresholds for each day are determined by analyzing the statistical characteristics of the transit time differences within the datasets. Finally, these dual thresholds facilitate the precise extraction of transfer behaviors. Furthermore, we establish a classification framework for these behaviors, classifying them into nine distinct transfer modes. These modes are defined based on the duration of travel time in the first and second journeys, encompassing variations including long-long, long-medium, long-short, middle-long, middle-middle, middle-short, short-long, short-middle, and short-short. We analyze these models individually for their travel characteristics. Results reveal that the morning peak for all transfer trips precedes that of buses and subways, with short-long transfers leading by up to 30 minutes. This underscores the added effort required by commuters who rely on transfers. In contrast, evening peak times vary, with certain transfer modes like long-long and long-short lagging notably behind the general evening peak. This further emphasizes the increased commuting burden associated with transfers. In terms of travel distances, the peak of regular subway travel distances is around 10 km, while that of the bus travel distances is around 1 km. The peak commuting distances for all nine transfer behaviors are greater than those of typical trips and are distributed within a range of 20~40 km. In summary, our method for extracting and analyzing transfer behaviors offers a robust and effective tool for urban transportation research, urban vitality assessment, public transportation planning, and urban planning.

Triple Collocation (TC) is a technique for assessing the uncertainties of three samples individually without knowledge of the true values. This method is based on the assumptions of linearity, orthogonality, and zero cross-correlation. In practical use, these three assumptions are often difficult to achieve, particularly the orthogonality and zero cross-correlation assumptions, which often encounter significant violations. Moreover, we are uncertain about the impact of these assumption violations on the errors of the method's results. In this study, we simulated multiple sets of synthetic samples with varying degrees of two assumption violations to investigate the impact of assumption violations on the accuracy of the TC method. The results of synthetic samples experiment indicate that, in general, when there is an increase in the violation of orthogonality or zero cross-correlation assumptions, the error of the method's results increases linearly or quadratically. However, under certain specific conditions of assumption violation, there is a sudden and spike-like increase in the error of TC method results. This phenomenon is referred to as "outliers". To understand the origin of the outliers, we derived the complete mathematical relationship between the violation of assumptions and the errors of the results. This relationship exhibits a fractional structure rather than a linear one, contributing to the emergence of outliers. From the perspective of the difference notation, this fractional structure results from rescaling coefficients. Continuing to analyze this mathematical relationship, we can draw two conclusions. Firstly, merely ensuring the approximate independence of the three samples does not necessarily lead to improved method results. When the structural relationships among the three samples meet certain conditions, outliers emerge. Additionally, previous attempts at method improvement have aimed at overall reducing the sensitivity of this method to assumptions, neglecting the presence of outliers. Considering these factors, the key to suppressing outliers lies in better designing these rescaling coefficients. The paper presents two possible improvement methods:(1) Ignoring the additive bias, so that the rescaling coefficients are not affected by the orthogonality or zero cross-correlation assumptions. (2) Limiting the upper and lower bounds of the rescaling coefficients. We achieved favorable results in suppressing outliers by constraining the absolute values of the rescaling coefficients between 0.25 and 4. Both improvement methods can suppress the occurrence of outliers. However, when the additive bias is significant, the first improvement method generates substantial extreme errors due to its inherent structure, which is insufficient to eliminate outliers. The second method performs effectively even in complex scenarios. Lastly, we conducted a simple estimation of the probability of outliers occurring in practical usage, which was approximately 3.2%. In addition, we used SMOS, SMAP, and AMSR2 soil moisture data to validate the phenomenon of outliers and compared the two improved methods. According to real data, some outliers appear as negative values and are removed because the calculated results cannot be negative. Therefore, A portion of the outlier does not cause a significant deviation in the calculation result; instead, they simply prevent the calculation of meaningful results. Therefore, when employing the TC method with fewer repetitions for calculations (e.g., with fewer than 500 repetitions), the influence of outliers can be disregarded.

The geographically and temporally weighted regression method based on weighted least squares estimation achieves optimal estimates under the assumption of Gauss-Markov independent identical distributions. However, these conditions cannot be always satisfied. If there are outliers or heavy-tailed distributions in the data, the least squares estimates may be significantly biased. On the other hand, quantile regression is less affected by outliers and is more robust than least squares regression, which can be applied in a broader range of applications under more relaxed conditions. More importantly, the least squares regression model only focuses on the mean of the response, while quantile regression explores the global distribution of the response variable (e.g., quantiles of the response variable) and can obtain richer information. In this paper, we propose the geographically and temporally weighted quantile regression model based on the local polynomial estimation. This model allows for different optimal bandwidths for different explanatory variables and use a two-step estimation method to obtain the estimates of the coefficients. To illustrate the superiority of the proposed method, we compare the proposed method with the geographically and temporally weighted least squares regression through numerical simulations. The simulation results show that the mean square error and the mean absolute error of the coefficient estimates for the proposed quantile regression model are both smaller than those of the least squares regression model. For example, at the 0.75 quantile, the mean square error and mean absolute error of the coefficient estimates based on the least squares regression are 10 times and 4 times those based on the quantile regression, respectively. This indicates that our proposed method is robust and can explore the global distribution of the response variable compared to the least squares regression model. Finally, to illustrate the practical ability of the method, we apply it to the data of Shanghai's commercial residential neighborhoods from 2017 to 2021 to investigate the effects of different factors on residential prices at different quantiles (e.g., high house prices, medium house prices, and low house prices). The results show that the explanatory variables have different effects on house prices at different quantiles. The spatial and temporal distributions of the coefficients of the variables differ significantly among high house prices, medium house prices, and low house prices, and the optimal bandwidths for different explanatory variables also differ. Compared to the MGTWR based on least squares regression, the quantile regression model proposed in this paper is more robust with the presence of outliers. After removing 1% of extreme values, the change in the mean absolute error of the fitting based on the quantile regression model is 1% smaller than that based on the least squares regression model. Additionally, the quantile regression model can explore the factors affecting the different price levels of the housing such as the high house prices, medium house prices, and low house prices.

The way we capture and analyze human activity and behavior is changing because of big data. A variety of new data sources have emerged to supplement the official data, offering a significant amount of data with potential application value for the research of tourism and leisure while overcoming the common problem of insufficient data in traditional tourism research. Based on the research frontier of big geodata, this paper explains the theoretical foundation of tourism under the background of geographic multi-source big data at three levels: human tourism activities, tourism geographical environments and destinations, and the relationship between tourists and tourist destinations. Secondly, this paper summarizes the application of big geodata, such as human tourism activity data (e.g., UGC data, device data, transaction data) and tourism geographical environment data (e.g., POI, environmental data). Finally, this paper discusses the challenges and prospects of big geodata in three aspects: research paradigm and theory, multi-source data fusion, and analysis methods. For the research paradigm and theory, there is the requirement for standardize and systematize the scientific research paradigm by combining different events and scenarios to create an interpretation system of Chinese tourism geography based on "process-structure-mechanism". In terms of multi-source data fusion, the combination of big data and other data is necessary. In terms of analysis methods, efforts are still needed to improve the adaptability of analysis methods and incorporate the specific variables of tourism phenomena.

The movement of people within urban areas serves as a driving force for the development of social phenomena. Accurate Origin-Destination (OD) flow data record spatial interaction patterns of individuals, goods, or information from their starting points (Origin [O]) to their destinations (Destination [D]). Precise prediction of internal city OD flows is crucial for optimizing urban traffic operational efficiency, enhancing resource utilization, and fostering sustainable urban development. However, obtaining high-quality OD flow data is challenging due to issues such as privacy protection. There are significant hurdles, including high acquisition costs, limited coverage within large areas, and sparse spatial distribution, which hinder extensive research in urban computation. Current research often relies on a single scale, utilizing extensive historical traffic data between geographic locations to predict future flows. Yet, there has been limited exploration into crucial features and model accuracy for different spatial scales. This study addresses this gap by employing taxi trajectories in Beijing and leveraging the Deep Gravity model to predict OD flow at different spatial scales. Additionally, the integration of SHapley Additive exPlanations (SHAP) values sheds light on the pivotal features influencing OD flow predictions across diverse scales. Results show that: 1) Compared to Gravity model and Radiation model, the Deep Gravity model at the street scale exhibits the highest accuracy in predicting OD flows, achieving an impressive Common Part of Commuters (CPC) value of 0.83. The Deep Gravity model effectively captures the overall structure of the OD flow network during peak morning and evening hours in Beijing, revealing a distinctive "circular dispersal" pattern; 2) For the selected spatial scales, the four features with the most significant impact on OD flow prediction accuracy are the travel distance between O and D points, the number of businesses around O and D points, the quantity of dining establishments, and the number of shopping services; 3) The local impact of the same feature on OD flow prediction models differs from its global impact. For instance, features related to education, science, and culture, as well as sports and leisure Points of Interest (POI), exhibit relatively minor effects on the model at a global scale. However, on a local scale, these features demonstrate a significant influence. This study has achieved high-precision prediction of OD flows at various spatial scales. Additionally, it quantitatively reveals the crucial factors influencing OD flow modeling at different spatial scales, thereby providing valuable insights into understanding population movements within urban areas. The findings of this research hold significant practical implications for urban planning, traffic management, and the development of smart cities.

The mobile communication technology and social media has been deeply embedded into people's daily life, affecting people's choices of leisure activities. However, there is still limited understanding of the spatial regularity characteristics of its impact, particularly due to the lack of empirical analysis utilizing specific quantitative indicators. Given that the layout of leisure spaces is closely linked to social equity, it is essential to obtain a better understanding of the emerging spatial patterns in order to improve residents' well-being. To address this gap, leisure check-ins on Xiaohongshu (a Chinese social media platform) and leisure Points of Interest (POI) in Dalian are used to measure the characteristics of these two types of leisure spaces in two dimensions: concentration and clustering, and at two scales: the main urban area and subdistricts. Various spatial analysis methods, including kernel density estimation, head/tail breaks, hot spot analysis (Getis-Ord Gi*), and DBSCAN (Density-Based Clustering), are employed to analyze the data. The findings are that: (1) Leisure check-ins are mostly located in the urban central area, with a smaller distribution range and fewer hotspot cores; (2) At both the main urban area and subdistricts scales, the distribution of leisure check-ins exhibits lower concentration and clustering, with obvious "decentralized dispersion" characteristics. However, the degree of significance of these features varies across different subdistricts; (3) The majority hotspots of leisure check-ins are located in traditional hotspots, with a few emerging in expansion of urban central area or regions with unique features, such as historic urban landscape district and marina space; (4) The distribution patterns of leisure check-ins can be grouped into four types based on differences in subdistricts' concentration and clustering ratio: "original center cluster type", "original center scattered type", "new center scattered type", and "no center scattered type". The subdistricts with these different distribution patterns exhibit differences in functionality, location, and other characteristics. This study analyses the behavioral processes of leisure activities under the influence of social media through the lens of Actor-Network-Theory. Based on the fundamental principles of temporal geography and differences between "space of places" and "space of flows", it is argued that social media engenders a novel "local order" of leisure pursuits, marked by a desire for spatial exploration. This new order reflects the impact of "space of flows" based on virtual connections on "space of places" based on physical presence, which strengthens the role of node attractors, reduces the constraints of accessibility at micro scales, and increases the flexibility of location.

Accurate and explainable prediction of PM_2.5 concentration can help humans avoid exposure risks to air pollution, which is of great significance for human health risk assessment and policy implementation. Currently, the existing PM_2.5 concentration prediction models focus on improving the model prediction accuracy without considering model interpretability, resulting in poor model reusability and trustworthiness. Therefore, this paper proposes an Attentional Spatiotemporal Ordinary Differential Equation (ASTODE) model for PM_2.5 concentration prediction tasks considering both prediction accuracy and model interpretability. Specifically, this paper integrates the Neural Ordinary Differential Equation (NODE) into the PM_2.5 concentration prediction task to improve the interpretability of the prediction model. In addition, to address the challenge of traditional NODE in mining spatial dependencies in PM_2.5 concentration data, this paper proposes a novel spatiotemporal derivative network that extends the traditional NODE to spatiotemporal ordinary differential equations. To address the challenges of traditional NODE in mining long-term dependencies in PM_2.5 concentration data, this paper proposes a spatiotemporal attention mechanism to fuse hidden states of multiple time nodes. In the experimental section, the proposed ASTODE model is validated using a real PM_2.5 concentration dataset. This paper quantifies the prediction errors of the ASTODE model in both temporal and spatial dimensions. By comparing with six existing baseline methods, our proposed ASTODE model obtains a similar or higher prediction accuracy. This paper also analyzes the interpretability of our proposed ASTODE model from a visualization perspective, demonstrating that the proposed ASTODE model balances the prediction accuracy and interpretability to some extent.

Geographic Information Science (GIS) is not only the demand for the development of the discipline itself, but also the technical method to support the exploration of the frontiers of geography, earth system science and future geography, and the supporting technology to serve the national strategy and social development. In view of the intrinsic law of the development of geographic information science, the extrinsic drive of the development of related disciplines, and the pull of new technologies such as Artificial Intelligence (AI), this paper firstly analyses the development process of GIS and explores its development law from six dimensions, such as description content, expression dimension, expression mode, analysis method and service mode, etc.; then, on the basis of interpreting the original intention and goal of the development of geography, a geography discipline system oriented to the "physical-humanistic-informational" triadic world is proposed, the research object of information geography is discussed, and a conceptual model integrating the seven elements of information and seven dimensions of geographic descriptions is put forward; then, the development trend of geographic information science is analysed from three aspects, including geography from the perspective of information science, information geography from the perspective of geography, and geo-linguistics from the perspective of linguistics, information geography from the perspective of geography, and geolinguistics from the perspective of linguistics, the development trend of geographic information discipline is analysed. Finally, the paper summarises the possible directions and points of development of GIS, geography in the information age, geo-scenario, and geo-big model. We hope that our work can contribute to enriching the understanding of geographic information disciplines, promoting the development of geographic information related sciences, and enhancing the ability of the discipline to support national development needs and serve society.

With the continuous advancement of the globalization process, communication and cooperation among countries and regions around the world are becoming increasingly closer, and the scale of international migration flows is also expanding. Asia stands out as an active region for international migration, with a large portion of migratory movements occurring within its borders. In addition to the social and economic factors of the origin and destination regions, spatial and temporal dependence among migration flows is crucial in understanding international migration dynamics, indicating that migration is influenced by neighboring and past migration flows. Different from other kinds of data (e.g., regional GDP), migration flows between different regions often contain many zero values, necessitating specific methods for handling them. Additionally, spatial and temporal dependence among migration flows can be categorized into space-time contemporaneous and lagged structures, with the former reflecting the links to the preceding location and the instantaneous neighboring locations, and the latter pertaining to the preceding location and the preceding neighboring locations. Based on the bilateral migration data of Asian countries in six periods from 1990 to 2020, this study utilizes eigenvector space-time filtering models, along with contemporaneous and lagged dependent structures, as well as eigenvector spatial filtering models and zero-inflated negative binomial regression models, to explore the influential factors of the international migration flows within Asia and their changes during 1990-2020. Finally, this study aims to forecast international flows within Asia between 2020 and 2025 based on two types of space-time filtering models. Preliminary results indicate significant space-time autocorrelation of international migration flows within Asia, with neighboring migration flows exerting a greater influence over the same time period compared to the past. Incorporating eigenvectors to represent spatial and temporal dependence effectively improves the goodness-of-fit of the models. Main factors affecting international migration flows within Asia include population size, economic level, war situation, and proximity. During the 30 years (1990-2020), the influence of population size fluctuated, economic disparities initially increased before weakening, wars continued to drive emigration, geographical barriers decreased, and factors like language proximity and economic cooperation significantly influenced migration. Looking ahead from 2020 to 2025, migration trends are evident between Pakistan and India, as well as from India to Saudi Arabia, from Pakistan to Afghanistan and from Syria to Jordan. Combining the forecasting results of the two eigenvector space-time filtering models, the mean value of the total volume of international migration flows within Asia from 2020 to 2025 is projected to be approximately 1.8×10⁷. India emerges as a major country for international migration. Understanding the spatial and temporal dependence and other characteristics of international migration within Asia is crucial for accurately forecasting future migration flows and providing scientific reference for policy making.

Network science provides abstract models for analyzing complex phenomena in the real world. With the support of network science theories and methods, researchers are able to explore the dynamic relationship between research objects in tourism domains from a more systematic perspective. This unique viewpoint is of great significance for further understanding the operation rules of tourism and promoting the balanced and sustainable development of related industries. With the digitalization of tourism, tourism information has become more flexible and scalable, which has significantly increased the applicability of network science theories and methods in tourism domains. Against this background, research on the applications of network science theories and methods in tourism domains has received extensive attention in recent years. In view of this, this paper systematically reviews the published articles related to the applications of network science theories and methods in tourism domains and summarizes the main research contents through a multi-scale perspective. Specifically, this paper first outlines the backgrounds of related theoretical foundations and application scenarios. The most common types of tourism networks, including interpersonal networks, tourist flow networks, economic networks, etc., are summarized through a "node-edge" structure. Important concepts and terms in network science, especially the differences and relations between complex networks and social networks as two "research paradigms", are also highlighted. Following that, this paper summarizes the progress of the applications of network science theories and methods in tourism domains at different scales of observation (i.e., microscopic, mesoscopic, and macroscopic). Among these scales, the microscopic scale focuses on the interactive properties of tourism actors, the mesoscopic scale is often used to describe the aggregation phenomena of tourism actors, and the macroscopic scale focuses on the global topological structural features of the tourism actor networks in tourism domains. Common methods or measures in network science, such as centrality, structural holes, community/cohesive subgroups, core-periphery structure, small worlds, and scale-free effect are also introduced. Based on the review of the research progress, this paper identifies the research problems in current research, including reliability deficiencies in the research data, negligence of multi-scale phenomena, interpretability challenges in the research results, and lack of highlighting theoretical contributions in tourism domains. The aim of this paper is to review the research literature on applications of network science theories and methods in tourism domains from the perspective of research practice, in order to effectively present the substance and compatibility of research at the intersection of network science and tourism.

Traditional settlements have gathered a wealth of traditional cultural resources such as ancient architecture and folklore, which have attracted significant attention for their outstanding historical, cultural and artistic values, and it is of positive significance to extract their abundant historical and cultural information and serve them for modern industrial development. Currently, there is a lack of knowledge extraction, organization and expression of the rich historical and cultural information of traditional settlements based on geographic knowledge extraction and expression perspectives to achieve the transformation of "data-information-knowledge-wisdom", this paper proposes the geographic ontology of cultural landscape genes of traditional settlements (GeoOnto-CLGTS) and explores the intrinsic correlation characteristics of the traditional landscape genes of traditional settlements. Firstly, combining the geographic information ontology and characteristics of traditional settlement landscape genes, the concept and expression method of GeoOnto-CLGTS are analyzed, and this paper proposes the construction method of GeoOnto-CLGTS model. Secondly, combing the landscape gene concepts, association relationships and data attribute characteristics, the seven-step geographic information ontology modeling method is applied to construct the conceptual layer of GeoOnto-CLGTS from top-down. By utilizing Protege tool to supplement examples using 123 traditional Chinese settlements as cases, the instance layer construction of the GeoOnto-CLGTS model is achieved. Finally, the GeoOnto-CLGTS data is stored through the Neo4j graph database to complete the construction of the knowledge graph of traditional settlement landscape genes, enabling the retrieval of landscape gene information. The results show that the GeoOnto-CLGTS constructed in this paper can provide a valuable reference for carrying out knowledge discovery of traditional settlement cultural resources and promoting digital preservation of traditional settlements in the future.

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.

Tourism and leisure have become important aspects of modern life, enhancing the quality of life through recreational activities. However, the development of tourism and leisure is characterized by imbalances and deficiencies that need immediate attention. Geo-information Science provides a spatial analytical framework and methods for studying tourism and leisure. Additionally, the rapid advancement of big data technology has facilitated the widespread application and interest in Geo-information Science in the field of tourism and leisure. This article aims to critically review the current state of research, disciplinary contributions, limitations, and future directions of Geo-Information Science in the field of tourism and leisure. To achieve this objective, we conducted interviews with representative scholars from various fields such as tourism management, Geo-information Science, and geography to gather their insights. Through interviews with twelve experts, we found that one of the major contributions of Geo-information Science to tourism and leisure research is the integration of spatial thinking, including the spatial and temporal dimensions. On one hand, by emphasizing the importance of space, Geo-information Science allows for a deeper understanding of how the geographical environment influences tourist behavior and decision-making processes. Analytical techniques such as spatial analysis, geographic visualization, and spatial modeling offer technical opportunities for valuable insights into various aspects of tourism, including the spatial behavior of tourists, distribution patterns, and the utilization of tourism resources. On the other hand, the use of Geo-information Science rooted in spatiotemporal cognitive logic helps in understanding the generation and evolution of tourism patterns. This approach can analyze changes and impacts of tourism processes at different time and spatial scales, revealing underlying behavioral mechanisms, spatial-temporal distribution patterns of tourist attractions, and temporal trends in the tourism market. However, challenges remain in interpreting research findings, integrating data from multiple sources, and promoting interdisciplinary exchanges. Addressing these challenges requires further exploration and research from scholars. Nonetheless, it is important to recognize the tremendous potential of Geo-information Science in future applications in the field of tourism and leisure. In the era of Artificial Intelligence 2.0, the integration and breakthroughs in combining 3D GIS with human sensory devices, enhancing decision-making abilities through spatiotemporal modeling technologies, the integration of AIGC with Geo-information Science technologies, and the automatic generation of multidimensional virtual spaces all hold exciting prospects. This study aims to provide guidance for the fusion of Geo-information Science with tourism and leisure research and anticipate future directions in this field. By addressing current limitations and exploring future directions, researchers can further enhance our understanding of these fields and contribute to their sustainable development.

Due to technical gaps in using satellite carbon observations for regional emission reduction, high-resolution, global-scale Fossil Fuel Carbon Dioxide (FFCO₂) emission inventories have become the main data sources for regional FFCO₂ emission research. However, there are still significant uncertainties in use of existing global-scale FFCO₂ emission inventories for regional research. Therefore, this paper quantitatively analyzed the differences and variabilities of high-resolution FFCO₂ emission inventories (ODIAC 2020b, EDGAR v6.0, and PKU-CO₂-v2) at the regional scale and fused these three inventories based on Kalman filtering algorithm. Then, this paper explored the spatial and temporal evolution pattern of FFCO₂ emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The results show that: (1) There were significant differences and variability among the current FFCO₂ emission inventories. Taking the GBA as an example, under the optimal representation spatial resolution of 3 km × 3 km, the average difference of grid cells within the region reached 140%, and the coefficient of variation was 16.3%. The use of a single global scale FFCO₂ emission inventory data for regional or urban FFCO₂ emission studies resulted in inaccurate results; (2) The reconstructed long term data from 2000 to 2018 using Kalman filter showed that the uncertainty decreased from ±15%~20% to ±10%; (3) From 2000 to 2018, the overall pattern of FFCO₂ emissions in the GBA was characterized by high emissions in Guangzhou, Shenzhen, Hong Kong, and Macao, low emission areas in the peripheral areas, and an emission transfer path from Shenzhen, Hong Kong → Guangzhou → Foshan, Dongguan → Zhongshan. The approach for regional FFCO₂ emissions proposed in this paper is demonstrated in the GBA and is applicable to other regions and cities. The conclusions of this research will provide a scientific basis for the optimal layout of energy and resources in the Greater Bay Area, which is of great significance for low-carbon transformation, high-quality development, and the construction of Beautiful Bay Area.

In the context of culture-tourism integration, digital China, and activated utilization of heritage, heritage tourism has become a hot topic in academia and industry. The mismatch between spatial representation of heritage value and tourists' spatial perception is one of the most prominent contradictions in current heritage tourism. From the perspective of heritage value, this paper combines bibliometric analysis and systematic review to discuss relevant research from four aspects: interpretation and quantification of heritage value, spatial calculation and representation of heritage value, tourists' perception of heritage value and space, and tourists' spatial behavior in heritage site. Besides, comparisons between Chinese and foreign literature of these four themes are conducted to figure out the similarity and difference. The main findings are as follows: (1) there are abundant achievements in the interpretation of heritage value, which mainly focus on the connotation and interpretation technology of heritage value, but lack of quantitative methods; (2) the spatial calculation and representation of heritage value is object-oriented and application-oriented, and the geographic information system and spatial information technology are commonly used methods; (3) studies on tourists’ perception of heritage value and space are mostly from the perspective of tourism destinations of heritage sites but ignore the heritage value and spatial attributes, lacking the exploration of relationship between heritage value, heritage space, and tourists. The measurement dimension of sensory perception is mainly visual; (4) the research on tourist behavior in heritage site mainly focuses on the characteristics, patterns, causes, and influencing factors of behavior. It emphasizes the importance of practical application and reflects the orientation of heritage responsibility; (5) the spatial calculation and representation of heritage value, as well as tourists' perception of heritage value and space, are still lack of concern in the context of natural heritage and mixed heritage; (6) there are similarities and differences in the research objects, methods, and contents of Chinese and foreign literatures; (7) in the future, the interpretation and representation of heritage value will transition from traditional narrative to spatial quantification, and the perception and calculation of heritage space will shift from spatial footprint to perceptual behavior. Based on above findings, this paper puts forward a theoretical framework and methodological path from multidisciplinary perspective for tourists' spatial perception and calculation of heritage value, in order to promote the interdisciplinary theory and technology integration of heritage research. In conclusion, this paper provides theoretical references for related research and practical references for heritage protection, heritage site management, tourism development, and heritage value inheritance.

The ecotourism suitability assessment is the basis and a crucial reference for evaluating development potential, formulating plans, and implementing exploitation in ecotourism. In this study, we first analyze the feasibility of machine learning methods for modeling ecotourism suitability, and the Random Forest (RF) algorithm is selected for conducting an empirical study in the Wuling Mountain area in Hunan Province. In the study area, there are abundant tourism resources with an urgent need for ecotourism development, which can not only consolidate and expand the achievements of poverty alleviation, but also effectively connect with rural revitalization, thereby promoting sustainable development of tourism. The results show that: (1) Machine learning, as a new regional ecotourism suitability assessment approach, provides new insights and solutions for further improvement of suitability assessment; (2) The RF algorithm as a typical machine learning method can be effectively applied in the regional ecotourism suitability assessment. The optimized RF model achieves an average testing accuracy of 86.49%, with an area under the curve (AUC) of 0.95. These results also indicate the ecotourism suitability of the Wuling Mountain area in Hunan Province; (3) The ranking of feature importance reveals that land use type contributes most to the model, accounting for 28.98%, followed by other significant factors including population density (16.34%), distance from scenic spots (12.2%), and biological richness (10.65%). The above factors should be all considered in ecotourism development efforts; (4) The ecotourism suitability results show a high proportion of highly and moderately suitable areas, suggesting significant potential for ecotourism development in the study area. Based on the ecotourism suitability assessment, different development directions are proposed: A protective pattern and experiential education-oriented ecotourism are well-suited in highly suitable areas; a joint pattern and supportive ecotourism are appropriate for moderately suitable areas; a restrictive pattern is recommended for marginally suitable areas; and for unsuitable areas, the development should be prohibited. Finally, we present a new development strategy known as "two centers, one belt, and one plate," providing theoretical and technical guidance for ecotourism development and the consolidation of poverty alleviation achievements in the Wuling Mountain area of Hunan Province.

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.

The distribution characteristics, accessibility, fairness, and other spatial configuration of urban public sports facilities are directly related to the equality of basic public services and the integrity of urban spatial structure. Taking Beijing as an example, this paper uses POI data to characterize urban public sports facilities, and uses kernel density estimation, nearest neighbor index, improved two-step mobile search method, and coverage index analysis methods to study the spatial agglomeration characteristics of urban public sports facilities at the street scale in Beijing, as well as the accessibility and fairness from the perspective of supply and demand. The results show that: (1) The improved two-step mobile search method considers the supply scale of public sports facilities at different levels of cities and the travel distance of residents under the concept of "15-min life circle", which is suitable for the accessibility analysis of block level and community level sports facilities. The spatial accessibility of block level and community level facilities in central urban areas is higher; (2) There is a significant spatial agglomeration trend of public sports facilities at all levels in Beijing. The block-level facilities show a trend of "point-like agglomeration and area-like dispersion", and the district-level facilities show a "core-edge" pattern, with more facilities in the central city and less in the surrounding areas. Community-level facilities are in the mode of "small agglomeration and large dispersion", with a uniform spatial distribution; (3) There are many streets with high coverage of public sports facilities at the community level in Beijing, and the spatial allocation is well balanced. There are few streets with high coverage index of public sports facilities at the block level, and the coverage is relatively limited. The results can provide reference for the planning and optimization of public sports facilities in Beijing.

In recent years, with the improvement of public awareness of environmental protection, the pursuit of harmonious coexistence between man and nature of ecotourism has attracted more and more people's attention. In the face of rich and diverse ecotourism resources and the contradiction between supply and demand of domestic ecotourism, how to orderly develop and make rational use of resources under the premise of maximum protection, and transform "lucid waters and lush mountains" into "gold and silver mountains" scientifically and rationally is a major issue to be addressed at present. Taking Shennongjia Forestry District as an example, based on multi-source geospatial data, we use the XGBoost algorithm to evaluate its ecotourism suitability. The following conclusions are drawn: (1) The ecotourism suitability evaluation model based on XGBoost algorithm integrates machine learning technology and achieves good classification results with the idea of ​​data mining. The overall classification accuracy of the model under 10-fold cross-validation is 89.44%, with a high recall rate (89.68%). The F1 score is 0.8745, based on both the precision and recall. The AUC value of the model is 0.9593, and the overall classification performance of the model is excellent; (2) According to the ranking results of feature importance, the NDVI of ecological environment elements (26.86%), annual average temperature (11.61%), and distance from social and economic factors to the road (8.90%) has the highest contribution to the model; (3) The classification results of ecotourism suitability show that the overall ecotourism resources in Shennongjia Forestry District are rich. Highly suitable areas, moderately suitable areas, marginally suitable areas, and unsuitable areas account for 44.13%, 15.93%, 11.89%, and 28.05% of the total forest area, respectively. The research method of this paper breaks through the limitations of traditional ecotourism suitability assessment methods which are highly subjective, and solves practical problems based on data mining ideas and machine learning technology.

Street space is the main space that affects tourists' experience of tourism sites. The visual quality of street space is crucial to the development of tourism. However, the evaluation method of visual quality needs further exploration. This paper selected Gulangyu, the famous tourism site in Xiamen, as a study case. First, we established a quantitative model of visual quality combining the existing research on street space and the visual elements of tourism sites. Then, we collected street view data of each intersection by traveling like tourists, corrected imaging parameters, and encoded the street view images. Second, based on the deep learning method (Fully Convolutional Networks, FCN), we segmented the collected street view images semantically and extracted the visual elements in street view data. Finally, by combining with GIS, we set up a geographic information database to analyze the visual and spatial characteristics of each sampling point's visual elements. This database was aimed at providing a basis for further evaluation of the visual quality of street space. It was aggregated using the street line as the smallest unit. In our study, we calculated the visual quality indicators to evaluate the street space in Gulangyu. The results show that: (1) There is obvious spatial differentiation in the visual elements of street space in Gulangyu; (2) Building density, street width, and vegetation sketches are the basic visual elements that shape the visual quality of street space; (3) The distribution of botanical parks, major docks, and commercial facilities significantly impacts the street space's visual quality. In detail, green plants, buildings, roads, sky, and street facilities show the differences between a center and a roundabout. While the distribution of pedestrians shows differences between the east and the west. The green view rate, enclosure, sky openness, and diversity of street space also have obvious center-roundabout spatial differentiation. Moreover, there is an obvious spatial agglomeration effect in the green view rate, crowding degree, and diversity of street space. The agglomeration points are mainly parks, docks, and commercial streets. The method in this paper provides a new collection method in street visual quality evaluation. The visual element extraction accuracy based on FCN is fairly high, which can provide a reference for street view images and other types of image data analysis. This paper provides a valuable reference for street space management and planning, resource integration and allocation, human flow guidance, and regulation in tourism sites.

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.

The coastline is located at land-sea interaction zone, and its utilization pattern is closely related to the spatial planning on both sides of the coastline (i.e., land and sea). This study primarily used the 2022 SDGSAT-1 satellite imagery and considered both the land and sea sides of the coastline to investigate the correlation characteristics of mainland coastlines and the spatial utilization on land and sea sides in China from the three levels: the entire coastal zone, the provincial (municipal) coastal zones, and the profile lines. The results show that: (1) The overall utilization ratio of the mainland coastline was high, with only 35.11% remaining undeveloped. Among the developed coastline types, aquaculture embankment coastlines accounted for the highest proportion, followed by port and dock coastlines; (2) In terms of land use, the largest area was utilized for construction purposes, with a significant proportion in Tianjin and Shanghai, followed by the artificial wetland area, with a high proportion in Liaoning and Shandong provinces. In terms of sea area use, fishery dominated in Shandong and Fujian provinces, marine transportation was most prominent in Shanghai, and the energy development accounted for a large proportion in Shanghai, Tianjin, and Hebei; (3) Regarding utilization intensity, there were clear positive correlations between the Index of Coastline Utilization Degree (ICUD), Index of Land Utilization Degree (ILUD), and Index of Sea Utilization Degree (ISUD). Especially, the correlation between coastline utilization and land use activities was relatively strong. Spatially, the regions with high and low values for the three indices were generally consistent. High-value regions were mainly distributed in northern regions such as Tianjin and Hebei, while low-value regions were predominantly found in southern provinces such as Zhejiang and Fujian; (4) Observing both land and sea sides from the coastline perspective, the landward side of unused coastlines was mostly covered by forests, with a lowest index of land utilization degree, while on the seaward side of unused coastlines, the proportion of tourism and recreational sea usage was relatively high. Port and dock coastlines exhibited the highest comprehensive indices on both land and sea sides. Construction lands dominated the landward side and energy development and transportation dominated the seaward side. The proportion of ecological protection sea usage on the sea side of aquaculture and salt embankment coastlines was relatively high. These findings provide scientific references and decision support for the scientific planning and management of coastlines, as well as the optimization of land-sea spatial structures.

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.

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.