[Objectives] Geographic Knowledge Graph (GeoKG) employs knowledge graph techniques to represent geographic knowledge as a computer-interpretable, reusable, and inferable knowledge network. However, due to the sparsity of geographic information distribution and outdated updates, GeoKGs are often incomplete, which restricts their breadth and depth of application. Geographic knowledge graph completion techniques are needed to address this incompleteness. Nevertheless, existing knowledge graph completion methods fail to fully account for the semantic information within GeoKGs and the distance-decaying effect governing interactions among geographic entities, resulting in an embedding space that inadequately captures the true distribution of geographic entities and relations, thereby limiting completion performance. [Methods] To address this issue, this study proposes a Distance-Decaying Effect-Aware Geographic Knowledge Graph Completion method (DDGKGC). The method first captures semantic information and distance-related features between entities and relations through a semantic information aggregation module and a distance-decaying effect-aware module. Then, a dual-attention mechanism-based representation learning module adaptively learns neighborhood information of entities and relations to derive their embeddings. Finally, the ConvE scoring function is used for prediction, and the results are applied to complete the GeoKGs. [Results] To comprehensively evaluate model performance,this study conducts comparative experiments, ablation studies, and multi-dimensional validation analyses on the self-constructed datasets Multi-Geo, CityDirection, and CountyDistance, as well as the public dataset Countries-S3. Experimental results demonstrate that DDGKGC achieves outstanding performance across multiple metrics including MRR, Hits@1, Hits@3, and Hits@10. Particularly in terms of MRR, which comprehensively reflects model performance, DDGKGC outperforms the baseline methods by 4%, 3.1%, 1.8%, and 5.2% on the four datasets, respectively. Moreover, through multi-dimensional validation and analysis, it is proven that DDGKGC can more effectively model the spatial and semantic relationships among geographic entities, thereby enhancing the accuracy and geographic plausibility of completion results. [Conclusions] The results demonstrate that the proposed method not only effectively enhances the performance of the geographic knowledge graph completion task but also exhibits strong generalization capability and application potential. Furthermore, it provides reliable support for the advanced application of GeoKGs.

[Significance] Urban digital twin models simulate comprehensive urban scenes by digitally mapping physical entities through real-time data integration. These models serve as visual, real-time representations of urban dynamics within smart cities, incorporating technologies such as the Internet of Things (IoT), spatial information systems, artificial intelligence, and others. Building on the Physical-Social-Information (PSI) three-dimensional framework, this paper reviews the current research progress of urban digital twin models and innovatively proposes a four-dimensional coupling framework: Physical-Social-Information-Time (PSIT). [Progress] The main research findings are as follows: (1) Since the introduction of digital twin technology into urban research in 2017, related literature has grown rapidly, with theoretical foundations and functional design frameworks gradually maturing. Urban digital twin models have initially been developed along three dimensions, PSI, including the digital mapping of geographic entities, spatial analysis of human activities, and the fusion and mining of geographic big data. (2) To more accurately reflect the real urban operations, current models require breakthroughs in data, technology, and algorithms. The PSI framework tends to overemphasize spatial features while oversimplifying the temporal dimension, lacking a representation of the spatiotemporal differentiation inherent in urban systems. (3) Recognizing the critical role of spatiotemporal coupling in urban modeling, this paper elevates time from a background variable to an independent dimension. This is based on the unidirectional nature of time, the temporal constraints on social behavior, the allometric time scales of urban element evolution, and the time-dependent mechanisms behind system phase transitions. Accordingly, the PSIT four-dimensional coupling framework is proposed to enhance the logic of urban system evolution and advance the theoretical paradigm of urban digital twin modeling. The CitySPS platform is presented as a case study for detailed illustration. [Prospect] The PSIT four-dimensional coupling framework offers the potential for more precise simulation and accurate prediction in digital urban spaces, representing a promising direction for future "intelligent" urban governance.

[Objectives] To systematically investigate the key pathways and application models for leveraging intelligent technologies in urban-rural integrated planning. It seeks to explore potential responses to the complex challenges arising from China's strategic developmental shift towards the renewal and quality enhancement of its existing urban and rural stock. In this new era, traditional planning methodologies, which often rely on static data and experience-driven decision-making, face significant limitations in addressing the intricate stakeholder relationships, intertwined land uses, and dynamic nature of established built environments. This review, therefore, explores how a more intelligent, data-informed approach could better support contemporary planning objectives. [Discussion] Addressing challenges such as the precise identification of multi-source features, the issue of persistent data silos, and the need for in-depth cognition of complex human-environment relationships, this paper reviews and proposes a closed-loop "Perception-Fusion-Cognition-Planning" conceptual framework. This framework is intended to guide the application of intelligent digitalization across the planning lifecycle. It begins with Intelligent Perception, which suggests integrating multi-modal data from sources like high-altitude cameras and drones with AI-driven algorithms for the automated semantic analysis and dynamic monitoring of urban-rural elements. This is followed by Spatio-temporal Data Fusion, which focuses on standardizing and integrating these heterogeneous data streams onto a unified baseline, creating a consistent and reliable digital foundation for analysis. The third stage, Cognition, employs knowledge graph technology to transform the integrated data into deep, systemic insights by explicitly modeling the implicit relationships between spatial entities, socio-economic factors, and regulatory policies. Finally, the Planning and Governance stage describes how an integrated enabling platform can translate these insights into actionable decision support, facilitating scenario analysis and collaborative workflows. A case study focused on farmland protection in Zengcheng District, Guangzhou, is presented to illustrate the potential and feasibility of this integrated technological pathway. The case demonstrates how the framework can be applied to achieve end-to-end governance, from automated change detection to policy-based reasoning and targeted enforcement. [Prospect] This systematic framework offers a potential conceptual approach for addressing the multifaceted challenges inherent in the intelligent planning for urban-rural integration. It is hoped that this work can help facilitate a paradigm shift in planning from traditional, experience-driven methods toward modern, data-informed "platform-based governance," characterized by more dynamic, evidence-based, and collaborative processes. By providing both theoretical references and practical guidance, this review aims to contribute to the ongoing efforts to modernize spatial governance capabilities, thereby supporting the overarching goals of sustainable and high-quality integrated development in China.

[Background] In recent years, confronted with emerging phenomena and new challenges in cartographic practice, an increasing number of scholars have called for a critical reassessment of existing paradigms in cartography, aiming to address both the disciplinary challenges and societal demands arising from technological transformations. [Objectives and Methods] Following a research approach that integrates critical inheritance and innovative transcendence, this study employs theoretical deduction to first review and synthesize existing paradigms in cartography. It then analyzes the predicaments encountered during the structural transformation of mapping practices and, finally, proposes a new paradigm for the discipline. [Results] Traditional cartographic research tends to equate “maps” with practices defined by specific professional norms, thereby endowing cartography with distinct characteristics of professional map-making in its knowledge sources, focal concerns, and practical pathways, ultimately forming what may be termed the professional map-making paradigm. However, this paradigm increasingly reveals two prominent dilemmas. On the one hand, the professional map-making paradigm struggles to capture the complexity and fluidity of maps as they are embedded in everyday social life, often resulting in theoretical lag and explanatory failure when addressing new forms of cartographic practices and their associated meaning-making mechanisms. On the other hand, the paradigm tends to operate within a closed cycle of internal knowledge reproduction, lacking substantial theoretical innovation and failing to cultivate problem-oriented thinking, thereby weakening its capacity to guide and regulate cartographical practice. In response, this study, grounded in a networked and relational understanding of the world, proposes a social practice paradigm for cartography. This paradigm conceptualizes maps as social practices embedded within social networks and linked to social actors, emphasizing the unique meanings and social values that maps generate in connecting individuals with the external world. [Conclusions] The social practice paradigm advocates for a transcendent perspective in understanding and interpreting maps, incorporates interdisciplinary integration and pluralistic methodological approaches, and promotes the coordination of local experiences with global perspectives. This paradigm not only deepens holistic understanding of mapping practices but also offers new theoretical resources and analytical frameworks for cartography to address pressing issues in contemporary digital, intelligent, and networked societies. Future research should systematically analyze the research context and theoretical foundations of cartography within the social practice paradigm. A conceptual, discursive, knowledge, theoretical, and methodological system, distinct from the professional map-making paradigm, needs to be gradually established to address the challenges faced by cartographic practice in complex and evolving contexts. The fundamental goal is to expand the boundaries of cartographic research and intellectual resources, transcending existing disciplinary categories and knowledge frameworks.

[Significance] As a critical pathway to achieving Sustainable Development Goal (SDG) 2, “Zero Hunger,” and ensuring long-term ecological sustainability, the concept and practice of sustainable agriculture are undergoing a paradigm shift toward data-driven and system-oriented approaches. In recent years, Big Earth Data—comprising remote sensing, geospatial, meteorological, and agricultural Internet of Things (IoT) data—has emerged as a foundational driver for agricultural monitoring, decision support, and technological innovation in sustainable development. [Analysis] Given the interdisciplinary, multi-stakeholder, cross-regional, and evolving-goal nature of sustainable agriculture, this study begins by systematically reviewing the conceptual evolution of the term. It highlights the multidimensional implications and diverse practical pathways of sustainable agriculture, noting its growing role as a core component of global development strategies. On this basis, the paper proposes a new, data-oriented and operational interpretation of sustainable agriculture. The study then establishes an analytical framework—“data-Technology”—to clarify the pivotal role of Big Earth Data in supporting sustainable agriculture. It examines the evolution of core datasets and key technical methods across three periods: before 2015, 2015—2019, and 2020 to the present. The applications reviewed include agricultural resource monitoring, multi-scale crop condition assessment, and evaluations of agriculture's environmental impacts. The findings suggest that sustainable agriculture, enabled by Big Earth Data, is rapidly shifting from a paradigm of "observational analysis" to one of "intelligent decision-making." Furthermore, the study conducts a comparative assessment of China, the United States, and the European Union across four critical dimensions: data infrastructure; technological advancement and application; scientific research capacity; and policy support. While China has made significant progress in all four areas—with strengths in remote sensing capabilities, rapid technological rollout and demonstration, substantial research output, and clearly defined policy directives—it continues to face challenges in data ecosystem development, original algorithm innovation, commercialization of scientific outputs, and the alignment of standards and incentive mechanisms. Finally, in light of the current needs for sustainable agricultural development, this study systematically analyzes the major challenges facing Big Earth Data from four aspects: data acquisition capacity, intelligent processing methods, application promotion and services, and data governance and ethical security. In response, it proposes multi-level strategies covering standardization, model optimization, improvements to service systems, and protection of data rights, with the aim of providing a reference pathway for the efficient utilization and sustainable development of agricultural big data in the future. [Prospect] The article aims to analyze the data-driven transformation pathway of sustainable agriculture and provide a systematic reference for its green, inclusive, and intelligent development.

[Objectives] Global climate change, accelerating sea-level rise, and intensifying anthropogenic pressures are rendering the intricate human-land-sea nexus within coastal zones increasingly complex, sensitive, and vulnerable. This growing challenge underscores the urgent need for integrated coastal research frameworks capable of synthesizing environmental sensing, dynamic process simulation, and scenario projection. Addressing this critical gap, Digital Twin (DT) technology emerges as a transformative paradigm. By integrating multi-source data, sophisticated models, and domain knowledge into intelligent systems, DT offers unprecedented potential for creating precise virtual replicas and enabling intelligent management of complex coastal socio-ecological systems. [Analysis] This paper systematically analyzes the state of coastal zone digitalization, highlighting the pressing need for robust digital frameworks that can effectively represent and analyze the strong coupling between natural processes and human activities under multifaceted pressures. Building on this foundation, we propose a novel conceptual framework and implementation pathway for constructing a Digital Twin Coastal Zone (DTCZ). This framework explicitly positions land-sea interface processes as the foundational scenario and centers on human-land-sea feedback mechanisms as the core analytical thread. The proposed DTCZ system architecture is articulated across four pivotal dimensions: (1) Comprehensive information integration and knowledge aggregation; (2) Simulation of natural processes integrated with coupled human-nature decision support; (3) Synergistic short-term forecasting and long-term monitoring capabilities; and (4) Realistic multidimensional representation enabling intelligent interaction. We critically discuss the key technological enablers supporting this vision, encompassing coastal data governance and fusion, multi-scale scenario modeling, predictive analytics for critical coastal elements, persistent long-term monitoring strategies, and the development of the integrated DTCZ platform itself. At its core, the envisioned DTCZ leverages spatiotemporally fused multi-source data as its foundation and prioritizes enhanced scenario simulation and intervention capabilities. [Prospects] This framework is designed to overcome the limitations, such as fragmented data and limited predictive power, that constrain traditional coastal digital systems. By significantly advancing the computational tractability and overall manageability of coastal systems, the DTCZ paradigm offers a powerful new methodological tool and operational framework. It holds strong potential for supporting sustainable coastal development and modernizing governance structures in the face of ongoing climate change, providing a robust platform for evidence-based planning and adaptive management.

[Significance] As a representative of new-quality productivity, the low-altitude economy is gradually emerging as a new engine for economic growth. This economy is based on the development and utilization of low-altitude airspace resources. While bringing development opportunities to geospatial information technology, it also poses entirely new challenges. [Progress and Analysis] In this paper, we introduce the division of low-altitude airspace resources and highlight typical drone application scenarios in the context of the low-altitude economy. Subsequently, we analyze the broad application prospects of geospatial information technology in key areas of the low-altitude economy, including the refined utilization of airspace resources, the construction of low-altitude environments, the planning, construction, and operation of new air traffic infrastructure, as well as the safe and efficient operation and regulatory oversight of drones. We emphasize that the geospatial information industry will benefit from development opportunities such as the integration and innovation of emerging scientific and technological advancements, growing market demand, policy support, industrial guidance, and industrial upgrading and transformation. [Prospect] Finally, we briefly address the challenges geospatial information technology must overcome to meet the development needs of the low-altitude economy. These include advancements in spatio-temporal dimension elevation, map and location-based services, high-frequency and rapid data acquisition systems, all-time and all-domain capabilities, and ubiquitous intelligent technologies. These areas will also serve as future directions for development and breakthroughs in geospatial information technology.

[Objectives] To systematically review recent advancements in text-to-image generation technology driven by large-scale AI models and explore its potential applications in urban and rural planning. [Discussion] This study provides a comprehensive review of the development of text-to-image generation technology from the perspectives of training datasets, model architectures, and evaluation methods, highlighting the key factors contributing to its success. While this technology has achieved remarkable progress in general computer science, its application in urban and rural planning remains constrained by several critical challenges. These include the lack of high-quality domain-specific data, limited controllability and reliability of generated content, and the absence of constraints informed by geoscience expertise. To address these challenges, this paper proposes several research strategies, including domain-specific data augmentation techniques, text-to-image generation models enhanced with spatial information through instruction-based extensions, and locally editable models guided by induced layouts. Furthermore, through multiple case studies, the paper demonstrates the value and potential of text-to-image generation technology in facilitating innovative practices in urban and rural planning and design. [Prospect] With continued technological advancements and interdisciplinary integration, text-to-image generation technology holds promise as a significant driver of innovation in urban and rural planning and design. It is expected to support more efficient and intelligent design practices, paving the way for groundbreaking applications in this field.

[Objectives] All meaningful forms of human discourse are rhetorical, and the purpose of rhetoric is to enable communication and foster sympathy between parties with certain views. Narrative maps are essentially a discursive practice for communicating information and exchanging ideas, characterized by the strategic use of rhetoric to construct persuasive discourse and achieve the goal of "agreement" or "persuasion". In the current era, where visual dominance is increasingly prominent, rhetoric has garnered growing attention in cartography. This turn not only addresses core issues in narrative map research but also provides a realistic path for enriching and reconstructing the existing knowledge of modern cartography. However, the academic community has yet to establish a systematic framework, leaving three key issues unresolved: (1) How to conceptualize the rhetoric of narrative maps? (2) How to categorize the rhetoric of narrative maps? (3) What is the working mechanism of rhetoric in narrative maps? [Methods] To address these research gaps, this article, firstly, follows the research paradigm of rhetoric to clarify the essence of rhetoric in narrative maps, and defines it as: "During the design process of narrative maps, cartographers use certain visualization strategies to facilitate the representation of events, thereby weaving explicit narrative intentions into the mapping space in an implicit way to create persuasive discourse or emotional agreement for viewers." Secondly, a classification criterion is proposed based on the differences between content semantic representation and logical semantic representation. Two major categories, semantic rhetoric and structural rhetoric, along with 24 minor classes, are divided for rhetoric of narrative map. Semantic rhetoric mainly focuses on enhancing the understanding of content, expressing the connotation and imaginative tension of map "text". Structural rhetoric aims to emphasize the logic semantic relationships in narrative discourse, presenting the narrative logic of events. Semantic rhetoric often manifests as the design of visual symbols to describe events, serving as the "visual punctum" of narrative maps. Structural rhetoric typically involves adjusting the arrangement and structure of different event units, functioning as the "visual stadium" of narrative maps. Next, the mechanism of rhetoric in narrative maps is explored from four aspects: the dimensions of rhetoric, the hierarchy of rhetoric, the integrated use of rhetoric, and the applicability principles of rhetoric. Finally, this study demonstrates the applicability of the proposed theoretical framework through a case study of "Jiangnan Canal", illustrating how the framework can facilitate narrative map design. [Conclusions] This paper lays a theoretical foundation for narrative map research and contributes to the theoretical innovation of contemporary cartography.

[Objectives] The outdoor pedestrian navigation road network is a vital component of maps and a crucial basis for outdoor activity route planning and navigation. It plays a significant role in promoting outdoor travel development and ensuring safety management. However, existing research on road network generation mainly focuses on the construction of urban vehicular navigation networks, with relatively less emphasis on hiking navigation road networks in complex outdoor environments. Moreover, existing methods primarily emphasize the extraction of two-dimensional geometric information of roads, while the reconstruction of real three-dimensional geometric and topological structures remains underdeveloped. [Methods] To address these limitations, this study proposes a method for constructing the three-dimensional outdoor pedestrian navigation road network maps using crowdsourced trajectory data. This approach leverages a road network generation layer and an elevation extraction layer to extract the two-dimensional structure and three-dimensional elevation information of the road network. In the road network generation layer, a trajectory density stratification strategy is adopted to construct the two-dimensional vector road network. In the elevation extraction layer, elevation estimation and optimization are performed to generate an elevation grid raster map, which is then matched with the two-dimensional road network to produce the three-dimensional hiking navigation road network. [Results] To demonstrate the effectiveness of the proposed approach, experiments were conducted using 1 170 outdoor trajectories collected in 2021 from Yuelu Mountain Scenic Area in Changsha through an online outdoor website. The constructed outdoor three-dimensional hiking road network map achieved an average positional offset of 4.201 meters in two-dimensional space and an average elevation estimation error of 7.656 meters. The results demonstrate that the proposed method effectively handles outdoor trajectory data with high noise and varied trajectory density distribution differences, generating high-quality three-dimensional hiking road network maps. [Conclusions] Compared to traditional outdoor two-dimensional road networks, the three-dimensional navigation road networks constructed this study provide more comprehensive and accurate map information, facilitating improved pedestrian path planning and navigation services in complex outdoor environments.

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.

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.

With high quality development becoming the primary task of comprehensively building a socialist modernized country, the importance of geographic spatiotemporal information in supporting national and local socio-economic development has been raised to new heights. Based on the urgent need for high-quality development to empower geographic spatiotemporal information, this paper first comprehensively reviews the theoretical and methodological research status of geographic spatiotemporal expression and computation from the perspectives of complex land surface system expression, spatiotemporal uncertainty analysis, and geographic spatial intelligent computing. It is pointed out that there is an urgent need to update concepts, integrate across borders, and innovate technologies to improve the production level of spatiotemporal information products and assist in the high-quality transformation and development of social and economic activities in the three living spaces. Furthermore, driven by the problems of deconstructing complex land surface and analyzing precise parameters, we propose relevant theoretical thinking and research ideas of geographic spatiotemporal digital base (GST-DB) with an overview of basic concepts and technical points. The GST-DB is based on the uniqueness and distribution of time and space, and is proposed by three basic elements around brackets, containers, and engines. The paper focuses on analyzing three key scientific issues, including multiple representations and knowledge association for complex land surface systems, uncertainty analysis of spectral feature reconstruction under spatial form constraints, signal transmission and optimized control with the collaboration of satellite, ground, and human. The three key objectives, namely deconstruction of global space, analyticity of local space, and transferability between spaces, cut into the process of connecting the two-step process of spatial expression and parameter calculation, and further explain the difficulties and feasible solution paths of reliable expression, reliable analysis, and controllable computing. Through the analysis of the solution approach, the feasibility and necessity of the organic synergy of geoscientific analysis ideas, remote sensing mechanism knowledge, and machine intelligence algorithms are demonstrated. On this basis, this paper focuses on the monitoring and supervision of agricultural production as a demand-oriented problem for introducing agricultural application cases of GST-DB. Four types of application models for people, land, money, and things are preliminarily described. By demonstrating the construction process and implementation effectiveness of integrated intelligent computing, the advantages and basic supporting role of the base in carrying and utilizing spatiotemporal data elements are highlighted. This case study demonstrates the potential to provide high-quality spatiotemporal information services for the development of modern agriculture in complex mountain areas.

Human mobility data play a crucial role in many real-world applications such as infectious diseases, transportation, and public safety. The development of modern Information and Communication Technologies (ICT) has made it easier to collect large-scale individual-level human mobility data, however, the availability and usability of the raw data are still significantly limited due to privacy concerns, as well as issues of data redundancy, missing, and noise. Generating synthetic human mobility data through modeling approaches to statistically approximate the real data is a promising solution. From the data perspective, the generated human mobility data can serve as a substitute for real data, mitigating concerns about personal privacy and data security, and enhance the low-quality real data. From the modeling perspective, the constructed models for human mobility data generation can be used for scenario simulations and mechanism exploration. The human mobility data generation tasks include individual trajectory data generation and collective mobility data generation, and the research methods primarily consist of mechanistic models and machine learning models. This article firstly provides a systematic review of the research progress in human mobility data generation and then summarizes its development trends and challenges. It can be observed that mechanistic-model-based methods are predominantly studied in the field of statistical physics, while machine-learning-based methods are primarily studied in the field of computer science. Although the two types of models have complementary advantages, they are still developing independently. The article suggests that future research in human mobility data generation should focus on: 1) exploring and revealing the underlying mechanisms of human mobility behavior from a multidisciplinary perspective; 2) designing hybrid approaches by coupling machine learning and mechanistic models; 3) leveraging cutting-edge generative Artificial Intelligence (AI) and Large Language Model (LLM) technologies; 4) improving the models' spatial generalization and transfer-learning capabilities; 5) controlling the costs of model training and implementation; and 6) designing reasonable evaluation metrics and balancing data utility with privacy-preserving effectiveness. The article asserts that human mobility processes are typical phenomenon of human-environment interactions. On the one hand, research in Geographic Information Science (GIS) field should integrate with theories and technologies from other disciplines such as computer science, statistical physics, complexity science, transportation, and others. While on the other hand, research in GIS field should harness the unique characteristics of GIS by explicitly incorporating geographic spatial effects, including spatial dependency, distance decay, spatial heterogeneity, scale, and more into the modeling process to enhance the rationality and performance of the human mobility data generation models.

Cyberspace surveying and mapping has become a hot research topic of widespread concern across various fields. Its core task involves surveying the components of cyberspace, analyzing the laws of cyberspace phenomena, and mapping the structure of cyberspace. Research on cyberspace surveying and mapping faces issues such as diverse conceptual terminologies which is lack of unified research frameworks, unclear understanding of elements and laws, non-standardized methods of cyberspace map expression, and the absence of unified standards. Based on systematically reviewing the current status of cyberspace surveying and mapping research across fields, a common understanding of the essence of cyberspace has been analyzed. Starting from the spatial, geographical, and cultural characteristics of cyberspace, the features and advantages of studying and utilizing cyberspace from the perspective of mapping geography are dissected. A research framework for cyberspace surveying and mapping is proposed, focusing on the core content and key technologies of "surveying " and "mapping" in cyberspace, and explaining its relationship with 3D Real Scene, Digital Twins and Metaverse. Cyberspace surveying has been divided into narrow and broad senses, pointing out the lack of holistic measurement of cyberspace features and the lack of research on measuring the phenomena and patterns of human activity in cyberspace. From the perspective of cyberspace cognitive needs, a conceptual model and classification system for cyberspace maps have been proposed. Focusing on the cyberspace coordinate system, "geo-cyber" correlation mapping, and methods of expressing cyberspace maps, the key technologies for creating cyberspace maps are described in detail, and the methods of representing cyberspace maps and their applicability are systematically analyzed. Finally, key scientific questions and critical technologies that need focused research, such as the top-level concepts of cyberspace, cyberspace modeling methods, theories and methods of cyberspace maps, and the design of application scenarios for cyberspace maps, are discussed.

The development of hydrologic time series analysis is crucial for the effective management and utilization of water resources. Based on the WoS Core Collection database and the CNKI database, this paper employs bibliometrics and CiteSpace software to reveal the development trends, research hotspots, and future directions in the field of hydrologic time series analysis both domestically and internationally. Firstly, starting with the randomness, nonlinearity, and uncertainty of hydrologic time series, as well as emerging methods such as machine learning and neural networks, this paper divides the recent advances in the field of hydrologic time series analysis into six aspects. Then, a detailed introduction for each advance is provided, and a comparison with traditional methods is also made to summarize the shortcomings of traditional methods. Finally, the directions for improving the accuracy of hydrologic time series analysis are pointed out, including:1) modeling at spatiotemporal scales and integrating multi-source data for analysis; 2) incorporating physical mechanisms into machine learning models to enhance interpretability and generalization capabilities; 3) considering the coupling of climate change (extreme weather events) and hydrologic processes in research advances; 4) conducting comprehensive research on multiple complex characteristics and improving the research level of each complex characteristic. By revealing the development trends, research hotspots, and future directions of hydrologic time series analysis both domestically and internationally, we can better understand and respond to the impacts of climate change, extreme weather events, and human activities on water resources, enhance our understanding of hydrologic processes, and provide scientific basis for water resources planning, flood risk management, and sustainable development.

Moving target detection plays a pivotal role in extracting temporal information from time-series images, particularly from satellite data. This method enables the rapid acquisition, analysis, and utilization of dynamic change information, meeting the demand for "real-time target discovery and delivery." In the processing of optical image-based moving target detection, existing methods often fall short of meeting the requirements for large-scale target discovery, accommodating diverse speeds, and ensuring hardware acceleration compatibility. This study aims to achieve swift perception of large-scale moving targets using optical remote sensing satellites, with a primary focus on both camera innovation and algorithm research in terms of target discovery and target information processing. This paper proposes a novel imaging mode, leveraging a dual-linear array push-broom optical remote sensing camera to capture dual-strip images containing temporal changes associated with moving targets. The camera principle prototype was successfully deployed on the "Taijing-4 Satellite" on February 27, 2022, thereby validating the technical approach for large-scale detections. Furthermore, this paper introduces a pioneering approach for detecting moving targets based on saliency region proposal for dual-band images, which significantly enhances the temporal information captured in dual-linear array push-broom imaging. Subsequently, we employ a sophisticated saliency region proposal method to extract the prominent regions of moving targets by utilizing the temporal and spatial change information within the image. These salient regions encompass dynamic targets across the entire image, effectively reducing the amount of intermediate data processed by the algorithm. Finally, a lightweight and efficient deep learning object detection model is leveraged to classify moving targets and eliminate false positives from the initial detection outcomes. The results indicate that the proposed method can efficiently detect moving targets in dual-strip images, substantially improving the accuracy of dynamic target shape extraction and optimizing the results of target matching. Notably, by enhancing the recall rate of the moving target detection algorithm, the algorithm's execution efficiency is also increased by 61.4%. This paper demonstrates two remarkable strengths in its viewpoints and discussion. Firstly, it puts forth a groundbreaking imaging mode and method to enhance the temporal information of images, effectively addressing the challenge of observing large-scale moving targets without relying on satellite attitude maneuvering. Secondly, it proposes a highly efficient moving target detection model based on saliency region proposal, resolving the problem of detecting moving targets in complex backgrounds. The acquisition of key information about moving targets can significantly reduce the bandwidth requirements for ground transmission of remote sensing data, providing a new way of data acquisition and on-orbit processing for mega Earth observation systems.

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.

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.

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.

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 effect of "space-time compression" caused by "space flow" breaks the independent allocation of resources between cities and drives the formation of regionally integrated development pattern, and the organizational structure and operation mechanism of the urban network cannot be separated from the inter-city relationship. Based on Baidu migration big data from October 2021 to September 2022, this paper constructs the intercity population flow network for 366 cities in China. At the node level, a population flow surpassing index is proposed to measure urban centrality and explore the spatial clustering characteristics of urban centrality. At the network community level, the monthly intercity population flow pattern and characteristics of 366 cities are analyzed. The results show that: (1) The population flow surpassing index considering flow direction meets the actual needs of intercity population mobility evaluation for measuring urban centrality and can effectively characterize the centrality of cities in the intercity population flow network. Using Baidu Migration big data from January 2023 to April 2023 after the end of the epidemic for comparison, we found that the central impact on national central city is small due to the prevention and control of COVID-19 transmission; (2) Cities in the intercity population flow network exhibit "High-High (HH)" and "Low-Low (LL)" agglomeration characteristics according to their centrality. HH clustering areas are formed in the eastern coastal and central regions, while LL clustering areas are mainly located at the edge of the Qinghai Tibet Plateau, the edge of the three northeastern provinces, and some areas in Hainan Island; (3) The intercity population flow pattern shows different characteristics in different months due to the influence of holidays, COVID-19 transmission, etc., generally in accordance with the first law of geography, and exhibits provincial differentiation characteristics; (4) The finding of urban cohesive subgroups shows that the intercity population flow patterns of Chengdu-Chongqing Urban Agglomeration, Greater Bay Area, Central Plains Urban Agglomeration, Guanzhong Plain Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and other urban clusters are relatively stable, characterized by cross-provincial population flow integration. The Shandong Peninsula Urban Agglomeration and the Beijing-Tianjin-Hebei Urban Agglomeration have close connection in intercity population flow patterns, characterized by cross-urban cluster intercity population flow. The intercity population flow pattern within Zhejiang Province is gradually enhanced, and the urban clusters in middle reaches of Yangtze River and the west bank of the Taiwan Strait haven’t yet formed a stable population flow pattern across provincial borders.

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.

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.

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.

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.

Food attracts a large number of foodie tourists to travel together. Although previous research has discussed food tourism mainly from the point of view of customer satisfaction, there is still an evident gap in our knowledge about the travel behavior of foodie tourists and the influences of their travel partners on travel patterns. This paper uses travel diary data from Qunar.com and takes foodie tourists in Chongqing, China as an example to analyze the influence of travel partner types on tourists' "dining trajectories". In this paper, we proposed a research framework for the dining behavior characteristics of foodie tourists from the perspective of travel partners based on travel diaries. As restaurants have the characteristics of various categories and dense distribution, the characteristics of tourists' dining behavior were explored from two aspects: food types and spatial distribution of restaurants. Firstly, the foodie tourists' dining behavior network (the flow network between food types and the flow network between restaurants) was constructed. Secondly, the social network analysis of food network was carried out, and the changes of community relationship between food types were explored by community detection and the results of food network structure index analysis. Then the social network analysis of the restaurant network was carried out, and foodie tourists' dining behavior characteristics were explored by structural indicators of the restaurant network. The results show that: (1) The food network characteristics of tourists with different travel partner roles differed significantly. The nodes of food network of solo tourist (no travel partners) were not connected closely, while the food network of other travel partner role showed obvious small-world characteristics; (2) The role of travel partners influenced tourists' choice of specialty food types. In particular, solo tourists showed a "passive and conservative" type of dining, tourists with three or five friends showed a "try all the specialties" type of dining, and tourists with other travel partner roles showed a "casual/interesting" dinning behavior; (3) The characteristics of restaurant network of tourists with different travel partner roles differed significantly. The restaurant network of travel partners as a couple showed obvious small-world characteristics, while the nodes of restaurant network of other travel partner roles were not connected closely. The results provide basis for destination marketing organizations to formulate marketing material and dining route recommendations for foodie tourists. In the future, it is necessary to understand the impact of interpersonal relationships on human mobility and develop spatiotemporal analysis theory and models for dealing with mobile location big data.