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.

. Online travel notes are self-reported records published by tourists on the Internet, which describe the process of their trip and experience. Extracting itinerary chain from online travel notes and analyzing itinerary structure, can provide important reference for tourists' itinerary formulation and route design. The traditional itinerary extraction mostly relies on manual recognition, and some methods proposed in current studies require extensive data annotation, which is a large workload. Automatic extraction of itinerary chain from online travel notes accurately can improve the efficiency of data processing, which is an open issue and worth of study. In this paper, a syntactic rule-based travel chain extraction method was proposed based on natural language processing technology, which includes the identification of travel nodes, the recognition of nodes order and the generation of itinerary chain. First of all, the paragraph structure and expression characteristics of itinerary in online travel notes were analyzed, and the syntactic expression rules of travel nodes and nodes order were summarized based on word segmentation and dependency syntax analysis of related statements. Secondly, the travel nodes matched by syntactic rules, can be divided into deterministic travel nodes, uncertain travel nodes and non-travel nodes. Thirdly, through regular expression and syntactic rules match, the order of travel nodes was recognized from the specific itinerary description statement. Finally, the uncertain travel nodes were distinguished based on nodes context analysis, and the sequential and cross-arranged travel nodes were merged and connected in series. Meanwhile, the order of nodes in the connected series were verified and adjusted based on previously recognized node orders, and the itinerary chain was generated. In order to verify the effectiveness of proposed method, 17 226 online travel notes text data of Nanjing city were collected on Mafengwo platform, and the longest common subsequence algorithm was used to carry out the experimental verification. Through comparative analysis, the similarity between the extracted result by this method and the real travel chain identified by manual is 86.14%, which is higher than the BERT-BiLSTM-CasRel deep learning model in the field of entity relation extraction (83.1%). Compared with the existed relation extraction method in deep learning field, the proposed method is more convenient in calculation and does not require extensive data annotation. The limitation of method is the construction of regional travel site directory. In the future work, the strong semantic understanding ability of large language model would be carried out to improve the accuracy and data processing efficiency in itinerary chain extraction.

Prediction of users' geolocation plays an important role in location-based applications such as natural disaster monitoring, flu trend prediction, and targeted advertising promotion. Integrating multi-source information, mining user behavior characteristics, and analyzing user social attributes can help improve prediction accuracy and reduce distance error. Existing methods primarily rely on textual content and social networks for location prediction without considering the fusion of these two types of information, and have difficulty in predicting the locations of isolated users in social networks. Therefore, this paper proposes a home location prediction method for social network users integrating text topic and social relationship graph neural network. In the method, first, hybrid features are extracted from text content, using TF-IDF to obtain text feature vectors, and an initial social relationship graph is established based on the mentioned information between users. Then, to address the issue of isolated users in the user social relationship graph and difficulty in estimating their locations, a topic model is established to establish connections for isolated users based on topic vector similarity and supplement the social relationship graph. Finally, based on graph convolutional neural network, social relationship graph data are processed, and text features and network structure are jointly modeled to effectively predict users' geolocation. The effect of topic similarity threshold on prediction performance and graph size is explored on a real-world benchmark dataset GeoText. The experimental results show that our method is able to aggregate most of the user nodes belonging to the same class and increase the proportion of locatable users. The network constructed using multiple types of relationships can maintain the diversity of user relationships and can achieve better prediction accuracy of graph neural network. SRGCN outperforms the existing methods in terms of the average distance error, the median distance error, and the prediction accuracy, which indicates that the multi-view feature learning model is superior for geolocation prediction compared to models based on a single source of information. On the GeoText dataset, the Acc@161 of SRGCN is 1% higher than that of GCN method, and the average error distance is reduced by 16km, which indicates that the SRGCN method is more competitive than the existing best-performing method. Our experimental results demonstrate the effectiveness of SRGCN, which can improve the accuracy of home location prediction of users.

The 31st International Cartography Conference (ICC) was held in Cape Town, South Africa from August 13 to August 18 in 2023. This paper first introduced the overview of the 31st ICC, the participation of Chinese experts and enterprise. Secondly, based on the technical reports during the ICC2023, new research hotspots of cartography were analyzed and summarized from eight aspects including the basic theory of cartography and technologies of cartography, map data, map products, and Spatial Data Infrastructure (SDI) construction, public applications, sustainable development applications and historical and cultural ethics. We concluded some obvious hotspots that the traditional mapping fields have paid more attention to multi-element fusion mapping, user and scenario experience enhancement and rapid mapping capabilities; On the other hand, the emerging geographical information fields have focused on multi-modal ubiquitous sensing, big data fusion processing, artificial intelligence analysis, knowledge construction and services which have been deepened continuously; In addition, government agencies, scientific research institutions, industrial enterprises across the world have continuous passion on global, regional, national and urban sustainable development cartographic applications for resource management, ecological protection, social development. Subsequently, the new characteristics of map visualization methods from the award-winning maps were explored as well which incorporate more modern elements and cultural imprints, and emphasize people-map interaction. Afterwards, in the era of big data and artificial intelligence, the development trend of theoretical systems, technical methods and application services for cartography in the next few years are discussed. That is, the theoretical system of cartography becomes more professional and refined. In the era of artificial intelligence, the technical content of cartography becomes more knowledgeable. Cartography application services become more ubiquitous driven by big data. Cartography plays a more profound role in supporting the sustainable development of the United Nations. In the last place, some suggestions were put forward for the development of the cartography discipline in China. For example, in the future, we must make full use of the ICA international platform to continuously establish and improve a new theoretical system of cartography in the intelligent era, break through the key core technologies of cartography, and promote the high-quality development of cartography in our country with a global perspective. Meanwhile, we should pay more attention to the international frontier developments in important and emerging research fields such as geospatial data fusion, knowledge construction, spatial analysis, ubiquitous mapping, geographic intelligence, and data quality, and strengthen scientific and technological exchanges and cooperation between relevant domestic and foreign research institutions. A more proactive and proactive opening-up strategy should be implemented to promote the continuous improvement of the international influence of cartography research.

The advent of intelligent connected vehicles has seamlessly integrated into the fabric of contemporary intelligent transportation systems, emerging as an indispensable and transformative constituent. At the nucleus of this paradigm shift lies the autonomous driving high definition maps, assuming a pivotal role in propelling the evolution of intelligent transportation. The high definition maps, as a core element in intelligent connected vehicles, stand as a linchpin in advancing the development of intelligent transportation systems. Effectively establishing intricate connections among drivers, vehicles, road environments, driving conditions, significant landmarks, and the broader social environment, high definition maps act as a catalyst, propelling autonomous driving technology from Level 0 to Level 5. This article delves into the urgent imperatives steering the progression of intelligent connected vehicles and the critical role played by autonomous driving high definition maps. Beginning with an exploration of the essence, mainstream foundational data models, concepts, and characteristics of high definition maps, the discussion underscores their transformative role as a groundbreaking map data paradigm, crucial for realizing autonomous driving in intelligent connected vehicles. Subsequently, a nuanced analysis unfolds, dissecting the multifaceted characteristics woven into the entire lifecycle of high definition maps. This comprehensive examination spans diverse perceptual data types, encompassing multiple map construction methodologies, a variety of crowd-sourced updating techniques, various map application methods, the inherent intelligence embedded in map auditing processes, and innovative management modalities. Additionally, a prototypical route for high definition maps crowd-sourced updating technology is proposed, elucidating the dynamic landscape of map data refinement. Addressing the current challenges in high definition maps auditing, the study introduces an online intelligent map auditing methodology, providing a promising avenue to navigate the intricacies of the auditing process. This approach not only addresses key issues but also ensures the precision and reliability of map data. The practical application of these conceptual frameworks is exemplified through an extensive case study of the Shenzhen high definition maps pilot, offering valuable insights derived from practical experiences and explorations. In conclusion, this paper provides a forward-looking perspective on the developmental trajectory of high definition maps. It envisions their sustained significance and potential advancements, anticipating the continuous refinement and innovation in high definition maps. This ongoing evolution is expected to significantly contribute to the further enhancement of intelligent transportation systems and the maturation of autonomous driving technologies. The transformative impact of high definition maps is poised to usher in a new era of seamless and intelligent mobility, reshaping the landscape of contemporary transportation systems.

The autonomous cognition ability of unmanned platforms in complex environments is a key issue that restricts their real-world applications and has become a research hotspot in cartography, artificial intelligence, and robotics. Although the research on environment modeling and learning for unmanned platforms has achieved substantial progress, these platforms still face problems maintaining robustness, adaptability, and continuous learning when leaving well-trained environments for real-world environments. Motivated by cartographic research, this paper reviews the research work from artificial intelligence, robotics, and cognitive science and proposes a novel environment cognition model of unmanned platforms, the machine map. We first rationalize the similarity between the machine map and the mental map with a brief review of the mental map for human spatial cognition and then summarizes the machine map's characteristics. Having reviewed the research findings on the cognitive mechanism of mental maps, we propose the conceptual model of the machine map that features an architecture of "two cycles and three composition maps." The architecture follows design principles drawn from the research on the core cognitive capabilities of artificial cognitive systems. As for the two cycles, the outer cycle demonstrates the machine map's function in an autonomous unmanned platform, while the inner cycle illustrates the key components and the operation logic among them. Motivated by the structure theory of a mental map, the machine map is modeled as a multi-store memory system that consists of a perception map, a working map, and a long-term map. The overall information processing procedure among these three composition maps is discussed to finalize the model design. The conceptual development of machine maps benefits from studying the mental map in cognitive research and the technical innovations in autonomous driving and robotics fields, such as High-Definition maps, SLAM, and BEV. The proposed conceptual model can serve as a top-level research framework and a route map for further research on machine maps. In the end, the paper suggests that the research of machine maps needs a two-way methodology. On the top level, the deductive reasoning of the conceptual model can promote the understanding of the connotation and architecture of machine maps. While on the bottom level, the continuous development of machine learning and artificial intelligence technology can mitigate the restrictions on the environmental cognitive ability of unmanned platforms, resulting in a continuous improvement of the technical framework of machine maps. The research on the machine map can improve the cognitive capabilities of autonomous unmanned platforms in complex environments and illuminate a new path for the development of cartography in the intelligent era. We hope this paper can raise interest in machine maps among the cartographic community and thus promote the development of this emerging field.

As Artificial Intelligence Generated Content(AIGC) rapidly advances, various disciplines are shifting toward AI-driven scientific research. GeoAI technology, which focuses on geographic spatial intelligence, has the potential to outperform traditional methods in solving cartographic tasks. This shift presents both new opportunities and challenges for cartography. Despite some progress in integrating AI into cartographic research, limitations in computational power and other factors have hindered significant success in the past. As we enter the era of intelligence, both humans and machines will play critical roles in map creation and interpretation. Through artificial intelligence algorithms, maps can be produced quickly, at low cost, and on a large scale. However, there are also issues such as the instability of the quality of map works. The generation of map content has gone through the stages of expert-generated content and user-generated content and is developing towards the stage of artificial intelligence-generated content. In the traditional map-making phase, professional maps are produced by cartographic experts. While the quality of these maps is assured, the number of experts is limited. Consequently, the production cycle is long, the cost is high, the quantity of map products is limited, and they have not been produced on a large scale. At the current stage, generative artificial intelligence can produce map content in three forms: text-to-map (txt2map), map-to-text explanation (map2txt), and map style transfer (map2map). People can already use ChatGPT to generate maps by entering a piece of text, produce a textual explanation of a map by uploading an image of the map to ChatGPT, and even achieve map style transfer from images using Generative Adversarial Networks (GANs). The integration of artificial intelligence with the map transmission model has derived an intelligent map transmission model. It includes four stages: (1) Intelligent acquisition of mapping information: Sampling and collecting information about the real-world geographical environment through artificial intelligence methods, which is then processed and filtered into structured information for mapping; (2) Intelligent mapping: The process of intelligently generating maps through the use of colors, symbols, grading, and other representational methods based on mapping information; (3) Intelligent map reading: The process by which readers use artificial intelligence methods, combined with map language, domain knowledge, and personal understanding, to recognize the real world; (4) Intelligent interpretation of map information: Using artificial intelligence to interpret maps, thereby gaining cognition and understanding of the real world. Although progress has been made, research on using intelligent methods to address cartographic challenges is still in its early stages. Challenges include the lack of comprehensive training datasets, limited model algorithm generalization, and interpretability. These areas offer promising directions for future development.

The map is one of the most powerful and lasting geographical thinking, reflecting the way of human observing and understanding geographical elements and phenomena. In the era of information and communications technology, the map is defined as a visual information representation of ternary space consisting of physical space, social space, and information space. In the three stages of digitalization, intelligentization, and smartization, the knowledge system of cartography has also undergone profound changes and evolution, whose connotation and extension are constantly expanding and generalizing, and it is constantly cross-penetrating with multiple disciplines in terms of depth and breadth. In this process, map thinking has become an important way of knowing, understanding and constructing spatial thinking, which can be specifically distilled into four ways of thinking, namely, Digital-Shape-Graph-Spectrum thinking. Map thinking is an important research method in geographic science and even in earth science, and refining and coalescing map thinking can form a new path for understanding spatial thinking in ternary space. Respectively，digital thinking is a quantitative description and expression of geographical entities and phenomena in the traditional binary space, pursuing the "precision and accuracy" of spatial description; Shaped thinking is a symbolic form of mapping of "digital", which focuses on "similarity and detail" expressing human geographical insights in a visual form; Thinking about geographical problems through graph thinking is to show the connections between things and form a logical abstraction. It is the foundation for establishing the "map/GIS + professional applications" ecology, and its basic paths include metaphorical inspiration, generalized expression, and knowledge linking. Spectral thinking is a system that organizes a series of maps based on the category or system of objects, using certain characteristics to form a dynamic evolutionary system. Driven by multidisciplinary, the four modes of thinking of Digital-Shape-Graph-Spectrum have significant differences in grammatical features, semantic functions, pragmatic characteristics, implementation paths, expression types, and key technologies. Map thinking is an advanced cognitive activity in which humans use graph and images to understand, analyze and express spatial problems, from the perspective of Number-Shape-Graph-Spectrum thinking, it is clear that maps occupy a unique position at the intersection of geography and surveying and mapping science. Surveying and mapping science is more based on the Digital-Shape thinking of map, focusing on scientific measurement and expressing the spatial distribution of geographical elements; Geography is more inclined to the Graph-Spectrum thinking of map, focusing on revealing the laws and mechanisms of geographical phenomena in nature.

Sound maps have great potential for a series of application, such as describing natural and humanistic environments, recording history and culture, and assisting urban planning, etc. However, current sound maps are dominated by the topic of noise mapping, with insufficient attention to various sound landscapes. Sound maps should not only focus on the location and physical properties of sound, but also involve people's experience and emotion of the sound environment. They also suffer from high cost of acquiring sound source data and much difficulty of visualizing auditory attributes. To address those issues, this paper proposes a sound mapping method by using geo-tagged sound data. Firstly, we sort out four types of constituent elements of sound maps from the theory of sound landscape and the image of city: sound landmark, sound path, sound area, and background sound. Then, deep learning and spatial clustering methods are then introduced to parse sound map elements from geo-tagged sound data and extract sound attributes such as types, sound pressure levels, frequencies, and emotions. On this basis, the idea of synaesthesia and metaphor are introduced to design symbols of sound landmarks, sound paths, and sound areas. Multivariate color-coding schemes are also crafted to colorize those sound symbols. Finally, the proposed data analysis and symbolization methods are experimentally evaluated by using Nanjing Xianlin University Area as an example. And from the four tasks of listening matching, recognition, comparison, and distribution, the performance of the proposed method and the pictographic-based method is compared and evaluated. The results show that the proposed method outperforms the pictographic-based method in both effectiveness and efficiency. The method proposed in this paper is expected to enrich the means of sound data analysis and sound symbolization. In future studies, the method could be applied to the preservation of sound intangible cultural heritage and urban planning.

As an integration of map and visual metaphor, metaphorical map has become a new means for cultural communication in the "Image Age". However, related studies are still in their infancy and the fundamental theoretical issues such as the conceptualization, generation mechanism, and discourse production principle remain underexplored. Most importantly, there exists no unified framework for metaphorical map design, which have substantially hindered the progress of theoretical innovation and practical application. Aiming to fill in these research gaps and taking the generative mechanism and discourse production principle as the breakthrough points, this paper first elaborates the concept of "map" from the perspective of semiotics, and on this basis defines the basic concept and connotation of metaphorical map from the lens of cognitive linguistics and identifies the two basic characteristics of metaphorical map, namely the figurativeness and polysemy. Second, we unravel the generation mechanism of metaphorical map -"similarity fusion - similarity highlighting - symbolization"- through clarifying the principle of visual metaphor. Third, we propose the symbolic fabric principle based on "syntagmatic relation - paradigmatic relation" and the semantic transfer principle of "point-generation" after clarifying the principle of discourse production of metaphorical map in reference to language semiotics. Finally, we construct a theoretical framework for the design of metaphorical map-“defining subject, choosing metaphor, and creating context”, and further demonstrate its rationality and operability with practical cases. This paper provides theoretical reference for the in-depth research and practical application of metaphorical map.

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

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