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  • ZHANG Xinchang, HUA Shuzhen, QI Ji, RUAN Yongjian
    Journal of Geo-information Science. 2024, 26(4): 779-789. https://doi.org/10.12082/dqxxkx.2024.240065

    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.

  • LV Guonian, YUAN Linwang, CHEN Min, ZHANG Xueying, ZHOU Liangchen, YU Zhaoyuan, LUO Wen, YUE Songshan, WU Mingguang
    Journal of Geo-information Science. 2024, 26(4): 767-778. https://doi.org/10.12082/dqxxkx.2024.240149

    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.

  • GU Jinyuan, YANG Dongfeng
    Journal of Geo-information Science. 2024, 26(2): 332-351. https://doi.org/10.12082/dqxxkx.2024.230136

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

  • WU Tianjun, LUO Jiancheng, LI Manjia, ZHANG Jing, ZHAO Xin, HU Xiaodong, ZUO Jin, MIN Fan, WANG Lingyu, HUANG Qiting
    Journal of Geo-information Science. 2024, 26(4): 799-830. https://doi.org/10.12082/dqxxkx.2024.230747

    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.

  • JIANG Yiyi, GAO Jie, GUO Jiaming, XU Haibin
    Journal of Geo-information Science. 2024, 26(2): 242-258. https://doi.org/10.12082/dqxxkx.2024.230017

    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.

  • YANG Fei, Li Xiang, CAO Yibing, ZHAO Xinke, WANG Lina, WU Ye
    Journal of Geo-information Science. 2024, 26(3): 543-555. https://doi.org/10.12082/dqxxkx.2024.230497

    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.

  • JIANG Bingchuan, SI Dongyu, LIU Jingxu, REN Yan, YOU Xiong, CAO Zhe, LI Jiawei
    Journal of Geo-information Science. 2024, 26(4): 848-865. https://doi.org/10.12082/dqxxkx.2024.240151

    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.

  • YAN Minzu, DONG Guanpeng, LU Binbin
    Journal of Geo-information Science. 2024, 26(6): 1351-1362. https://doi.org/10.12082/dqxxkx.2024.230709

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

  • CAO Yi, BAI Hanwen, WANG Yixiao
    Journal of Geo-information Science. 2024, 26(3): 556-566. https://doi.org/10.12082/dqxxkx.2024.230407

    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.

  • HUANG Jingxiong, LIANG Jiaqi, YANG Mengsheng, LI Yuan
    Journal of Geo-information Science. 2024, 26(2): 352-366. https://doi.org/10.12082/dqxxkx.2024.220404

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

  • LI Xinran, HE Rixing, JIANG Chao, JIN Xin, TANG Zongdi, LONG Wei, DENG Yue
    Journal of Geo-information Science. 2024, 26(6): 1390-1406. https://doi.org/10.12082/dqxxkx.2024.230643

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

  • TAN Songlin, WANG Jie, JI Jingjing, LIU Meili, ZHAN Zhongyu, LIU Miao, WANG Lirong, HU Xiaodong
    Journal of Geo-information Science. 2024, 26(3): 591-603. https://doi.org/10.12082/dqxxkx.2024.230502

    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.

  • LI Yuan, LIANG Jiaqi, ZHAO Long, DU Ya'nan, YANG Mengsheng, ZHANG Na
    Journal of Geo-information Science. 2024, 26(2): 274-302. https://doi.org/10.12082/dqxxkx.2024.220723

    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.

  • LIU Yihan, NING Nianwen, YANG Donglin, LI Wei, WU Bin, ZHOU Yi
    Journal of Geo-information Science. 2024, 26(4): 946-966. https://doi.org/10.12082/dqxxkx.2024.230572

    In the field of intelligent transportation, various information collection devices have produced a massive amount of multi-source heterogeneous data. These data encompass various types of information, including vehicle trajectories, road conditions, and traffic incidents, soured from devices such as traffic cameras, sensors, and GPS. However, the current challenge faced by researchers and practitioners is how to correlate and integrate the massive amount of heterogeneous data to facilitate decision support. To address this challenge, knowledge graph technology, with its powerful entity-to-entity modeling ability, has shown great potential in knowledge mining, representation, management, and reasoning, making it well-suited for intelligent transportation applications. In this paper, we first review the construction techniques for geographic traffic graphs, multimodal knowledge graphs, and dynamic knowledge graphs, demonstrating the broad applicability of knowledge graphs in the field of intelligent transportation. Secondly, we summarize relevant algorithms of multi-modal knowledge graph representation learning and discuss dynamic knowledge graph representation learning in the field of intelligent transportation. Knowledge graph representation learning technology plays a crucial role in creating high-quality knowledge graphs by capturing and organizing the relationships between entities and their attributes within the transportation domain. This technology utilizes advanced machine learning algorithms to analyze and process the heterogeneous data from various sources to extract meaningful patterns and structures. We also introduce the completion technology and causal reasoning technology in dynamic transportation multi-modal knowledge graph, which is useful for improving the data of intelligent transportation systems. Comprehension ability and decision-making reasoning level have important theoretical significance and practical application prospects. Thirdly, we summarize the solutions of knowledge graph that provide important support for intelligent decision-making in several application scenarios. The utilization of knowledge graphs in intelligent transportation systems facilitates real-time data integration and enables correlation analysis of diverse data sources to provide a holistic view of the traffic ecosystem. This comprehensive understanding empowers decision-makers to implement targeted interventions and proactive measures, ultimately mitigating traffic congestion and reducing the occurrence of accidents. Through the continuous refinement and enrichment of the traffic knowledge graph, the intelligent transportation system can adapt and evolve to address emerging challenges and optimize transport networks for enhanced efficiency and safety. Finally, we analyze and discuss the existing technical bottlenecks. The future of traffic knowledge graphs and their auxiliary applications are also prospected and discussed, highlighting the potential impact of this important technology on intelligent transportation systems.

  • YANG Mingwang, ZHAO Like, YE Linfeng, JIANG Huawei, YANG Zhen
    Journal of Geo-information Science. 2024, 26(6): 1500-1516. https://doi.org/10.12082/dqxxkx.2024.240057

    Building extraction is one of the important research directions that has attracted great attention in the field of remote sensing image processing. It refers to the process of accurately extracting building information such as the location and shape of buildings by analyzing and processing remote sensing images. This technology plays an irreplaceable and important role in urban planning, disaster management, map production, smart city construction, and other fields. In recent years, with the advancement of science and technology, especially the continuous evolution of earth observation technology and the rapid development of deep learning algorithms, Convolutional Neural Networks (CNNs) have become an emerging solution for extracting buildings from remote sensing images because of their powerful feature extraction capability. The aim of this paper is to provide a comprehensive and systematic overview and analysis of building extraction methods based on convolutional neural networks. We conduct a comprehensive literature review to summarize the building extraction methods from perspectives of model structure, multi-scale feature differences, lack of boundary information, and model complexity. This will help researchers to better understand the advantages and disadvantages of different methods and the applicable scenarios. In addition, several typical building datasets in this field are described in detail, as well as the potential issues associated with these datasets. Subsequently, by collecting experimental results of relevant algorithms on these typical datasets, a detailed discussion on the accuracy and parameter quantities of various methods is conducted, aiming to provide a comprehensive assessment of performance and applicability of these methods. Finally, based on the current research status of this field and looking forward to the new era of high-quality development in artificial intelligence, the future directions for building extraction are prospected. Specifically, this paper discusses the combination of Transformers and CNNs, the combination of deep learning and reinforcement learning, multi-modal data fusion, unsupervised or semi-supervised learning methods, real-time extraction based on large-scale remote sensing model, building instance segmentation, and building contour vector extraction. In conclusion, our review can provide some valuable references and inspirations for future related research, so as to promote the practical application and innovation of building extraction from remote sensing images. This will fulfill the demand for efficient and precise map information in remote sensing technology and other related fields, contributing to the sustainable and high-quality development of human society.

  • LU Huijia, HU Zui
    Journal of Geo-information Science. 2024, 26(6): 1407-1425. https://doi.org/10.12082/dqxxkx.2024.240008

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

  • ZHANG Lei, DOU Wangsheng, QIN Bo
    Journal of Geo-information Science. 2024, 26(2): 381-392. https://doi.org/10.12082/dqxxkx.2024.220473

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

  • WANG Shoufen, WANG Shouxia, GU Jianxiang
    Journal of Geo-information Science. 2024, 26(3): 567-590. https://doi.org/10.12082/dqxxkx.2024.230413

    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.

  • LIN Liangguo, ZHAO Yaolong, KE Entong
    Journal of Geo-information Science. 2024, 26(4): 898-914. https://doi.org/10.12082/dqxxkx.2024.240198

    In China, urbanization has entered a later stage characterized by a slowdown in growth rates and a focus on quality enhancement. The urban growth paradigm is transitioning gradually from "incremental development" to "quality improvement of existing urban stock", marking the adoption of a new urbanization mode centered around urban renewal. Urban renewal, as a spatial governance activity within the scope of national territory, aims to continuously enhance city functions, optimize spatial layout, improve environmental quality, and stimulate economic and social vitality. However, challenges of urban renewal, such as the ambiguous definition of urban renewal oriented towards national spatial planning and the lack of a systematic logical framework for geographic information technology tailored for urban renewal, still persist. Therefore, this study reexamines the connotations of urban renewal research from the perspective of the "Production-Living-Ecological" space, expecting to achieve "intensive and efficient production space", "livable and moderate living space", and "beautiful and ecofriendly ecological space". Furthermore, with reference to the three processes of perception, assessment and optimization in "Urban Cognition", the logical architecture of geospatial information technology application for urban renewal is constructed, and based on this framework, the contributions of geographic spatial information technology in data collection, model assessment, and simulation optimization are elucidated. In the production space, geospatial information technology is able to perceive the production elements of urban renewal in real time, rapidly construct the economic benefit assessment index system and spatial assessment model, simulate the geographical process of industrial development, and optimize the spatial pattern of production. In the living space, the application of geospatial information technology helps to integrate the resources of living elements by means of spatial and temporal digitization, comprehensively assess the social benefits and carry out the spatial optimization of the allocation of public service facilities. In the ecological space, geospatial information technology provides an efficient and fast technical method for perceiving the elements of the natural environment and natural resources in a timely manner, constructing an ecological efficiency assessment index system to identify "urban diseases", optimizing the ecological spatial pattern, and exploring coping strategies to solve "urban diseases". Finally, based on the actual needs of urban renewal, the prospects for application of geographic spatial information technology in urban renewal research are discussed. This paper proposes comprehensive perception, comprehensive assessment, comprehensive optimization of urban renewal and construct an urban renewal technology system covering the whole process of "Perception-Assessment-Optimization", so as to improve the city's ability to adapt to the future development of regulation. These efforts will facilitate the modernization of national spatial governance systems and capabilities.

  • CAO Wei, XIAO Yao, LIANG Xun, GUAN Qingfeng
    Journal of Geo-information Science. 2024, 26(7): 1611-1628. https://doi.org/10.12082/dqxxkx.2024.230571

    Cellular Automata (CA) provides an important tool for land use/land cover change simulation. However, previous CA models based on pure cells ignore the mixed land cover structure within cells, making it difficult to simulate the continuous evolution of mixed land systems during rapid urbanization. The Mixed-Cell Cellular Automata (MCCA) can address this issue, but its widespread application is hindered by the difficulty in obtaining fine-scale mixed structure data. To solve these problems, this study proposes a simulation analysis framework that couples the mixed pixel decomposition method with the MCCA model. This framework uses the mixed pixel decomposition algorithm to directly obtain the sub-pixel scale mixed structure data required by the MCCA model from Landsat images. The SHAP method is utilized to explore the driving forces of sub-pixel scale land cover change. To verify the proposed framework, we conducts an experiment in Wuhan city. Results show that: 1) The decomposition accuracy of the land cover data is above 0.8, and the mcFoM index of the simulation results is 0.38, indicating that this coupled model has high accuracy in characterizing the spatial pattern of mixed land structures and simulating future changes; 2) The proposed coupling model can effectively simulate the fine-scale dynamic changes of land cover proportions and discover relevant patterns of regional land use changes. For example, future land cover structure changes will mainly concentrate in built-up areas, and land mixture will experience a process of increasing first and then decreasing. Socio-economic factors such as proximity to companies, the municipal government, and high population and GDP are important driving factors for the expansion of impervious surfaces, and impervious surfaces in urban centers relatively far from high-speed railway stations grow more rapidly; 3) The future land cover change trends simulated by the proposed model are consistent with the future planning layout of Wuhan. The comparison between multiple scenarios demonstrates the MCCA model’s ability to accurately capture the subtle differences in land cover proportion between pixels. This method couples the mixed pixel decomposition method from the field of remote sensing with the mixed Cellular Automata (CA) model from the field of GIS, solving the problem of lacking fine-scale data sources for simulating mixed land cover structures. It simulates future changes in mixed land cover structures at the sub-pixel scale, which can enrich existing research on mixed land structures and provide a certain theoretical basis for urban development decisions. Additionally, it opens up new avenues for the application of CA models in other areas.

  • YE Qilin, PU Yingxia, YE Cui
    Journal of Geo-information Science. 2024, 26(6): 1374-1389. https://doi.org/10.12082/dqxxkx.2024.240003

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

  • WU Peng, Hasibagen, QIN Fuying
    Journal of Geo-information Science. 2024, 26(7): 1594-1610. https://doi.org/10.12082/dqxxkx.2024.240039

    Points of Interest(POI), which are rich in semantic information, reflect current situations, and indicate areas of interest, serve as the primary data source in studies related to urban functionalization studies. These studies aim to deepen the understanding of human activities and environmental features within geographical spaces. An important research issue for enhancing the understanding of the human-environment system is detecting outliers, namely elements considerably different from the rest in large-scale spatial data. The detection of POI outliers can be broadly discussed from three perspectives: (1) spatial distribution differences, (2) spatial contextual differences, and (3) variations in the usage frequency of some POI instances and their surrounding points in specific areas due to factors such as special events, changes in urban population behavior, cultural activities, etc., leading to outliers. This paper focuses on discussing the phenomenon of POI outliers caused by spatial distribution differences. However, current outlier detection methods face with challenges. They fall short of adequately expressing and quantifying POIs' local spatial distribution features. The effectiveness of these methods needs further investigation. Given these considerations, this study proposed a novel approach for detecting POI outliers based on determination of local aggregation scales. Initially, we constructed spatial adjacency relationships of the POIs using Delaunay triangulation. Subsequently, the local aggregation characteristic scales of these points were determined by combining cross K-nearest distances and multi-scale feature parameters. Thereafter, based on the scale constraint, the points and their adjacent edge sets that met the conditions were extracted. Finally, we employed the edge length constraint index to systematically remove local long edges that did not meet the prescribed criteria. This meticulous process ensured the integration of the refined point set, thus facilitating the comprehensive detection of outliers within the POI context. The comparative experimental results, drawn from real-world data, suggested that the proposed method possessed a strong generalization ability. Moreover, it effectively and robustly detected outliers without compromising the inherent distribution characteristics of POI. We also performed an interpretability analysis of outlier detection results. The analysis revealed a close correlation between the causes of outlier distribution and various factors including the proportion of POI types, spatial layout, footprint area, and public awareness level. This study provides novel methodologies and academic perspectives for a comprehensive understanding of urban development trends, optimal resource allocation, and the enhancement of urban sustainability and quality of life.

  • JIANG Yiyi, DENG Ning, GAO Bingbo, LI Yuan, LI Yunpeng, LIU Yi, LIU Zhenhuan, MOU Naixia, PENG Peng, TANG Chengcai, ZHANG Honglei, ZHANG Xiang, XU Haibin
    Journal of Geo-information Science. 2024, 26(2): 227-241. https://doi.org/10.12082/dqxxkx.2024.240023

    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.

  • ZHENG Yunhao, ZHANG Yi, MOU Naixia, JIANG Qi, LIU Yu
    Journal of Geo-information Science. 2024, 26(2): 259-273. https://doi.org/10.12082/dqxxkx.2024.230354

    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.

  • QI Ziyin, LI Junyi, HE Zhe, YANG Xiping
    Journal of Geo-information Science. 2024, 26(2): 514-529. https://doi.org/10.12082/dqxxkx.2024.230181

    Streets are an important attraction for urban tourism. Exploring the influence of street landscape color characteristics on tourists' emotional perception holds important reference value for the rational planning and layout of urban street landscape. This study takes the built-up area within the third ring road of Xi'an city as a study case, and employs the Full Convolutional Neural Network (FCN) and Random Forest (RF) algorithms to construct an emotional perception dataset of street images. We use the streetscape images as the basis to extract the color features of the streetscape using machine learning algorithms, and color quantifiers are constructed and spatially visualized; The RF regression algorithm is used to explore the relationship between streetscape color characteristics and tourists' emotional perception, and the optimal color characteristic parameters are derived. The results show that: (1) There is a distinct spatial distribution pattern of tourists' emotional perception. The emotions of beauty and liveness gradually increase from the central area outward, and emotions of safety and wealth emotions score higher in the area within the second ring road outside the main city. While boring emotions score lower in this area, and depressing emotions gradually decrease from the central area outward. This suggests that the spatial distribution pattern of emotional perception shares somewhat homogeneity between tourists' emotional perception in non-routine environment and residents' perception in familiar environment; (2) The color characteristics of the streetscape show a complex non-linear relationship with tourists' emotional perception. For example, color complexity has less effect on emotions of beauty and liveness compared to color coordination and has a greater effect on emotions of boredom, depression, safety, and wealth than color coordination. Moreover, when the value of color complexity is 0.86 and the value of color coordination is 0.84, tourists can obtain better emotional perception across six dimensions; (3) Under non-routine conditions, the more significant the color characteristics of the street landscape, the better the emotional perception of visitors. Theoretically, this study confirms the conclusion that the more colorful environment leads to better experience for tourists; and methodologically, this paper not only expands the traditional text-based and manually-assigned research methods in the field of tourism emotion, but also enriches the application of streetscape big data and machine learning methods in the field of tourism. This study provides a reference for city managers to understand tourists' visual preferences for streetscapes and to optimize streetscape design.

  • HUANG Qin, TAN Cui, YANG Bo
    Journal of Geo-information Science. 2024, 26(2): 303-317. https://doi.org/10.12082/dqxxkx.2024.220245

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

  • ZHAO Qunqun, ZHAO Jing, ZHANG Lingxian, WANG Tuo, YANG Tengfei, ZHAO Chen, MOU Naixia
    Journal of Geo-information Science. 2024, 26(6): 1439-1451. https://doi.org/10.12082/dqxxkx.2024.230685

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

  • TAN Cui, HUANG Qin, YANG Bo, LI Tao, LEI Jihua
    Journal of Geo-information Science. 2024, 26(2): 318-331. https://doi.org/10.12082/dqxxkx.2024.230198

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

  • GAO Feifan, LIU Yi, LI Zhigang
    Journal of Geo-information Science. 2024, 26(2): 530-541. https://doi.org/10.12082/dqxxkx.2024.220165

    With China entering the stage of high-quality development in an all-round way, high-level urban leisure supply becomes increasingly important. It is of great significance to systematically explore the relationship between urban leisure supply and residents' mental health to meet people's yearning for a better life. Therefore, this paper calculates the residents' Psychological Distress Index (PDI) from 2011 to 2019 based on Baidu index, applies spatial autocorrelation analysis to specify the spatial-temporal pattern of PDI, and uses stepwise regression analysis to further explore the relationship between PDI and leisure facilities with economic, population, and environmental factors controlled. The results show that: (1) In recent years, the PDI of the whole country and sub-regions has been rising continuously; (2) The more developed the region/city, the higher the PDI of residents. PDI presents a decreasing pattern of East-Central-Northeast-West. It also shows a significant spatial agglomeration, with high-high, low-high, high-low, and low-low agglomeration patterns from the coast to the inland; (3) PDI is significantly associated with the supply level of commercial leisure facilities at night (e.g., midnight snack, bar, and KTV), but not significantly associated with that in the daytime (e.g., shopping mall and cinema). The density of midnight snack, which reflects the degree of overtime work, is significantly positively associated with PDI. While as typical nightlife places, the densities of bars and KTV are significantly negatively associated with PDI. Besides, typical public leisure facilities (e.g., park) can effectively alleviate PDI. This study tentatively verifies the correlation between residents' PDI and different leisure facilities. Such effort offers a better understanding of urban health problem and how to cure it. It is important to improve the pedigree and enrich the supply of urban leisure facilities, so as to meet residents' demand for high-quality leisure life and promote their mental health.

  • WANG Peixiao, ZHANG Hengcai, ZHANG Tong, LU Feng
    Journal of Geo-information Science. 2024, 26(6): 1363-1373. https://doi.org/10.12082/dqxxkx.2024.230678

    Accurate and explainable prediction of PM2.5 concentration can help humans avoid exposure risks to air pollution, which is of great significance for human health risk assessment and policy implementation. Currently, the existing PM2.5 concentration prediction models focus on improving the model prediction accuracy without considering model interpretability, resulting in poor model reusability and trustworthiness. Therefore, this paper proposes an Attentional Spatiotemporal Ordinary Differential Equation (ASTODE) model for PM2.5 concentration prediction tasks considering both prediction accuracy and model interpretability. Specifically, this paper integrates the Neural Ordinary Differential Equation (NODE) into the PM2.5 concentration prediction task to improve the interpretability of the prediction model. In addition, to address the challenge of traditional NODE in mining spatial dependencies in PM2.5 concentration data, this paper proposes a novel spatiotemporal derivative network that extends the traditional NODE to spatiotemporal ordinary differential equations. To address the challenges of traditional NODE in mining long-term dependencies in PM2.5 concentration data, this paper proposes a spatiotemporal attention mechanism to fuse hidden states of multiple time nodes. In the experimental section, the proposed ASTODE model is validated using a real PM2.5 concentration dataset. This paper quantifies the prediction errors of the ASTODE model in both temporal and spatial dimensions. By comparing with six existing baseline methods, our proposed ASTODE model obtains a similar or higher prediction accuracy. This paper also analyzes the interpretability of our proposed ASTODE model from a visualization perspective, demonstrating that the proposed ASTODE model balances the prediction accuracy and interpretability to some extent.