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学者姓名：赵志远
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Fever visit management (FVM) played a critical role in reducing the risk of local outbreaks caused by positive cases during the coronavirus disease 2019 (COVID-19) pandemic under the dynamic zero-COVID-19 policy. Fever clinics were established to satisfy the healthcare needs of citizens with fever symptoms, including those with and without COVID-19. Learning how FVM affects fever medical care accessibility for citizens in different places can support decision making in establishing fever clinics more equitably. However, the dynamic nature of the population at different times has rarely been considered in evaluating healthcare facility accessibility. To fill this gap, we adjusted the Gaussian-based two-step floating catchment area method (G2SFCA) by considering the hourly dynamics of the population distribution derived from mobile phone location data. The results generated from Xining city, China, showed that (1) the accessibility of fever clinics explicitly exhibited spatial distribution patterns, being high in the center and low in surrounding areas; (2) the accessibility reduction in suburban areas caused by FVM was approximately 2.8 times greater than that in the central city for the 15 min drive conditions; and (3) the accessibility of fever clinics based on the nighttime anchor point was overestimated in central areas, but underestimated in suburban areas.
Keyword ：
COVID-19 pandemic COVID-19 pandemic fever medical care accessibility fever medical care accessibility G2SFCA G2SFCA mobile phone location data mobile phone location data spatial distribution patterns spatial distribution patterns
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| GB/T 7714 | Zhao, Zhiyuan , Tu, Youjun , Ding, Yicheng . How Did the Fever Visit Management Policy During the COVID-19 Epidemic Impact Fever Medical Care Accessibility? [J]. | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2025 , 14 (3) . |
| MLA | Zhao, Zhiyuan 等. "How Did the Fever Visit Management Policy During the COVID-19 Epidemic Impact Fever Medical Care Accessibility?" . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 14 . 3 (2025) . |
| APA | Zhao, Zhiyuan , Tu, Youjun , Ding, Yicheng . How Did the Fever Visit Management Policy During the COVID-19 Epidemic Impact Fever Medical Care Accessibility? . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2025 , 14 (3) . |
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Large-scale crowd flow prediction is a critical task in urban management and public safety. However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. Meanwhile, the massive spatial units significantly increase the computational cost, limiting model efficiency. To address these limitations, we propose a novel model for large-scale crowd flow prediction, namely the Stratified Compressive Sensing Network (SCS-Net). First, we develop a spatially stratified module that posterior adaptively extracts the underlying spatially stratified structure, effectively modeling spatial heterogeneity. Then, we develop compressive sensing modules to compress redundant information from massive spatial units and learn shared crowd flow patterns, enabling efficient prediction. Finally, we conduct experiments on a large-scale real-world dataset. The results demonstrate that SCS-Net outperforms deep learning baseline models by 35.25-139.2% in MAE and 26.3-112.4% in RMSE while reducing GFLOPs by 53-1067 times and shortening training time by 3.1-83.2 times compared to prevalent spatio-temporal prediction models. Moreover, the spatially stratified structure extracted by SCS-Net offers valuable interpretability for spatial heterogeneity in crowd flow patterns, providing deeper insights into urban functional layouts.
Keyword ：
compressive sensing compressive sensing crowd flow prediction crowd flow prediction large scale large scale spatial heterogeneity spatial heterogeneity spatially stratified structure spatially stratified structure
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| GB/T 7714 | Tan, Xiaoyong , Chen, Kaiqi , Deng, Min et al. SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction [J]. | MATHEMATICS , 2025 , 13 (10) . |
| MLA | Tan, Xiaoyong et al. "SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction" . | MATHEMATICS 13 . 10 (2025) . |
| APA | Tan, Xiaoyong , Chen, Kaiqi , Deng, Min , Liu, Baoju , Zhao, Zhiyuan , Tu, Youjun et al. SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction . | MATHEMATICS , 2025 , 13 (10) . |
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The use of the current global and regional models for predicting large-scale urban crowd flows often involves trade-offs between computational efficiency and accuracy. Global models are computationally efficient but struggle to fully capture the spatial heterogeneity in crowd dynamics and often lead to unsatisfactory performance. Region-specific models are highly accurate in capturing fine-grained spatial heterogeneity, but their computational costs are high when applied to numerous regions. We took a GeoAI approach to develop a novel spatiotemporal compressed sensing-based prediction framework (STCSP) to address these challenges. This framework employs compressed sensing techniques to identify the shared structures in crowd flow data. STCSP transforms spatiotemporal predictions in a complex geographical space into simplified predictions in an embedding space, which is more efficient than existing models. STCSP combines these simplified predictions, modeling the spatial heterogeneity in detail to increase the accuracy of crowd-flow predictions. We evaluated STCSP on a small-scale benchmark dataset and a large-scale citywide dataset and showed that STCSP outperformed 12 baseline models in accuracy and efficiency in predicting crowd flows. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
Keyword ：
compressed sensing compressed sensing Crowd-flow prediction Crowd-flow prediction spatial heterogeneity spatial heterogeneity
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| GB/T 7714 | Deng, M. , Tan, X. , Chen, K. et al. Predicting crowd flows via compressed sensing with spatial heterogeneity: an efficient GeoAI framework [J]. | International Journal of Geographical Information Science , 2025 . |
| MLA | Deng, M. et al. "Predicting crowd flows via compressed sensing with spatial heterogeneity: an efficient GeoAI framework" . | International Journal of Geographical Information Science (2025) . |
| APA | Deng, M. , Tan, X. , Chen, K. , Liu, B. , Zhao, Z. , Tu, Y. et al. Predicting crowd flows via compressed sensing with spatial heterogeneity: an efficient GeoAI framework . | International Journal of Geographical Information Science , 2025 . |
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Artificial intelligence (AI) has played a key role in advancing autonomous navigation for unmanned ships, where ship trajectory prediction is crucial for ensuring maritime safety. As the shipping industry grows and the number of ships increases, especially with autonomous ships operating in complex environments, collision risks have become a major concern. Accurate trajectory prediction, supported by advanced AI techniques, is crucial for the safe operation of these ships. While current models predict ship trajectories with high precision using Automatic Identification System (AIS) data, they often fail to incorporate prior knowledge of collision risks and struggle to model ship interactions that could lead to collisions. To overcome these limitations, the DGCN-Transformer (Dynamic Graph Convolution Network-Transformer) model is proposed. This model enhances the accuracy and reliability of ship trajectory predictions by incorporating collision risk modeling into the prediction framework. It uses the Quaternion Ship Domain (QSD) to model potential collision scenarios, integrating an advanced understanding of ships' spatial and kinematic properties. The model integrates QSD-based prior knowledge into an advanced Graph Convolutional Network (GCN) for spatial modeling, while the Transformer component captures and analyzes temporal features, overcoming the limitations of traditional Long Short-Term Memory (LSTM) networks. Experiments with AIS data from Tianjin, Caofeidian, and Chengshanjiao ports demonstrate that the DGCN-Transformer model outperforms state-of-the-art models, significantly improving trajectory prediction accuracy. Specifically, at Tianjin Port, the DGCN-Transformer model reduces Final Displacement Error (FDE) by 36.1%, Maximum Displacement Error (MDE) by 15.4%, and Average Displacement Error (ADE) by 50% compared to the best baseline model, highlighting the model's effectiveness in enhancing the safety of autonomous ship navigation.
Keyword ：
Autonomous navigation Autonomous navigation Collision risk Collision risk Graph convolution network Graph convolution network Quaternion ship domain Quaternion ship domain Ship trajectory prediction Ship trajectory prediction Transformer Transformer
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| GB/T 7714 | Zhang, Yu , Tu, Ping , Zhao, Zhiyuan et al. Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 146 . |
| MLA | Zhang, Yu et al. "Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 146 (2025) . |
| APA | Zhang, Yu , Tu, Ping , Zhao, Zhiyuan , Chen, Xuan-Yan . Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 146 . |
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The flow of essential elements such as people, goods, and information through complex networks has become a critical factor in shaping urban dynamics and regional development. Quantifying location centrality plays an indispensable role not only in urban infrastructure planning but also in National central city planning. Two vital aspects should be considered for central nodes in flow-based complex networks: their impact on adjacent nodes and the diversity of nodes they affect. In this paper, we present a centrality measure index (C-index) that accounts for flow volume and flow directions, offering a high degree of interpretability. We applied the C-index to four public weighted complex networks, demonstrating that our method outperforms classical methods. Furthermore, we validated the effectiveness and advantages of C-index on quantifying location centrality both in inter-city and intra-city population mobility network. The centrality findings from the perspective of population mobility can reinforce guidelines for understanding National central cities and polycentric structure of cities, thereby facilitating policy-making of sustainable urban development.
Keyword ：
Centrality measure Centrality measure Interactions between locations Interactions between locations Population mobility networks Population mobility networks Sustainable urban development Sustainable urban development
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| GB/T 7714 | Yin, Yanzhong , Wu, Qunyong , Zhao, Zhiyuan et al. Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development [J]. | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS , 2025 , 116 . |
| MLA | Yin, Yanzhong et al. "Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development" . | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 116 (2025) . |
| APA | Yin, Yanzhong , Wu, Qunyong , Zhao, Zhiyuan , Chen, Xuanyu . Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development . | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS , 2025 , 116 . |
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CONTEXT: Long-term historical information on national -scale grain production is critical for ensuring food security but often limited by the lack of geospatial data. OBJECTIVE: This study aims to conduct the first systematic investigation of grain Cropping Patterns (CP) in China over the past two decades, shedding light on the roles of grain expansion and intensification in sustainable agriculture. METHODS: This study proposes a framework to fully characterize grain production patterns considering crop types, cropping intensity and patterns based on spatiotemporal continuous ChinaCP datasets (2005-2020). Four indicators were developed for measuring the Reality to Capability Ratio (RCR) of grain production regarding the total yield and sow area, the cropland extent and cropping intensity. The capability of grain production was derived based on grain cultivation history. RESULTS AND CONCLUSION: There was a huge gap between the reality and capability of grain production in China, which varied with grain crop types and cropping patterns. At national level, a vast majority (96%) of cropland was capable of grain production, and two fifths of cropland quantified for double grain cropping. However, only 46.65% and 24.89% of the capability was implemented for grain or double -grain cropping in 2020. Maize, rice, and wheat was ever cultivated in 76.88%, 57.05%, and 25.18% of national cropland, respectively. Winter wheat plays an important role in stabilizing grain production by double grain cropping, accounting for 7/8 continuously grain -cultivated areas. However, the RCR of double rice was only 7% in 2020. Bridging these gaps could potentially triple grain production, however, achieving this increase poses challenges due to a series of constraints related to cropland fraction, topographic conditions and lack of agricultural labors along with rapid urbanization. This study found that there was a continuous Northeastward movement & countryside shift in grain production. Continuous support for long-term active agricultural systems is crucial to ensure sustainable grain production in China, with a special emphasis on key grain productive regions, considering targeted cropping patterns and regional disparities. SIGNIFICANCE: This study enhances our understanding of grain production systems in China based on long-term cultivation histories. Findings can inform the development of more geographic -targeted policies concerning grain cropping intensifications to ensure food security and environmental sustainability in developing countries. The long term spatiotemporal continuous CPChina datasets during 2005-2020 was are publicly accessed at: https ://doi.org/10.6084/m9.figshare.25106948.
Keyword ：
China China Cropping patterns Cropping patterns Grain security Grain security Non-grain production Non-grain production Spatiotemporal process Spatiotemporal process
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| GB/T 7714 | Qiu, Bingwen , Jian, Zeyu , Yang, Peng et al. Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification [J]. | AGRICULTURAL SYSTEMS , 2024 , 216 . |
| MLA | Qiu, Bingwen et al. "Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification" . | AGRICULTURAL SYSTEMS 216 (2024) . |
| APA | Qiu, Bingwen , Jian, Zeyu , Yang, Peng , Tang, Zhenghong , Zhu, Xiaolin , Duan, Mingjie et al. Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification . | AGRICULTURAL SYSTEMS , 2024 , 216 . |
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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. © 2024 Science Press. All rights reserved.
Keyword ：
Agglomeration Agglomeration Big data Big data Digital storage Digital storage Disease control Disease control Flow patterns Flow patterns Population dynamics Population dynamics Population statistics Population statistics
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| GB/T 7714 | Yin, Yanzhong , Wu, Qunyong , Lin, Han et al. Analysis of Urban Centrality and Community Patterns from the Perspective of 'Intercity Mobility Flow' in China [J]. | Journal of Geo-Information Science , 2024 , 26 (3) : 666-678 . |
| MLA | Yin, Yanzhong et al. "Analysis of Urban Centrality and Community Patterns from the Perspective of 'Intercity Mobility Flow' in China" . | Journal of Geo-Information Science 26 . 3 (2024) : 666-678 . |
| APA | Yin, Yanzhong , Wu, Qunyong , Lin, Han , Zhao, Zhiyuan . Analysis of Urban Centrality and Community Patterns from the Perspective of 'Intercity Mobility Flow' in China . | Journal of Geo-Information Science , 2024 , 26 (3) , 666-678 . |
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The emergence of large-scale spatiotemporal trajectory data offers an excellent opportunity to characterize collective human mobility patterns and their relationship with the urban built environment. Such an approach can generate a complementary understanding of traditional individual travel behavior based on travel survey data. Using mobile phone data, this study aims to investigate how residential built environments affect residents' mobility at an aggregated level. Specifically, three indicators (movement distance, activity space, and the number of stops) were derived from raw mobile phone data to characterize human mobility. The decay co-efficients and average values were then employed to reveal the aggregated characteristics of movement distance, activity space, and the number of stops. Furthermore, linear regression was applied to examine the relationship between human mobility indicators and residential built environments. The results indicate that the living-built environment could better explain the activity space than the movement distance and number of stops. In addition, some differences in the relationship between human mobility and residential built environments are identified between weekdays and weekends. These findings could provide new insights into human mobility and its interaction with the built environment, thus advancing our understanding of human travel behavior from both individual and collective perspectives.
Keyword ：
Built environment Built environment Human mobility Human mobility Mobile phone data Mobile phone data
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| GB/T 7714 | Yang, Xiping , Li, Junyi , Fang, Zhixiang et al. Influence of residential built environment on human mobility in Xining: A mobile phone data perspective [J]. | TRAVEL BEHAVIOUR AND SOCIETY , 2024 , 34 . |
| MLA | Yang, Xiping et al. "Influence of residential built environment on human mobility in Xining: A mobile phone data perspective" . | TRAVEL BEHAVIOUR AND SOCIETY 34 (2024) . |
| APA | Yang, Xiping , Li, Junyi , Fang, Zhixiang , Chen, Hongfei , Li, Jiyuan , Zhao, Zhiyuan . Influence of residential built environment on human mobility in Xining: A mobile phone data perspective . | TRAVEL BEHAVIOUR AND SOCIETY , 2024 , 34 . |
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Tidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integrates the COntinuous monitoring of Land Disturbance (COLD) algorithm and Median Composite (MC) based on the dense Landsat time series to track the dynamic processes of tidal wetlands in the YRE from 1990 to 2020. The results showed that the COLD-MC demonstrated remarkable effectiveness in detecting the change of tidal wetlands and excellent overall accuracy and kappa coefficient ranging from 90% to 96% and 0.89-0.95, respectively. The overall accuracy of change detection was 97% with an absolute error of 0.4 years. We found that the total area of tidal wetlands experienced a net loss of 59.75 km2 in the YRE, but the gain and loss of the study period were 1556.07 and 1615.82 km2, respectively. Land reclamation, sediment reduction, and Spartina alterniflora invasion pose significant threats to tidal wetlands. Sustainable management could be implemented through the establishment of nature reserves and ecological sediment enhancement engineering projects.
Keyword ：
COLD-MC COLD-MC dynamic equilibrium dynamic equilibrium land reclamation land reclamation landsat time-series landsat time-series sediment starvation sediment starvation Tidal wetlands Tidal wetlands
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| GB/T 7714 | Wu, Wenting , Lin, Zhibin , Chen, Chunpeng et al. Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
| MLA | Wu, Wenting et al. "Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 17 . 1 (2024) . |
| APA | Wu, Wenting , Lin, Zhibin , Chen, Chunpeng , Chen, Zuoqi , Zhao, Zhiyuan , Su, Hua . Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
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Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries' GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.
Keyword ：
Gaussian process Gaussian process GDP GDP industrial agglomeration industrial agglomeration nighttime light nighttime light points of interest points of interest
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| GB/T 7714 | Chen, Zuoqi , Xu, Wenxiang , Zhao, Zhiyuan . The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data [J]. | REMOTE SENSING , 2024 , 16 (2) . |
| MLA | Chen, Zuoqi et al. "The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data" . | REMOTE SENSING 16 . 2 (2024) . |
| APA | Chen, Zuoqi , Xu, Wenxiang , Zhao, Zhiyuan . The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data . | REMOTE SENSING , 2024 , 16 (2) . |
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