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地理学报（英文版）
地理学报（英文版）

郑度

双月刊

1009-637X

jgs@igsnrr.ac.cn;SDC-journals@springer-sbm.com

010-64889293

100101

北京安外大屯路甲11号

地理学报（英文版）/Journal Journal of Geographical SciencesCSCDCSTPCD北大核心SCI
查看更多>>《地理学报》英文版主要刊登国际上地理学界前沿性的重大研究成果，对国家建设有较大应用价值的地理学论文。除部分论文是中文版优秀稿件的英译文外，还刊有大量的国外来稿和中外合作完成的资源环境热点问题研究成果。文中附有彩色插图和地图。《地理学报》英文版在国内外的发行量均居中国英文版科技期刊发行量的前列，已被SCIE等国内外主要检索机构收录，一些论文还在国际、国内学术评比中获奖。
正式出版
收录年代

    Urban economic efficiency under the interactive effect of urban hierarchy and connection networks in China

    ZHOU YingZHENG WenshengWANG XiaofangDU Nanqiao...
    2315-2332页
    查看更多>>摘要：The efficient development of the urban economy is a major concern of scholars in the fields of geography and urban science.In the context of globalization,informatization,industrialization,and urbanization,the external relationships of China's cities are experienc-ing the joint action of urban scale hierarchies and connection networks("hierarchy-network").However,under the interactive effect of the two,the mechanism of urban economic efficiency(UEE)is unclear.Therefore,based on Baidu migration data,the regionalization with dynam-ically constrained agglomerative clustering and partitioning(REDCAP)method,and a spatial simultaneous equation model,this paper analyzes the UEE spatial pattern and mechanism in China.The results indicate that:(1)the urban economy has a superlinear relationship with the population size.However,the benefit of this superlinear growth is in marginal decline.(2)The UEE shows a pattern of differentiation between China's eastern,then central,and then western region.Also,local differences are found within the three major sub-regions.(3)The increase of urban network centrality can promote UEE,while the impact of urban scale is negative.(4)There is regional heterogeneity of the interactive effect of"hierarchy-network"on UEE.This study reveals the influencing mechanism of UEE and also provides policy implica-tions for the development of UEE.
      原文链接:
    • 万方数据
    • 维普

    Spatio-temporal characteristics and influencing factors of Pseudo Human Settlements in Northeast China based on the Baidu Index

    LI XuemingZHANG XiaohuiTIAN ShenzhenGAO Mengke...
    2333-2363页
    查看更多>>摘要：Pseudo Human Settlements(PHS)are a fundamental element in human settle-ments geography,serving as an innovative frontier in the exploration of human-land rela-tionships.Since entering the information age,PHS have emerged as a new catalyst for peo-ple's lives and urban development.Based on the Baidu Index,cold hot spot analysis and the Pearson correlation coefficient method were used to evaluate the spatiotemporal variation characteristics of the development of the quality of PHS at different levels in the three prov-inces of Northeast China(TPNC)during 2011-2022 and to characterize the influence of the system and factors.The results indicated that:1)temporally,PHS exhibits significant fluctua-tions,with an overall pattern of rapid increase followed by a gradual decline;2)spatially,PHS is marked by regional differentiation,with"three-core"dominance and a"cluster-like"distri-bution;3)systematically,the five major PHS systems generally exhibit an ascending and then a descending trend;4)in terms of influence,the socialization system serves as the core in-fluence of PHS,with WeChat,JD.COM,and others are identified as the core influencing factors of subsystems.The findings of this study can provide scientific guidance for diversi-fying approaches to human settlements,promoting sustainable urban development,and re-vitalizing Northeast China.
      原文链接:
    • 万方数据
    • 维普

    A large-scale village classification model for tailored rural revitalization:A case study of Hubei province,China

    PAN YupiaoZHAO XiangZHANG YiqingLUO Haifeng...
    2364-2392页
    查看更多>>摘要：A comprehensive understanding of village development patterns and the identifica-tion of different village types is crucial for formulating tailored planning for rural revitalization.However,a model for large-scale village classification to support tailored rural revitalization planning is still lacking.This study aims to develop a large-scale village classification model using the Gaussian Mixture Models to support tailored rural revitalization efforts.Firstly,we propose a multi-dimensional index system to capture the diverse features of massive villages.Secondly,the GMM clustering algorithm is applied to identify distinct village types based on their unique features.The model was employed to classify the 25,409 villages in Hubei province in China into four classes.Villages in these classes exhibit discernible differences in spatial distribution,topography,location,economic development level,industrial structure,infrastructure,and resource endowment.In addition,the GMM-based village classification model demonstrates a high level of agreement with evaluations made by planning experts,confirming its accuracy and reliability.In the empirical study,our model achieves an overall accuracy of 95.29％,signifying substantial concordance between the classifications made by planning experts and the results generated by our model.Based on the identified features,tailored paths are proposed for each village class for rural revitalization efforts.
      原文链接:
    • 万方数据
    • 维普

    Green transformation mechanisms and implementation path for agricultural clusters:A case study of the vegetable cluster in Shouguang city,Shandong province,China

    LI ErlingREN ShixinYANG Yang
    2393-2420页
    查看更多>>摘要：Promoting the green transformation of agricultural clusters represents an effective strategy to address pressing issues related to agricultural resources and environmental concerns.However,existing literature provides limited insights into the internal mechanisms and pathways for achieving green transformation of agricultural clusters.To address the challenges in international research on the collaborative green transformation of entire ag-ricultural value chains,a theoretical analysis framework is constructed in this study,which is characterized by"point-line-plane three-layer embeddedness and four-force interaction,"positioning green innovation as a pivotal entry point.Through social network analysis,this study examines the processes and mechanisms underlying the collaborative green trans-formation of agricultural clusters and proposes viable pathways for implementation using the Shouguang vegetable industrial cluster as a case study.The research findings are as follows:(1)The green transformation of agricultural clusters includes the green transformation of cluster behavior actors(point),formation of a green innovation network(line),construction of a green environment(plane),and embedded integration and coordinated transformation of the three.Green innovations generated by leading enterprises,universities,and research institutions serve as the foundation for this transformation,whereas farmers'adoption of these innovations forms the basis,and government policies provide regulatory environment to ensure successful implementation.The transformation is realized through green collab-orative innovation and governance,achieving the"three-layer embeddedness."(2)Under the influence of four driving forces,namely,market-driven mechanisms,environmental reg-ulations,green innovation,and multidimensional proximity,actors at various levels form and embed green innovation networks that are integrated into regional environments through institutional constraints.This results in a"five-in-one"system of collaborative green inno-vation and governance encompassing enterprises,industries,technologies,institutions,and spatial dimensions,which constitute the internal mechanisms for the green transformation and upgrading of agricultural clusters.(3)Building on the"three-layer and four-force"framework,this study proposes pathways for achieving the green transformation of agri-cultural clusters,thereby providing theoretical insights and policy recommendations for de-veloping countries to foster green agricultural clusters and enhance their agricultural sectors'international competitiveness.
      原文链接:
    • 万方数据
    • 维普

    The effects of human activities on windbreak and sand fixation services in Inner Mongolia grasslands from 2000 to 2020

    YAN HuiminXIE GegeYAN FengNIU Zhongen...
    2421-2439页
    查看更多>>摘要：Windbreak and sand fixation services(SR)provided by grasslands are a joint result of climate change and human activities.Series of grassland protection measurements have been successively implemented on Inner Mongolia grasslands since 2000,but their effects on SR remains unclear.Based on satellite-derived vegetation dynamics and the Revised Wind Erosion Equation(RWEQ)model,this paper developed a method for quantitatively separating the impact of human activities on SR and revealed the contribution of human activities to SR in the Inner Mongolia grasslands from 2000 to 2020.In 2020,the actual sand fixation(SRA)of Inner Mongolia grasslands was 12.50 t·ha-1,spatially characterized as lower in the eastern and western parts,which was dominated by the sparse vegetation coverage and the low po-tential wind erosion respectively,while higher in the central part,due to the grassland vul-nerability.The human-driven sand fixation(SRH)of Inner Mongolia grasslands changed from-1.28 t·ha-1 to-0.14 t·ha-1 from 2000 to 2020,indicating human activities inhibited SR,but the inhibition was gradually weakened.In semidesert and meadow steppes,the SRH changed from-3.00 t·ha-1 to 0.00 t·ha-1 and-0.16 t·ha-1 to 0.00 t·ha-1,respectively,which showed that the effect of human activities changed from inhibition to promotion.However,it should be noted that human activities still inhibited the SR in typical steppes.The results implicated that grassland ecological protection should pay much more attention to reasonable use of vul-nerable typical steppes.Future grassland use requires quantitative evaluation on the effects of human activity for precise monitoring and sustainable management.
      原文链接:
    • 万方数据
    • 维普

    Evaluating urban development and socio-economic disparity in India through nighttime light data

    YU JingtongLIU LingcenBAN YifangZHANG Qian...
    2440-2456页
    查看更多>>摘要：Balanced development and the reduction of inequality are central objectives of the United Nations Sustainable Development Goals(SDGs).This study explores the use of Nighttime Light(NTL)brightness and the Nighttime Light Development Index(NLDI)as indi-cators of socioeconomic development in urban centers,focusing on six Indian cities.It ex-amines the correlation between these indices and socioeconomic inequality across affluent neighborhoods,urban slums,downtown areas,and general urban areas in 2015,2018,and 2021.The results reveal that lighting brightness in affluent areas can be lower than that in bustling downtowns,due to factors such as lower residential density.This challenges the conventional assumption that higher NTL necessarily indicates greater prosperity.This study further confirmed significant developmental disparities between well-lit downtowns and poorly illuminated peripheral slum areas,as reflected by lower NLDI scores.Notably,the results uncover a phenomenon termed"same value but different spectrum"based on a careful ex-amination of NLDI values of urban centers and their corresponding curves.This suggests that NLDI alone may not fully capture the complexity of urban development,and that underlying development trajectories,along with on-the-ground realities,must be further examined.The findings emphasize the importance of applying NLDI for urban internal analyses.In addition,the study highlights the necessity for nuanced urban planning and targeted policy interven-tions specifically tailored to the unique conditions of different urban areas.
      原文链接:
    • 万方数据
    • 维普

    Classification of architectural styles in Chinese traditional settlements using remote sensing images and building facade pictures

    ZHANG XiaoxiaLI ShaodanCHEN Changyao
    2457-2476页
    查看更多>>摘要：The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classifica-tion model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4％compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were im-proved by more than 2％compared with other deep learning models.The results demon-strated that the proposed model performed well in the classification of architectural styles in CTSs.
      原文链接:
    • 万方数据
    • 维普

    Enhancing flood risk assessment in northern Morocco with tuned machine learning and advanced geospatial techniques

    MOUTAOUAKIL WassimaHAMIDA SoufianeSALEH ShawkiLAMRANI Driss...
    2477-2508页
    查看更多>>摘要：Mapping floods is crucial for effective disaster management.This study focuses on flood assessment in northern Morocco,specifically Tangier,Tetouan,and Larache.Due to the lack of a comprehensive flood inventory map,we used unsupervised learning techniques,such as K-means clustering and fuzzy logic algorithms,to predict flood-prone areas.We identified nine conditioning factors influencing flood risk:elevation,slope,aspect,plan cur-vature,profile curvature,land use,soil type,normalized difference vegetation index(NDVI),and topographic position index(TPI).Using Landsat-8 imagery and a Digital Elevation Model(DEM)within a Geographic Information System(GIS),we analyzed topographic and geo-environmental variables.K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan,while the fuzzy logic method in Larache produced a Da-vies-Bouldin Index(DBI)score of 0.35.The maps classified flood risk levels into low,mod-erate,and high categories.This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps.Our findings highlight the main factors contributing to flash floods and assess their impact,enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.
      原文链接:
    • 万方数据
    • 维普

    Examining the nonlinear and threshold effects of the 5Ds built environment to land values using interpretable machine learning models

    Quang Cuong DOANKhac Hung VUThi Kieu Trang TRINHThi Cam Ngoc BUI...
    2509-2533页
    查看更多>>摘要：Previous studies have extensively explored the critical influence of the built envi-ronment on land values,but the non-linear relationship has yet to be fully revealed.This study aims to uncover the non-linear relationship between land values and the five built environ-ment dimensions using machine learning algorithms and Shapley Additive exPlanation(SHAP).The results highlight that the Gradient Boost Decision Tree(GBDT)outperforms eXtreme Gradient Boosting(XGBoost),Ordinary Least Squares(OLS),and Multiscale Geo-graphically Weighted Regression(MGWR)in land value estimation,exhibiting higher R2 and lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results illustrate that density and destination accessibility are the dominant factors,contributing 32.48％and 37.38％to land value variation,respectively.We observed that the top three factors affecting land values are the built-floor area ratio,the number of floors and the number of restaurants.Additionally,the results revealed the non-linear relationship between the built environment and land values,suggesting that maintaining built environment features at optimal thresholds may increase land values.Neglecting interaction effects may lead to bias in determining re-lationships between land values and the built environment.This study contributes to the lit-erature by providing non-linear and threshold identification evidence in land value determi-nants,offering valuable insights for urban planners and real estate managers.
      原文链接:
    • 万方数据
    • 维普

    The segmentation of debris-flow fans based on local features and spatial attention mechanism

    SONG XinWANG Baoyun
    2534-2550页
    查看更多>>摘要：In response to issues such as incomplete segmentation and the presence of breakpoints encountered in extracting debris-flow fans using semantic segmentation models,this paper proposes a local feature and spatial attention mechanism to achieve precise segmentation of debris-flow fans.Firstly,leveraging the spatial inhibition mechanism from neuroscience theory as a foundation,an energy function for the local feature and spatial at-tention mechanism is formulated.Subsequently,by employing optimization theory,a closed-form solution for the energy function is derived,which ensures the lightweight nature of the proposed attention mechanism algorithm.Finally,the performance of this algorithm is compared with other mainstream attention mechanism algorithms embedded in semantic segmentation models through comparative experiments.Experimental results demonstrate that the proposed method outperforms both the original models and mainstream attention mechanisms across various classic models,effectively enhancing the performance of net-work models in debris-flow fan segmentation tasks.
      原文链接:
    • 万方数据
    • 维普