期刊：
Computers and Electronics in Agriculture,2022年193:106667 ISSN：0168-1699
通讯作者：
Wenbin Wu
作者机构：
[Tao, Jianbin; Wang, Yun] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Qiu, Bingwen] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Natl Engn Res Ctr Geospatial Informat Technol, Fuzhou 350116, Fujian, Peoples R China.;[Wu, Wenbin] Chinese Acad Agr Sci, Key Lab Agr Remote Sensing AGRIRS, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China.
通讯机构：
[Wenbin Wu] K;Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
关键词：
Bayesian network;Cropping intensity dynamics;MODIS time-series;Semantic information
作者：
Li, Zhuofan;Zhang, Xiangmin;Liu, Xiaoyong;Yu, Bin
期刊：
Atmosphere,2022年13(10):1696- ISSN：2073-4433
通讯作者：
Zhuofan Li
作者机构：
[Li, Zhuofan; Zhang, Xiangmin; Yu, Bin] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Li, Zhuofan; Zhang, Xiangmin; Yu, Bin] Hubei Dev & Reform Commiss, Acad Wuhan Metropolitan Area, Wuhan 430079, Peoples R China.;[Li, Zhuofan; Zhang, Xiangmin; Yu, Bin] Cent China Normal Univ, Wuhan 430079, Peoples R China.;[Zhang, Xiangmin; Liu, Xiaoyong] Xinyang Normal Univ, Sch Geog Sci, Xinyang 464000, Peoples R China.;[Liu, Xiaoyong] Xinyang Normal Univ, Henan Key Lab Synergist Prevent Water & Soil Envi, Xinyang 464000, Peoples R China.
通讯机构：
[Zhuofan Li] A;Academy of Wuhan Metropolitan Area, Hubei Development and Reform Commission & Central China Normal University, Wuhan 430079, China<&wdkj&>Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
To investigate the spatiotemporal patterns of fine particulate matter (PM2.5) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM2.5 concentrations data during the period of 2017-2020 in six typical major urban agglomerations. During the period of 2017-2020, PM2.5 concentrations in the Beijing-Tianjin-Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu-Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 mu g/m(3)/year, respectively. PM2.5 concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 mu g/m(3) in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM2.5 concentrations with high levels formed a high-high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM2.5 concentrations. RH has a positive correlation with PM2.5 concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7-63.9% of the PM2.5 changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM2.5 changes. Among the six urban agglomerations, PM2.5 concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM2.5 concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.
作者机构：
[Li, Mengyao; Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi; He, Nan] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zhou, Yong] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Zhou, Yong] Cent China Normal Univ, Land Sci Res Ctr, Wuhan 430079, Peoples R China.
通讯机构：
[Yong Zhou] C;College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China<&wdkj&>Key Laboratory for Geographical Process Analysis & Simulation in Hubei Province, Central China Normal University, Wuhan 430079, China<&wdkj&>Land Science Research Center, Central China Normal University, Wuhan 430079, China
关键词：
Ecological security;Spatiotemporal variation;Driving mechanism;Enshi autonomous prefecture;Sustainable development
摘要：
The land cover maps of Lhasa River Basin with long time series and high spatial resolution was drawn. Our study verified the relevance between hulless barley gravity center and river gravity center as well as the internal relationship between hulless barley growth and river area. Furthermore, we explored the interrelation relationships among hulless barley, Lhasa River area and climate factors. Abstract Accurate understanding of the impacts of climate change on hulless barley and river in the Lhasa River Basin is of great significance to food security and water resources management in the plateau region. It is important to explore the relationship between hulless barley and river under the background of climate change for the comprehensive and coordinated development of agriculture and water conservancy. Based on the Google Earth Engine (GEE) cloud computing platform, the Random Forest algorithm was used to obtain the spatio‐temporal variation of hulless barley and river in the Lhasa River Basin from 2010 to 2020. The overall accuracy and Kappa coefficient of classification results were 89.54% and 85.96%, respectively. The average area and Normalized Difference Vegetation Index (NDVI) of hulless barley from 2010 to 2020 were 178.46 km2 and 0.69, respectively. The increase of accumulated precipitation, number of precipitation days, average dew point temperature (ADPT) and average wind speed (AWS) promoted the growth of hulless barley, with NDVI significant increasing rate of 0.0173 (R2 = 0.764, p < 0.001). The combined effects of human activities (construction of water conservancy facilities and mining activities), ADPT and AWS resulted in a significant decrease (decreasing rate: 10.8682 km2/year, p < 0.01) in river area during 2010–2020. There was a significant negative correlation (R2 = 0.722, p < 0.01) between hulless barley NDVI and river area driven by climate factors. The changes in hulless barley gravity center and river gravity center were consistent, and both shifted in the northeast direction. These results provide a scientific understanding of the impacts of plateau climate change on agriculture and water resource. The land cover maps of the Lhasa River Basin with long time series and high spatial resolution were drawn. Our study verified the relevance between hulless barley gravity center and river gravity center as well as the internal relationship between hulless barley growth and river area. Furthermore, we explored the interrelation relationships among hulless barley, the Lhasa River area and climate factors.
关键词：
*Impact mechanisms;*Microplastics phytotoxicity;*Prevention and control;*Soil-plant system;*Tolerance system
摘要：
Microplastics (MPs) are creating an emerging threat on the soil ecosystems and are of great global concern. However, the distribution in soil-plant system, as well as the phytotoxicity and impact mechanisms of MPs remain largely unexplored so far. This study introduced the diverse sources of MPs and showed the significant spatial variation in the global geographic distribution of MPs contamination based on data collected from 116 studies (1003 sampling sites). We systematically discussed MPs phytotoxicity, such as plant uptake and migration to stems and leaves, delaying seed germination, impeding plant growth, inhibiting photosynthesis, interfering with nutrient metabolism, causing oxidative damage, and producing genotoxicity. We further highlighted the alterations of soil structure and function by MPs, as well as their self and load toxicity, as potential mechanisms that threaten plants. Finally, this paper provided several preventive strategies to mitigate soil MPs pollution and presented research gaps in the biogeochemical behavior of MPs in soil-plant systems. Meanwhile, we recommended that methods for the quantitative detection of MPs accumulated in plant tissues should be explored and established as soon as possible. This review will improve the understanding of the environmental behavior of MPs in soil-plant systems and provide a theoretical reference to better assess the ecological risk of MPs.
期刊：
Journal of Cleaner Production,2022年367:132922 ISSN：0959-6526
通讯作者：
Yong Zhou
作者机构：
[Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi; Liu, Yujie] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi; Liu, Yujie] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Yong Zhou] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, 430079, China<&wdkj&>The College of Urban & Environmental Sciences, Central China Normal University, Wuhan, 430079, China
关键词：
Soil heavy metal;Sentinel-2A images;Estimation mechanism;Dimidiate pixel model;Random forest
摘要：
Rapid and accurate predictions of heavy metal contents in farmland are of great significance to ensure the safety of agricultural products and maintain ecosystem balance. Combining multispectral images and chemometric modeling provides a feasible means of estimating cadmium (Cd) and lead (Pb) contents in farmland. We collected 640 samples from the surface soils of farmland in Xiangzhou District, Hubei Province, China. The Cd, Pb, soil organic matter (SOM), pH, and Fe of the soil samples were measured in a laboratory. In this study, the dimidiate pixel model was used to process remote sensing images. In particular, we used random forest (RF) to screen the best spectral indices for use as input variables. Partial least squares regression (PLSR), backward propagation neural network (BPNN), and RF were used to calibrate the spectral data with Cd and Pb contents, and the optimal model was used for the regional mapping of soil Cd and Pb contents. Additionally, we explored the potential of using spectral estimation mechanisms to estimate Cd and Pb contents. The mechanism for estimating Cd and Pb contents with multispectral images depended mainly on the covariance of Cd and Pb contents with that of SOM. For both Cd and Pb estimations, the double-date image estimation model performed better than the single-date image estimation model, and the unmixed image estimation model was more accurate than the original image estimation model. Overall, the estimation model using the best spectral indices as input variables performed better than the model using full-band data as input variables. The RF model outperformed the PLSR and BPNN models in all cases. Relatively satisfactory estimates of Cd and Pb contents in farmland of the study area (maximum R-val(2) (determination coefficient of the validation set) = 0.60 for Cd and R-val(2) = 0.63 for Pb) were obtained. Our results show that areas of farmland contaminated by Cd and Pb throughout the study area have increased and that contamination levels have worsened since 1990. In addition, the spatial patterns of Cd and Pb contents in farmland throughout the study area were analyzed and validated using field survey results. The results of the study provide a theoretical basis and methodological reference for the rapid prediction of Cd and Pb contents in regional farmland.
期刊：
Remote Sensing of Environment,2022年269:112822 ISSN：0034-4257
通讯作者：
Ran Meng
作者机构：
[Wang, Mengyu; Zhao, Feng] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Meng, Ran; Zeng, Xiaoxi; Li, Yaxin; Sun, Rui] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Zhong, Liheng] Ant Grp, Hangzhou 311121, Peoples R China.;[Meng, Ran] Huazhong Agr Univ, Interdisciplinary Sci Res Inst, Wuhan 430070, Peoples R China.;[Huang, Chengquan] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA.
通讯机构：
[Ran Meng] C;College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China<&wdkj&>Interdisciplinary Sciences Research Institute, Huazhong Agricultural University, Wuhan 430070, China
期刊：
GEOPHYSICAL RESEARCH LETTERS,2022年49(3):e2021GL096666- ISSN：0094-8276
通讯作者：
Li, J
作者机构：
[Wang, Cong; Liu, Qinhuo; Dong, Yadong; Zhao, Jing; Li, Jing] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.;[Wang, Cong; Liu, Qinhuo; Dong, Yadong; Zhao, Jing; Li, Jing] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing, Peoples R China.;[Wang, Cong] Cent China Normal Univ, Sch Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.;[Liu, Qinhuo; Li, Jing] Univ Chinese Acad Sci, Beijing, Peoples R China.;[Huete, Alfredo] Univ Technol Sydney, Sch Life Sci, Ultimo, NSW, Australia.
通讯机构：
[Li, J ] C;Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.;Beijing Normal Univ, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing, Peoples R China.;Univ Chinese Acad Sci, Beijing, Peoples R China.
期刊：
Urban Water Journal,2022年20(10):1375-1391 ISSN：1573-062X
通讯作者：
Jie Xu
作者机构：
[Xiao, Pengnan] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Hubei, Peoples R China.;[Xu, Jie] Hubei Univ, Fac Resources & Environm Sci, Wuhan, Hubei, Peoples R China.
通讯机构：
[Jie Xu] F;Faculty of Resources and Environmental Science, Hubei University, Wuhan, Hubei, China
关键词：
Urban water management;land space planning;waterfront city;Yuanjiang City
摘要：
Taking the water space landscape in the city as the theme, the relationship between city and water as the core, and Chinese traditional culture as the ideological context, this paper puts forward the theoretical framework and practical cases of urban water management. Urban water management theory provides corresponding theoretical support for the construction of water characteristics and guides practice in many aspects, including water ecology, water environment, water safety, water cycle, water culture, water space and water economy, to optimize the structure and function of the water space. It is necessary to explore measures for constructing water space features from the standpoint of ecological security patterns, urban and rural quality improvement and feature shaping, central urban space layout, and regional coordinated development. The research on the land spatial planning method of waterfront cities proposed by this study can provide new ideas for the spatial land planning of other waterfront cities.
期刊：
Journal of Environmental and Public Health,2022年2022 ISSN：1687-9805
通讯作者：
Huican Zhang<&wdkj&>Deng Pan<&wdkj&>Bing Zhao<&wdkj&>Hongjing Cui
作者机构：
[Cui, Hongjing] Harbin Univ, Sch Foreign Languages, Harbin 150086, Peoples R China.;[Zhang, Huican] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Pan, Deng] Hubei Univ Sci & Technol, Sch Foreign Languages, Xianning 437100, Peoples R China.;[Zhao, Bing] Huzhou Univ, Sch Humanities, Huzhou 313000, Peoples R China.;[Zhao, Bing] Philippine Christian Univ Ctr Int Educ, Manila 1004, Philippines.
通讯机构：
[Huican Zhang] C;[Deng Pan; Bing Zhao; Hongjing Cui] S;College of Urban and Environmental Sciences,Central China Normal University,Wuhan 430079,China<&wdkj&>School of Foreign Languages,Harbin University,Harbin 150086,China<&wdkj&>School of Humanities,Huzhou University,Huzhou,China<&wdkj&>Philippine Christian University Center for International Education,Manila 1004,null,Philippines<&wdkj&>School of Foreign Languages,Hubei University of Science and Technology,Xianning 437100,China
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
This study aims to make public sports health emergency corpus as a way to deal with public health emergency such as COVID-19, reducing the losses affected by an illness or health condition that has occurred frequently in recent years. On this basis, this paper analyzes the research status of emergency language services at home and abroad, discusses the significance and principles of Multimodal Aligned Corpus Public Health Emergency (shorted for MACPHE) construction, and develops technical processing paths and building procedures for MACPHE. Finally, it was emphasized that the construction of MACPHE and emergency language resources are important parts of the national language service capacity. Furthermore, on the basis of big data, a modal architecture of MACPHE was given and analyzed in the field of public health service.
作者机构：
[Liu, Peizhuo; Zhang, Xuming; Gu, Xingfa; Yan, Jian; Yang, Jian; Zang, Wenqian; Mi, Xiaofei; Jia, Wenqi; Zhang, Zhouwei] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China.;[Jia, Wenqi] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Gu, Xingfa] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.;[Gu, Xingfa; Zhu, Hongbo] North China Inst Aerosp Engn, Sch Remote Sensing & Informat Engn, Langfang 065000, Peoples R China.;[Zang, Wenqian; Mi, Xiaofei] Langfang Res & Dev Ctr Spatial Informat Technol, Langfang 065001, Peoples R China.
通讯机构：
[Xiaofei Mi] L;Langfang Research and Development Center for Spatial Information Technology, Langfang 065001, China<&wdkj&>Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
MSPAS;SDGs;spatiotemporal pattern;multi-scale analysis;land suitability with driving factors;CA-Markov;urban expansion;farmland abandonment
摘要：
In pursuit of Sustainable Development Goals (SDGs), land cover change (LCC) has been utilized to explore different dynamic processes such as farmland abandonment and urban expansion. The study proposed a multi-scale spatiotemporal pattern analysis and simulation (MSPAS) model with driving factors for SDGs. With population information from the census, multi-scale analysis criteria were designed using the combination of administrative and regional divisions, i.e., district, province, nation and ecological region. Contribution and correlation of LCC or population were quantified between multiple scales. Different kinds of driving factors were explored in the pattern analysis and then utilized for the definition of adaptive land suitability rules using the Cellular Automata-Markov (CA-Markov) simulation. As a case study of the MSPAS model, Nepal entered into a new era by the establishment of a Federal Republic in 2015. The model focused on four specific land cover classes of urban, farmland, forest and grassland to explore the pattern of Nepal's LCC from 2016 to 2019. The result demonstrated the performance of the MSPAS model. The spatiotemporal pattern had consistency, and characteristics between multiple scales and population were related to LCC. Urban area nearly doubled while farmland decreased by 3% in these years. Urban areas expanded at the expense of farmland, especially in Kathmandu and some districts of the Terai region, which tended to occur on flat areas near the existing urban centers or along the roads. Farmland abandonment was relatively intense with scattered abandoned areas widely distributed in the Hill region under conditions of steep topography and sparse population. The MSPAS model can provide references for the development of sustainable urbanization and agriculture in SDGs.
作者机构：
[Lu, Wei; Lu, W; Peng, Feifei; Tan, Wenxia] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Lu, Wei; Lu, W; Peng, Feifei; Tan, Wenxia] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Qi, Kunlun] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430078, Peoples R China.;[Zhang, Xiaokang] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China.;[Zhu, Quansheng] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
通讯机构：
[Lu, W ] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
摘要：
Scene classification is an active research area in the remote sensing (RS) domain. Some categories of RS scenes, such as medium residential and dense residential scenes, would contain the same type of geographical objects but have various spatial distributions among these objects. The adjacency and disjointness relationships among geographical objects are normally neglected by existing RS scene classification methods using convolutional neural networks (CNNs). In this study, a multi-output network (MopNet) combining a graph neural network (GNN) and a CNN is proposed for RS scene classification with a joint loss. In a candidate RS image for scene classification, superpixel regions are constructed through image segmentation and are represented as graph nodes, while graph edges between nodes are created according to the spatial adjacency among corresponding superpixel regions. A training strategy of a jointly learning CNN and GNN is adopted in the MopNet. Through the message propagation mechanism of MopNet, spatial and topological relationships imbedded in the edges of graphs are employed. The parameters of the CNN and GNN in MopNet are updated simultaneously with the guidance of a joint loss via the backpropagation mechanism. Experimental results on the OPTIMAL-31 and aerial image dataset (AID) datasets show that the proposed MopNet combining a graph convolutional network (GCN) or graph attention network (GAT) and ResNet50 achieves state-of-the-art accuracy. The overall accuracy obtained on OPTIMAL-31 is 96.06% and those on AID are 95.53% and 97.11% under training ratios of 20% and 50%, respectively. Spatial and topological relationships imbedded in RS images are helpful for improving the performance of scene classification.
作者机构：
[Zhu, Lei; Li, Yannan; Xu, Jiahui; Liang, Mangmang] Anqing Normal Univ, Coll Resources & Environm, Anqing 246011, Peoples R China.;[Hu, Jing] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Jing Hu] C;College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
rural tourism;pro-poor tourism villages;spatial distribution characteristics;geographic detector
摘要：
This paper aims to contribute to the effectiveness of pro-poor tourism in rural areas. We use 5770 pro-poor tourism villages in China as the research objects; the spatial distribution characteristics of pro-poor tourism villages in China are analyzed using a combination of disequilibrium index, kernel density analysis, and spatial autocorrelation; their influencing factors are detected using a geographical detector and overlay analysis. The study results show the following: (1) The distribution of pro-poor tourism villages is exceptionally uneven in three zones, eight regions, and inter-provincial levels, forming a high-density cluster belt that includes Hebei, Henan, Anhui, and Hubei, and five high-density cluster cores that include southern Gansu, Sichuan–Gansu–Shaanxi border area; Guizhou, Hunan, and Chongqing border area; southern Sichuan; and southwest Guizhou. (2) Regarding spatial correlations, the pro-poor tourism villages in central and western regions are in hot spots, while those in eastern regions are in cold spots. The hot spots gradually increase, while cold spots gradually decrease, and the clustering trend of the distribution of the pro-poor tourism villages is increasingly apparent. (3) Pro-poor tourism villages are affected by social, economic, industrial, and other human factors as well as natural geographical factors such as terrain, precipitation, river, and climate, among which the industrial factors have a more significant impact. Pro-poor tourism villages are concentrated in humid mountainous areas with an altitude of about 1000 m and an annual precipitation of more than 800 mm, and they are mostly distributed in the subtropical monsoon climate zone closer to the river and more suitable climate. (4) It is suggested that pro-poor tourism villages can be divided into four types: the resource underutilization type, mountain environment restriction type, traffic location non-optimization type, and industrial development lagging type, and the sustainable development strategies of different types of pro-poor tourism villages are proposed.
