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spatial analysis increases efficiency of progeny testing of Chinese fir

Journal of Forestry Research 2017年第3期28卷 445-452页

作者： Liming Bian Renhua Zheng Shunde Su Huazhong Lin Hui Xiao Harry Xiaming Wu Jisen Shi South China Forestry Multidisciplinary Collaborative Innovation Center College of ForestryNanjing Forestry University159 Longpan RoadNanjing 210037People'sRepublic of China Fujian Academy of Forestry 35 ShangchiqiaoFuzhou 350012People's Republic of China Jiangle Forest Farm Jiangle 353300People's Republic of China Department of Forest Genetics and Plant Physiology Umea Plant Science CentreSwedish University of Agricultural Sciences90183 UmeaSweden College of Forestry Nanjing Forestry University159 Longpan RoadNanjing 210037People's Republic of China

We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir （Cunninghamia lanceolata （Lamb.） Hook） on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height （DBH） and tree height data had significant spatial auto-correlations among rows and columns. Adding a firstorder separable autoregressive term more effectively modelled the spatial variation than did the incomplete block （IB） model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis rel- ative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3-5%. Fitting global trend and postblocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.

关键词： Chinese fir Genetic variance Heritabilities Progeny testing spatial analysis

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Resampling in neural networks with application to spatial analysis

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Geo-spatial Information Science 2022年第3期25卷 413-424页

作者： Bruno Póvoa Rodrigues Vinicius Francisco Rofatto Marcelo Tomio Matsuoka Talita Teles Assunção Graduate Program in Agriculture and Geospatial Information Federal University of UberlândiaMonte CarmeloBrazil Institute of Geography Federal University of UberlândiaMonte CarmeloBrazil

In developing Artificial Neural Networks(ANNs),the available dataset is split into three categories:training,validation and ***,an important problem arises:How to trust the predic-tion provided by a particular ANN?Due to the randomness related to the network itself(architecture,initialization and learning procedure),there is usually no best *** this issue,we provide a framework,which captures the randomness related to the network *** idea is to perform several training and test trials based on the Jackknife resampling *** consists of iteratively deleting a single observation each time from the sample and recomputing the ANN on the rest of the sample ***,interval prediction is available instead of point *** proposed method was applied and tested using pH,Ca and P data obtained by analyzing 118 georeferenced soil *** results,based on the dataset size simulation,showed that 60%reduction in available dataset offers compatible accuracy in relation to full dataset,and therefore a higher cost of sampling in the field would not be *** re-sampling method spatially characterizes the points of greater or lesser accuracy and *** re-sampling method increased the success rate by using interval prediction instead of using the mean as the most probable *** we restrict it to the regression neural network model,the resampling method proposed can also be extended to other modern statistical tools,such as Kriging,Least Squares Collocation(LSC),Convolutional Neural Network(CNN),and so on.

关键词： Artificial Neural Network(ANN) data splitting resampling delete-1 Jackknife spatial analysis

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spatial analysis of synergies and trade-offs between the Sustainable Development Goals (SDGs) in Africa

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Geography and Sustainability 2022年第3期3卷 220-231页

作者： Adams Osman Emmanuel Abeashi Mensah Collins Adjei Mensah Yaw Asamoah Suleman Dauda Kofi Adu-Boahen Charles Atanga Adongo Department of Geography Education Faculty of Social Sciences EducationUniversity of EducationWinnebaP.O.Box 25WinnebaGhana Department of Geography and Regional Planning Faculty of Social SciencesUniversity of Cape CoastPrivate Mail BagCape CoastGhana Department of Tourism and Hospitality Management Faculty of Social SciencesUniversity of Cape CoastPrivate Mail BagCape CoastGhana Schools of Tourism and Hospitality University of JohannesburgP.O.Box 524Auckland Park 2006South Africa Emperiks Research Limited Cape Coast Stadium AvenueCape CoastGhana

Challenges faced by African countries in achieving the goals of sustainable development are similar and trans-boundary. Previous analysis of Africa’s progress on the Sustainable Development Goals (SDGs) has largely beennon-spatial, reducing the ability to find spatial relationships between countries and SDGs to help cooperationand proffer country-specific interventions. This study adopted techniques of exploratory and inferential spatialstatistics to assess the successes of African countries from 2016 to 2020 in achieving the goals of sustainable de-velopment. Also, the study sought to understand how the spatial synergies and trade-offs between SDGs vary percountry and time. The results revealed that spatial hotspots of countries with high SDGs scores were mostly con-fined to northern African countries with significant coldspots within central and eastern Africa and few patchesin western and southern Africa for 2016. In 2020, the number of countries forming hotspots reduced, with Cen-tral African countries as significant cold spots. Five main spatial relationships: positive linear, negative linear,concave, convex and undefined complex, were found among countries and the SDGs. However, these spatialrelationships were fluid as they changed over time and with different levels of influence from 2016 to *** study concludes that generic solutions and policies by development agencies, governments, developmentfinance instiutions and other impact investors will not be enough in achieving the SDGs because of the spatialheterogeneity of the continent. Tailored and country-specific policies based on results of spatial statistics matter.

关键词： Africa Development Sustainable development spatial analysis Hotspots Random forest

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Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

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Frontiers of Earth Science 2018年第2期12卷 311-324页

作者： Bilasco STEFAN Rosca SANDA Fodorean IOAN Vescan IULIU Filip SORIN Petrea DANUT Faculty of Geography "Babes-Bolyai" University 400006 Cluj-Napoca Romania Romanian Academy Cluj-Napoca Subsidiary Geography Section 400015 Cluj-Napoca Romania

Maramures Land is mostly characterized byagricultural and forestry land use due to its specificconfiguration of topography and its specific pedoclimaticconditions. Taking into consideration the trend of the lastcentury from the perspective of land management, adecrease in the surface of agricultural lands to theadvantage of built-up and grass lands, as well as anaccelerated decrease in the forest cover due to uncontrolledand irrational forest exploitation, has become obvious. Thefield analysis performed on the territory of Maramures Land has highlighted a high frequency of two geomor-phologic processes - landslides and soil erosion - whichhave a major negative impact on land use due to their rateof occurrence. The main aim of the present study is the GISmodeling of the two geomorphologic processes, determin-ing a state of vulnerability （the USLE model for soilerosion and a quantitative model based on the morpho-metric characteristics of the territory, derived from the HG.447/2003） and their integration in a complex model ofcumulated vulnerability identification. The modeling of therisk exposure was performed using a quantitative approachbased on models and equations of spatial analysis, whichwere developed with modeled raster data structures andprimary vector data, through a matrix highlighting thecorrespondence between vulnerability and land useclasses. The quantitative analysis of the risk was performedby taking into consideration the exposure classes asmodeled databases and the land price as a primaryalphanumeric database using spatial analysis techniquesfor each class by means of the attribute table. The spatialresults highlight the territories with a high risk to presentgeomorphologic processes that have a high degree ofoccurrence and represent a useful tool in the process ofspatial planning.

关键词： risk evaluation spatial analysis GIS modeling soil erosion landslides spatial planning

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A prototype auto-human support system for spatial analysis

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Progress in Natural Science:Materials International 2006年第9期16卷 954-966页

作者： LI Lianfa and WANG Jinfeng (State Key Laboratory of Resources & Environmental Information System, Institute of Geographical Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China) State Key Laboratory of Resources & Environmental Information System Institute of Geographical Sciences & Natural Resources Re- search Chinese Academy of Sciences Beijing 100101 China

spatial analysis is a multidisciplinary field that involves multiple influential factors, variation and uncertainty, and modeling of geospatial data is a complex procedure affected by spatial context, mechanism and assumptions. In order to make spatial modeling easier, some scholars have suggested a lot of knowledge from exploratory data analysis (EDA), specification of the model, fitness and diagnosis of the model, to interpretation of the model. Also an amount of software has improved some functionalities of spatial analysis, e. g. EDA by the dynamic link (GeoDa) and robust statistical calculation (R). However, there are few programs for spatial analysis that can automatically deal with unstructured declarative issues and uncertainty in machine modeling using the domain knowledge. Under this context, this paper suggests a prototype support system for spatial analysis that can automatically use experience and knowledge from the experts to deal with complexity and uncertainty in modeling. The knowledge base component, as the major contribution of the system, in support of the expert system shell, codes and stores declarative modeling knowledge, e.g. spatial context, mechanisms and prior knowledge to deal with declarative issues during the modeling procedure. With the open architecture, the system integrates functionalities of other components, e.g. GIS’ visualization, DBMS, and robust calculation in an interactive environment. An application case of spatial sampling, design and implementation of spatial modeling with such a system is demonstrated.

关键词： spatial analysis automatic support system modeling of spatial data artificial inference for modeling.

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Gold Resources Potential Assessment in Eastern Kunlun Mountains of China Combining Weights-of-evidence Model with GIS spatial analysis Technique

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Chinese Geographical Science 2010年第5期20卷 461-470页

作者： HE Binbin CHEN Cuihua LIU Yue Institute of Geo-spatial Information Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China College of Geosciences Chengdu University of Technology Chengdu 610059 China

Resources potential assessment is one of the fields in geosciences,which is able to take great advantage of GIS technology as a substitution of traditional working *** gold resources potential in the eastern Kunlun Mountains,Qinghai Province,China was assessed by combining weights-of-evidence model with GIS spatial analysis *** the data sets used in this paper were derived from an established multi-source geological spatial database,which contains geological,geophysical,geochemical and remote sensing *** multi-class variables,i.e.,structural intersection,Indosinian k-feldspar granite and regional fault,were used in proximity analysis to examine their spatial association with known gold deposits.A prospectivity map was produced by weights-of-evidence model based on seven binary evidential maps,all of which had passed a conditional independence *** study area was divided into three target zones of high potential,moderate potential and low potential areas,among which high potential areas and moderate potential areas accounted for 20% of the total area and contained 32 of the 43 gold *** results show that the gold resources potential assessment in the eastern Kunlun Mountains has a higher precision.

关键词： weights-of-evidence model mineral resources potential assessment eastern Kunlun Mountains spatial analysis conditional independence

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Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools

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Journal of Forestry Research 2020年第2期31卷 649-659页

作者： Antonio Gonzalez-Hernandez Rene Morales-Villafana Martin Enrique Romero-Sanchez Brenda Islas-Trejo Ramiro Perez-Miranda Conservation and Enhancement of Forest Ecosystems National Institute of ForestryAgriculture and Livestock ResearchProgreso 504010 Coyoacan Mexico CityMexico Geography Department The National Autonomous University of MexicoUniversidad 300004010 Coyoacan Mexico CityMexico

Accurate and reliable predictions of pest species distributions in forest ecosystems are urgently needed by forest managers to develop management plans and monitor new areas of potential ***-only species distribution models are commonly used in these *** maximum entropy algorithm(MaxEnt)has gained popularity for modelling species ***,MaxEnt was used to model the spatial distribution of the Mexican pine bark beetle(Dendroctonus mexicanus)in a daily fashion by using forecast data from the Weather Research and Forecasting *** study aimed to exploit freely available geographic and environmental data and software and thus provide a pathway to overcome the lack of costly data and technical guidance that are a challenge to implementing national monitoring and management strategies in developing *** results showed overall agreement values between 60 and 87%.The results of this research can be used for *** monitoring and management and may aid as a model to monitor similar species.

关键词： spatial analysis Dendroctonus mexicanus Geodatabases MaxEnt Forest modelling

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Identifying disaster-related tweets and their semantic,spatial and temporal context using deep learning,natural language processing and spatial analysis:a case study of Hurricane Irma

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International Journal of Digital Earth 2019年第11期12卷 1205-1229页

作者： Muhammed Ali Sit Caglar Koylu Ibrahim Demir Department of Computer Science University of IowaIowa CityIAUSA Department of Geographical and Sustainability Sciences University of IowaIowa CityIAUSA Department of Civil and Environmental Engineering University of IowaIowa CityIAUSA

We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and *** first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of *** employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature ***,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted ***,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,*** Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the *** results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.

关键词： Social sensing Twitter deep learning natural language processing spatial analysis hurricane

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spatial analysis on the Contribution of Industrial Structural Adjustment to Regional Energy Efficiency:ACase Study of 31 Provinces across China

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Journal of Resources and Ecology 2012年第2期3卷 129-137页

作者：刘佳骏董锁成李宇毛琦梁李俊王隽妮中国科学院地理科学与资源研究所北京100101 中国科学院研究生院北京100049

Energy is an important production factor. When more energy is put into production activities, labor productivity increases and the economy and social development improve. Industrial structural change is an important factor that affects the growth of regional energy efficiency. In modern development, industrial structural improvement is closely related to regional energy efficiency and the status of industrial structure. Energy efficiency can together reflect a country＇s development level, sustainable development and the direction in which its economy will head. This paper discusses the contribution of industrial structural adjustment to regional energy efficiency growing with a spatial perspective. This study based on 31 mainland provinces in China, uses statistics of industrial structure and corresponding energy utilization data, adopting mathematic model and spatial analysis approaches, reveals that the 31 provinces in China differed in their contribution to industrial structural adjustment to regional energy efficiency. The outcome reflected contribution is obvious in those provinces with appropriate industrial structure and good economies or the ones developed late as well as the undeveloped middle and western provinces that are in initial stage of industrial restructuring. But that is not obvious in those provinces with the undeveloped leading industries, with weak economic basis, and with a relatively low level of industrial structure.

关键词： energy energy efficiency industrial structure contribution spatial analysis

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SOIL spatial analysis AND AGRICULTURAL LAND USE OPTIMIZATION BY USING GIS

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Chinese Geographical Science 2003年第1期13卷 25-29页

作者： GAOJun-feng LIChang-feng ZHANGHong-hui NanjingInstituteofGeographyandLimnology theChineseAcademyofSciencesNanjing21008P.R.China GraduateSchool theChineseAcademyofSciencesBejing100039P.R.China CollegeofLandManagement NanjingAgriculturalUniversityNanjing210095P.R.China

This paper discusses the methods and process of Geographic Information System (GIS) applied in soil spatial analysis, involving the collection of soil spatial data that GIS required, spatial analysis method of soil nutrient, land use, slope and exposure of geography, crop yield and other factors, and also including acquiring soil spatial information and creating thematic map, and so on. Taking Hengjing Town in Wuxian County of Jiangsu Province as a case study and the maximum income as a principle, and applying the GIS methods and their interrelated models, we have calculated the most optimized agricultural land and the possible maximum income of Hengjing Town. According to the method, we can rationally regulate local agricultural production, and put forward some scenarios for optimizing agricultural structure of Hengjing Town. The paper puts forward an evaluation method of land adaptability based on soil spatial analysis, and offers some research clews to optimize agricultural land use constitution, so the paper will be have important reference value for soil study.

关键词： GIS soil spatial analysis optimization agricultural landuse

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