Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data     

Xiaoyu Sun  Xia Peng  Yiran Chen  Yu Liu 《International Journal of Digital Earth》2019,12(6):661-678

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.  相似文献   

Geospatial data mining for digital raster mapping     

Bruce K. Wylie  Neal J. Pastick  Joshua J. Picotte  Carol A. Deering 《地理信息系统科学与遥感》2019,56(3):406-429

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.  相似文献   

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)     

Pijush Samui  T. G. Sitharam 《国际地质力学数值与分析法杂志》2010,34(7):755-770

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

基于空间技术实测数据的板块构造运动研究     

孙建军 《测绘与空间地理信息》2016,(3)

将ITRF2008解算结果 GPS、SLR、VLBI实测站速度信息和北美CORS网络、中国陆态网络、欧洲CORS网络站速度信息进行基准统一,联合解算IERS组织划定的全球14个主要大陆板块的欧拉矢量,对原有的板块运动参数进行精化;提出了基于最小二乘法的海量台站选择方法,实验结果表明,该台站选取方法能够有效提高海量台站的筛选效率和可靠性。稳定台站的数量越多,分布越均匀,越能够体现板块运动的现势性。  相似文献   

基于EzMap的矢量专题图与影像专题图编制的比较研究     

马红利  刘晓明  王祖亮  李宏建 《测绘与空间地理信息》2016,(10):209-211

矢量专题图与影像专题图的区别在于是否具有影像内容以及矢量要素内容的选取和表示。本文基于Ez Map软件,从制图工艺流程、地理底图、专题要素内容、符号库设计、制图综合、表示方法、数据量和成图周期8个方面比较,详细讨论了矢量专题图与影像专题图编制的异同,指出了影像专题图的一些优势。  相似文献   

高光谱影像概率分类向量机分类方法研究     

薛志祥  余旭初  张鹏强  谭熊  魏祥坡 《测绘科学技术学报》2016,(4):426-430

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。  相似文献   

并行处理技术在地理信息数据处理中的应用     

徐道柱  金澄  焦洋洋 《测绘科学技术学报》2016,(6):629-634

随着地理信息存储量的飞速增长,传统的单进程、集中式的数据处理方式已不能满足基于网络的地理信息服务的效能要求。分析对比了OpenMP,MPI和MapReduce等主流并行编程模式,将关系型数据库与分布式空间数据管理系统相结合,提出了面向并行处理的地理信息存储模型和数据组织模型,将该模型与传统模型进行了对比分析,并基于MapReduce实现了地理空间数据并行处理框架,选取了矢量数据装载、影像数据装载以及数据切片作为典型数据处理案例开展对比实验,该技术方案的处理效率均数倍于传统技术方案。实验表明,该模型能够很好地支持并行处理框架,可为分布式环境下数据处理中心构建提供一个有效解决方案。  相似文献   

面向WebGL的矢量数据三维绘制技术     

欧阳峰  龚桂荣  何列松 《测绘科学技术学报》2016,(6)

矢量数据的叠加显示能够提高三维虚拟地球的表达效果与分析能力。受限于GPU的计算精度,在三维虚拟地球中矢量数据绘制普遍存在抖动现象和深度冲突现象。对基于WebGL的矢量数据三维绘制中计算精度问题进行了分析,提出了使用CPU RTC技术和GPU RTE技术提高顶点变换的精度,使用多视锥渲染算法和深度平面技术解决深度缓存精度问题。实验证明,这几种技术和算法可以有效缓解抖动现象和深度冲突现象导致的视觉干扰,改善了各种尺度和范围的矢量数据在三维地形上的叠加显示效果。  相似文献   

局部区域模型重力异常快速算法     

邢志斌  李姗姗  曲政豪  王伟  范昊鹏  贾鲁 《测绘科学技术学报》2016,(6):566-571

地球重力场位系数模型可以用于计算局部重力扰动场元。然而随着地球重力场模型阶次的提高、局域重力场计算范围的增大,其计算速度往往不能满足工程需求。针对这一问题,在对位系数模型泰勒级数展开的基础上提出了采用向量运算、混合编程的方法,同时对连带勒让德函数Belikov递推方法中与经纬度无关的量进行了预先计算,有效提高了计算速度。提出的方法对于利用超高阶次重力场模型快速解算大范围、高分辨率重力场元数据以及累加求和计算具有一定的参考与借鉴意义。  相似文献   

Short-lived polyphase deformation during crustal thickening and exhumation of a collisional orogen (Ribeira Belt,Brazil)     

《Journal of Structural Geology》2016

The Ribeira Belt (Brazil) is a Neoproterozoic collisional-related feature that was located in a south-central position in West Gondwana. We present quantitative data on finite strain, flow vorticity and deformation temperatures for the Curitiba Terrane, a major segment of the southern Ribeira Belt. Six deformation phases (D₁-D₆) related with crustal thickening and exhumation were recognized. D₁ and D₂-related microstructures are preserved exclusively within porphyroblasts, in part grown during stages of high-pressure (∼9–12 kbar) isobaric heating after crustal thickening. D₃ phase was active from peak metamorphism attained in contrasting crustal levels (810–400 °C), to the early stage of exhumation (500–400 °C), as indicated by petrological, microstructural and quartz c-axis fabric evidence. Kinematic vorticity results indicate that the SL₃ mylonitic fabric resulted from a simple shear-dominated deformation related with westward thrusting. North-verging overturned D₄ folds with E-W-trending subhorizontal axes derived from a pure shear-dominated deformation. Regional D₅ open folds with subvertical axes and NNE-SSW-trending traces were produced by indentation tectonics. D₆ phase comprises retrograde orogen-parallel transcurrent shear zones related with scape tectonics. Geochronological data indicate that D₃-D₆ phases occurred between 584 and 580 Ma, suggesting a fast exhumation rate of ∼8 mm/year for the deepest rocks from the southern Ribeira Belt.  相似文献