Contemporary American cartographic research: a review and prospective

ABSTRACT

We review recent developments in cartographic research in North America, in the context of informing the 29th International Cartographic Conference, and 18th General Assembly in 2019. The titles of papers published since 2015 in four leading cartographic journals yielded a corpus of 245 documents containing 1109 unique terms. These terms were analyzed using Latent Dirichlet Allocation and by visual analytics to produce 14 topic groups that mapped onto five classes. These classes were named as information visualization, cartographic data, spatial analysis and applications, methods and models, and GIScience. The classes were then used as themes to discuss the recent cartographic literature more broadly, first, to review recent trends in the research and to identify research gaps, and second, to examine prospects for new research over the next 20 years. A conclusion draws some broad findings from the review, suggesting that cartographic research in the future will be aimed less at dealing with data, and more at generating insight and knowledge to better inform society about global challenges.     

Exploratory analysis of the interrelations between co-located boolean spatial features using network graphs

Visual data mining of spatial data is a challenging task. As exploratory analysis is fundamental, it is beneficial to explore the data using different potential visualisations. In this article, we propose and analyse network graphs as a useful visualisation tool to mine spatial data. Due to their ability to represent complex systems of relationships in a visually insightful and intuitive way, network graphs offer a rich structure that has been recognised in many fields as a powerful visual representation. However, they have not been sufficiently exploited in spatial data mining, where they have principally been used on data that come with an explicit pre-specified network graph structure. This research presents a methodology with which to infer relationship network graphs for large collections of boolean spatial features. The methodology consists of four principal stages: (1) define a co-location model, (2) select the type of co-association of interest, (3) compute statistical diagnostics for these co-associations and (4) construct and visualise a network graph of the statistic from step (3). We illustrate the potential usefulness of the methodology using an example taken from an ecological setting. Specifically, we use network graphs to understand and analyse the potential interactions between potential vector and reservoir species that enable the propagation of leishmaniasis, a disease transmitted by the bite of sandflies.     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.     

无黏性土压缩曲线的一种数学模式

总结和分析了已有考虑状态相关的无黏性土压缩曲线数学模式做了。进行了不同初始密度砂土的等向压缩试验,提出了一个新的状态相关压缩曲线数学模式。该数学模式能够反映无黏性土不同初始状态等向压缩下的应力-应变关系,参数物理意义明确、试验确定方法简单、方便。利用4种不同砂土的等向压缩试验数据,验证了所提出的数学模式的合理性和有效性。该模式可应用于一般应力、应变条件下状态相关本构模型的建立     

FVCOM模型关键参数的处理及其在涌潮模拟中的应用

通过改进海床阻力系数和设置合适的垂向紊动背景系数,应用FVCOM模型成功再现了钱塘江河口强涌潮的演进过程。海床阻力系数采用Manning公式形式,取值随水深、地形在0.000 2~0.002 9之间变化;垂向紊动背景系数取1×10^-4 m²/s。模拟结果较好地复演了涌潮到达时刻、涌潮高度及涌潮抬升过程、涌潮水平流速以及其沿垂向分布规律,表明阻力系数及垂向紊动背景系数等关键参数的改进和处理是合理的,可应用于涌潮三维潮流运动特征模拟。     

阿特拉津在地下水中迁移转化的实验研究

阿特拉津是研究区内广泛使用的旱田除草剂。为治理其污染的地下水 ,采用静态和动态实验的方法研究其在含水介质砂层中的吸附特性和在地下水中的迁移转化规律。实验结果表明 :砂层对阿特拉津的吸附量小 ,不同固液比 (1.0、0 .5、0 .2 )时的分配系数分别为 0 .10 ,0 .15 ,0 .19cm3 /g ;含水层的弥散度为 0 .0 336m ,阻滞因子为 1.2 9,自然净化系数为 0 .0 0 2 8/d。由此确定所建立的数学模型和参数 ,为研究区阿特拉津污染地下水的治理提供可靠的依据     

由降雨侵蚀引起的山坡面水土流失的研究进展

20世纪末，坡面降雨水土流失的研究日益深入。经验性模型是根据实际资料应用统计相关的方法建立起来的，其结构简单，使用方便。物理过程模拟模型试图对流域内发生的侵蚀产沙过程进行概化和近似，并用数学方程描述流域上侵蚀产沙的主要物理过程，再用比较严格的数值解法计算水沙运动过程，同时人们对侵蚀机理的研究也得到了深入发展。     

曲线放样中的坐标转换及转换精度分析

从误差分析的角度全面分析了坐标转换计算中的误差类型,提出了克服转换计算误差的途径与方法,为提高土木工程,特别是高精度土木工程的施工质量提供了理论保障。     

河南栾川叫河—大清沟地区地球化学异常特征

根据叫河—大清沟地区1∶50000水系沉积物测量成果,在分析区内元素地球化学特征的基础上,利用R型因子分析和马氏距离对元素分布特征和规律进行了进一步的总结与探讨,系统阐述了测区内地层的地球化学特征、(衬值)异常特征、马氏距离异常特征,指明了寻找铅锌银多金属矿的有利层位和成矿元素主要富集地段,为区内地质找矿工作提供了地球化学依据。     

热带气旋的路径及登陆预报

用几个非线性数学模型制作热带气旋短期路径预报及热带气旋个数、登陆时段、地段的短期气候预报。5年多的研究和预报试验结果表明：用指数曲线模型制作热带气旋路径预报，准确率较高。24h预报，199次平均误差123km，达到国内先进水平。用多项式等非线性模型，制作登陆我国及登陆广东热带气旋的年、月个数预测，经过3年实际应用检验，准确率达到70％～90％。用非线性预测模型的逐日气压场、逐日雨量场长期预测结果进行分析，制作广东热带气旋登陆时段、地段和南海海面热带气旋出现时间的预报，准确率达到70％～80％，2002年热带气旋的预报，采用长中短期预报相结合，数值预报与统计预报相结合，预报效果较佳。