Experimental design for feature matching of geospatial data: results of the quality control service

This dataset contains the results of the experimental design for feature matching presented in the thesis 'Automatic evaluation of geospatial data quality using web services'. Design of experiment techniques were developed to assess how some variables are influenced by a list of factors. In this study we chose four variables: precision, recall, F-measure, and time. We selected six factors that might influence these variables: similarity measures, matching methods, morphology of features, geographic context, systematic disturbance, and random disturbance. Regarding our four variables, precision and recall are concepts that come from the Information Retrieval field. Precision evaluates the presence of wrong matches (false positives) against the real matches (true positives). Recall evaluates the presence of non-matches (false negatives) against those real matches. F-measure represents the harmonic mean between precision and recall. Time represents the time consumed to run some matching procedure. In a previous study we identified that the geospatial data matching problem can be organized in two key aspects: similarity measures and matching methods. So these are the two first factors to be investigate: measures and methods. The third controlled factor is the morphology of objects, i.e., how the roughness of lines or areas influences the variables. Other factor is the geographic context of features, which refers to the spatial relationships between objects in a neighbourhood. The last two factors refers to some disturbances applied to source data: systematic and random perturbations. This experiment is divided according to the geometric primitive: point, line, and area. Figure Fig_Overview_of_DOE.png shows an overview of this DOE with the factors considered for each geometry, and the respective number of treatments for each essay. Each type of geometry has its own essays, 20 in total: points (P1-P5), lines (L1-L6), and areas (A1-A9). The results of each essay are presented in CSV format: blank space as separator, first line as header.本数据集包含论文《基于Web服务的地理空间数据质量自动评估》（*Automatic evaluation of geospatial data quality using web services*）中提出的特征匹配实验设计结果。本研究采用实验设计技术，量化评估若干变量受一系列因素的影响程度。本次研究选取了四项核心变量：准确率（precision）、召回率（recall）、F值（F-measure）与运行耗时。同时选定了六项可能影响上述变量的因素：相似度度量、匹配方法、地物形态、地理上下文、系统性扰动与随机性扰动。 就四项核心变量而言，准确率与召回率均源自信息检索（Information Retrieval）领域。准确率用于衡量真实匹配（真阳性，true positives）中错误匹配（假阳性，false positives）的占比；召回率则用于衡量真实匹配中未被匹配出的项（假阴性，false negatives）的占比。F值为准确率与召回率的调和平均值，运行耗时则指代执行单次匹配流程所消耗的总时间。 前期研究表明，地理空间数据匹配问题可归纳为两个核心维度：相似度度量与匹配方法，此二者即为首批待研究的两项因素。第三项可控因素为对象形态，即线状或面状地物的粗糙程度如何影响各变量。第四项因素为地物的地理上下文，指代邻域内对象间的空间关系。最后两项因素则为施加于源数据的两类扰动：系统性扰动与随机性扰动。 本实验按照几何基元（geometric primitive）分为点、线、面三类。图Fig_Overview_of_DOE.png展示了本次实验设计（DOE，Design of Experiment）的概览，包含各几何类型对应的考量因素，以及各测试用例的处理组数量。三类几何分别对应独立的测试用例，总计20组：点集测试（P1-P5）、线集测试（L1-L6）与面集测试（A1-A9）。每组测试的结果均以CSV格式（CSV）存储：以空格作为字段分隔符，首行为表头。