Darghan C. Aquiles E., Taborda L. Darlley S., González S. Nair J., Rivera M. Carlos A., Ospina N. Jesús E.
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引用次数: 0
Abstract
In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.
近年来,已经开发出了一些包含空间因素的统计方法,例如那些包含地理参照数据的 统计方法。目前,地理信息系统的描述部分提供了许多可视化和分析工具;然而,在分析方面,这些系统仍有相当大的局限性,因此,如果忽视这些局限性,就可能导致用传统的统计方法处理具有空间效应的数据,用于非空间用途,这无疑会使使用先进工具(如日常在地学领域使用的工具)进行数据采集的出色工作变得无效。这促使我们编写了本文件,提请人们注意用统计方法分析的地学信息是如何失效的,这些方法意味着模型观测的独立性。本文将莫兰指数与实验设计中的空间滞后系数建议进行了比较,这样方差分析的使用者就不会以一种仪式化的方式应用这一众所周知的程序,也许会修改某些假设,也许会忽略更重要的假设。在存在空间依赖性的情况下,方差分析得出的 p 值的扭曲是显而易见的。在这种情况下,它与滞后或空间重叠有关。通过设计矩阵的开发、重新参数化和各自权重矩阵的选择,该方法很容易应用于其他设计。这可能会促使用户重新考虑传统的分析方法,并采用一些适当的分析方法来解决数据或建模过程输出中存在的空间效应问题。
期刊介绍:
Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences.
The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology.
Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements