Elisa Fusco, Matheus Pereira Libório, Hamidreza Rabiei-Dastjerdi, Francesco Vidoli, Chris Brunsdon, Petr Iakovlevitch Ekel
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引用次数: 0
Abstract
Spatially heterogeneous weights and a non-compensatory aggregation scheme, are two important properties needed to construct a composite indicator capable of summarizing properly the multidimensional phenomenon of local spatial units. Such a composite indicator takes into account, on the one hand, the latent characteristics of the specific units related to their location in the territory, and on the other hand, the relative importance of sub-indicators highlighting both positive and negative aspects of the studied phenomena. Under these premises, this article proposes a new method called Ordered Geographically Weighted Averaging (OGWA), which can consider different degrees of non-compensability between sub-indicators and, at the same time, the spatial heterogeneity for continuous, ordinal, and mixed data. The properties of the method are evaluated through a simulation study. Finally, the method is applied to construct a composite indicator to map the urban public infrastructure of São Sebastião do Paraíso, a city located in the southeastern region of Brazil.
空间异质性权重和非补偿性汇总方案,是构建能够正确概括地方空间单位多维现象的综合指标所需的两个重要属性。这种综合指标一方面要考虑到特定单位与其在地域中的位置有关的潜在特征,另一方面要考虑到突出所研究现象的积极和消极方面的子指标的相对重要性。在这些前提下,本文提出了一种名为有序地理加权平均法(OGWA)的新方法,该方法可以考虑子指标之间不同程度的不可补偿性,同时还可以考虑连续数据、序数数据和混合数据的空间异质性。通过模拟研究对该方法的特性进行了评估。最后,应用该方法构建了一个综合指标,用于绘制巴西东南部城市圣塞巴斯蒂安-杜帕拉伊索(São Sebastião do Paraíso)的城市公共基础设施地图。
期刊介绍:
First in its specialty area and one of the most frequently cited publications in geography, Geographical Analysis has, since 1969, presented significant advances in geographical theory, model building, and quantitative methods to geographers and scholars in a wide spectrum of related fields. Traditionally, mathematical and nonmathematical articulations of geographical theory, and statements and discussions of the analytic paradigm are published in the journal. Spatial data analyses and spatial econometrics and statistics are strongly represented.