空间数据：性质、影响和分析方法

doi:10.11867/j.issn.1001-8166.2005.01.0049

20世纪90年代以来，以信息技术为主要标志的科学进步日新月异，正深刻地改变着人类社会的生产和生活方式。人类活动所涉及的信息80％以上属空间信息，可由地理坐标确定其空间区位，美国已将发展空间信息科学视为提升其国家竞争力水平的重要途径之一。2000年美国国家地理信息与分析中心（Nat ionalCenterforGeo graphicInformat ionandAnalysis，NCGIA）在其跨世纪的研究规划中提出了发展地理信息科学的Varenius计划。Varenius计划要求研究空间信息的不确定性理论，并开发处理这种不确定性的空间数据分析技术。鉴于空间数据的两维多方向性，空间数据所包含的误差远比单一方向性的时间序列数据来得复杂。最近发展起来的空间统计学和空间计量经济学不仅解决了标准统计方法在处理空间数据时的失误问题，更重要的是为测量这种空间联系及其性质、并在建模时明确地引入空间联系变量以估算与检验其贡献提供了全新的手段。基于美国地理信息科学的发展动态，重点阐释空间统计学和空间计量经济学的理论基础，包括空间数据的性质、空间信息的误差理论、空间数据分析的类型和内容。

关键词: 空间数据, 空间误差, 空间数据统计分析, 探索性分析, 确认性分析

    With the exponentially growing use of geographic information systems (GIS) to store, manipulate and visualize geocoded information, it is increasingly important to understand the particular nature of geographic data and the specialized statistical techniques required for its spatial analysis. Spatial analysis is often broadly defined as a “set of methods useful when the data are spatial”. More specifically, it encompasses a collection of techniques to add value to data contained in a geographic information system. As such, spatial analysis forms an important component of the evolving discipline of Geographic Information Science.
    Aggregate spatial data are characterized by dependence (spatial autocorrelation) and heterogeneity (spatial structure). These spatial effects are important in applied statistical analysis, in that they may invalidate certain standard methodological results, demand adaptations to others, and in some contexts, necessitate the development of a specialized set of techniques. These issues are typically ignored by classical statistics and now are vigorously approached in the separate field of spatial statistics.
    In this paper, some general ideas on fundamental issues are outlined, related to the distinctive characteristics of spatial data analysis, as opposed to data analysis in general. The emphasis is on the relevance for spatial data analysis of the ongoing debate about methodology in the disciplines of statistics and econometrics, and on the role of spatial errors in modeling and analysis. First, some general remarks are formulated on two opposing viewpoints regarding spatial analysis and spatial data: a datadriven approach versus a model-driven approach. This is followed by a review of a number of competing inferential frameworks that can be used as the basis for spatial data analysis. Next, the focus shifts to spatial errors and to the implications of various forms of spatial errors for spatial data analysis. Finally, some concluding remarks are formulated on future research directions in spatial statistics and spatial econometrics.

Key words: Spatial data, Spatial error, Statistical analysis of spatial data, ESDA

中图分类号:

TP75

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Spatial Data: Its Nature, Effects and Ananlysis