地理空间分析需要不同的思维方式：空间异质性问题
Geospatial Analysis Requires a Different Way of Thinking: The Problem of Spatial Heterogeneity
摘要：
地理空间分析在很大程度上受高斯思维方式的支配，这种思维方式假设世界上的事物可以用定义明确的平均值来表征，即，大小差不多。然而，这种假设并不总是有效的。事实上，世界上许多事物都缺乏一个明确的平均值，因此小的事物远远多于大的事物。本文试图论证地理空间分析需要一种不同的思维方式--一种基于偏态分布（如幂律、帕累托分布和对数正态分布）的帕累托思维方式。本文回顾了空间相关性和空间异质性的两个性质，指出目前空间统计学中的空间异质性概念仅用于表征空间相关性的局部方差。随后，我主张对空间异质性的一个广泛的视角，并建议它被制定为一个缩放定律。我进一步讨论了帕累托思想和尺度定律的影响，以更好地理解地理形式和过程，特别是在面对大量的社交媒体数据。在帕累托思想的精神下，地理空间分析应该寻求自下而上模拟地理事件和现象，而不是像高斯思想所指导的那样相互关联。
英文摘要：
Geospatial analysis is very much dominated by a Gaussian way of thinking, which assumes that things in the world can be characterized by a well-defined mean, i.e., things are more or less similar in size. However, this assumption is not always valid. In fact, many things in the world lack a well-defined mean, and therefore there are far more small things than large ones. This paper attempts to argue that geospatial analysis requires a different way of thinking - a Paretian way of thinking that underlies skewed distribution such as power laws, Pareto and lognormal distributions. I review two properties of spatial dependence and spatial heterogeneity, and point out that the notion of spatial heterogeneity in current spatial statistics is only used to characterize local variance of spatial dependence. I subsequently argue for a broad perspective on spatial heterogeneity, and suggest it be formulated as a scaling law. I further discuss the implications of Paretian thinking and the scaling law for better understanding of geographic forms and processes, in particular while facing massive amounts of social media data. In the spirit of Paretian thinking, geospatial analysis should seek to simulate geographic events and phenomena from the bottom up rather than correlations as guided by Gaussian thinking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
