为什么拓扑学在预测人类活动中很重要
Why Topology Matters in Predicting Human Activities
摘要：
通过底层街道的拓扑关系（注意：整条街道而不是街道段）可以更好地理解地理空间，这使我们能够看到连通性差的街道的缩放或分形或生活结构。正是这种潜在的尺度结构使得人类活动是可预测的，尽管是在集体而不是个人的人类移动行为的意义上。这种拓扑分析在文献中尚未得到应有的重视，因为许多研究人员继续依赖于片段分析来预测人类活动。基于分段分析的方法本质上是几何的，重点是位置，长度和方向的几何细节，并且无法揭示缩放特性，这意味着它们不能用于人类活动预测。我们使用伦敦街道和tweet位置数据进行了一系列案例研究，基于相关概念，如自然街道和自然街道段（或简称为街道段），轴线和轴线段（或简称为线段）。我们发现，自然街道是人类活动或交通预测方面的最佳代表，其次是轴线，并且街道段和线段都没有网络参数和鸣叫位置之间的良好相关性。这些发现表明，基于轴线的空间句法或拓扑分析之所以有效，与人类个体的旅行行为或人类对距离或空间的概念化方式无关。相反，正是潜在的街道规模等级--大量连接最少的街道，极少数连接最多的街道，以及一些介于连接最少和最多之间的街道--使人类活动变得可预测。
英文摘要：
Geographic space is better understood through the topological relationship of the underlying streets (note: entire streets rather than street segments), which enables us to see scaling or fractal or living structure of far more less-connected streets than well-connected ones. It is this underlying scaling structure that makes human activities predictable, albeit in the sense of collective rather than individual human moving behavior. This topological analysis has not yet received its deserved attention in the literature, as many researchers continue to rely on segment analysis for predicting human activities. The segment-analysis-based methods are essentially geometric, with a focus on geometric details of locations, lengths, and directions, and are unable to reveal the scaling property, which means they cannot be used for human activities prediction. We conducted a series of case studies using London streets and tweet location data, based on related concepts such as natural streets, and natural street segments (or street segments for short), axial lines, and axial line segments (or line segments for short). We found that natural streets are the best representation in terms of human activities or traffic prediction, followed by axial lines, and that neither street segments nor line segments bear a good correlation between network parameters and tweet locations. These findings point to the fact that the reason why axial lines-based space syntax, or the kind of topological analysis in general, works has little to do with individual human travel behavior or ways that human conceptualize distances or spaces. Instead, it is the underlying scaling hierarchy of streets - numerous least-connected, a very few most-connected, and some in between the least- and most-connected - that makes human activities predictable.
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。
