使用表面网络检测和分析移动热点
Detecting and Analyzing Mobility Hotspots using Surface Networks
摘要：
在过去几十年中，收集和存储移动物体上的数据的能力急剧增加。一个持续的困难是总结移动对象的大集合。本文开发了用于提取和分析具有相对高水平的移动活动的热点或位置的方法。我们使用内核密度估计（KDE）转换成一个光滑，连续的表面移动对象的大集合。然后，我们开发了一个拓扑算法来提取表面的关键几何特征，这些特征包括临界点（峰，坑和通行证）和临界线（脊线和路线）。我们将山峰和相应的山脊线连接起来，以产生一个表面网络，该网络总结了表面的拓扑结构。我们应用图论指数分析表征的表面及其随时间的变化。为了说明我们的方法，我们将这些技术应用于在中国上海收集的出租车数据。我们发现，在上午晚些时候，下午和晚上的正常活动时间的热点空间分布的复杂性增加，并在上午的热点空间分布的连接性的峰值，因为出租车集中服务的旅行工作。这些结果与研究区域人类活动模式的科学和轶事知识相匹配。
英文摘要：
Capabilities for collecting and storing data on mobile objects have increased dramatically over the past few decades. A persistent difficulty is summarizing large collections of mobile objects. This paper develops methods for extracting and analyzing hotspots or locations with relatively high levels of mobility activity. We use kernel density estimation (KDE) to convert a large collection of mobile objects into a smooth, continuous surface. We then develop a topological algorithm to extract critical geometric features of the surface; these include critical points (peaks, pits and passes) and critical lines (ridgelines and course-lines). We connect the peaks and corresponding ridgelines to produce a surface network that summarizes the topological structure of the surface. We apply graph theoretic indices to analytically characterize the surface and its changes over time. To illustrate our approach, we apply the techniques to taxi cab data collected in Shanghai, China. We find increases in the complexity of the hotspot spatial distribution during normal activity hours in the late morning, afternoon and evening and a spike in the connectivity of the hotspot spatial distribution in the morning as taxis concentrate on servicing travel to work. These results match with scientific and anecdotal knowledge about human activity patterns in the study area.
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