利用导航大数据挖掘城市热点区域关联性

导航大数据是大量与导航相关且具有泛在导航、定位、授时特征的数据集合。城市环境的特性影响居民的出行活动,而居民出行活动中产生的导航大数据则蕴含了城市环境的时空信息。热点区域空间分布以及热点区域之间的关联性特征是城市环境时空特性的重要组成部分,由客观的环境现状和主观的人为活动造成。通过挖掘导航大数据可以揭示这些特征。本文提出了利用导航大数据的城市热点区域关联性挖掘方法。首先,通过对居民出行的起点和终点坐标进行空间聚类,挖掘城市中的热点区域,并依据点的分布特点对城市热点区域进行离散化;然后,利用基于谱聚类和蚁群算法的方法分析居民出行特征,揭示城市中热点区域之间存在的关联性。本文提出的方法能够充分利用导航大数据对城市动态的感知能力。以上海市2007年2月20日的出租车轨迹数据为例进行分析,结果表明：利用导航大数据分析城市热点区域之间的关联性,可以得到具有紧密关联性的热点区域的空间分布特征;上海市居民出行活动频繁的热点区域被划分为15个内部紧密关联的子图,形成该分布特征的内在机制以及居民流通规律与上海市的土地资源利用及道路交通建设现状密切相关。分析方法和结果可为合理的城市功能区域规划,智慧城市建设等提供决策支持和参考信息。

Detecting Urban Hotspot Region Association by Navigation Big Data Mining

Navigation big data is a set of massive data produced by navigation and positioning technology with both the characteristics of PNT (position, navigation, and time) and 5V (volume, velocity, variety, value, and veracity). Urban environment affects the travel behavior of residents. Consequently, navigation big data generated in daily travel activities contain abundant spatiotemporal information about both residents’ behavior and environment. The spatial distribution of hotspots and their latent association are key aspects of urban dynamics. This kind of characteristics is influenced by objective urban environment condition and subjective residents' activities. The knowledge about urban hotspot region association, which can be detected via mining navigation big data, is useful for urban environment management and intelligent transportation, etc. However, most studies focus on only determining hotspots. To better understand urban dynamics, a method based on spectral clustering and ant colony algorithm was proposed in the present study to mine the knowledge of association among urban hotspots from navigation big data. Implementation of this method included three main steps. Firstly, data preprocessing including data cleaning and OD (Origin and Destination) points extraction was performed on raw data. Then, the hotspot regions were extracted by two-step density-based clustering of the OD points. These hotspot regions were discretized by k-means clustering and Voronoi polygon. The proposed discretization strategy efficiently retained the intrinsic spatial distribution characteristics of the OD points. Lastly, we defined the degree of association among hotspot regions based on travel frequency. This measurement intuitively described the relationship among different regions embedded in travel activities. The association among hotspot regions was investigated using spectral clustering and ant colony algorithm. The two algorithms converted the association mining into the issues of graph partition and optimal path solution. The proposed method takes advantage of using navigation big data as a proxy of urban dynamics. We applied it to real-world taxi trajectory data in Shanghai. Results show the spatial distribution characteristics of hotspot regions with tight association and the pattern of residents' travel behavior. Discretized hotspot regions frequently visited by residents were clustered into 15 groups. Regions of each group formed a strongly associated structure. With land-use and road network information, the intrinsic mechanism of this characteristics was also analyzed. Findings of this paper could provide decision-making support and useful knowledge for layout design of urban function region and the construction of smart cities.