网络约束下局部空间同位模式的扫描统计方法

空间同位模式挖掘旨在发现空间数据库中频繁发生在邻近位置的地理事件。由于空间异质性，地理事件在不同区域邻近出现的频繁程度亦存在差异，进而形成局部同位模式。现有局部同位模式挖掘方法多基于欧氏空间的平面假设，难以客观揭示网络空间（如城市道路）内地理事件间的局部同位规律，因此基于空间扫描统计思想，提出了一种网络约束下的局部同位模式挖掘方法。首先，发展了网络约束下的路径扩展方法，识别可能存在局部网络空间同位模式的候选路径；其次，基于网络约束下的二元泊松分布构建显著性检验的零模型，判别候选路径中局部网络空间同位模式的有效性。通过模拟实验与北京市出租车供需模式分析，发现该方法比现有方法得到的结果更精细、更客观，能够有效地挖掘网络约束下的局部同位模式。

Spatial Scan Statistic Method for Discovering Regional Network Co-location Patterns

  Objectives  Currently, most methods for mining regional co-location patterns focus on the planar geospatial space which can hardly support the analysis on the network space such as urban roads. Therefore, a regional network co-location pattern mining method is proposed based on spatial scan statistics.  Methods  A network-constrained path expansion method is developed to detect the candidate paths where co-location patterns could occur. These candidates are further validated using significance tests, where the null model is constructed using a network-constrained bivariate Poisson distribution.  Results  Experiment results of simulated data and taxi datasets show that the proposed method is more effective for discovering regional co-location patterns on the network space than a baseline method.Compared with traditional methods for discovering regional co-location patterns, our proposed method fully considers the network constraint properties of events, in both phases of the candidate path detection and the significance testing.  Conclusions  Our method can effectively assist the resource allocation of taxis within the urban road network in Beijing by analyzing the taxi supply-demand patterns in different urban districts.