A bivariate spatiotemporal scan statistics method for geographical flows
In response to the problem that existing bivariate anomaly clustering methods for geographic flows overlook the temporal dimension,this article proposes a bivariate spatiotemporal scan statistics method for geographical flows. Firstly,multi-scale spatiotemporal scanning windows for geographical flows are constructed. Secondly,the scan statistics of Bernoulli model are used to detect anomalous flow clusters in the spatiotemporal scanning window. Thirdly,the Monte Carlo simulation method is used to test the statistical significance of the scan statistics. Finally,a series of bivariate anomalous flow clusters with non-overlapping spatiotemporal distributions are screened. The method proposed in this paper is applied to the detect spatiotemporal anomalous flow clusters of ride-hailing flows and taxi flows in Xiamen City. The results show that the proposed method can reveal the spatiotemporal pattern of the competition mode between taxi flows and ride-hailing flows. The spatiotemporal anomalous clusters in which taxi flows occupy competitive advantages often occur in entertainment,catering and homestays places in the wee hours;The spatiotemporal anomalous clusters in which ride-hailing flows occupy competitive advantages often occur in offices and residences in the morning or evening. The presented method can identify the accurate spatiotemporal distribution of the anomalous clusters,which can provide support for optimizing the allocation of traffic resources.
geographical flowsbivariate anomalous flow clustersspatiotemporal scan statisticsspatiotemporal distribution differences
万方数据
维普
