Geographical flows represent meaningful interactions of geographical objects between pairs of locations.Mining the spatio-temporal association patterns of geographical flows is of great significance for uncovering the spatio-temporal dependency and heterogeneity among flows,as well as understanding the underlying flow mechanisms and spatio-temporal interactions.Currently,there is an increasing number of methods for spatial association analysis of geographical flows.However,there is limited research considering the spatio-temporal coupling characteristics of geographical flows and focusing on the impact of time effects on association pattern detection.Accurately capturing the spatio-temporal dynamics of dependencies between geo-graphical flows remains a challenging issue in the flow association analysis field.To address this gap,this paper extends the spatio-temporal cross K-function of point process to the flow spatio-temporal cross K-function.The method takes the geo-graphical flow as the research object,which can be used to detect spatio-temporal association patterns between any two types of geographical flows.Specifically,the global flow spatio-temporal cross K-function can detect the overall association patterns of geographical flows in the study area,while the local flow spatio-temporal cross K-function can identify the spatio-temporal as-sociations at the local scale that does not follow the global pattern.This work utilizes the flow spatio-temporal cross K-function to analyze the spatio-temporal association patterns between taxi flows and ride-hailing flows on Xiamen Island.The global re-sults show an isolated pattern between taxi flows and ride-hailing flows,suggesting that there is no intense positive competition between the two types of vehicles on Xiamen island.Whereas,the local results indicate competition between the two types of vehicles during the morning,afternoon,and evening peak hours.This competition is mainly concentrated in several specific ar-eas,such as residential areas to industrial parks,residential areas to the airport,train stations to business districts,and busi-ness districts to tourist attractions.
关键词
地理流/时空关联模式/时空交叉K函数/网约车服务/巡游车服务
Key words
geographical flows/spatio-temporal association patterns/spatio-temporal cross K-function/ride-hailing service/taxi service
维普
万方数据