【摘要】 通过louvain算法时空数据进行挖掘，对比洛杉矶地区共享单车在2019年和2020年的出行数据，研究疫情期间不同社区共享单车出行特征。分析结果显示，疫情对于不同种类的社区影响大相径庭，主要体现在出行次数、出行距离、出行时长等方面具有不同的变化，其原因，面对疫情的常态化防控，对比研究了共享单车调度模型、预测模型在疫情期间的时空特性，得出是疫情改变共享单车使用者的生活习惯及出行习惯。更多还原

【Abstract】 Through spatio-temporal data mining with Louvain algorithm, we compare the travel data of shared bicycles in Los Angeles area in 2019 and 2020 to study the travel characteristics of shared bicycles in different communities during the epidemic.The analysis results show that the epidemic has a very different impact on different kinds of communities, with different changes on the number of trips, travel distance, travel duration in different communities.The spatio-temporal characteristics of the bike-sharing scheduling model and the prediction model during the epidemic are studied in comparison, and it is concluded that the epidemic has changed the living habits and travel habits of bike-sharing users.In the face of the normalization of epidemic prevention and control, the research on the space-time characteristics of shared bicycles during the epidemic period will have implications for the modifications of the shared bicycle scheduling model and the prediction model in the future.更多还原